diff --git a/nes/interpolation/horizontal_interpolation.py b/nes/interpolation/horizontal_interpolation.py
index 74dadd918532cfc0b205b13da10361380816b95e..972c990a949003fa9498780c2259aa50510f63e5 100644
--- a/nes/interpolation/horizontal_interpolation.py
+++ b/nes/interpolation/horizontal_interpolation.py
@@ -44,10 +44,16 @@ def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='Neares
         raise NotImplemented("Parallel method {0} is not implemented yet for horizontal interpolations. Use 'T'".format(
             self.parallel_method))
 
-    # Apply weights
+    # Copy NES
     final_dst = dst_grid.copy()
     final_dst.set_communicator(dst_grid.comm)
-    # return final_dst
+
+    # Remove original file information
+    final_dst.__ini_path = None
+    final_dst.netcdf = None
+    final_dst.dataset = None
+
+    # Return final_dst
     final_dst.lev = self.lev
     final_dst._lev = self._lev
     final_dst.time = self.time
@@ -55,6 +61,7 @@ def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='Neares
     final_dst.hours_start = self.hours_start
     final_dst.hours_end = self.hours_end
 
+    # Apply weights
     for var_name, var_info in self.variables.items():
         if info and self.master:
             print("\t{var} horizontal interpolation".format(var=var_name))
@@ -100,6 +107,12 @@ def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='Neares
         else:
             msg = "The final projection must be points to interpolate an experiment and get it in Providentia format."
             warnings.warn(msg)
+    else:
+        # Convert dimensions (time, lev, lat, lon) to (time, station) for interpolated variables and reshape data
+        if isinstance(final_dst, nes.PointsNes):
+            for var_name, var_info in final_dst.variables.items():
+                if var_info['dimensions'] == ('time', 'lev', 'lat', 'lon') and len(var_info['data'].shape) == 2:
+                    final_dst.variables[var_name]['dimensions'] = ('time', 'station')
 
     return final_dst
 
diff --git a/nes/interpolation/vertical_interpolation.py b/nes/interpolation/vertical_interpolation.py
index 461dbcba90441444aa5ae779c4b35947006caeb1..88e2a86fc5597e8f5c4448bc8a8510b7a24b2d5a 100644
--- a/nes/interpolation/vertical_interpolation.py
+++ b/nes/interpolation/vertical_interpolation.py
@@ -99,7 +99,7 @@ def interpolate_vertical(self, new_levels, new_src_vertical=None, kind='linear',
     # Loop over variables
     for var_name in self.variables.keys():
         if self.variables[var_name]['data'] is None:
-            # Loading data if it is not loaded yet
+            # Load data if it is not loaded yet
             self.load(var_name)
 
         if var_name != self.vertical_var_name:
@@ -119,7 +119,7 @@ def interpolate_vertical(self, new_levels, new_src_vertical=None, kind='linear',
                     for i in range(nx):
                         curr_level_values = src_levels[t, :, j, i]
                         try:
-                            # check if all values are identical or masked
+                            # Check if all values are identical or masked
                             if ((isinstance(curr_level_values, np.ndarray) and
                                  (curr_level_values == curr_level_values[0]).all()) or
                                     (isinstance(curr_level_values, np.ma.core.MaskedArray) and
@@ -171,7 +171,7 @@ def interpolate_vertical(self, new_levels, new_src_vertical=None, kind='linear',
             self.variables[var_name]['data'] = copy(dst_data)
             # print(self.variables[var_name]['data'])
 
-    # Updating level information
+    # Update level information
     new_lev_info = {'data': new_levels}
     for var_attr, attr_info in self.variables[self.vertical_var_name].items():
         if var_attr not in ['data', 'dimensions', 'crs', 'grid_mapping']:
@@ -180,5 +180,10 @@ def interpolate_vertical(self, new_levels, new_src_vertical=None, kind='linear',
     self._lev = new_lev_info
     self.free_vars(self.vertical_var_name)
     self.vertical_var_name = None
+    
+    # Remove original file information
+    self.__ini_path = None
+    self.dataset = None
+    self.netcdf = None
 
     return self
diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py
index 8f03f5041cd3145b8cbd1783bf104f31b82ba374..33d86b139cfd3f29bc80c7f26bf60fb20f8584fe 100644
--- a/nes/nc_projections/default_nes.py
+++ b/nes/nc_projections/default_nes.py
@@ -622,6 +622,7 @@ class Nes(object):
 
     def sel(self, hours_start=None, time_min=None, hours_end=None, time_max=None, lev_min=None, lev_max=None,
             lat_min=None, lat_max=None, lon_min=None, lon_max=None):
+            
         loaded_vars = False
         for var_info in self.variables.values():
             if var_info['data'] is not None:
@@ -664,6 +665,7 @@ class Nes(object):
 
         # New Axis limits
         self.read_axis_limits = self.get_read_axis_limits()
+
         # Dimensions screening
         self.time = self._time[self.read_axis_limits['t_min']:self.read_axis_limits['t_max']]
         self.time_bnds = self._time_bnds
@@ -1209,13 +1211,14 @@ class Nes(object):
         dataset : xr.Dataset
             Opened dataset.
         """
-        
+
         if self.master:
             warnings.filterwarnings('ignore')  # Disabling warnings while reading MONARCH original file
             dataset = open_dataset(self.__ini_path, decode_coords='all')
             warnings.filterwarnings('default')  # Re-activating warnings
         else:
             dataset = None
+
         dataset = self.comm.bcast(dataset, root=0)
         self.dataset = dataset
 
@@ -1500,11 +1503,14 @@ class Nes(object):
             Dictionary with the 'data' key with the coordinate variable values. and the attributes as other keys.
         coordinate_axis : str
             Name of the coordinate to extract. Accepted values: ['Z', 'Y', 'X'].
+        bounds : bool
+            Boolean variable to know if there are coordinate bounds.
         Returns
         -------
         values : dict
             Dictionary with the portion of data corresponding to the rank.
         """
+
         if coordinate_info is None:
             return None
 
@@ -1644,6 +1650,9 @@ class Nes(object):
         var_list : list, str
             List (or single string) of the variables to be loaded.
         """
+        
+        if (self.__ini_path is None) and (self.dataset is None) and (self.netcdf is None):
+            raise RuntimeError('Only data from existent files can be loaded.')
 
         if self.netcdf is None:
             self.__open_dataset()
@@ -1661,6 +1670,9 @@ class Nes(object):
                 print("Rank {0:03d}: Loading {1} var ({2}/{3})".format(self.rank, var_name, i + 1, len(var_list)))
             if self.variables[var_name]['data'] is None:
                 self.variables[var_name]['data'] = self._read_variable(var_name)
+            else:
+                if self.master:
+                    print("Data for {0} was previously loaded. Skipping variable.".format(var_name))
             if self.info:
                 print("Rank {0:03d}: Loaded {1} var ({2})".format(
                     self.rank, var_name, self.variables[var_name]['data'].shape))
diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py
index d5ef1f2457f9dbfb8af23fcf7e463e7d976c6617..218a6a66d669a37d7ff4f82f3ea6ca99c124ba8b 100644
--- a/nes/nc_projections/points_nes.py
+++ b/nes/nc_projections/points_nes.py
@@ -219,7 +219,7 @@ class PointsNes(Nes):
 
         return None
 
-    def _get_coordinate_values(self, coordinate_info, coordinate_axis):
+    def _get_coordinate_values(self, coordinate_info, coordinate_axis, bounds=False):
         """
         Get the coordinate data of the current portion.
 
@@ -229,16 +229,25 @@ class PointsNes(Nes):
             Dictionary with the 'data' key with the coordinate variable values. and the attributes as other keys.
         coordinate_axis : str
             Name of the coordinate to extract. Accepted values: ['X'].
+        bounds : bool
+            Boolean variable to know if there are coordinate bounds.
         Returns
         -------
         values : dict
             Dictionary with the portion of data corresponding to the rank.
         """
 
-        values = deepcopy(coordinate_info)
-        if isinstance(coordinate_info, list):
+        if coordinate_info is None:
+            return None
+
+        if not isinstance(coordinate_info, dict):
             values = {'data': deepcopy(coordinate_info)}
+        else:
+            values = deepcopy(coordinate_info)
+
         coordinate_len = len(values['data'].shape)
+        if bounds:
+            coordinate_len -= 1
 
         if coordinate_axis == 'X':
             if coordinate_len == 1:
@@ -601,12 +610,12 @@ class PointsNes(Nes):
                                                       lon=self.lon['data']
                                                       )
         
-        # Convert dimensions (time, lat, lon) to (time, station) for interpolated variables and reshape data
+        # Convert dimensions (time, lev, lat, lon) to (station, time) for interpolated variables and reshape data
         variables = {}
         interpolated_variables = deepcopy(self.variables)
         for var_name, var_info in interpolated_variables.items():
             variables[var_name] = {}
-            if var_info['dimensions'] == ('time', 'lat', 'lon') and len(var_info['data'].shape) == 2:
+            if var_info['dimensions'] == ('time', 'lev', 'lat', 'lon') and len(var_info['data'].shape) == 2:
                 variables[var_name]['data'] = var_info['data'].T
                 variables[var_name]['dimensions'] = ('station', 'time')
             else:
diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py
index 7951bcfe77fbea75d2b68e82635f2435ef085fc7..6dece347f36312e19ded3ed0a0baf72bcdffc8c9 100644
--- a/nes/nc_projections/points_nes_ghost.py
+++ b/nes/nc_projections/points_nes_ghost.py
@@ -227,7 +227,7 @@ class PointsNesGHOST(PointsNes):
 
         return None
 
-    def _get_coordinate_values(self, coordinate_info, coordinate_axis):
+    def _get_coordinate_values(self, coordinate_info, coordinate_axis, bounds=False):
         """
         Get the coordinate data of the current portion.
 
@@ -237,16 +237,25 @@ class PointsNesGHOST(PointsNes):
             Dictionary with the 'data' key with the coordinate variable values. and the attributes as other keys.
         coordinate_axis : str
             Name of the coordinate to extract. Accepted values: ['X'].
+        bounds : bool
+            Boolean variable to know if there are coordinate bounds.
         Returns
         -------
         values : dict
             Dictionary with the portion of data corresponding to the rank.
         """
 
-        values = deepcopy(coordinate_info)
-        if isinstance(coordinate_info, list):
+        if coordinate_info is None:
+            return None
+
+        if not isinstance(coordinate_info, dict):
             values = {'data': deepcopy(coordinate_info)}
+        else:
+            values = deepcopy(coordinate_info)
+
         coordinate_len = len(values['data'].shape)
+        if bounds:
+            coordinate_len -= 1
 
         if coordinate_axis == 'X':
             if coordinate_len == 1:
@@ -609,8 +618,12 @@ class PointsNesGHOST(PointsNes):
                                lon=self.lon['data'], 
                                times=self.time
                                )
-        
-        GHOST_version = str(float(np.unique(self.variables['GHOST_version']['data'])))
+
+        # The version attribute in GHOST files prior to 1.3.3 is called data_version, after it is version
+        if 'version' in self.global_attrs:
+            GHOST_version = self.global_attrs['version']
+        elif 'data_version' in self.global_attrs:
+            GHOST_version = self.global_attrs['data_version']
         metadata_variables = self.get_standard_metadata(GHOST_version)
         self.free_vars(metadata_variables)
         self.free_vars('station')
diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py
index 499cd7af261e3fc43250ae905547d731d0b81f7e..f0457f81e3bc825be24cd078403a39eb9b0da870 100644
--- a/nes/nc_projections/points_nes_providentia.py
+++ b/nes/nc_projections/points_nes_providentia.py
@@ -260,7 +260,7 @@ class PointsNesProvidentia(PointsNes):
 
         self.free_vars(('model_centre_longitude', 'model_centre_latitude', 'grid_edge_longitude', 'grid_edge_latitude'))
 
-    def _get_coordinate_values(self, coordinate_info, coordinate_axis):
+    def _get_coordinate_values(self, coordinate_info, coordinate_axis, bounds=False):
         """
         Get the coordinate data of the current portion.
 
@@ -270,16 +270,25 @@ class PointsNesProvidentia(PointsNes):
             Dictionary with the 'data' key with the coordinate variable values. and the attributes as other keys.
         coordinate_axis : str
             Name of the coordinate to extract. Accepted values: ['X'].
+        bounds : bool
+            Boolean variable to know if there are coordinate bounds.
         Returns
         -------
         values : dict
             Dictionary with the portion of data corresponding to the rank.
         """
 
-        values = deepcopy(coordinate_info)
-        if isinstance(coordinate_info, list):
+        if coordinate_info is None:
+            return None
+
+        if not isinstance(coordinate_info, dict):
             values = {'data': deepcopy(coordinate_info)}
+        else:
+            values = deepcopy(coordinate_info)
+
         coordinate_len = len(values['data'].shape)
+        if bounds:
+            coordinate_len -= 1
 
         if coordinate_axis == 'X':
             if coordinate_len == 1:
diff --git a/tests/3-test_spatial_join.py b/tests/3-test_spatial_join.py
index e456c64ee2cd1ea24e63e864f851678d37cc5936..77662f94197e4c3d6f04605510bb482b048aad31 100644
--- a/tests/3-test_spatial_join.py
+++ b/tests/3-test_spatial_join.py
@@ -79,7 +79,7 @@ for method in ['spatial_overlay', 'spatial_join']:
             coordinates.write_shapefile('coordinates_{0}_{1}'.format(projection, 
                                                                      projection_type))
 
-            mask_path = '/esarchive/scratch/avilanova/software/NES/tutorials/data/timezones_2021c/timezones_2021c.shp'
+            mask_path = '/esarchive/shapefiles/timezones/timezones_2021c/timezones_2021c.shp'
             mask = gpd.read_file(mask_path)
 
             # Spatial overlay (old method)
diff --git a/tutorials/5.Others/5.4.Spatial_Join.ipynb b/tutorials/5.Others/5.4.Spatial_Join.ipynb
index b00e62288dc5f117c373579760892b9c5d86452a..097ff78f928fee83629e49a7360514e29341691a 100644
--- a/tutorials/5.Others/5.4.Spatial_Join.ipynb
+++ b/tutorials/5.Others/5.4.Spatial_Join.ipynb
@@ -153,31 +153,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mask_path = '/esarchive/scratch/avilanova/software/NES/tutorials/data/timezones_2021c/timezones_2021c.shp'\n",
+    "mask_path = '/esarchive/shapefiles/timezones/timezones_2021c/timezones_2021c.shp'\n",
     "mask = gpd.read_file(mask_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2190: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
+      "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2470: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
       "\n",
       "  shapefile_aux.geometry = self.shapefile.centroid\n"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 15.2 s, sys: 680 ms, total: 15.9 s\n",
-      "Wall time: 15.9 s\n"
-     ]
     }
    ],
    "source": [
@@ -186,123 +178,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "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>geometry</th>\n",
-       "      <th>tzid</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>FID</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...</td>\n",
-       "      <td>Europe/Madrid</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...</td>\n",
-       "      <td>Europe/Madrid</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...</td>\n",
-       "      <td>Europe/Madrid</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9995</th>\n",
-       "      <td>POLYGON ((20.80000 60.90000, 21.00000 60.90000...</td>\n",
-       "      <td>Europe/Helsinki</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9996</th>\n",
-       "      <td>POLYGON ((21.00000 60.90000, 21.20000 60.90000...</td>\n",
-       "      <td>Europe/Helsinki</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9997</th>\n",
-       "      <td>POLYGON ((21.20000 60.90000, 21.40000 60.90000...</td>\n",
-       "      <td>Europe/Helsinki</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9998</th>\n",
-       "      <td>POLYGON ((21.40000 60.90000, 21.60000 60.90000...</td>\n",
-       "      <td>Europe/Helsinki</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9999</th>\n",
-       "      <td>POLYGON ((21.60000 60.90000, 21.80000 60.90000...</td>\n",
-       "      <td>Europe/Helsinki</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10000 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                               geometry             tzid\n",
-       "FID                                                                     \n",
-       "0     POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...    Europe/Madrid\n",
-       "1     POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...    Europe/Madrid\n",
-       "2     POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...    Europe/Madrid\n",
-       "3     POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...              NaN\n",
-       "4     POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...              NaN\n",
-       "...                                                 ...              ...\n",
-       "9995  POLYGON ((20.80000 60.90000, 21.00000 60.90000...  Europe/Helsinki\n",
-       "9996  POLYGON ((21.00000 60.90000, 21.20000 60.90000...  Europe/Helsinki\n",
-       "9997  POLYGON ((21.20000 60.90000, 21.40000 60.90000...  Europe/Helsinki\n",
-       "9998  POLYGON ((21.40000 60.90000, 21.60000 60.90000...  Europe/Helsinki\n",
-       "9999  POLYGON ((21.60000 60.90000, 21.80000 60.90000...  Europe/Helsinki\n",
-       "\n",
-       "[10000 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "coordinates.shapefile"
    ]
@@ -325,110 +203,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "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>geometry</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>FID</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>POLYGON ((2.80000 42.90000, 3.00000 42.90000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>POLYGON ((3.00000 42.90000, 3.20000 42.90000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>POLYGON ((3.20000 42.90000, 3.40000 42.90000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>POLYGON ((3.40000 42.90000, 3.60000 42.90000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>POLYGON ((3.60000 42.90000, 3.80000 42.90000, ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                              geometry\n",
-       "FID                                                   \n",
-       "0    POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...\n",
-       "1    POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...\n",
-       "2    POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...\n",
-       "3    POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...\n",
-       "4    POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...\n",
-       "..                                                 ...\n",
-       "95   POLYGON ((2.80000 42.90000, 3.00000 42.90000, ...\n",
-       "96   POLYGON ((3.00000 42.90000, 3.20000 42.90000, ...\n",
-       "97   POLYGON ((3.20000 42.90000, 3.40000 42.90000, ...\n",
-       "98   POLYGON ((3.40000 42.90000, 3.60000 42.90000, ...\n",
-       "99   POLYGON ((3.60000 42.90000, 3.80000 42.90000, ...\n",
-       "\n",
-       "[100 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "lat_orig = 41.1\n",
     "lon_orig = 1.8\n",
@@ -444,17 +221,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "mask_path = '/esarchive/scratch/avilanova/software/NES/tutorials/data/timezones_2021c/timezones_2021c.shp'\n",
+    "mask_path = '/esarchive/shapefiles/timezones/timezones_2021c/timezones_2021c.shp'\n",
     "mask = gpd.read_file(mask_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -463,110 +240,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "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>geometry</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>FID</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>95</th>\n",
-       "      <td>POLYGON ((2.80000 42.90000, 3.00000 42.90000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>96</th>\n",
-       "      <td>POLYGON ((3.00000 42.90000, 3.20000 42.90000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>POLYGON ((3.20000 42.90000, 3.40000 42.90000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>98</th>\n",
-       "      <td>POLYGON ((3.40000 42.90000, 3.60000 42.90000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>99</th>\n",
-       "      <td>POLYGON ((3.60000 42.90000, 3.80000 42.90000, ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                              geometry\n",
-       "FID                                                   \n",
-       "0    POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...\n",
-       "1    POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...\n",
-       "2    POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...\n",
-       "3    POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...\n",
-       "4    POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...\n",
-       "..                                                 ...\n",
-       "95   POLYGON ((2.80000 42.90000, 3.00000 42.90000, ...\n",
-       "96   POLYGON ((3.00000 42.90000, 3.20000 42.90000, ...\n",
-       "97   POLYGON ((3.20000 42.90000, 3.40000 42.90000, ...\n",
-       "98   POLYGON ((3.40000 42.90000, 3.60000 42.90000, ...\n",
-       "99   POLYGON ((3.60000 42.90000, 3.80000 42.90000, ...\n",
-       "\n",
-       "[100 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "coordinates.shapefile"
    ]
@@ -589,110 +265,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "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>geometry</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>FID</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9995</th>\n",
-       "      <td>POLYGON ((20.80000 60.90000, 21.00000 60.90000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9996</th>\n",
-       "      <td>POLYGON ((21.00000 60.90000, 21.20000 60.90000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9997</th>\n",
-       "      <td>POLYGON ((21.20000 60.90000, 21.40000 60.90000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9998</th>\n",
-       "      <td>POLYGON ((21.40000 60.90000, 21.60000 60.90000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9999</th>\n",
-       "      <td>POLYGON ((21.60000 60.90000, 21.80000 60.90000...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10000 rows × 1 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                               geometry\n",
-       "FID                                                    \n",
-       "0     POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...\n",
-       "1     POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...\n",
-       "2     POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...\n",
-       "3     POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...\n",
-       "4     POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...\n",
-       "...                                                 ...\n",
-       "9995  POLYGON ((20.80000 60.90000, 21.00000 60.90000...\n",
-       "9996  POLYGON ((21.00000 60.90000, 21.20000 60.90000...\n",
-       "9997  POLYGON ((21.20000 60.90000, 21.40000 60.90000...\n",
-       "9998  POLYGON ((21.40000 60.90000, 21.60000 60.90000...\n",
-       "9999  POLYGON ((21.60000 60.90000, 21.80000 60.90000...\n",
-       "\n",
-       "[10000 rows x 1 columns]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "lat_orig = 41.1\n",
     "lon_orig = 1.8\n",
@@ -708,7 +283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -718,149 +293,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Rank 000: 9270 intersected areas found\n",
-      "CPU times: user 8min 19s, sys: 9.29 s, total: 8min 29s\n",
-      "Wall time: 8min 30s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%time coordinates.spatial_join(mask, method='intersection')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "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>geometry</th>\n",
-       "      <th>tzid</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>FID</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...</td>\n",
-       "      <td>Europe/Madrid</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...</td>\n",
-       "      <td>Europe/Madrid</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...</td>\n",
-       "      <td>Europe/Madrid</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...</td>\n",
-       "      <td>Europe/Madrid</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9995</th>\n",
-       "      <td>POLYGON ((20.80000 60.90000, 21.00000 60.90000...</td>\n",
-       "      <td>Europe/Helsinki</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9996</th>\n",
-       "      <td>POLYGON ((21.00000 60.90000, 21.20000 60.90000...</td>\n",
-       "      <td>Europe/Helsinki</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9997</th>\n",
-       "      <td>POLYGON ((21.20000 60.90000, 21.40000 60.90000...</td>\n",
-       "      <td>Europe/Helsinki</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9998</th>\n",
-       "      <td>POLYGON ((21.40000 60.90000, 21.60000 60.90000...</td>\n",
-       "      <td>Europe/Helsinki</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9999</th>\n",
-       "      <td>POLYGON ((21.60000 60.90000, 21.80000 60.90000...</td>\n",
-       "      <td>Europe/Helsinki</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>10000 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                               geometry             tzid\n",
-       "FID                                                                     \n",
-       "0     POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...    Europe/Madrid\n",
-       "1     POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...    Europe/Madrid\n",
-       "2     POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...    Europe/Madrid\n",
-       "3     POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...    Europe/Madrid\n",
-       "4     POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...              NaN\n",
-       "...                                                 ...              ...\n",
-       "9995  POLYGON ((20.80000 60.90000, 21.00000 60.90000...  Europe/Helsinki\n",
-       "9996  POLYGON ((21.00000 60.90000, 21.20000 60.90000...  Europe/Helsinki\n",
-       "9997  POLYGON ((21.20000 60.90000, 21.40000 60.90000...  Europe/Helsinki\n",
-       "9998  POLYGON ((21.40000 60.90000, 21.60000 60.90000...  Europe/Helsinki\n",
-       "9999  POLYGON ((21.60000 60.90000, 21.80000 60.90000...  Europe/Helsinki\n",
-       "\n",
-       "[10000 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "coordinates.shapefile"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -885,6 +336,11 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.7.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
   }
  },
  "nbformat": 4,
diff --git a/tutorials/5.Others/5.6.Interpolation_Test.ipynb b/tutorials/5.Others/5.6.Interpolation_Test.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..461ef4c027839bb92cbe35ed848c1acbcc5ae975
--- /dev/null
+++ b/tutorials/5.Others/5.6.Interpolation_Test.ipynb
@@ -0,0 +1,4773 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Testing Providentia Interpolation and NES Interpolation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nes import *\n",
+    "import geopandas as gpd\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "import copy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Original model (a4s2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.1. Read data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_0 = '/esarchive/exp/monarch/a4s2/global/hourly/od550du/od550du-000_2020060100.nc'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (time: 25, lev: 1, lat: 181, lon: 257)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2020-06-01 2020-06-01T01:00:00 ... 2020-06-02\n",
+       "  * lev      (lev) float64 0.0\n",
+       "  * lat      (lat) float64 -90.0 -89.0 -88.0 -87.0 -86.0 ... 87.0 88.0 89.0 90.0\n",
+       "  * lon      (lon) float64 -180.0 -178.6 -177.2 -175.8 ... 177.2 178.6 180.0\n",
+       "Data variables:\n",
+       "    od550du  (time, lev, lat, lon) float32 ...\n",
+       "    crs      int32 -2147483647\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.7\n",
+       "    comment:      Generated on marenostrum4</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-58dd96f1-77c1-4484-b8c6-3e7681cbc60f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-58dd96f1-77c1-4484-b8c6-3e7681cbc60f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 25</li><li><span class='xr-has-index'>lev</span>: 1</li><li><span class='xr-has-index'>lat</span>: 181</li><li><span class='xr-has-index'>lon</span>: 257</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-87f46cb0-2290-4398-9323-e199989e90d8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-87f46cb0-2290-4398-9323-e199989e90d8' 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'>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'>2020-06-01 ... 2020-06-02</div><input id='attrs-a67554ef-7979-4d38-9a1b-93e1b61cc393' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a67554ef-7979-4d38-9a1b-93e1b61cc393' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26c6b4f4-d517-475c-a441-06278222ce17' class='xr-var-data-in' type='checkbox'><label for='data-26c6b4f4-d517-475c-a441-06278222ce17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-06-01T01:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-01T02:00:00.000012999&#x27;, &#x27;2020-06-01T03:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-01T04:00:00.000000000&#x27;, &#x27;2020-06-01T05:00:00.000013000&#x27;,\n",
+       "       &#x27;2020-06-01T06:00:00.000000000&#x27;, &#x27;2020-06-01T07:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-01T08:00:00.000013000&#x27;, &#x27;2020-06-01T09:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-01T10:00:00.000000000&#x27;, &#x27;2020-06-01T11:00:00.000012999&#x27;,\n",
+       "       &#x27;2020-06-01T12:00:00.000000000&#x27;, &#x27;2020-06-01T13:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-01T14:00:00.000012999&#x27;, &#x27;2020-06-01T15:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-01T16:00:00.000000000&#x27;, &#x27;2020-06-01T17:00:00.000012999&#x27;,\n",
+       "       &#x27;2020-06-01T18:00:00.000000000&#x27;, &#x27;2020-06-01T19:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-01T20:00:00.000013000&#x27;, &#x27;2020-06-01T21:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-01T22:00:00.000000000&#x27;, &#x27;2020-06-01T23:00:00.000013000&#x27;,\n",
+       "       &#x27;2020-06-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-a7dea364-d088-454e-91f4-5ce62e67cfcb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7dea364-d088-454e-91f4-5ce62e67cfcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61e19c2a-9683-4116-aa64-e74c343aeb29' class='xr-var-data-in' type='checkbox'><label for='data-61e19c2a-9683-4116-aa64-e74c343aeb29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>-</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array([0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.0 -88.0 ... 89.0 90.0</div><input id='attrs-09ec6052-ce27-4669-8147-876580838416' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09ec6052-ce27-4669-8147-876580838416' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ae8104d-16eb-4594-b6bf-883e22925c5c' class='xr-var-data-in' type='checkbox'><label for='data-4ae8104d-16eb-4594-b6bf-883e22925c5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-90., -89., -88., -87., -86., -85., -84., -83., -82., -81., -80., -79.,\n",
+       "       -78., -77., -76., -75., -74., -73., -72., -71., -70., -69., -68., -67.,\n",
+       "       -66., -65., -64., -63., -62., -61., -60., -59., -58., -57., -56., -55.,\n",
+       "       -54., -53., -52., -51., -50., -49., -48., -47., -46., -45., -44., -43.,\n",
+       "       -42., -41., -40., -39., -38., -37., -36., -35., -34., -33., -32., -31.,\n",
+       "       -30., -29., -28., -27., -26., -25., -24., -23., -22., -21., -20., -19.,\n",
+       "       -18., -17., -16., -15., -14., -13., -12., -11., -10.,  -9.,  -8.,  -7.,\n",
+       "        -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,   1.,   2.,   3.,   4.,   5.,\n",
+       "         6.,   7.,   8.,   9.,  10.,  11.,  12.,  13.,  14.,  15.,  16.,  17.,\n",
+       "        18.,  19.,  20.,  21.,  22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,\n",
+       "        30.,  31.,  32.,  33.,  34.,  35.,  36.,  37.,  38.,  39.,  40.,  41.,\n",
+       "        42.,  43.,  44.,  45.,  46.,  47.,  48.,  49.,  50.,  51.,  52.,  53.,\n",
+       "        54.,  55.,  56.,  57.,  58.,  59.,  60.,  61.,  62.,  63.,  64.,  65.,\n",
+       "        66.,  67.,  68.,  69.,  70.,  71.,  72.,  73.,  74.,  75.,  76.,  77.,\n",
+       "        78.,  79.,  80.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,  88.,  89.,\n",
+       "        90.])</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'>-180.0 -178.6 ... 178.6 180.0</div><input id='attrs-a53342fa-ab24-47f8-81da-f610fd3e681a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a53342fa-ab24-47f8-81da-f610fd3e681a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-382377f1-b842-4c83-a560-3f71ce05a574' class='xr-var-data-in' type='checkbox'><label for='data-382377f1-b842-4c83-a560-3f71ce05a574' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "        180.     ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-98a311b9-ef4c-4849-8d41-565195ae7b0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98a311b9-ef4c-4849-8d41-565195ae7b0a' 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>od550du</span></div><div class='xr-var-dims'>(time, lev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9ba49c0f-10b5-40f4-8d9a-ba76c88bc9b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ba49c0f-10b5-40f4-8d9a-ba76c88bc9b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c7ede4b-69c5-4d3f-861e-95f8ee3f1e4f' class='xr-var-data-in' type='checkbox'><label for='data-0c7ede4b-69c5-4d3f-861e-95f8ee3f1e4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>-</dd><dt><span>long_name :</span></dt><dd>Aerosol optical depth</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd></dl></div><div class='xr-var-data'><pre>[1162925 values with dtype=float32]</pre></div></li><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'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-22834f57-8cc2-4424-984d-a413816e9de8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22834f57-8cc2-4424-984d-a413816e9de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5aacbe9-3b67-4236-8866-848b97a3e73f' class='xr-var-data-in' type='checkbox'><label for='data-f5aacbe9-3b67-4236-8866-848b97a3e73f' 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>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>semi_major_axis :</span></dt><dd>6371000.0</dd><dt><span>inverse_flattening :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>array(-2147483647, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eb37a6d6-0411-4ba6-8afa-29b6609dbec0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eb37a6d6-0411-4ba6-8afa-29b6609dbec0' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>comment :</span></dt><dd>Generated on marenostrum4</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (time: 25, lev: 1, lat: 181, lon: 257)\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 2020-06-01 2020-06-01T01:00:00 ... 2020-06-02\n",
+       "  * lev      (lev) float64 0.0\n",
+       "  * lat      (lat) float64 -90.0 -89.0 -88.0 -87.0 -86.0 ... 87.0 88.0 89.0 90.0\n",
+       "  * lon      (lon) float64 -180.0 -178.6 -177.2 -175.8 ... 177.2 178.6 180.0\n",
+       "Data variables:\n",
+       "    od550du  (time, lev, lat, lon) float32 ...\n",
+       "    crs      int32 ...\n",
+       "Attributes:\n",
+       "    Conventions:  CF-1.7\n",
+       "    comment:      Generated on marenostrum4"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xr.open_dataset(path_0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<nes.nc_projections.latlon_nes.LatLonNes at 0x7f510113d4d0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_model = open_netcdf(path=path_0, info=True)\n",
+    "data_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rank 000: Loading od550du var (1/1)\n",
+      "Rank 000: Loaded od550du var ((25, 1, 181, 257))\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_model.keep_vars(['od550du'])\n",
+    "data_model.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.2. Create shapefile"
+   ]
+  },
+  {
+   "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>geometry</th>\n",
+       "      <th>od550du</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POLYGON ((-180.70312 -90.50000, -179.29688 -90...</td>\n",
+       "      <td>0.000144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POLYGON ((-179.29688 -90.50000, -177.89062 -90...</td>\n",
+       "      <td>0.000144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POLYGON ((-177.89062 -90.50000, -176.48438 -90...</td>\n",
+       "      <td>0.000144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POLYGON ((-176.48438 -90.50000, -175.07812 -90...</td>\n",
+       "      <td>0.000144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POLYGON ((-175.07812 -90.50000, -173.67188 -90...</td>\n",
+       "      <td>0.000144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46512</th>\n",
+       "      <td>POLYGON ((173.67188 89.50000, 175.07812 89.500...</td>\n",
+       "      <td>0.023041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46513</th>\n",
+       "      <td>POLYGON ((175.07812 89.50000, 176.48438 89.500...</td>\n",
+       "      <td>0.023041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46514</th>\n",
+       "      <td>POLYGON ((176.48438 89.50000, 177.89062 89.500...</td>\n",
+       "      <td>0.023041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46515</th>\n",
+       "      <td>POLYGON ((177.89062 89.50000, 179.29688 89.500...</td>\n",
+       "      <td>0.023041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46516</th>\n",
+       "      <td>POLYGON ((179.29688 89.50000, 180.70312 89.500...</td>\n",
+       "      <td>0.023041</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>46517 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                geometry   od550du\n",
+       "FID                                                               \n",
+       "0      POLYGON ((-180.70312 -90.50000, -179.29688 -90...  0.000144\n",
+       "1      POLYGON ((-179.29688 -90.50000, -177.89062 -90...  0.000144\n",
+       "2      POLYGON ((-177.89062 -90.50000, -176.48438 -90...  0.000144\n",
+       "3      POLYGON ((-176.48438 -90.50000, -175.07812 -90...  0.000144\n",
+       "4      POLYGON ((-175.07812 -90.50000, -173.67188 -90...  0.000144\n",
+       "...                                                  ...       ...\n",
+       "46512  POLYGON ((173.67188 89.50000, 175.07812 89.500...  0.023041\n",
+       "46513  POLYGON ((175.07812 89.50000, 176.48438 89.500...  0.023041\n",
+       "46514  POLYGON ((176.48438 89.50000, 177.89062 89.500...  0.023041\n",
+       "46515  POLYGON ((177.89062 89.50000, 179.29688 89.500...  0.023041\n",
+       "46516  POLYGON ((179.29688 89.50000, 180.70312 89.500...  0.023041\n",
+       "\n",
+       "[46517 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_model.create_shapefile()\n",
+    "data_model.shapefile['od550du'] = data_model.variables['od550du']['data'][0, 0, :].ravel()\n",
+    "data_model.shapefile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1.3. Plot data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1, figsize=(20, 7))\n",
+    "\n",
+    "gdf_model = data_model.shapefile\n",
+    "gdf_model.plot(ax=ax, column='od550du', cmap='viridis', legend=True)\n",
+    "\n",
+    "gdf_earth = gpd.read_file('/esarchive/shapefiles/timezones/timezones_2021c/timezones_2021c.shp')\n",
+    "gdf_earth.plot(ax=ax, facecolor=\"none\", edgecolor='white', lw=0.5)\n",
+    "\n",
+    "ax.margins(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Observations (AERONET_v3_lev1.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1. Read data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_obs = '/esarchive/obs/ghost/AERONET_v3_lev1.5/1.4/hourly/od550aero/od550aero_202006.nc'"
+   ]
+  },
+  {
+   "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",
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+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
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+       "\n",
+       ".xr-attrs dt:hover span {\n",
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+       "  padding-right: 10px;\n",
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+       "\n",
+       ".xr-attrs dd {\n",
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+       "  stroke: currentColor;\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                                                           (station: 366, time: 720, N_flag_codes: 190, N_qa_codes: 77)\n",
+       "Coordinates:\n",
+       "  * time                                                              (time) datetime64[ns] ...\n",
+       "Dimensions without coordinates: station, N_flag_codes, N_qa_codes\n",
+       "Data variables: (12/180)\n",
+       "    ASTER_v3_altitude                                                 (station) float32 ...\n",
+       "    EDGAR_v4.3.2_annual_average_BC_emissions                          (station) float32 ...\n",
+       "    EDGAR_v4.3.2_annual_average_CO_emissions                          (station) float32 ...\n",
+       "    EDGAR_v4.3.2_annual_average_NH3_emissions                         (station) float32 ...\n",
+       "    EDGAR_v4.3.2_annual_average_NMVOC_emissions                       (station) float32 ...\n",
+       "    EDGAR_v4.3.2_annual_average_NOx_emissions                         (station) float32 ...\n",
+       "    ...                                                                ...\n",
+       "    station_timezone                                                  (station) object ...\n",
+       "    street_type                                                       (station) object ...\n",
+       "    street_width                                                      (station) float32 ...\n",
+       "    terrain                                                           (station) object ...\n",
+       "    vertical_datum                                                    (station) object ...\n",
+       "    weekday_weekend_code                                              (station, time) uint8 ...\n",
+       "Attributes:\n",
+       "    title:          Surface aerosol optical depth at 550nm data in the AERONE...\n",
+       "    institution:    Barcelona Supercomputing Center\n",
+       "    source:         Surface observations\n",
+       "    creator_name:   Dene R. Bowdalo\n",
+       "    creator_email:  dene.bowdalo@bsc.es\n",
+       "    version:        1.4</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-916e7689-c413-4c50-9a5b-780f75e766af' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-916e7689-c413-4c50-9a5b-780f75e766af' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>station</span>: 366</li><li><span class='xr-has-index'>time</span>: 720</li><li><span>N_flag_codes</span>: 190</li><li><span>N_qa_codes</span>: 77</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ff2453df-b509-42cb-a86f-f7072ca5fd8c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff2453df-b509-42cb-a86f-f7072ca5fd8c' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>2020-06-01 ... 2020-06-30T23:00:00</div><input id='attrs-64f43cec-3aaa-4fba-bc45-705ee7841923' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64f43cec-3aaa-4fba-bc45-705ee7841923' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9f52496-ddea-45fc-abea-5c330ffab7f7' class='xr-var-data-in' type='checkbox'><label for='data-d9f52496-ddea-45fc-abea-5c330ffab7f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>description :</span></dt><dd>Time in hours since 2020-06-01 00:00 UTC. Time given refers to the start of the time window the measurement is representative of (temporal resolution).</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>tz :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-06-01T01:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-01T02:00:00.000000000&#x27;, ..., &#x27;2020-06-30T21:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-30T22:00:00.000000000&#x27;, &#x27;2020-06-30T23:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b35fe32-7bae-4797-a708-e7d3d447984b' class='xr-section-summary-in' type='checkbox'  ><label for='section-9b35fe32-7bae-4797-a708-e7d3d447984b' class='xr-section-summary' >Data variables: <span>(180)</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>ASTER_v3_altitude</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-258e633a-7690-405f-870f-92a2628302bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-258e633a-7690-405f-870f-92a2628302bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68e6b0f3-66ac-4b5f-b6a5-ac9dfb80da8d' class='xr-var-data-in' type='checkbox'><label for='data-68e6b0f3-66ac-4b5f-b6a5-ac9dfb80da8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ASTER v3 altitude</dd><dt><span>long_name :</span></dt><dd>ASTER v3 altitude, relative to EGM96 geoid datum</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>description :</span></dt><dd>Altitude from ASTER v3 digital elevation model, relative to EGM96 geoid vertical datum, in metres. The dataset was generated using 1,880,306 Level-1A scenes (taken from the NASA TERRA spacecraft) acquired between March 1, 2000 and November 30, 2013. The ASTER GDEM was created by stacking all individual cloud-masked scene DEMs and non-cloud-masked scene DEMs, then applying various algorithms to remove abnormal data. A statistical approach is not always effective for anomaly removal in areas with a limited number of images. Several existing reference DEMs were used to replace residual anomalies caused by the insufficient number of stacked scenes. In addition to ASTER GDEM, the ASTER Global Water Body Database (ASTWBD) was generated as a by-product to correct elevation values of water body surfaces like sea, rivers, and lakes. The ASTWBD was applied to GDEM to provide proper elevation values for water body surfaces. The sea and lake have a flattened elevation value. The river has a stepped-down elevation value from the upper stream to the lower stream. Native resolution of 1 arc second ~= 30m at the equator.</dd></dl></div><div class='xr-var-data'><pre>array([1313.,   18.,   15., ...,  234.,  454.,  179.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EDGAR_v4.3.2_annual_average_BC_emissions</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a224d7c8-9ea3-424b-acd9-5e6142877552' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a224d7c8-9ea3-424b-acd9-5e6142877552' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43525cc0-b329-4da3-96d4-9930bc4f09b1' class='xr-var-data-in' type='checkbox'><label for='data-43525cc0-b329-4da3-96d4-9930bc4f09b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>EDGAR v4.3.2 annual average BC emissions</dd><dt><span>long_name :</span></dt><dd>EDGAR v4.3.2 annual average black carbon emissions</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>description :</span></dt><dd>EDGAR v4.3.2 annual average BC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([2.489332e-13, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
+       "       0.000000e+00, 2.421291e-13], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EDGAR_v4.3.2_annual_average_CO_emissions</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b0294f7f-0675-4338-b008-b1710af4e022' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0294f7f-0675-4338-b008-b1710af4e022' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5106c750-491a-4e5c-bf75-64c4559c74ec' class='xr-var-data-in' type='checkbox'><label for='data-5106c750-491a-4e5c-bf75-64c4559c74ec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>EDGAR v4.3.2 annual average CO emissions</dd><dt><span>long_name :</span></dt><dd>EDGAR v4.3.2 annual average carbon monoxide emissions</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>description :</span></dt><dd>EDGAR v4.3.2 annual average CO emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([3.161767e-11, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
+       "       0.000000e+00, 6.931332e-13], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EDGAR_v4.3.2_annual_average_NH3_emissions</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f1652db6-8d3b-4708-bb11-1806ed5433ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1652db6-8d3b-4708-bb11-1806ed5433ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-027ca82f-b6ab-4a0c-bee1-d27b876e872c' class='xr-var-data-in' type='checkbox'><label for='data-027ca82f-b6ab-4a0c-bee1-d27b876e872c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>EDGAR v4.3.2 annual average NH3 emissions</dd><dt><span>long_name :</span></dt><dd>EDGAR v4.3.2 annual average ammonia emissions</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>description :</span></dt><dd>EDGAR v4.3.2 annual average NH3 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([9.423107e-16, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
+       "       0.000000e+00, 1.087701e-15], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EDGAR_v4.3.2_annual_average_NMVOC_emissions</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6196dbd9-d01f-411a-b0a7-51c546bb9c27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6196dbd9-d01f-411a-b0a7-51c546bb9c27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cc630b3-69d5-482b-9999-7d74958241e6' class='xr-var-data-in' type='checkbox'><label for='data-6cc630b3-69d5-482b-9999-7d74958241e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>EDGAR v4.3.2 annual average NMVOC emissions</dd><dt><span>long_name :</span></dt><dd>EDGAR v4.3.2 annual average non-methane volatile organic compound emissions</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>description :</span></dt><dd>EDGAR v4.3.2 annual average NMVOC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([6.275388e-12, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
+       "       0.000000e+00, 4.111606e-12], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EDGAR_v4.3.2_annual_average_NOx_emissions</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-42abfc4b-a1b6-4862-8401-0bfeb1fecad7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42abfc4b-a1b6-4862-8401-0bfeb1fecad7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0975fbce-9d48-4cd5-a9b0-d67fab635eaf' class='xr-var-data-in' type='checkbox'><label for='data-0975fbce-9d48-4cd5-a9b0-d67fab635eaf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>EDGAR v4.3.2 annual average NOx emissions</dd><dt><span>long_name :</span></dt><dd>EDGAR v4.3.2 annual average emissions of nitrogen oxides</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>description :</span></dt><dd>EDGAR v4.3.2 annual average NOx emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([6.169598e-12, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
+       "       0.000000e+00, 1.253201e-11], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EDGAR_v4.3.2_annual_average_OC_emissions</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cab87eae-e41f-4ff5-b60f-73400abfee3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cab87eae-e41f-4ff5-b60f-73400abfee3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cb839bb-faf6-4eac-87db-f2c3a7854553' class='xr-var-data-in' type='checkbox'><label for='data-6cb839bb-faf6-4eac-87db-f2c3a7854553' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>EDGAR v4.3.2 annual average OC emissions</dd><dt><span>long_name :</span></dt><dd>EDGAR v4.3.2 annual average organic carbon emissions</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>description :</span></dt><dd>EDGAR v4.3.2 annual average OC emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([1.244637e-13, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
+       "       0.000000e+00, 1.210442e-13], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EDGAR_v4.3.2_annual_average_PM10_emissions</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad3e5cb9-ee6c-4a41-8fd1-da444978befa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad3e5cb9-ee6c-4a41-8fd1-da444978befa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76b7d786-4260-4c37-a969-4fdf9d9ff407' class='xr-var-data-in' type='checkbox'><label for='data-76b7d786-4260-4c37-a969-4fdf9d9ff407' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>EDGAR v4.3.2 annual average PM10 emissions</dd><dt><span>long_name :</span></dt><dd>EDGAR v4.3.2 annual average PM10 emissions</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>description :</span></dt><dd>EDGAR v4.3.2 annual average PM10 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([1.244739e-12, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
+       "       0.000000e+00, 1.211099e-12], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EDGAR_v4.3.2_annual_average_SO2_emissions</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e93e2f6d-7d6a-43c8-a1b0-d5586bb9e4d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e93e2f6d-7d6a-43c8-a1b0-d5586bb9e4d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d7de53a-33d5-4c65-97e5-cda7914bf533' class='xr-var-data-in' type='checkbox'><label for='data-5d7de53a-33d5-4c65-97e5-cda7914bf533' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>EDGAR v4.3.2 annual average SO2 emissions</dd><dt><span>long_name :</span></dt><dd>EDGAR v4.3.2 annual average sulphur dioxide emissions</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>description :</span></dt><dd>EDGAR v4.3.2 annual average SO2 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([4.236494e-12, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
+       "       0.000000e+00, 7.338124e-12], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aae7e157-ee53-446a-93f5-0f4b5aabaec2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aae7e157-ee53-446a-93f5-0f4b5aabaec2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3d376ac-e642-4b04-b51e-1a5fbac1d4e3' class='xr-var-data-in' type='checkbox'><label for='data-b3d376ac-e642-4b04-b51e-1a5fbac1d4e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>EDGAR v4.3.2 annual average biogenic PM2.5 emissions</dd><dt><span>long_name :</span></dt><dd>EDGAR v4.3.2 annual average biogenic PM2.5 emissions</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>description :</span></dt><dd>EDGAR v4.3.2 annual average biogenic PM2.5 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([3.510429e-17, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
+       "       0.000000e+00, 3.058946e-16], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d47f1bd3-f86f-4846-9698-91abd0a637ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d47f1bd3-f86f-4846-9698-91abd0a637ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-048ac5ca-c4e2-43f6-9c67-2619eef17a81' class='xr-var-data-in' type='checkbox'><label for='data-048ac5ca-c4e2-43f6-9c67-2619eef17a81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions</dd><dt><span>long_name :</span></dt><dd>EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>description :</span></dt><dd>EDGAR v4.3.2 annual average fossil fuel PM2.5 emissions, in kilograms per squared metre per second. Native resolution of 0.1 x 0.1 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([1.244664e-12, 0.000000e+00, 0.000000e+00, ..., 0.000000e+00,\n",
+       "       0.000000e+00, 1.210459e-12], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ESDAC_Iwahashi_landform_classification</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-89eb76f9-2efd-4daf-8abb-480f15c09d1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89eb76f9-2efd-4daf-8abb-480f15c09d1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d22897af-0705-47f4-a79c-2451b3725445' class='xr-var-data-in' type='checkbox'><label for='data-d22897af-0705-47f4-a79c-2451b3725445' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ESDAC Iwahashi landform classification</dd><dt><span>long_name :</span></dt><dd>ESDAC Iwahashi landform classification</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>European Soil Data Centre (ESDAC) Iwahashi landform classification. The classification presents relief classes which are classified using an unsupervised nested-means algorithms and a three part geometric signature. Slope gradient, surface texture and local convexity are calculated based on the SRTM30 digital elevation model, within a given window size and classified according to the inherent data set properties. This is a dynamic landform classification method. Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is &quot;water&quot;, then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an &quot;ocean&quot; site, all the surrounding gridcells will be water also, and therefore the class will be maintained as &quot;water&quot;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;medium steep - coarse texture - low convexity&#x27;,\n",
+       "       &#x27;medium steep - fine texture - low convexity&#x27;,\n",
+       "       &#x27;gentle - coarse texture - low convexity&#x27;, ...,\n",
+       "       &#x27;medium steep - coarse texture - low convexity&#x27;,\n",
+       "       &#x27;medium gentle - fine texture - low convexity&#x27;,\n",
+       "       &#x27;medium gentle - fine texture - high convexity&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ESDAC_Meybeck_landform_classification</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-61b5a512-2748-48c6-b077-ab473216e126' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61b5a512-2748-48c6-b077-ab473216e126' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-717c7911-7789-4027-9d52-1c773c43eb08' class='xr-var-data-in' type='checkbox'><label for='data-717c7911-7789-4027-9d52-1c773c43eb08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ESDAC Meybeck landform classification</dd><dt><span>long_name :</span></dt><dd>ESDAC Meybeck landform classification</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>European Soil Data Centre (ESDAC) Meybeck landform classification. The classification presents relief classes which are calculated based on the relief roughness. Roughness and elevation are classified based on a digital elevation model according to static thresholds, with a given window size. This is a static landform classification method. Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is &quot;water&quot;, then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an &quot;ocean&quot; site, all the surrounding gridcells will be water also, and therefore the class will be maintained as &quot;water&quot;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;high altitude plains&#x27;, &#x27;water&#x27;, &#x27;nan&#x27;, ..., &#x27;very low plateaus&#x27;,\n",
+       "       &#x27;mid altitude plains&#x27;, &#x27;lowlands&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ESDAC_modal_Iwahashi_landform_classification_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-41269783-a640-4d0c-84ab-08d0bc3879ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41269783-a640-4d0c-84ab-08d0bc3879ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8b11ba7-8d43-4668-b676-b891f2f0b893' class='xr-var-data-in' type='checkbox'><label for='data-b8b11ba7-8d43-4668-b676-b891f2f0b893' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ESDAC modal Iwahashi landform classification 25km</dd><dt><span>long_name :</span></dt><dd>ESDAC modal Iwahashi landform classification in 25km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal European Soil Data Centre (ESDAC) Iwahashi landform classification in radius of 25km around station location.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;medium steep - coarse texture - low convexity&#x27;, &#x27;water&#x27;, &#x27;water&#x27;, ...,\n",
+       "       &#x27;medium steep - fine texture - high convexity&#x27;,\n",
+       "       &#x27;medium gentle - fine texture - high convexity&#x27;,\n",
+       "       &#x27;medium steep - fine texture - high convexity&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ESDAC_modal_Iwahashi_landform_classification_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-25a24290-76ed-4937-ab6f-b388a17ce66e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25a24290-76ed-4937-ab6f-b388a17ce66e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44f05587-92a0-4ca8-b82d-3c38d315651b' class='xr-var-data-in' type='checkbox'><label for='data-44f05587-92a0-4ca8-b82d-3c38d315651b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ESDAC modal Iwahashi landform classification 5km</dd><dt><span>long_name :</span></dt><dd>ESDAC modal Iwahashi landform classification in 5km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal European Soil Data Centre (ESDAC) Iwahashi landform classification in radius of 5km around station location.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;medium steep - coarse texture - low convexity&#x27;, &#x27;water&#x27;, &#x27;water&#x27;, ...,\n",
+       "       &#x27;medium steep - coarse texture - low convexity&#x27;,\n",
+       "       &#x27;medium gentle - fine texture - low convexity&#x27;,\n",
+       "       &#x27;medium steep - fine texture - high convexity&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ESDAC_modal_Meybeck_landform_classification_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a0c14ca9-7349-46e7-a952-724a5dcbf37c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0c14ca9-7349-46e7-a952-724a5dcbf37c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04a47689-33d4-44f2-b3e0-b3ea78451993' class='xr-var-data-in' type='checkbox'><label for='data-04a47689-33d4-44f2-b3e0-b3ea78451993' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ESDAC modal Meybeck landform classification 25km</dd><dt><span>long_name :</span></dt><dd>ESDAC modal Meybeck landform classification in 25km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal European Soil Data Centre (ESDAC) Meybeck landform classification in radius of 25km around station location.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;mid altitude plateaus&#x27;, &#x27;water&#x27;, &#x27;nan&#x27;, ..., &#x27;very low plateaus&#x27;,\n",
+       "       &#x27;mid altitude plains&#x27;, &#x27;plains&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ESDAC_modal_Meybeck_landform_classification_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d13d8479-1e5a-406b-b8d5-9dbb39d76393' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d13d8479-1e5a-406b-b8d5-9dbb39d76393' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b110be06-6857-46cf-a0ad-aae724465f05' class='xr-var-data-in' type='checkbox'><label for='data-b110be06-6857-46cf-a0ad-aae724465f05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ESDAC modal Meybeck landform classification 5km</dd><dt><span>long_name :</span></dt><dd>ESDAC modal Meybeck landform classification in 5km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal European Soil Data Centre (ESDAC) Meybeck landform classification in radius of 5km around station location.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;high altitude plains&#x27;, &#x27;water&#x27;, &#x27;nan&#x27;, ..., &#x27;very low plateaus&#x27;,\n",
+       "       &#x27;mid altitude plains&#x27;, &#x27;plains&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ETOPO1_altitude</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0235f8ac-6292-4470-9d43-3c1bbba6bb16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0235f8ac-6292-4470-9d43-3c1bbba6bb16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd427960-7d06-4efb-b7e5-0cf84b434b3d' class='xr-var-data-in' type='checkbox'><label for='data-fd427960-7d06-4efb-b7e5-0cf84b434b3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ETOPO1 altitude</dd><dt><span>long_name :</span></dt><dd>ETOPO1 altitude, relative to sea level datum</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>description :</span></dt><dd>Altitude from ETOPO1 digital elevation model, relative to sea level vertical datum, in metres. Over Antarctica and Greenland the elevation given is on top of the ice sheets. Native resolution of 1 arc minute. A correction for coastal sites is made: if the derived altitude is &lt;= -5 m, the maximum altitude of the neighbouring grid boxes will be used instead. If all neighbouring grid boxes have altitudes &lt;= -5 m, the original value will be retained.</dd></dl></div><div class='xr-var-data'><pre>array([1299.,   26.,    2., ...,  216.,  456.,  176.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ETOPO1_max_altitude_difference_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4964a76e-86cc-4b95-8c88-09d89c05cacd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4964a76e-86cc-4b95-8c88-09d89c05cacd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48159f6a-4528-4089-88dc-9e8d4a460a7e' class='xr-var-data-in' type='checkbox'><label for='data-48159f6a-4528-4089-88dc-9e8d4a460a7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ETOPO1 max altitude difference 5km</dd><dt><span>long_name :</span></dt><dd>ETOPO1 maximum altitude difference between the ETOPO1_altitude and all ETOPO1 altitudes in 5km radius</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>description :</span></dt><dd>Altitude difference between the ETOPO1_altitude, and the minimum ETOP1 altitude in a radius of 5 km around the station location, in metres.</dd></dl></div><div class='xr-var-data'><pre>array([ 24., 145.,   5., ...,  31.,   9.,  26.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHOST_version</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5dd27336-4942-48ae-b936-67c464c23b40' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5dd27336-4942-48ae-b936-67c464c23b40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-913ffb72-dbf6-4125-8eef-601f4043687e' class='xr-var-data-in' type='checkbox'><label for='data-913ffb72-dbf6-4125-8eef-601f4043687e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHOST version</dd><dt><span>long_name :</span></dt><dd>Globally Harmonised Observational Surface Treatment (GHOST) version</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Version of the Globally Harmonised Observational Surface Treatment (GHOST).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1.4&#x27;, &#x27;1.4&#x27;, &#x27;1.4&#x27;, ..., &#x27;1.4&#x27;, &#x27;1.4&#x27;, &#x27;1.4&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_average_built_up_area_density_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-15a4a0ed-1a62-4fdf-ac4a-6f3dae67f80e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15a4a0ed-1a62-4fdf-ac4a-6f3dae67f80e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0943439-aa70-4ec3-aec1-efd5311083e8' class='xr-var-data-in' type='checkbox'><label for='data-a0943439-aa70-4ec3-aec1-efd5311083e8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL average built up area density 25km</dd><dt><span>long_name :</span></dt><dd>GHSL average built up area density in 25km radius</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Global Human Settlement Layer (GHSL) average built up area density in a radius of 25km around the station location.</dd></dl></div><div class='xr-var-data'><pre>array([ 0.973138,  0.069935,  0.118563, ...,  4.052589,  0.882098, 13.686182],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_average_built_up_area_density_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-22bed924-2fca-41bb-947a-a097c6cfdec5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22bed924-2fca-41bb-947a-a097c6cfdec5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c25912d3-a90b-4441-bb70-2e187c014e8e' class='xr-var-data-in' type='checkbox'><label for='data-c25912d3-a90b-4441-bb70-2e187c014e8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL average built up area density 5km</dd><dt><span>long_name :</span></dt><dd>GHSL average built up area density in 5km radius</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Global Human Settlement Layer (GHSL) average built up area density in a radius of 5km around the station location.</dd></dl></div><div class='xr-var-data'><pre>array([ 0.779646,  1.149631,  0.344782, ..., 41.303318, 15.780167, 12.955944],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_average_population_density_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-66eac089-7748-4ff9-9df2-0ac71b99a894' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66eac089-7748-4ff9-9df2-0ac71b99a894' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ab62db2-5c3f-48f2-979c-36a7fa081d7c' class='xr-var-data-in' type='checkbox'><label for='data-6ab62db2-5c3f-48f2-979c-36a7fa081d7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL average population density 25km</dd><dt><span>long_name :</span></dt><dd>GHSL average population density in 25km radius</dd><dt><span>units :</span></dt><dd>xx km–2</dd><dt><span>description :</span></dt><dd>Global Human Settlement Layer (GHSL) average population density in a radius of 25km around the station location.</dd></dl></div><div class='xr-var-data'><pre>array([1.933994e+01, 2.114581e+00, 3.526324e-01, ..., 3.771514e+02,\n",
+       "       3.493256e+02, 1.995498e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_average_population_density_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-20c64045-9667-4798-a8a0-185906d984f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20c64045-9667-4798-a8a0-185906d984f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f2072b7-c00d-49ea-bcf4-ba7e8755e6f3' class='xr-var-data-in' type='checkbox'><label for='data-6f2072b7-c00d-49ea-bcf4-ba7e8755e6f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL average population density 5km</dd><dt><span>long_name :</span></dt><dd>GHSL average population density in 5km radius</dd><dt><span>units :</span></dt><dd>xx km–2</dd><dt><span>description :</span></dt><dd>Global Human Settlement Layer (GHSL) average population density in a radius of 5km around the station location.</dd></dl></div><div class='xr-var-data'><pre>array([8.326788e+01, 3.396778e+01, 4.800151e-01, ..., 4.657755e+03,\n",
+       "       6.104640e+03, 2.213394e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_built_up_area_density</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f60ba6e9-e6ea-4da0-a277-4015bb0bc64f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f60ba6e9-e6ea-4da0-a277-4015bb0bc64f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdd16f5c-61ac-4e3b-8a7f-a11a176b7e73' class='xr-var-data-in' type='checkbox'><label for='data-fdd16f5c-61ac-4e3b-8a7f-a11a176b7e73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL built up area density</dd><dt><span>long_name :</span></dt><dd>GHSL built up area density</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Global Human Settlement Layer (GHSL) built up area density (technical label: GHS_BUILT_LDSMT_GLOBE_R2018A), in units of built-up area percent per gridcell (0-100). The product is a multitemporal information layer on built-up presence as derived from Landsat image collections (GLS1975, GLS1990, GLS2000, and ad-hoc Landsat 8 collection 2013/2014). Native resolution of 0.25 x 0.25 kilometres.</dd></dl></div><div class='xr-var-data'><pre>array([ 0.    ,  0.    ,  0.    , ..., 83.7568,  0.    , 33.7712],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_max_built_up_area_density_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8c9a7d99-bea7-4d24-8b0b-29a421813d78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c9a7d99-bea7-4d24-8b0b-29a421813d78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f27ede1-592d-4da1-a907-5a199a06c252' class='xr-var-data-in' type='checkbox'><label for='data-5f27ede1-592d-4da1-a907-5a199a06c252' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL max built up area density 25km</dd><dt><span>long_name :</span></dt><dd>GHSL max built up area density in 25km radius</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Global Human Settlement Layer (GHSL) max built up area density in a radius of 25km around the station location.</dd></dl></div><div class='xr-var-data'><pre>array([ 98.,  95.,  65., ..., 100., 100., 100.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_max_built_up_area_density_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-382ec1e7-a080-46d9-abaf-1b06be85a89f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-382ec1e7-a080-46d9-abaf-1b06be85a89f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99e60244-b67e-40f2-b97e-0e5110c65ca2' class='xr-var-data-in' type='checkbox'><label for='data-99e60244-b67e-40f2-b97e-0e5110c65ca2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL max built up area density 5km</dd><dt><span>long_name :</span></dt><dd>GHSL max built up area density in 5km radius</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Global Human Settlement Layer (GHSL) max built up area density in a radius of 5km around the station location.</dd></dl></div><div class='xr-var-data'><pre>array([ 87.,  95.,  26., ..., 100., 100.,  99.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_max_population_density_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8c8c3a53-ccd3-4f78-b37c-4e9d82d15960' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c8c3a53-ccd3-4f78-b37c-4e9d82d15960' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca1983f4-cc06-438d-81b4-459293d110f3' class='xr-var-data-in' type='checkbox'><label for='data-ca1983f4-cc06-438d-81b4-459293d110f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL max population density 25km</dd><dt><span>long_name :</span></dt><dd>GHSL max population density in 25km radius</dd><dt><span>units :</span></dt><dd>xx km–2</dd><dt><span>description :</span></dt><dd>Global Human Settlement Layer (GHSL) max population density in a radius of 25km around the station location.</dd></dl></div><div class='xr-var-data'><pre>array([ 8958.,  2350.,   549., ..., 11219., 45549.,  9135.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_max_population_density_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fbad23e7-36c3-4a1e-9dca-86cd6883ea52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbad23e7-36c3-4a1e-9dca-86cd6883ea52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7c3c71a-15c5-4278-ab7b-d43fa371e781' class='xr-var-data-in' type='checkbox'><label for='data-a7c3c71a-15c5-4278-ab7b-d43fa371e781' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL max population density 5km</dd><dt><span>long_name :</span></dt><dd>GHSL max population density in 5km radius</dd><dt><span>units :</span></dt><dd>xx km–2</dd><dt><span>description :</span></dt><dd>Global Human Settlement Layer (GHSL) max population density in a radius of 5km around the station location.</dd></dl></div><div class='xr-var-data'><pre>array([8.9580e+03, 2.3500e+03, 4.3000e+01, ..., 1.1219e+04, 4.5549e+04,\n",
+       "       1.7360e+03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_modal_settlement_model_classification_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a5784f25-bd8c-439c-a36f-a5a267844906' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5784f25-bd8c-439c-a36f-a5a267844906' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e5b44b1-b9dc-4d19-ac8e-53296f7547ae' class='xr-var-data-in' type='checkbox'><label for='data-1e5b44b1-b9dc-4d19-ac8e-53296f7547ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL modal settlement model classification 25km</dd><dt><span>long_name :</span></dt><dd>GHSL modal settlement model classification in 25km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal Global Human Settlement Layer (GHSL) settlement model classification in radius of 25km around station location.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;very low density rural&#x27;, &#x27;water&#x27;, &#x27;water&#x27;, ...,\n",
+       "       &#x27;very low density rural&#x27;, &#x27;very low density rural&#x27;,\n",
+       "       &#x27;very low density rural&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_modal_settlement_model_classification_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a0d9be7b-868d-4d56-a248-055e489004c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0d9be7b-868d-4d56-a248-055e489004c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24f4a177-9a0b-4111-aa99-ac8d5386b987' class='xr-var-data-in' type='checkbox'><label for='data-24f4a177-9a0b-4111-aa99-ac8d5386b987' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL modal settlement model classification 5km</dd><dt><span>long_name :</span></dt><dd>GHSL modal settlement model classification in 5km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal Global Human Settlement Layer (GHSL) settlement model classification in radius of 5km around station location.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;very low density rural&#x27;, &#x27;water&#x27;, &#x27;water&#x27;, ..., &#x27;urban centre&#x27;,\n",
+       "       &#x27;very low density rural&#x27;, &#x27;low density rural&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_population_density</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-391db14c-0630-4ec6-8191-d9b01f828d4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-391db14c-0630-4ec6-8191-d9b01f828d4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9b99bbc-96b3-4186-9295-9579f2bff679' class='xr-var-data-in' type='checkbox'><label for='data-c9b99bbc-96b3-4186-9295-9579f2bff679' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL population density</dd><dt><span>long_name :</span></dt><dd>GHSL population density</dd><dt><span>units :</span></dt><dd>xx km–2</dd><dt><span>description :</span></dt><dd>Global Human Settlement Layer (GHSL) population density (technical label: GHS_POP_MT_GLOBE_R2019A), in populus per squared kilometre. It depicts the distribution of population, expressed as the number of people per cell. Residential population estimates for target years 1975, 1990, 2000 and 2015 provided by CIESIN GPWv4.10 were disaggregated from census or administrative units to grid cells, informed by the distribution and density of built-up as mapped in the GHSL global layer per corresponding epoch. Native resolution of 0.25 x 0.25 kilometres.</dd></dl></div><div class='xr-var-data'><pre>array([   0.    ,    0.    ,    0.    , ..., 9396.993 ,    0.    ,  556.0051],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GHSL_settlement_model_classification</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c767ba4-bb59-44e4-904b-79a184a69fda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c767ba4-bb59-44e4-904b-79a184a69fda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dfcdd8f-e75b-45e2-825b-7f2a1c67f606' class='xr-var-data-in' type='checkbox'><label for='data-3dfcdd8f-e75b-45e2-825b-7f2a1c67f606' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GHSL settlement model classification</dd><dt><span>long_name :</span></dt><dd>GHSL settlement model classification</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Global Human Settlement Layer (GHSL) settlement model classification (technical label: GHS_SMOD_POPMT_GLOBE_R2019A). The classification delineates and classify settlement typologies via a logic of population size, population and built-up area densities as a refinement of the ‘degree of urbanization’ method described by EUROSTAT. The classification is derived by using the GHS_POP_MT_GLOBE_R2019A and GHS_BUILT_LDSMT_GLOBE_R2018A products. The GHS Settlement Model grid is an improvement of the GHS Settlement Grid (R2016A) introducing a more detailed classification of settlements in two levels, also called ‘refined degree of urbanization’. The Settlement Model is provided at detailed level (Second Level - L2). The First Level, as a porting of the Degree of Urbanization adopted by EUROSTAT can be obtained aggregating L2. Native resolution of 1.0 x 1.0 kilometres.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;very low density rural&#x27;, &#x27;very low density rural&#x27;,\n",
+       "       &#x27;very low density rural&#x27;, ..., &#x27;urban centre&#x27;, &#x27;rural cluster&#x27;,\n",
+       "       &#x27;low density rural&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GPW_average_population_density_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1025dc44-e7d4-4e2e-81da-ccd552eddb5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1025dc44-e7d4-4e2e-81da-ccd552eddb5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e96c70d-868e-4f54-9275-01b764ae6dab' class='xr-var-data-in' type='checkbox'><label for='data-1e96c70d-868e-4f54-9275-01b764ae6dab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GPW average population density 25km</dd><dt><span>long_name :</span></dt><dd>GPW average population density in 25km radius</dd><dt><span>units :</span></dt><dd>xx km–2</dd><dt><span>description :</span></dt><dd>Gridded Population of the World (GPW), average population density in a radius of 25 km around the station location.</dd></dl></div><div class='xr-var-data'><pre>array([124.32709 ,   2.79323 ,   0.764419, ..., 357.12894 , 273.1742  ,\n",
+       "       212.34114 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GPW_average_population_density_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-068c7dea-7b20-489b-a69d-437678708279' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-068c7dea-7b20-489b-a69d-437678708279' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29ce14cb-db57-4c57-98e0-0b4f60ef1697' class='xr-var-data-in' type='checkbox'><label for='data-29ce14cb-db57-4c57-98e0-0b4f60ef1697' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GPW average population density 5km</dd><dt><span>long_name :</span></dt><dd>GPW average population density in 5km radius</dd><dt><span>units :</span></dt><dd>xx km–2</dd><dt><span>description :</span></dt><dd>Gridded Population of the World (GPW), average population density in a radius of 5 km around the station location.</dd></dl></div><div class='xr-var-data'><pre>array([1.226605e+01, 4.290382e+01, 5.904162e-02, ..., 7.327646e+02,\n",
+       "       4.929010e+02, 2.843317e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GPW_max_population_density_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-17ddecd3-34f1-4121-908b-d106612bd6d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17ddecd3-34f1-4121-908b-d106612bd6d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99c8b85e-fcf1-4753-8c4a-b4b27b8c7b37' class='xr-var-data-in' type='checkbox'><label for='data-99c8b85e-fcf1-4753-8c4a-b4b27b8c7b37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GPW max population density 25km</dd><dt><span>long_name :</span></dt><dd>GPW average population density in 25km radius</dd><dt><span>units :</span></dt><dd>xx km–2</dd><dt><span>description :</span></dt><dd>Gridded Population of the World (GPW), maximum population density in a radius of 25 km around the station location.</dd></dl></div><div class='xr-var-data'><pre>array([75960.96   ,   832.58167,   280.91034, ...,  1197.667  ,  2789.2234 ,\n",
+       "        5616.408  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GPW_max_population_density_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e3f4a060-2478-449e-9b3e-c6b6fd176df8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3f4a060-2478-449e-9b3e-c6b6fd176df8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d1eb20c-db52-4275-b608-4dc9f9d51785' class='xr-var-data-in' type='checkbox'><label for='data-5d1eb20c-db52-4275-b608-4dc9f9d51785' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GPW max population density 5km</dd><dt><span>long_name :</span></dt><dd>GPW average population density in 5km radius</dd><dt><span>units :</span></dt><dd>xx km–2</dd><dt><span>description :</span></dt><dd>Gridded Population of the World (GPW), maximum population density in a radius of 5 km around the station location.</dd></dl></div><div class='xr-var-data'><pre>array([1.226605e+01, 8.325817e+02, 9.681910e-01, ..., 1.085922e+03,\n",
+       "       2.789223e+03, 3.627036e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GPW_population_density</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7a6f71f0-ca26-4c66-9d15-5ddd569dd341' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a6f71f0-ca26-4c66-9d15-5ddd569dd341' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4f8136a-1f4a-441e-9d90-976fd9a29019' class='xr-var-data-in' type='checkbox'><label for='data-b4f8136a-1f4a-441e-9d90-976fd9a29019' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GPW population density</dd><dt><span>long_name :</span></dt><dd>GPW population density</dd><dt><span>units :</span></dt><dd>xx km–2</dd><dt><span>description :</span></dt><dd>Gridded Population of the World (GPW), population density, in populus per squared kilometre, from either version 3 and 4 of the provided gridded datasets, dependent on the data year: v3 (1990-2000), v4 (2000-2015). Native resolution of 0.04166 x 0.04166 for v3 data; native resolution of 0.0083 x 0.0083 degrees for v4 data.</dd></dl></div><div class='xr-var-data'><pre>array([1.226605e+01, 1.010118e+02, 8.580016e-02, ..., 7.309171e+02,\n",
+       "       1.188315e+02, 2.668087e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>GSFC_coastline_proximity</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4431cc6f-3932-4ec7-8388-559c5bee94e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4431cc6f-3932-4ec7-8388-559c5bee94e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0677357-3b3c-46ad-ae10-f0f5dca81a57' class='xr-var-data-in' type='checkbox'><label for='data-c0677357-3b3c-46ad-ae10-f0f5dca81a57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>GSFC coastline proximity</dd><dt><span>long_name :</span></dt><dd>GSFC proximity to the coastline</dd><dt><span>units :</span></dt><dd>km</dd><dt><span>description :</span></dt><dd>Proximity to the coastline provided by the NASA Goddard Space Flight Center (GSFC) Ocean Color Group, in kilometres, produced using the Generic Mapping Tools package. Native resolution of 0.01 x 0.01 degrees. Negative distances represent locations over land (including land-locked bodies of water), while positive distances represent locations over the ocean. There is an uncertainty of up to 1 km in the computed distance at any given point.</dd></dl></div><div class='xr-var-data'><pre>array([-718.,    0.,   -1., ..., -162., -950., -606.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Joly-Peuch_classification_code</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-58208105-a0dd-493d-ad87-8057694fc045' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58208105-a0dd-493d-ad87-8057694fc045' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15ca3116-a0fa-491a-9152-044411e02781' class='xr-var-data-in' type='checkbox'><label for='data-15ca3116-a0fa-491a-9152-044411e02781' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Joly-Peuch classification code</dd><dt><span>long_name :</span></dt><dd>Joly-Peuch classification code</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Joly-Peuch European classification code (range of 1-10) designed to objectively stratify stations between those diplaying rural and urban signatures (most rural == 1, most urban == 10). This classification is objectively made per species. The species that this is done for are: O3, NO2, SO2, CO, PM10, PM2.5. See reference here: https://www.sciencedirect.com/science/article/abs/pii/S1352231011012088</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Koppen-Geiger_classification</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1c85ad94-184d-4e5c-a8dc-df88aa33f4ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c85ad94-184d-4e5c-a8dc-df88aa33f4ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3c44c79-1829-416b-9660-f0a6f574f343' class='xr-var-data-in' type='checkbox'><label for='data-e3c44c79-1829-416b-9660-f0a6f574f343' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Koppen-Geiger classification</dd><dt><span>long_name :</span></dt><dd>Koppen-Geiger classification</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Koppen-Geiger classification, classifying the global climates into 5 main groups (30 total groups with subcategories). Native resolution of 0.0083 x 0.0083 degrees. A correction for costal sites is made: if the native class is &quot;water&quot;, then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an &quot;ocean&quot; site, all the surrounding gridcells will be water also, and therefore the class will be maintained as &quot;water&quot;. See citation: Beck, H.E., N.E. Zimmermann, T.R. McVicar, N. Vergopolan, A. Berg, E.F. Wood: Present and future Köppen-Geiger climate classification maps at 1-km resolution, Nature Scientific Data, 2018.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;arid - steppe - cold&#x27;, &#x27;temperate - dry summer - hot summer&#x27;,\n",
+       "       &#x27;polar - tundra&#x27;, ..., &#x27;arid - steppe - cold&#x27;, &#x27;arid - desert - hot&#x27;,\n",
+       "       &#x27;cold - no dry season - warm summer&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Koppen-Geiger_modal_classification_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fcb0cfd9-0012-4da1-9151-6cb70a5ccf51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcb0cfd9-0012-4da1-9151-6cb70a5ccf51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d8d3787-2a7d-4853-9744-52c710a6feb1' class='xr-var-data-in' type='checkbox'><label for='data-2d8d3787-2a7d-4853-9744-52c710a6feb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Koppen-Geiger modal classification 25km</dd><dt><span>long_name :</span></dt><dd>Koppen-Geiger classification</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal Koppen-Geiger classification in radius of 25km around station location.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;arid - steppe - cold&#x27;, &#x27;water&#x27;, &#x27;water&#x27;, ..., &#x27;arid - steppe - cold&#x27;,\n",
+       "       &#x27;arid - desert - hot&#x27;, &#x27;cold - no dry season - warm summer&#x27;],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Koppen-Geiger_modal_classification_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6e121279-fc8c-40c3-a4c6-8333108f7bd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e121279-fc8c-40c3-a4c6-8333108f7bd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2310cda-4abf-43df-ac30-78468ac3f11f' class='xr-var-data-in' type='checkbox'><label for='data-c2310cda-4abf-43df-ac30-78468ac3f11f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>Koppen-Geiger modal classification 5km</dd><dt><span>long_name :</span></dt><dd>Koppen-Geiger classification</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal Koppen-Geiger classification in radius of 5km around station location.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;arid - steppe - cold&#x27;, &#x27;water&#x27;, &#x27;water&#x27;, ..., &#x27;arid - steppe - cold&#x27;,\n",
+       "       &#x27;arid - desert - hot&#x27;, &#x27;cold - no dry season - warm summer&#x27;],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MODIS_MCD12C1_v6_IGBP_land_use</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-874511db-0983-4e72-b15b-6b260375b833' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-874511db-0983-4e72-b15b-6b260375b833' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b8737a6-d068-4661-94a9-5d626541ea96' class='xr-var-data-in' type='checkbox'><label for='data-0b8737a6-d068-4661-94a9-5d626541ea96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>MODIS MCD12C1 v6 IGBP land use</dd><dt><span>long_name :</span></dt><dd>MODIS MCD12C1 v6 IGBP land use</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Majority land use class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is &quot;water bodies&quot;, then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an &quot;ocean&quot; site, all the surrounding gridcells will be water also, and therefore the class will be maintained as &quot;water&quot;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;grasslands&#x27;, &#x27;grasslands&#x27;, &#x27;permanent wetlands&#x27;, ...,\n",
+       "       &#x27;urban and built-up lands&#x27;, &#x27;grasslands&#x27;, &#x27;woody savannas&#x27;],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MODIS_MCD12C1_v6_LAI</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0ccc4750-90c0-4e41-9020-d0ed490b287a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ccc4750-90c0-4e41-9020-d0ed490b287a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bde918e8-6069-481e-8b6c-9161777e6aa5' class='xr-var-data-in' type='checkbox'><label for='data-bde918e8-6069-481e-8b6c-9161777e6aa5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>MODIS MCD12C1 v6 LAI</dd><dt><span>long_name :</span></dt><dd>MODIS MCD12C1 v6 Leaf Area Index</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Majority Leaf Area Index class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is &quot;water bodies&quot;, then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an &quot;ocean&quot; site, all the surrounding gridcells will be water also, and therefore the class will be maintained as &quot;water&quot;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;grasslands&#x27;, &#x27;grasslands&#x27;, &#x27;grasslands&#x27;, ...,\n",
+       "       &#x27;urban and built-up lands&#x27;, &#x27;grasslands&#x27;, &#x27;savannas&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MODIS_MCD12C1_v6_UMD_land_use</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f15b0dca-efc9-422b-b015-597bb6ef9fd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f15b0dca-efc9-422b-b015-597bb6ef9fd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c155b69-f7b6-4f7a-af8e-05c475fcc0d1' class='xr-var-data-in' type='checkbox'><label for='data-9c155b69-f7b6-4f7a-af8e-05c475fcc0d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>MODIS MCD12C1 v6 UMD land use</dd><dt><span>long_name :</span></dt><dd>MODIS MCD12C1 v6 UMD land use</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Majority land use class from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification. Native resolution of 0.05 x 0.05 degrees. See dataset user guide here: https://lpdaac.usgs.gov/documents/101/MCD12_User_Guide_V6.pd. A correction for costal sites is made: if the native class is &quot;water bodies&quot;, then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an &quot;ocean&quot; site, all the surrounding gridcells will be water also, and therefore the class will be maintained as &quot;water&quot;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;grasslands&#x27;, &#x27;grasslands&#x27;, &#x27;permanent wetlands&#x27;, ...,\n",
+       "       &#x27;urban and built-up lands&#x27;, &#x27;grasslands&#x27;, &#x27;woody savannas&#x27;],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MODIS_MCD12C1_v6_modal_IGBP_land_use_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8040a9c5-4165-49bb-ae16-90e9d86a4876' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8040a9c5-4165-49bb-ae16-90e9d86a4876' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de1fc501-b9e0-4992-a87c-5a42a821c954' class='xr-var-data-in' type='checkbox'><label for='data-de1fc501-b9e0-4992-a87c-5a42a821c954' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>MODIS MCD12C1 v6 IGBP modal land use 25km</dd><dt><span>long_name :</span></dt><dd>MODIS MCD12C1 v6 IGBP modal land use in 25km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal land use in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;grasslands&#x27;, &#x27;water bodies&#x27;, &#x27;water bodies&#x27;, ..., &#x27;croplands&#x27;,\n",
+       "       &#x27;grasslands&#x27;, &#x27;mixed forests&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MODIS_MCD12C1_v6_modal_IGBP_land_use_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7c818288-cdd4-4af7-9721-adc2f824cdbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c818288-cdd4-4af7-9721-adc2f824cdbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7a4bbcb-c28e-46d3-b2ca-37f981c65db5' class='xr-var-data-in' type='checkbox'><label for='data-c7a4bbcb-c28e-46d3-b2ca-37f981c65db5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>MODIS MCD12C1 v6 IGBP modal land use 5km</dd><dt><span>long_name :</span></dt><dd>MODIS MCD12C1 v6 IGBP modal land use in 5km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal land use in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the International Geosphere-Biosphere Programme (IGBP) classification.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;grasslands&#x27;, &#x27;water bodies&#x27;, &#x27;water bodies&#x27;, ...,\n",
+       "       &#x27;urban and built-up lands&#x27;, &#x27;grasslands&#x27;, &#x27;mixed forests&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MODIS_MCD12C1_v6_modal_LAI_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-702cfee1-9387-4c20-97a7-66aaaea03609' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-702cfee1-9387-4c20-97a7-66aaaea03609' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc96b9a0-1764-44a2-acec-1fd108470e8c' class='xr-var-data-in' type='checkbox'><label for='data-cc96b9a0-1764-44a2-acec-1fd108470e8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>MODIS MCD12C1 v6 LAI modal 25km</dd><dt><span>long_name :</span></dt><dd>MODIS MCD12C1 v6 modal Leaf Area Index in 25km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal Leaf Area Index in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;grasslands&#x27;, &#x27;water bodies&#x27;, &#x27;water bodies&#x27;, ...,\n",
+       "       &#x27;broadleaf croplands&#x27;, &#x27;grasslands&#x27;, &#x27;savannas&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MODIS_MCD12C1_v6_modal_LAI_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2e1be4fb-cf25-4f0e-a26c-fe1705f24f82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e1be4fb-cf25-4f0e-a26c-fe1705f24f82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab559bf0-4ef3-41bc-a2e1-efd2a35ec257' class='xr-var-data-in' type='checkbox'><label for='data-ab559bf0-4ef3-41bc-a2e1-efd2a35ec257' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>MODIS MCD12C1 v6 LAI modal 5km</dd><dt><span>long_name :</span></dt><dd>MODIS MCD12C1 v6 modal Leaf Area Index in 5km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal Leaf Area Index in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;grasslands&#x27;, &#x27;water bodies&#x27;, &#x27;water bodies&#x27;, ...,\n",
+       "       &#x27;urban and built-up lands&#x27;, &#x27;grasslands&#x27;, &#x27;savannas&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MODIS_MCD12C1_v6_modal_UMD_land_use_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-52577ade-2e8a-40b3-8cd5-a52987298c67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52577ade-2e8a-40b3-8cd5-a52987298c67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1c63253-2e1f-4de4-abf6-1570f1aa8ed7' class='xr-var-data-in' type='checkbox'><label for='data-c1c63253-2e1f-4de4-abf6-1570f1aa8ed7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>MODIS MCD12C1 v6 UMD modal land use 25km</dd><dt><span>long_name :</span></dt><dd>MODIS MCD12C1 v6 UMD modal land use in 25km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal land use in radius of 25km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;grasslands&#x27;, &#x27;water bodies&#x27;, &#x27;water bodies&#x27;, ..., &#x27;croplands&#x27;,\n",
+       "       &#x27;grasslands&#x27;, &#x27;mixed forests&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MODIS_MCD12C1_v6_modal_UMD_land_use_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f589280a-48a3-46cf-adf1-bd5c75cbaf26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f589280a-48a3-46cf-adf1-bd5c75cbaf26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5501e632-647b-49aa-b58f-21a4b4fde1a1' class='xr-var-data-in' type='checkbox'><label for='data-5501e632-647b-49aa-b58f-21a4b4fde1a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>MODIS MCD12C1 v6 UMD modal land use 5km</dd><dt><span>long_name :</span></dt><dd>MODIS MCD12C1 v6 UMD modal land use in 5km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Modal land use in radius of 5km around the station location from the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) MCD12C1 version 6, using the University of Maryland (UMD) classification.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;grasslands&#x27;, &#x27;water bodies&#x27;, &#x27;water bodies&#x27;, ...,\n",
+       "       &#x27;urban and built-up lands&#x27;, &#x27;grasslands&#x27;, &#x27;mixed forests&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a4446252-b340-4161-ab99-c58c38e97fda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4446252-b340-4161-ab99-c58c38e97fda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19cf3ca9-fa1d-4952-9540-c18d6c03d502' class='xr-var-data-in' type='checkbox'><label for='data-19cf3ca9-fa1d-4952-9540-c18d6c03d502' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>NOAA-DMSP-OLS v4 average nighttime stable lights 25km</dd><dt><span>long_name :</span></dt><dd>NOAA DMSP-OLS version 4 average nighttime stable lights in 25km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 average nighttime stable lights in 25km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63.</dd></dl></div><div class='xr-var-data'><pre>array([ 6.,  0., 17., ..., 24.,  1., 33.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d50a776b-08da-4885-a0ee-0e177c23c83e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d50a776b-08da-4885-a0ee-0e177c23c83e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77c84d5e-0cc5-4bca-be77-f954c335313c' class='xr-var-data-in' type='checkbox'><label for='data-77c84d5e-0cc5-4bca-be77-f954c335313c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>NOAA-DMSP-OLS v4 average nighttime stable lights 5km</dd><dt><span>long_name :</span></dt><dd>NOAA DMSP-OLS version 4 average nighttime stable lights in 5km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 average nighttime stable lights in 5km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63.</dd></dl></div><div class='xr-var-data'><pre>array([ 6.,  6., 37., ..., 61., 13., 34.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-079192e1-2562-4d1e-9f4d-a7c69cdf869d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-079192e1-2562-4d1e-9f4d-a7c69cdf869d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7db49aa-7359-474c-bd9a-a65de6c6d880' class='xr-var-data-in' type='checkbox'><label for='data-b7db49aa-7359-474c-bd9a-a65de6c6d880' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>NOAA-DMSP-OLS v4 max nighttime stable lights 25km</dd><dt><span>long_name :</span></dt><dd>NOAA DMSP-OLS version 4 maximum nighttime stable lights in 25km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 maximum nighttime stable lights in 25km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63.</dd></dl></div><div class='xr-var-data'><pre>array([63., 16., 63., ..., 63., 56., 61.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aae93c6e-bd94-46a6-9b50-8c339777c817' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aae93c6e-bd94-46a6-9b50-8c339777c817' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c84c855e-12e2-4b33-9f59-1bfbc5a7d20e' class='xr-var-data-in' type='checkbox'><label for='data-c84c855e-12e2-4b33-9f59-1bfbc5a7d20e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>NOAA-DMSP-OLS v4 max nighttime stable lights 5km</dd><dt><span>long_name :</span></dt><dd>NOAA DMSP-OLS version 4 maximum nighttime stable lights in 5km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 maximum nighttime stable lights in 5km radius of measurement station. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63.</dd></dl></div><div class='xr-var-data'><pre>array([26., 16., 60., ..., 63., 56., 56.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NOAA-DMSP-OLS_v4_nighttime_stable_lights</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0164e13a-b3a3-4613-ac66-0e47de692d6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0164e13a-b3a3-4613-ac66-0e47de692d6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-504de6b4-e836-455b-bd6b-58a8d8bb3218' class='xr-var-data-in' type='checkbox'><label for='data-504de6b4-e836-455b-bd6b-58a8d8bb3218' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>NOAA-DMSP-OLS v4 nighttime stable lights</dd><dt><span>long_name :</span></dt><dd>NOAA DMSP-OLS version 4 nighttime stable lights</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>National Oceanic and Atmospheric Administration (NOAA), Defense Meteorological Satellite Program - Operational Linescane System (DMSP-OLS) version 4 nighttime stable lights. Native resolution of 0.0083 x 0.0083 degrees. The values represent a brightness index ranging from 0 to 63. The sensor saturates at a value of 63.</dd></dl></div><div class='xr-var-data'><pre>array([ 5., 11., 54., ..., 63.,  9., 28.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OMI_level3_column_annual_average_NO2</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-233e17bd-1810-45cb-8978-096855259fd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-233e17bd-1810-45cb-8978-096855259fd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5beadfa7-0ff4-4bb0-8d45-8eb1406b5917' class='xr-var-data-in' type='checkbox'><label for='data-5beadfa7-0ff4-4bb0-8d45-8eb1406b5917' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>OMI level3 column annual average NO2</dd><dt><span>long_name :</span></dt><dd>OMI level3 column annual average nitrogen dioxide</dd><dt><span>units :</span></dt><dd>xx cm-2</dd><dt><span>description :</span></dt><dd>AURA Ozone monitoring instrument (OMI) level3 column annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([6.301160e+15, 3.021980e+15, 3.681956e+15, ..., 4.460874e+15,\n",
+       "       2.814616e+15, 9.600188e+15], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OMI_level3_column_cloud_screened_annual_average_NO2</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-92296c37-3651-47fc-8e91-5cabd48aea6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-92296c37-3651-47fc-8e91-5cabd48aea6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c82182b7-fb7b-4181-b865-ede627fe6b6f' class='xr-var-data-in' type='checkbox'><label for='data-c82182b7-fb7b-4181-b865-ede627fe6b6f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>OMI level3 column cloud screened annual average NO2</dd><dt><span>long_name :</span></dt><dd>OMI level3 column cloud screened annual average nitrogen dioxide</dd><dt><span>units :</span></dt><dd>xx cm-2</dd><dt><span>description :</span></dt><dd>AURA Ozone monitoring instrument (OMI) level3 column cloud screened (where cloud fraction is less than 30 percent) annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([6.354764e+15, 3.141651e+15, 3.933728e+15, ..., 4.377470e+15,\n",
+       "       2.837691e+15, 7.143090e+15], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OMI_level3_tropospheric_column_annual_average_NO2</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3ce92a1-d092-4941-94af-21e518aebb9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3ce92a1-d092-4941-94af-21e518aebb9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29befe96-dbd0-40dc-ae79-bb27ce396f1a' class='xr-var-data-in' type='checkbox'><label for='data-29befe96-dbd0-40dc-ae79-bb27ce396f1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>OMI level3 tropospheric column annual average NO2</dd><dt><span>long_name :</span></dt><dd>OMI level3 tropospheric column annual average nitrogen dioxide</dd><dt><span>units :</span></dt><dd>xx cm-2</dd><dt><span>description :</span></dt><dd>AURA Ozone monitoring instrument (OMI) level3 tropospheric column annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([3.723849e+15, 4.691550e+14, 3.824027e+14, ..., 1.833370e+15,\n",
+       "       6.993819e+14, 7.325085e+15], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b16d2283-a58f-4d71-ac19-e8d5a1a6d28e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b16d2283-a58f-4d71-ac19-e8d5a1a6d28e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-417deb83-d7e4-4191-9e8b-0d6828e6806f' class='xr-var-data-in' type='checkbox'><label for='data-417deb83-d7e4-4191-9e8b-0d6828e6806f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>OMI level3 tropospheric column cloud screened annual average NO2</dd><dt><span>long_name :</span></dt><dd>OMI level3 tropospheric column cloud screened annual average nitrogen dioxide</dd><dt><span>units :</span></dt><dd>xx cm-2</dd><dt><span>description :</span></dt><dd>AURA Ozone monitoring instrument (OMI) level3 tropospheric column cloud screened (where cloud fraction is less than 30 percent) annual average NO2, in molecules per squared centimetres. Native resolution of 0.25 x 0.25 degrees.</dd></dl></div><div class='xr-var-data'><pre>array([3.713093e+15, 5.222752e+14, 5.292174e+14, ..., 1.688689e+15,\n",
+       "       7.069353e+14, 4.294799e+15], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UMBC_anthrome_classification</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4b8d3c99-4df3-4da1-bfd2-6819bdf64e45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b8d3c99-4df3-4da1-bfd2-6819bdf64e45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95e563bd-4150-4ca7-b68d-9d1a99157237' class='xr-var-data-in' type='checkbox'><label for='data-95e563bd-4150-4ca7-b68d-9d1a99157237' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>UMBC anthrome classification</dd><dt><span>long_name :</span></dt><dd>UMBC anthrome classification</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>University of Maryland Baltimore County (UMBC) anthrome classification, describing the anthropogenic land use (for the year 2000). There are 20 distinct classifications. Native resolution of 0.0833 x 0.0833 degrees. A correction for costal sites is made: if the native anthrome class is &quot;water&quot;, then the modal classification of the neighbouring grid boxes is used instead (lowest code kept preferentially in case of a tie). If the site is truly an &quot;ocean&quot; site, all the surrounding gridcells will be water also, and therefore the class will be maintained as &quot;water&quot;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;residential rangelands&#x27;, &#x27;water&#x27;, &#x27;wild treeless and barren lands&#x27;,\n",
+       "       ..., &#x27;urban&#x27;, &#x27;urban&#x27;, &#x27;pastoral villages&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UMBC_modal_anthrome_classification_25km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d3e063fe-f385-4130-a634-a7a75bad8399' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3e063fe-f385-4130-a634-a7a75bad8399' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8bb230d-2f13-4e2c-b4ff-0459310d7cef' class='xr-var-data-in' type='checkbox'><label for='data-a8bb230d-2f13-4e2c-b4ff-0459310d7cef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>UMBC modal anthrome classification 25km</dd><dt><span>long_name :</span></dt><dd>UMBC modal anthrome classification in 25km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>University of Maryland Baltimore County (UMBC) modal anthrome classification in radius of 25km around station location.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;residential rangelands&#x27;, &#x27;water&#x27;, &#x27;water&#x27;, ..., &#x27;populated croplands&#x27;,\n",
+       "       &#x27;residential rainfed croplands&#x27;, &#x27;residential rangelands&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UMBC_modal_anthrome_classification_5km</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e253d62b-5cd6-4f0c-a3be-12b125285e18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e253d62b-5cd6-4f0c-a3be-12b125285e18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ea9ed7a-8be5-47c0-8f5f-c2fc0123adb2' class='xr-var-data-in' type='checkbox'><label for='data-0ea9ed7a-8be5-47c0-8f5f-c2fc0123adb2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>UMBC modal anthrome classification 5km</dd><dt><span>long_name :</span></dt><dd>UMBC modal anthrome classification in 5km radius</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>University of Maryland Baltimore County (UMBC) modal anthrome classification in radius of 5km around station location.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;residential rangelands&#x27;, &#x27;water&#x27;, &#x27;wild treeless and barren lands&#x27;,\n",
+       "       ..., &#x27;urban&#x27;, &#x27;urban&#x27;, &#x27;pastoral villages&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WMO_region</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-36047d69-5a0d-42eb-bb36-3e2fdd22a858' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36047d69-5a0d-42eb-bb36-3e2fdd22a858' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f0e65f4-a58c-42d5-8c5d-0262dda0bdcb' class='xr-var-data-in' type='checkbox'><label for='data-8f0e65f4-a58c-42d5-8c5d-0262dda0bdcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>WMO region</dd><dt><span>long_name :</span></dt><dd>WMO region code</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>World Meteorological Organization (WMO) region of station. The available regions are: Africa, Asia, South America, &quot;Northern America, Central America and the Caribbean&quot;, South-West Pacific, Europe and Antarctica.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Asia&#x27;, &#x27;Europe&#x27;, &#x27;North America, Central America And The Caribbean&#x27;,\n",
+       "       ..., &#x27;Europe&#x27;, &#x27;Africa&#x27;, &#x27;Europe&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WWF_TEOW_biogeographical_realm</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-79f52a6f-0278-4cb3-897d-25b39f8b60b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79f52a6f-0278-4cb3-897d-25b39f8b60b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c624f51-b99d-4d6d-bba1-472caa616719' class='xr-var-data-in' type='checkbox'><label for='data-3c624f51-b99d-4d6d-bba1-472caa616719' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>WWF TEOW biogeographical realm</dd><dt><span>long_name :</span></dt><dd>WWF TEOW biogeographical realm</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 8 biogeographical realms. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;palearctic&#x27;, &#x27;palearctic&#x27;, &#x27;nearctic&#x27;, ..., &#x27;palearctic&#x27;,\n",
+       "       &#x27;afrotropics&#x27;, &#x27;palearctic&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WWF_TEOW_biome</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bb34a0b9-935f-4cc4-8248-efdbf5f22cbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb34a0b9-935f-4cc4-8248-efdbf5f22cbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-259f2ae5-d076-4908-801f-0f91811a5ca3' class='xr-var-data-in' type='checkbox'><label for='data-259f2ae5-d076-4908-801f-0f91811a5ca3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>WWF TEOW biome</dd><dt><span>long_name :</span></dt><dd>WWF TEOW biome</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 14 biomes. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;temperate grasslands, savannas and shrublands&#x27;,\n",
+       "       &#x27;temperate broadleaf and mixed forests&#x27;, &#x27;tundra&#x27;, ...,\n",
+       "       &#x27;mediterranean forests, woodlands and scrub&#x27;,\n",
+       "       &#x27;tropical and subtropical grasslands, savannas and shrublands&#x27;,\n",
+       "       &#x27;temperate broadleaf and mixed forests&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WWF_TEOW_terrestrial_ecoregion</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-beba8ba4-82d3-4680-81ab-75811a786a7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-beba8ba4-82d3-4680-81ab-75811a786a7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dbf7d23-9ff6-4f04-91f8-ac3ff331f120' class='xr-var-data-in' type='checkbox'><label for='data-7dbf7d23-9ff6-4f04-91f8-ac3ff331f120' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>WWF TEOW terrestrial ecoregion</dd><dt><span>long_name :</span></dt><dd>WWF TEOW terrestrial ecoregion</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Terrestrial Ecoregions of the World (TEOW) World Wildlife Foundation (WWF) classification. There are 825 terrestrial ecoregions. Ecoregions are relatively large units of land containing distinct assemblages of natural communities and species, with boundaries that approximate the original extent of natural communities prior to major land-use change. See citation: Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., DAmico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., Kassem, K. R. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51(11):933-938.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;mongolian-manchurian grassland&#x27;, &#x27;azores temperate mixed forests&#x27;,\n",
+       "       &#x27;arctic coastal tundra&#x27;, ...,\n",
+       "       &#x27;iberian sclerophyllous and semi-deciduous forests&#x27;,\n",
+       "       &#x27;west sudanian savanna&#x27;, &#x27;sarmatic mixed forests&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>administrative_country_division_1</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f3d20241-b5e9-4ac0-8859-6276e29ab9c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3d20241-b5e9-4ac0-8859-6276e29ab9c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b35fe006-efa8-40c4-ab5e-18588669d4b1' class='xr-var-data-in' type='checkbox'><label for='data-b35fe006-efa8-40c4-ab5e-18588669d4b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>administrative country division 1</dd><dt><span>long_name :</span></dt><dd>administrative country division 1</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Name of the first (i.e. largest) country administrative division in which the station lies, e.g. countries within soverign state, state, province, county etc. These are defined for the purposes of managing of land and the affairs of people. This is automatically generated using Reverse Geocoder Python package (taking longitude and latitude as inputs).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Inner Mongolia&#x27;, &#x27;Azores&#x27;, &#x27;Alaska&#x27;, ..., &#x27;Aragon&#x27;, &#x27;Zinder&#x27;,\n",
+       "       &#x27;Moskovskaya&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>administrative_country_division_2</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-30460472-e54e-44ce-b087-47fc91f4cb74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30460472-e54e-44ce-b087-47fc91f4cb74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25dad745-90ef-49c0-9793-4839cb7b40ca' class='xr-var-data-in' type='checkbox'><label for='data-25dad745-90ef-49c0-9793-4839cb7b40ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>administrative country division 2</dd><dt><span>long_name :</span></dt><dd>administrative country division 2</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Name of the second (i.e. second largest) country administrative division in which the station lies, e.g. countries within soverign state, state, province, county etc. These are defined for the purposes of managing of land and the affairs of people. This is automatically generated using Reverse Geocoder Python package (taking longitude and latitude as inputs).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;Santa Cruz Da Graciosa&#x27;, &#x27;North Slope Borough&#x27;, ...,\n",
+       "       &#x27;Provincia De Zaragoza&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>altitude</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f84ee8f5-3128-4359-a3a3-95096b650fa3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f84ee8f5-3128-4359-a3a3-95096b650fa3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d2cf11f-aca0-4ae3-bb1c-77d861b6a0d0' class='xr-var-data-in' type='checkbox'><label for='data-9d2cf11f-aca0-4ae3-bb1c-77d861b6a0d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>altitude</dd><dt><span>long_name :</span></dt><dd>altitude relative to mean sea level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>description :</span></dt><dd>Altitude of the ground level at the station, relative to the stated vertical datum, in metres.</dd></dl></div><div class='xr-var-data'><pre>array([1314. ,   15.2,    2. , ...,  250. ,  456. ,  200. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>annual_native_max_gap_percent</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ac129433-b0cc-42f7-9a28-4f906e980ddf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac129433-b0cc-42f7-9a28-4f906e980ddf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2bb121e-6a19-4580-9199-43912069e013' class='xr-var-data-in' type='checkbox'><label for='data-e2bb121e-6a19-4580-9199-43912069e013' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>annual native max gap percent</dd><dt><span>long_name :</span></dt><dd>annual native max gap percent</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Percentage of the maximum data gap in the annual averaged measurement UTC window filled by native resolution data, relative to the total window length.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>annual_native_representativity_percent</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1ab1525b-0356-4a48-adbe-cdeeb9f86dbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ab1525b-0356-4a48-adbe-cdeeb9f86dbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7bbaa80-3523-4ede-ab54-583b01f6b783' class='xr-var-data-in' type='checkbox'><label for='data-c7bbaa80-3523-4ede-ab54-583b01f6b783' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>annual native representativity percent</dd><dt><span>long_name :</span></dt><dd>annual native representativity percent</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Percentage of the annual averaged measurement UTC window represented by native resolution data.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>area_classification</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ac967fcb-668d-49a2-9669-80e108f5054c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac967fcb-668d-49a2-9669-80e108f5054c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dea1ff4-8373-4525-b45b-cd0e992c4691' class='xr-var-data-in' type='checkbox'><label for='data-5dea1ff4-8373-4525-b45b-cd0e992c4691' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>area classification</dd><dt><span>long_name :</span></dt><dd>standardised network provided area classification</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised network provided classification, describing type of area a measurement station is situated in.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>associated_networks</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c3aefb17-5bce-46a5-b3a9-c334dc57e31e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3aefb17-5bce-46a5-b3a9-c334dc57e31e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7cd2107-9e87-4ee1-8464-6b297ef59441' class='xr-var-data-in' type='checkbox'><label for='data-e7cd2107-9e87-4ee1-8464-6b297ef59441' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>other associated networks</dd><dt><span>long_name :</span></dt><dd>other associated networks</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>String pair of associated network name and station reference. Format: network1:station_reference1;network2:station_reference2</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>city</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cfddc6e8-4582-475f-b32b-b95e4daa071e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfddc6e8-4582-475f-b32b-b95e4daa071e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eaa562d7-03c4-44ba-85e7-a463df2581be' class='xr-var-data-in' type='checkbox'><label for='data-eaa562d7-03c4-44ba-85e7-a463df2581be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>city</dd><dt><span>long_name :</span></dt><dd>city</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Name of the city the station is located in.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Baliang&#x27;, &#x27;Lagoa&#x27;, &#x27;Nuiqsut&#x27;, ..., &#x27;Zaragoza&#x27;, &#x27;Zinder&#x27;, &#x27;Karinskoye&#x27;],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>climatology</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-71ac59a7-a385-412d-989f-93cc9c4cfb73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71ac59a7-a385-412d-989f-93cc9c4cfb73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66884964-dd1f-40e7-b0a5-4cde9942522a' class='xr-var-data-in' type='checkbox'><label for='data-66884964-dd1f-40e7-b0a5-4cde9942522a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>climatology</dd><dt><span>long_name :</span></dt><dd>climatology</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Name of the climatology of which the observations pertain to.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>contact_email_address</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-11221ec5-ea5b-4123-a7b8-7c2868a32a6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11221ec5-ea5b-4123-a7b8-7c2868a32a6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c926933-d6c3-4935-a1ca-512a37b62cd6' class='xr-var-data-in' type='checkbox'><label for='data-0c926933-d6c3-4935-a1ca-512a37b62cd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>contact email address</dd><dt><span>long_name :</span></dt><dd>contact email address</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Email address of the principal data contact for the specific reported data.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Brent.N.Holben@nasa.gov&#x27;, &#x27;Brent.N.Holben@nasa.gov&#x27;,\n",
+       "       &#x27;Brent.N.Holben@nasa.gov&#x27;, ..., &#x27;Brent.N.Holben@nasa.gov&#x27;,\n",
+       "       &#x27;Brent.N.Holben@nasa.gov&#x27;, &#x27;Brent.N.Holben@nasa.gov&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>contact_institution</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7979c18b-56f2-4ca0-a405-c584a57dc332' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7979c18b-56f2-4ca0-a405-c584a57dc332' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e8a7c92-1f39-4aec-ab11-2e3378d4c920' class='xr-var-data-in' type='checkbox'><label for='data-0e8a7c92-1f39-4aec-ab11-2e3378d4c920' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>contact institution</dd><dt><span>long_name :</span></dt><dd>contact institution</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Institution of the principal data contact for the specific reported data.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;NASA&#x27;, &#x27;NASA&#x27;, &#x27;NASA&#x27;, ..., &#x27;NASA&#x27;, &#x27;NASA&#x27;, &#x27;NASA&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>contact_name</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e392590d-e6bf-47c7-b9f2-215a6dcfc291' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e392590d-e6bf-47c7-b9f2-215a6dcfc291' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dae99c93-1afa-4b1e-b3b7-d1e4181959d2' class='xr-var-data-in' type='checkbox'><label for='data-dae99c93-1afa-4b1e-b3b7-d1e4181959d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>contact name</dd><dt><span>long_name :</span></dt><dd>contact name</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Full name of the principal data contact for the specific reported data.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Brent Holben&#x27;, &#x27;Brent Holben&#x27;, &#x27;Brent Holben&#x27;, ..., &#x27;Brent Holben&#x27;,\n",
+       "       &#x27;Brent Holben&#x27;, &#x27;Brent Holben&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>country</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7b781899-fe55-4236-80f4-c5fd929518b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b781899-fe55-4236-80f4-c5fd929518b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c15ea25b-7511-4a43-9c7a-254958ae88b1' class='xr-var-data-in' type='checkbox'><label for='data-c15ea25b-7511-4a43-9c7a-254958ae88b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>country</dd><dt><span>long_name :</span></dt><dd>country</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Name of the country the station is located in.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;China&#x27;, &#x27;Portugal&#x27;, &#x27;United States&#x27;, ..., &#x27;Spain&#x27;, &#x27;Niger&#x27;, &#x27;Russia&#x27;],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>daily_native_max_gap_percent</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1e0b3a68-9835-43fb-aeb7-287f554f9a33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e0b3a68-9835-43fb-aeb7-287f554f9a33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ba60500-0524-4a25-856c-5c0f8775ecc9' class='xr-var-data-in' type='checkbox'><label for='data-5ba60500-0524-4a25-856c-5c0f8775ecc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>daily native max gap percent</dd><dt><span>long_name :</span></dt><dd>daily native max gap percent</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Percentage of the maximum data gap in the daily averaged measurement UTC window filled by native resolution data, relative to the total window length.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>daily_native_representativity_percent</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7d292adb-9e84-4564-af15-34842eb6e9a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d292adb-9e84-4564-af15-34842eb6e9a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3e6e057-4b3d-4d49-b544-6371b9154c51' class='xr-var-data-in' type='checkbox'><label for='data-e3e6e057-4b3d-4d49-b544-6371b9154c51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>daily native representativity percent</dd><dt><span>long_name :</span></dt><dd>daily native representativity percent</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Percentage of the daily averaged measurement UTC window represented by native resolution data.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>daily_passing_vehicles</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1e8400d4-2013-4a57-9661-ad2f2f9c559e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e8400d4-2013-4a57-9661-ad2f2f9c559e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-569eceac-5bfc-4161-b44d-c740c7d4cd84' class='xr-var-data-in' type='checkbox'><label for='data-569eceac-5bfc-4161-b44d-c740c7d4cd84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>daily passing vehicles</dd><dt><span>long_name :</span></dt><dd>average daily number of passing vehicles</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Average number of vehicles passing daily.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_level</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9d62c9f-bd6a-44bb-8649-af2c14fd65b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d62c9f-bd6a-44bb-8649-af2c14fd65b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0570e597-7abd-449d-8240-799d32015139' class='xr-var-data-in' type='checkbox'><label for='data-0570e597-7abd-449d-8240-799d32015139' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>data level</dd><dt><span>long_name :</span></dt><dd>data level</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Data level of data reported. This varies per network. If data level is variable per measurement, and not static per reported file, then this is set as &quot;variable&quot;. If there is no reported data level this is set as &quot;none&quot;</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1.5&#x27;, &#x27;1.5&#x27;, &#x27;1.5&#x27;, ..., &#x27;1.5&#x27;, &#x27;1.5&#x27;, &#x27;1.5&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>data_licence</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0e3530c5-7430-4acf-834e-f9726b141797' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e3530c5-7430-4acf-834e-f9726b141797' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adfa0006-dd9f-4940-a729-86753fe28625' class='xr-var-data-in' type='checkbox'><label for='data-adfa0006-dd9f-4940-a729-86753fe28625' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>data licence</dd><dt><span>long_name :</span></dt><dd>data licence</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Information pertaining to the data licence governing the redistribution/publication of the ingested network data.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Public domain: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html&#x27;,\n",
+       "       &#x27;Public domain: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html&#x27;,\n",
+       "       &#x27;Public domain: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html&#x27;,\n",
+       "       ...,\n",
+       "       &#x27;Public domain: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html&#x27;,\n",
+       "       &#x27;Public domain: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html&#x27;,\n",
+       "       &#x27;Public domain: https://aeronet.gsfc.nasa.gov/new_web/data_usage.html&#x27;],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>day_night_code</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5d4e0d1-9921-4233-841d-e3396b70eaaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5d4e0d1-9921-4233-841d-e3396b70eaaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-934e7f2f-9437-4c9d-a90e-abef1afdf35c' class='xr-var-data-in' type='checkbox'><label for='data-934e7f2f-9437-4c9d-a90e-abef1afdf35c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>day/night code</dd><dt><span>long_name :</span></dt><dd>day/night code per measurement</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Binary indication if measurement was made during the day or night. Day=0, Night=1. The classification is made by calculating the solar elevation angle for a latitude/longitude/measurement height at a mid-measurement window timestamp. If the solar elevation angle is &gt; 0, it is classed as daytime, otherwise it is nightime. Classification is 255 if cannot be made.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>daytime_traffic_speed</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f28e8901-e96c-45fc-958d-14e80fd7a1d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f28e8901-e96c-45fc-958d-14e80fd7a1d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-903f0a3e-4cb0-4be4-a9ec-54c01a289227' class='xr-var-data-in' type='checkbox'><label for='data-903f0a3e-4cb0-4be4-a9ec-54c01a289227' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>daytime traffic speed</dd><dt><span>long_name :</span></dt><dd>average daytime speed of passing traffic</dd><dt><span>units :</span></dt><dd>km hr-1</dd><dt><span>description :</span></dt><dd>Average daytime speed of the passing traffic where measurements are being made (if applicable), in kilometres per hour.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>derived_uncertainty_per_measurement</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e388cb13-78a3-4f09-a99b-240be46b8e82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e388cb13-78a3-4f09-a99b-240be46b8e82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3749b4d6-c0df-4e2f-ae1c-df31ddd6d995' class='xr-var-data-in' type='checkbox'><label for='data-3749b4d6-c0df-4e2f-ae1c-df31ddd6d995' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>derived measurement uncertainty per measurement</dd><dt><span>long_name :</span></dt><dd>derived measurement uncertainty per measurement</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Derived measurement uncertainty (±) of methodology, for a specific measurement. This is calculated through the quadratic addition of reported (or if not available, documented) accuracy and precision metrics. This is given in absolute terms in unitless.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>distance_to_building</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-25715e97-7368-41c1-a0b7-882a924f672a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25715e97-7368-41c1-a0b7-882a924f672a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf29f29e-3076-4f4e-b4e0-110424e67b73' class='xr-var-data-in' type='checkbox'><label for='data-cf29f29e-3076-4f4e-b4e0-110424e67b73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>distance to building</dd><dt><span>long_name :</span></dt><dd>distance to the nearest building</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>description :</span></dt><dd>Distance to the nearest building of the inlet/instrument/sampler, in metres.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>distance_to_junction</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7d74289f-6b43-45f2-b078-e10ffcd66287' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d74289f-6b43-45f2-b078-e10ffcd66287' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5d25722-55fe-40bc-afeb-8b4e342ef066' class='xr-var-data-in' type='checkbox'><label for='data-f5d25722-55fe-40bc-afeb-8b4e342ef066' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>distance to junction</dd><dt><span>long_name :</span></dt><dd>distance to the nearest road junction</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>description :</span></dt><dd>Distance to the nearest road junction of the inlet/instrument/sampler, in metres.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>distance_to_kerb</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d8b724dc-bd56-44a7-8131-8e5f3134e83f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8b724dc-bd56-44a7-8131-8e5f3134e83f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7874f97-7fb5-46f3-931a-4ad4424e269d' class='xr-var-data-in' type='checkbox'><label for='data-e7874f97-7fb5-46f3-931a-4ad4424e269d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>distance to kerb</dd><dt><span>long_name :</span></dt><dd>distance to the street kerb</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>description :</span></dt><dd>Distance to the street kerb of the inlet/instrument/sampler, in metres.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>distance_to_source</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6c7ac979-faae-4c2e-8433-efbecd692810' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c7ac979-faae-4c2e-8433-efbecd692810' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-119f6690-bc51-4382-accd-fcbeb13093c2' class='xr-var-data-in' type='checkbox'><label for='data-119f6690-bc51-4382-accd-fcbeb13093c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>distance to source</dd><dt><span>long_name :</span></dt><dd>distance to the main emission source</dd><dt><span>units :</span></dt><dd>km</dd><dt><span>description :</span></dt><dd>Distance to the main emission source (see variable: &quot;main_emission_source&quot;) of the inlet/instrument/sampler, in kilometres.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ellipsoid</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b31fc229-f05b-4757-a46f-cdfde064a9f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b31fc229-f05b-4757-a46f-cdfde064a9f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa0e8fc3-5cf2-438f-86ce-5b111e86bdb5' class='xr-var-data-in' type='checkbox'><label for='data-fa0e8fc3-5cf2-438f-86ce-5b111e86bdb5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ellipsoid</dd><dt><span>long_name :</span></dt><dd>ellipsoid</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>The ellipsoidal model of the earth used as a basis for 2D and 3D geographic coordinate systems.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;WGS 84&#x27;, &#x27;WGS 84&#x27;, &#x27;WGS 84&#x27;, ..., &#x27;WGS 84&#x27;, &#x27;WGS 84&#x27;, &#x27;WGS 84&#x27;],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>flag</span></div><div class='xr-var-dims'>(station, time, N_flag_codes)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2cdb86cb-9667-4081-847c-3e64aa301d04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cdb86cb-9667-4081-847c-3e64aa301d04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98d9f392-9452-455e-855a-46f91c811251' class='xr-var-data-in' type='checkbox'><label for='data-98d9f392-9452-455e-855a-46f91c811251' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>flags</dd><dt><span>long_name :</span></dt><dd>data reporter provided standardised flags</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>List of associated data flag codes per measurement, indicating the data quality of a specific measurement, provided by the data reporter. Fill value code of 255.</dd></dl></div><div class='xr-var-data'><pre>[50068800 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>horizontal_datum</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-09a8f86f-6ccf-47b9-ab73-daa5af35e869' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09a8f86f-6ccf-47b9-ab73-daa5af35e869' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df3f8242-bdc7-4a9f-be84-1e2011cc384b' class='xr-var-data-in' type='checkbox'><label for='data-df3f8242-bdc7-4a9f-be84-1e2011cc384b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>horizontal datum</dd><dt><span>long_name :</span></dt><dd>horizontal datum</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Name of the horizontal datum used in defining geodetic latitudes and longitudes on the Earth&#x27;s surface. The datum is set when positioning an ellipsoid model of the Earth to an anchor point. If not explicitely stated then this is assumed to be &#x27;World Geodetic System 1984&#x27;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;WORLD GEODETIC SYSTEM 1984&#x27;, &#x27;WORLD GEODETIC SYSTEM 1984&#x27;,\n",
+       "       &#x27;WORLD GEODETIC SYSTEM 1984&#x27;, ..., &#x27;WORLD GEODETIC SYSTEM 1984&#x27;,\n",
+       "       &#x27;WORLD GEODETIC SYSTEM 1984&#x27;, &#x27;WORLD GEODETIC SYSTEM 1984&#x27;],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hourly_native_max_gap_percent</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dd45b4cd-6a1b-49da-b8d0-acacd79fc8fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd45b4cd-6a1b-49da-b8d0-acacd79fc8fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2545da6-8c80-45a4-b54f-01f5f6061184' class='xr-var-data-in' type='checkbox'><label for='data-f2545da6-8c80-45a4-b54f-01f5f6061184' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>hourly native max gap percent</dd><dt><span>long_name :</span></dt><dd>hourly native max gap percent</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Percentage of the maximum data gap in the hourly averaged measurement UTC window filled by native resolution data, relative to the total window length.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hourly_native_representativity_percent</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a382d3d5-eb45-497b-8b9a-58f6a0076898' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a382d3d5-eb45-497b-8b9a-58f6a0076898' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9946466b-55a0-4401-934d-e69889286280' class='xr-var-data-in' type='checkbox'><label for='data-9946466b-55a0-4401-934d-e69889286280' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>hourly native representativity percent</dd><dt><span>long_name :</span></dt><dd>hourly native representativity percent</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Percentage of the hourly averaged measurement UTC window represented by native resolution data.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>land_use</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-18860352-2630-4675-81c1-1e140eb86312' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18860352-2630-4675-81c1-1e140eb86312' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-130e278f-603e-49c7-a31a-59646f03ad1e' class='xr-var-data-in' type='checkbox'><label for='data-130e278f-603e-49c7-a31a-59646f03ad1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>land use</dd><dt><span>long_name :</span></dt><dd>standardised network provided land use type</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised network provided classification, describing the dominant land use in the area of the reporting station.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-754b8355-45ab-4232-9d24-b38bfb824e20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-754b8355-45ab-4232-9d24-b38bfb824e20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e157952-38ca-466c-8391-b2a41c0cddf9' class='xr-var-data-in' type='checkbox'><label for='data-8e157952-38ca-466c-8391-b2a41c0cddf9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>decimal degrees North</dd><dt><span>description :</span></dt><dd>Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum.</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([40.8517  , 39.09109 , 70.4995  , ..., 41.6334  , 13.776683, 55.695   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>local_time</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1e0de103-5a67-4c61-b19c-430da9c705d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e0de103-5a67-4c61-b19c-430da9c705d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72c29fc2-4347-4d26-ab3d-362e27760878' class='xr-var-data-in' type='checkbox'><label for='data-72c29fc2-4347-4d26-ab3d-362e27760878' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>local time</dd><dt><span>long_name :</span></dt><dd>local time</dd><dt><span>description :</span></dt><dd>Time in minutes since 0001-01-01 00:00 local time. Time given refers to the start of the time window the measurement is representative of (temporal resolution).</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>tz :</span></dt><dd>local</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f13e2000-d72d-46b5-9b24-0edd11dba3de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f13e2000-d72d-46b5-9b24-0edd11dba3de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68dceba4-7f60-41a6-9683-c45de3165a50' class='xr-var-data-in' type='checkbox'><label for='data-68dceba4-7f60-41a6-9683-c45de3165a50' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>decimal degrees East</dd><dt><span>description :</span></dt><dd>Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum.</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 109.6288  ,  -28.02917 , -149.88    , ...,   -0.88235 ,    8.990233,\n",
+       "         36.775   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>main_emission_source</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d9921fd-ce0f-4474-899f-db73d7f1f6b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d9921fd-ce0f-4474-899f-db73d7f1f6b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5137bd2-9e75-4621-9323-d6e98b88a78c' class='xr-var-data-in' type='checkbox'><label for='data-d5137bd2-9e75-4621-9323-d6e98b88a78c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>main emission source</dd><dt><span>long_name :</span></dt><dd>standardised network provided main emission source</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised network provided classification, describing the main emission source influencing air measured at a station.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mean_solar_time</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3e02a39d-4736-4040-a7be-61452662effc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e02a39d-4736-4040-a7be-61452662effc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0001a742-22cb-4c7d-934e-64fa335cb276' class='xr-var-data-in' type='checkbox'><label for='data-0001a742-22cb-4c7d-934e-64fa335cb276' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>mean solar time</dd><dt><span>long_name :</span></dt><dd>mean solar time</dd><dt><span>description :</span></dt><dd>Time in minutes since 0001-01-01 00:00 mean solar time. Time given refers to the start of the time window the measurement is representative of (temporal resolution).</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>tz :</span></dt><dd>mean solar</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=datetime64[ns]]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measurement_altitude</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-70ca13f8-3acf-49e3-add0-b85cb0f36f68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70ca13f8-3acf-49e3-add0-b85cb0f36f68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cac7f936-d7d3-4c7a-be7f-5580871551f2' class='xr-var-data-in' type='checkbox'><label for='data-cac7f936-d7d3-4c7a-be7f-5580871551f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measurement altitude</dd><dt><span>long_name :</span></dt><dd>measurement altitude relative to mean sea level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>description :</span></dt><dd>Altitude of the inlet/instrument/sampler, relative to the stated vertical datum, in metres.</dd></dl></div><div class='xr-var-data'><pre>array([1314. ,   15.2,    2. , ...,  250. ,  456. ,  200. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measurement_methodology</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1e297c92-dec6-42c8-a3c3-31fd776d65bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e297c92-dec6-42c8-a3c3-31fd776d65bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93d4f780-8af2-447a-83ad-3bbd40fbd00b' class='xr-var-data-in' type='checkbox'><label for='data-93d4f780-8af2-447a-83ad-3bbd40fbd00b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measurement methodology</dd><dt><span>long_name :</span></dt><dd>standardised measurement methodology</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised name of the measurement methodology.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;photometry - direct&#x27;, &#x27;photometry - direct&#x27;, &#x27;photometry - direct&#x27;,\n",
+       "       ..., &#x27;photometry - direct&#x27;, &#x27;photometry - direct&#x27;,\n",
+       "       &#x27;photometry - direct&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measurement_scale</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca31d330-985f-419c-a8ca-51caa6599a95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca31d330-985f-419c-a8ca-51caa6599a95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17c5b7ff-8070-458b-a2c9-055a0578028f' class='xr-var-data-in' type='checkbox'><label for='data-17c5b7ff-8070-458b-a2c9-055a0578028f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measurement scale</dd><dt><span>long_name :</span></dt><dd>standardised network provided measurement scale</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised network provided classification, a denotation of the geographic scope of the air quality measurements made.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_calibration_scale</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad00d914-104a-4cb0-9961-79b88d160ba4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad00d914-104a-4cb0-9961-79b88d160ba4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-882ea3c2-1c73-4420-8fe6-450edd600a04' class='xr-var-data-in' type='checkbox'><label for='data-882ea3c2-1c73-4420-8fe6-450edd600a04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument calibration scale</dd><dt><span>long_name :</span></dt><dd>measuring instrument calibration scale name</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Name of calibration scale used for the calibration of the measuring instrument.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_documented_absorption_cross_section</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c0bc5e25-15d1-4430-94e6-2073014ddca2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0bc5e25-15d1-4430-94e6-2073014ddca2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dd769dc-2a37-4004-9144-d441fc5c2ee4' class='xr-var-data-in' type='checkbox'><label for='data-6dd769dc-2a37-4004-9144-d441fc5c2ee4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument documented absorption cross section</dd><dt><span>long_name :</span></dt><dd>measuring instrument documented absorption cross section</dd><dt><span>units :</span></dt><dd>cm2</dd><dt><span>description :</span></dt><dd>Assumed molecule cross-section for parameter being measured (in cm2/molecule), as given in instrumental manual/documentation. This field is only used for parameters being measured using optical methods, where a molecule cross section is assumed for processing the measurement values. Physically it is the effective area of the molecule that photon needs to traverse in order to be absorbed. The larger the absorption cross section, the easier it is to photoexcite the molecule. Can be a range: e.g. 1e-15-1.5e-15.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_documented_accuracy</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-020b355e-20de-4f0e-9ae0-39abfe6827f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-020b355e-20de-4f0e-9ae0-39abfe6827f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-939288f1-3029-4522-9bc9-59a1334e144e' class='xr-var-data-in' type='checkbox'><label for='data-939288f1-3029-4522-9bc9-59a1334e144e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument documented measurement accuracy</dd><dt><span>long_name :</span></dt><dd>measuring instrument documented measurement accuracy</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Measurement accuracy (±), as given in the instrumental manual/documentation. Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes: Bias (a measure of the difference between the true value and the observed value of a part -- If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available) and Linearity (a measure of how the size of the part affects the bias of a measurement system -- It is the difference in the observed bias values through the expected range of measurement). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%&gt;=50).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_documented_flow_rate</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-10eaa0a7-73f6-43e7-89f6-b527792e7c36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10eaa0a7-73f6-43e7-89f6-b527792e7c36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adce0003-d183-4a86-8569-8cfd28f32a98' class='xr-var-data-in' type='checkbox'><label for='data-adce0003-d183-4a86-8569-8cfd28f32a98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument documented flow rate</dd><dt><span>long_name :</span></dt><dd>measuring instrument documented flow rate</dd><dt><span>units :</span></dt><dd>l min-1</dd><dt><span>description :</span></dt><dd>Volume (litres) of fluid which passes to the measuring instrument, per unit time (minutes), as given in instrumental manual/documentation. Can be a range: e.g. 1.0-3.0.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_documented_lower_limit_of_detection</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2452b8c0-c06c-42f7-a5e7-690843b4a1cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2452b8c0-c06c-42f7-a5e7-690843b4a1cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8bfbafd-a865-40b1-b3c9-934f9c69f80e' class='xr-var-data-in' type='checkbox'><label for='data-c8bfbafd-a865-40b1-b3c9-934f9c69f80e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument documented lower limit of detection</dd><dt><span>long_name :</span></dt><dd>measuring instrument documented lower limit of detection</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Lower limit of detection of measurement methodology, as given in the instrumental manual/documentation.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_documented_measurement_resolution</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b135c315-e11b-42b8-be9d-a825c772975a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b135c315-e11b-42b8-be9d-a825c772975a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-525d7fbd-2535-4a70-a002-89aa16bc849b' class='xr-var-data-in' type='checkbox'><label for='data-525d7fbd-2535-4a70-a002-89aa16bc849b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument documented measurement resolution</dd><dt><span>long_name :</span></dt><dd>measuring instrument documented measurement resolution</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Measurement resolution, as given in instrumental manual/documentation. The measurement resolution is defined as the smallest change or increment in the measured quantity that the instrument can detect. However it is often reported inconsistently, often being simply the number of digits an instrument can display, which does not relate to the actual physical resolution of the instrument.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_documented_precision</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c88da717-7a8c-47ac-b235-0b59a500e443' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c88da717-7a8c-47ac-b235-0b59a500e443' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3e62f9a-37b6-46bf-b81d-0e3256117073' class='xr-var-data-in' type='checkbox'><label for='data-e3e62f9a-37b6-46bf-b81d-0e3256117073' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument documented measurement precision</dd><dt><span>long_name :</span></dt><dd>measuring instrument documented measurement precision</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Measurement precision (±), as given in instrumental manual/documentation. Precision describes the variation you see when you measure the same part repeatedly with the same device. It includes the following two types of variation: Repeatability (variation due to the measuring device -- it is the variation observed when the same operator measures the same part repeatedly with the same device) and Reproducibility (variation due to the operators and the interaction between operator and part -- It is the variation of the bias observed when different operators measure the same parts using the same device). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%&gt;=50).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;0.1%&#x27;, &#x27;0.1%&#x27;, &#x27;0.1%&#x27;, ..., &#x27;0.1%&#x27;, &#x27;0.1%&#x27;, &#x27;0.1%&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_documented_span_drift</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4004acfb-c0b0-49ba-85ac-113383340adc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4004acfb-c0b0-49ba-85ac-113383340adc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82f72a3b-40ad-44f5-8730-06af84526a4c' class='xr-var-data-in' type='checkbox'><label for='data-82f72a3b-40ad-44f5-8730-06af84526a4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument documented span drift</dd><dt><span>long_name :</span></dt><dd>measuring instrument documented span drift</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Span drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Span drift (or sensitivity drift) refers to when there is proportional change in the indication of an instrument all along the upward scale, hence higher calibrations end up being shifted more than lower calibrations. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%&gt;=50/day).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_documented_uncertainty</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-88dcf3de-d34d-461f-954d-368548ba8a36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88dcf3de-d34d-461f-954d-368548ba8a36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1256c0c-27cf-42d7-9ab7-15663e9bc314' class='xr-var-data-in' type='checkbox'><label for='data-c1256c0c-27cf-42d7-9ab7-15663e9bc314' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument documented measurement uncertainty</dd><dt><span>long_name :</span></dt><dd>measuring instrument documented measurement uncertainty</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Measurement uncertainty (±), as given in the instrumental manual/documentation. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). This can be given in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%&gt;=50).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_documented_upper_limit_of_detection</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4aa0c361-e59e-47d7-976b-afbd71e6215d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4aa0c361-e59e-47d7-976b-afbd71e6215d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb5f3491-bd88-4574-bab8-caeb33e4932f' class='xr-var-data-in' type='checkbox'><label for='data-cb5f3491-bd88-4574-bab8-caeb33e4932f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument documented upper limit of detection</dd><dt><span>long_name :</span></dt><dd>measuring instrument documented upper limit of detection</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Upper limit of detection of measurement methodology, as given in the instrumental manual/documentation.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_documented_zero_drift</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ce8b476b-badf-4aeb-8445-e2be662e2e99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce8b476b-badf-4aeb-8445-e2be662e2e99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-552cd8be-5469-4252-9dc3-d2804fc578a2' class='xr-var-data-in' type='checkbox'><label for='data-552cd8be-5469-4252-9dc3-d2804fc578a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument documented zero drift</dd><dt><span>long_name :</span></dt><dd>measuring instrument documented zero drift</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Zero drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Zero drift (or baseline drift) refers to the shifting of the whole calibration by the same amount caused by slippage or due to undue warming up of the electronic circuits. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%&gt;=50/day).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_documented_zonal_drift</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b4d807aa-837f-4125-8f38-59b73beb7035' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4d807aa-837f-4125-8f38-59b73beb7035' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17ddba94-2245-4fbc-9220-62f479752d63' class='xr-var-data-in' type='checkbox'><label for='data-17ddba94-2245-4fbc-9220-62f479752d63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument documented zonal drift</dd><dt><span>long_name :</span></dt><dd>measuring instrument documented zonal drift</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Zonal drift of measuring instrument per unit of time, as given in instrumental manual/documentation. Zonal drift refers to when drift occurs only over a portion of the full scale or span of an instrument, while the remaining portion of the scale remains unaffected. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%&gt;=50/day).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_further_details</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0aa8863c-7c25-4619-991a-d858c43511c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0aa8863c-7c25-4619-991a-d858c43511c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fac3a17-f651-44bd-8bda-df52a5be47a6' class='xr-var-data-in' type='checkbox'><label for='data-2fac3a17-f651-44bd-8bda-df52a5be47a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument further details</dd><dt><span>long_name :</span></dt><dd>measuring instrument further details</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Further associated details regarding the specifics of the measurement methodology/instrument.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_inlet_information</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7865f866-422b-459d-9ca6-7cb76fe96265' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7865f866-422b-459d-9ca6-7cb76fe96265' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f963e21-ea6d-4b0d-bcee-7acd976c0f4e' class='xr-var-data-in' type='checkbox'><label for='data-0f963e21-ea6d-4b0d-bcee-7acd976c0f4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument inlet information</dd><dt><span>long_name :</span></dt><dd>measuring instrument measurement inlet information</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Description of sampling inlet of the measuring instrument.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_manual_name</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c72d380-bed8-4357-96bb-cfb6cc3a1c7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c72d380-bed8-4357-96bb-cfb6cc3a1c7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0628431f-b8f8-49ea-b726-0be2af0863a4' class='xr-var-data-in' type='checkbox'><label for='data-0628431f-b8f8-49ea-b726-0be2af0863a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument manual name</dd><dt><span>long_name :</span></dt><dd>measuring instrument manual name</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Path to the location in the esarchive of the manual for the specific measuring instrument.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Cimel_CE318T_Manual.pdf&#x27;, &#x27;Cimel_CE318T_Manual.pdf&#x27;,\n",
+       "       &#x27;Cimel_CE318T_Manual.pdf&#x27;, ..., &#x27;Cimel_CE318T_Manual.pdf&#x27;,\n",
+       "       &#x27;Cimel_CE318T_Manual.pdf&#x27;, &#x27;Cimel_CE318T_Manual.pdf&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_name</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d5713526-d243-428c-8074-38345b5f7517' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5713526-d243-428c-8074-38345b5f7517' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcbecfa8-6371-4135-8a99-7bf35af8a491' class='xr-var-data-in' type='checkbox'><label for='data-bcbecfa8-6371-4135-8a99-7bf35af8a491' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument name</dd><dt><span>long_name :</span></dt><dd>standardised measuring instrument name</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised name of the measuring instrument.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Cimel CE318-T&#x27;, &#x27;Cimel CE318-T&#x27;, &#x27;Cimel CE318-T&#x27;, ..., &#x27;Cimel CE318-T&#x27;,\n",
+       "       &#x27;Cimel CE318-T&#x27;, &#x27;Cimel CE318-T&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_process_details</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c3675874-e405-41f0-b0a3-91ad91316180' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3675874-e405-41f0-b0a3-91ad91316180' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2794489d-4787-498a-bdd7-22a356735234' class='xr-var-data-in' type='checkbox'><label for='data-2794489d-4787-498a-bdd7-22a356735234' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument process details</dd><dt><span>long_name :</span></dt><dd>measuring instrument process details</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Miscellaneous details regarding assumptions made in the standardisation of the measurement methodology/instrument.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_reported_absorption_cross_section</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ab52d60c-b33e-4f2c-8d7d-dcc593d47f56' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab52d60c-b33e-4f2c-8d7d-dcc593d47f56' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07efdf60-e6a0-4d36-81b6-c258dc087037' class='xr-var-data-in' type='checkbox'><label for='data-07efdf60-e6a0-4d36-81b6-c258dc087037' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument reported absorption cross section</dd><dt><span>long_name :</span></dt><dd>measuring instrument reported absorption cross section</dd><dt><span>units :</span></dt><dd>cm2</dd><dt><span>description :</span></dt><dd>Assumed molecule cross-section for parameter being measured (in cm2/molecule), as given in metadata. This field is only used for parameters being measured using optical methods, where a molecule cross section is assumed for processing the measurement values. Physically it is the effective area of the molecule that photon needs to traverse in order to be absorbed. The larger the absorption cross section, the easier it is to photoexcite the molecule. Can be a range: e.g. 1e-15-1.5e-15.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_reported_accuracy</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b09f7d7-7c4c-4ba0-832d-87d087608fea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b09f7d7-7c4c-4ba0-832d-87d087608fea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a78dd1ad-46ca-43a9-a52b-e079de6ee56a' class='xr-var-data-in' type='checkbox'><label for='data-a78dd1ad-46ca-43a9-a52b-e079de6ee56a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument reported measurement accuracy</dd><dt><span>long_name :</span></dt><dd>measuring instrument reported measurement accuracy</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Measurement accuracy (±), as given in metadata. Accuracy describes the difference between the measurement and the actual value of the part that is measured. It includes: Bias (a measure of the difference between the true value and the observed value of a part -- If the “true” value is unknown, it can be calculated by averaging several measurements with the most accurate measuring equipment available) and Linearity (a measure of how the size of the part affects the bias of a measurement system -- It is the difference in the observed bias values through the expected range of measurement). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%&gt;=50).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_reported_flow_rate</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1d63dc28-4906-4b5f-9594-d6906b156c30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d63dc28-4906-4b5f-9594-d6906b156c30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5edf73e5-607a-4682-bdab-c298f61e51f1' class='xr-var-data-in' type='checkbox'><label for='data-5edf73e5-607a-4682-bdab-c298f61e51f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument reported flow rate</dd><dt><span>long_name :</span></dt><dd>measuring instrument reported flow rate</dd><dt><span>units :</span></dt><dd>l min-1</dd><dt><span>description :</span></dt><dd>Volume (litres) of fluid which passes to the measuring instrument, per unit time (minutes), as given in metadata. Can be a range: e.g. 1.0-3.0.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_reported_lower_limit_of_detection</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e40ed552-8e48-4e9a-a47d-ef9ff9b0a8a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e40ed552-8e48-4e9a-a47d-ef9ff9b0a8a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b0a603a-a84c-417e-a939-9c0b1ec1d53c' class='xr-var-data-in' type='checkbox'><label for='data-5b0a603a-a84c-417e-a939-9c0b1ec1d53c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument reported lower limit of detection</dd><dt><span>long_name :</span></dt><dd>measuring instrument reported lower limit of detection</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Lower limit of detection of measurement methodology, as given in metadata.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_reported_measurement_resolution</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1ffe440e-a8d7-4105-9c12-f1498ee3f3a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ffe440e-a8d7-4105-9c12-f1498ee3f3a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b57fb8a-334a-4f28-b4ca-81ddfac62799' class='xr-var-data-in' type='checkbox'><label for='data-9b57fb8a-334a-4f28-b4ca-81ddfac62799' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument reported measurement resolution</dd><dt><span>long_name :</span></dt><dd>measuring instrument reported measurement resolution</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Measurement resolution, as given in metadata. The measurement resolution is defined as the smallest change or increment in the measured quantity that the instrument can detect. However it is often reported inconsistently, often being simply the number of digits an instrument can display, which does not relate to the actual physical resolution of the instrument.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_reported_precision</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8e70ed20-9009-4f17-80a3-4d35105eed9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e70ed20-9009-4f17-80a3-4d35105eed9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65e695c1-47a2-4662-8287-5818fc89816a' class='xr-var-data-in' type='checkbox'><label for='data-65e695c1-47a2-4662-8287-5818fc89816a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument reported measurement precision</dd><dt><span>long_name :</span></dt><dd>measuring instrument reported measurement precision</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Measurement precision (±), as given in metadata. Precision describes the variation you see when you measure the same part repeatedly with the same device. It includes the following two types of variation: Repeatability (variation due to the measuring device -- it is the variation observed when the same operator measures the same part repeatedly with the same device) and Reproducibility (variation due to the operators and the interaction between operator and part -- It is the variation of the bias observed when different operators measure the same parts using the same device). This can be given as in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%&gt;=50).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_reported_span_drift</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cbbdb85b-64d8-4aa1-ac9e-1597ee6b4225' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cbbdb85b-64d8-4aa1-ac9e-1597ee6b4225' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f91689f1-0fa0-4e6a-a6c9-51578486d6ed' class='xr-var-data-in' type='checkbox'><label for='data-f91689f1-0fa0-4e6a-a6c9-51578486d6ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument reported span drift</dd><dt><span>long_name :</span></dt><dd>measuring instrument reported span drift</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Span drift of measuring instrument per unit of time, as given in metadata. Span drift (or sensitivity drift) refers to when there is proportional change in the indication of an instrument all along the upward scale, hence higher calibrations end up being shifted more than lower calibrations. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%&gt;=50/day).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_reported_uncertainty</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-943e6850-739b-48d7-9dcb-1cbebd172e32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-943e6850-739b-48d7-9dcb-1cbebd172e32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8f96e25-d6e8-496d-a172-34ab7065d5a7' class='xr-var-data-in' type='checkbox'><label for='data-b8f96e25-d6e8-496d-a172-34ab7065d5a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument reported measurement uncertainty</dd><dt><span>long_name :</span></dt><dd>measuring instrument reported measurement uncertainty</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Measurement uncertainty (±), as given in metadata. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). It can be given in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%); or a percentage quantity after a fixed limit (i.e. 0.5%&gt;=50).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_reported_units</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-247d1040-198f-4713-b761-cba15fd74a41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-247d1040-198f-4713-b761-cba15fd74a41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fab5a7d-4389-42cd-99a9-300ffaa62375' class='xr-var-data-in' type='checkbox'><label for='data-8fab5a7d-4389-42cd-99a9-300ffaa62375' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument reported units</dd><dt><span>long_name :</span></dt><dd>measuring instrument reported measurement units</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Units that the measured parameter are natively reported in.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_reported_upper_limit_of_detection</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b228f91c-4065-41c7-abfd-090cd008ccd8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b228f91c-4065-41c7-abfd-090cd008ccd8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e7da8e2-8f6e-426c-a6d3-9e95f0603870' class='xr-var-data-in' type='checkbox'><label for='data-1e7da8e2-8f6e-426c-a6d3-9e95f0603870' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument reported upper limit of detection</dd><dt><span>long_name :</span></dt><dd>measuring instrument reported upper limit of detection</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Upper limit of detection of measurement methodology, as given in metadata.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_reported_zero_drift</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bea1f5cb-01bd-4787-b6f5-57bbad2c8616' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bea1f5cb-01bd-4787-b6f5-57bbad2c8616' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2c127ed-aca4-4fe4-a403-a3cd212a8764' class='xr-var-data-in' type='checkbox'><label for='data-a2c127ed-aca4-4fe4-a403-a3cd212a8764' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument reported zero drift</dd><dt><span>long_name :</span></dt><dd>measuring instrument reported zero drift</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Zero drift of measuring instrument per unit of time, as given in metadata. Zero drift (or baseline drift) refers to the shifting of the whole calibration by the same amount caused by slippage or due to undue warming up of the electronic circuits. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%&gt;=50/day).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_reported_zonal_drift</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-31677176-dd92-4048-a131-a2cf97acf509' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31677176-dd92-4048-a131-a2cf97acf509' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74898d9e-c603-4205-a9a9-720e6c8f73f0' class='xr-var-data-in' type='checkbox'><label for='data-74898d9e-c603-4205-a9a9-720e6c8f73f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument reported zonal drift</dd><dt><span>long_name :</span></dt><dd>measuring instrument reported zonal drift</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Zonal drift of measuring instrument per unit of time, as given in metadata. Zonal drift refers to when drift occurs only over a portion of the full scale or span of an instrument, while the remaining portion of the scale remains unaffected. It is reported as the maximum possible drift per unit of time in absolute terms; as a percentage; the greater of either an absolute value or percentage (i.e. 25.0/0.5%/day); or a percentage quantity after a fixed limit (i.e. 0.5%&gt;=50/day).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>measuring_instrument_sampling_type</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-37b46a70-04c1-4535-89f7-b3d8f58a64ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37b46a70-04c1-4535-89f7-b3d8f58a64ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76bf5455-2560-4e71-b4d2-308362cd7947' class='xr-var-data-in' type='checkbox'><label for='data-76bf5455-2560-4e71-b4d2-308362cd7947' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>measuring instrument sampling type</dd><dt><span>long_name :</span></dt><dd>standardised sampling type of the measuring instrument</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised name of the measuring instrument sampling type.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;remote&#x27;, &#x27;remote&#x27;, &#x27;remote&#x27;, ..., &#x27;remote&#x27;, &#x27;remote&#x27;, &#x27;remote&#x27;],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>monthly_native_max_gap_percent</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3b30addd-d7e7-40d5-8ad9-246dbc2e60f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b30addd-d7e7-40d5-8ad9-246dbc2e60f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c59e4b0b-4d72-4f74-b545-11b82c49dd94' class='xr-var-data-in' type='checkbox'><label for='data-c59e4b0b-4d72-4f74-b545-11b82c49dd94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>monthly native max gap percent</dd><dt><span>long_name :</span></dt><dd>monthly native max gap percent</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Percentage of the maximum data gap in the monthly averaged measurement UTC window filled by native resolution data, relative to the total window length.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>monthly_native_representativity_percent</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-82e388ab-e233-4363-bfdd-4edb51822e87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82e388ab-e233-4363-bfdd-4edb51822e87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5d166a0-f00b-42ed-a6e1-a2109729c5ba' class='xr-var-data-in' type='checkbox'><label for='data-b5d166a0-f00b-42ed-a6e1-a2109729c5ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>monthly native representativity percent</dd><dt><span>long_name :</span></dt><dd>monthly native representativity percent</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>description :</span></dt><dd>Percentage of the monthly averaged measurement UTC window represented by native resolution data.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>network</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6d84f634-68ef-4f61-8aba-941abe520bb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d84f634-68ef-4f61-8aba-941abe520bb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3855e3d-2240-4b49-85bf-b61150b8e8a0' class='xr-var-data-in' type='checkbox'><label for='data-f3855e3d-2240-4b49-85bf-b61150b8e8a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>network</dd><dt><span>long_name :</span></dt><dd>data network</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>The name of the network which reports data for the specific station in question.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;AERONET_v3_lev1.5&#x27;, &#x27;AERONET_v3_lev1.5&#x27;, &#x27;AERONET_v3_lev1.5&#x27;, ...,\n",
+       "       &#x27;AERONET_v3_lev1.5&#x27;, &#x27;AERONET_v3_lev1.5&#x27;, &#x27;AERONET_v3_lev1.5&#x27;],\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>network_maintenance_details</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-403fa7a0-ba92-43d6-a794-99aa33526b4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-403fa7a0-ba92-43d6-a794-99aa33526b4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2729c6e1-2fc3-4650-8e83-6793288eb095' class='xr-var-data-in' type='checkbox'><label for='data-2729c6e1-2fc3-4650-8e83-6793288eb095' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>network maintenance details</dd><dt><span>long_name :</span></dt><dd>network maintenance details</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Extra details provided by the reporting network about the operational maintenance done at the station.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>network_miscellaneous_details</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b8652522-cde3-47fe-8030-356ea5037d27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8652522-cde3-47fe-8030-356ea5037d27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b31ee11d-58d4-4c18-b7b4-336b3e391834' class='xr-var-data-in' type='checkbox'><label for='data-b31ee11d-58d4-4c18-b7b4-336b3e391834' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>network miscellaneous details</dd><dt><span>long_name :</span></dt><dd>network miscellaneous details</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Extra miscellanous details provided by the reporting network.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>network_provided_volume_standard_pressure</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df30e0f8-7ae2-4d46-aa4e-0dec4a450cfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df30e0f8-7ae2-4d46-aa4e-0dec4a450cfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7bec1f7-0913-4486-9de4-62f498bfcbe4' class='xr-var-data-in' type='checkbox'><label for='data-e7bec1f7-0913-4486-9de4-62f498bfcbe4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>network provided volume standard pressure</dd><dt><span>long_name :</span></dt><dd>network provided volume standard pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>description :</span></dt><dd>The pressure (in hPa) associated with the volume of the sampled gas (which varies with temperature and pressure). This volume is typically normalised in-instrument to a standard temperature and pressure. These standard values typically follow network/continental/global standards (e.g. European Union) for the measured component. If no in-instrument normalisation is done then the reported pressure should be reported as the internal pressure of the instrument (i.e. the measurement conditions). If no numbers are reported explicitly per measurement, then the sample gas pressure is assumed to be the known network standard pressure for the measured component.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>network_provided_volume_standard_temperature</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-242bdee7-5ce1-4eb3-a3ee-6615f5190dc4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-242bdee7-5ce1-4eb3-a3ee-6615f5190dc4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-889081c1-aeed-4990-ae6c-210a66e41c0a' class='xr-var-data-in' type='checkbox'><label for='data-889081c1-aeed-4990-ae6c-210a66e41c0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>network provided volume standard temperature</dd><dt><span>long_name :</span></dt><dd>network provided volume standard temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>description :</span></dt><dd>The temperature (in Kelvin) associated with the volume of the sampled gas (which varies with temperature and pressure). This volume is typically normalised in-instrument to a standard temperature and pressure. These standard values typically follow network/continental/global standards (e.g. European Union) for the measured component. If no in-instrument normalisation is done then the reported temperature should be reported as the internal temperature of the instrument (i.e. the measurement conditions). If no numbers are reported explicitly per measurement, then the sample gas temperature is assumed to be the known network standard temperature for the measured component.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>network_qa_details</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ca2d0ede-ed1b-459a-94c1-517e0033ac6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca2d0ede-ed1b-459a-94c1-517e0033ac6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfb00ba2-4b8e-4c7b-a385-91670fd9bdc3' class='xr-var-data-in' type='checkbox'><label for='data-dfb00ba2-4b8e-4c7b-a385-91670fd9bdc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>network qa details</dd><dt><span>long_name :</span></dt><dd>network qa details</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Extra details provided by the reporting network about the in-network quality assurance of measurements.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>network_sampling_details</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1fa198a8-8bd1-4ef0-9318-467ced2f80d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fa198a8-8bd1-4ef0-9318-467ced2f80d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa806a79-924f-4e30-90a7-00818d0324c8' class='xr-var-data-in' type='checkbox'><label for='data-fa806a79-924f-4e30-90a7-00818d0324c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>network sampling details</dd><dt><span>long_name :</span></dt><dd>network sampling details</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Extra details provided by the reporting network about the sampling methods employed.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>network_uncertainty_details</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-98a578f9-7e0e-4a71-9761-56906bf3d070' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98a578f9-7e0e-4a71-9761-56906bf3d070' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29109757-e7ae-4770-9185-705ecda369fc' class='xr-var-data-in' type='checkbox'><label for='data-29109757-e7ae-4770-9185-705ecda369fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>network uncertainty details</dd><dt><span>long_name :</span></dt><dd>network uncertainty details</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Extra details provided by the reporting network about the uncertainties involved with the measurement methods employed.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>od550aero</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ac3ef266-684c-4a91-87b5-7f1de5fdaab0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac3ef266-684c-4a91-87b5-7f1de5fdaab0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38b20dfd-0b07-454a-9d2c-af64c1b7a033' class='xr-var-data-in' type='checkbox'><label for='data-38b20dfd-0b07-454a-9d2c-af64c1b7a033' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>aerosol optical depth at 550nm</dd><dt><span>long_name :</span></dt><dd>aerosol optical depth at 550nm</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Measured value of surface aerosol optical depth at 550nm for the stated temporal resolution.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>population</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b596be4d-4e12-43d4-806f-de717b4f5833' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b596be4d-4e12-43d4-806f-de717b4f5833' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e62abd13-6f5f-4195-9409-cfe0b89ec1b4' class='xr-var-data-in' type='checkbox'><label for='data-e62abd13-6f5f-4195-9409-cfe0b89ec1b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>population</dd><dt><span>long_name :</span></dt><dd>population</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Population size of the nearest urban settlement.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>primary_sampling_further_details</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-007b5eda-f3ae-440d-b99a-4eb826e06617' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-007b5eda-f3ae-440d-b99a-4eb826e06617' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24dbea61-182b-496f-8b31-42f10c8e1ad4' class='xr-var-data-in' type='checkbox'><label for='data-24dbea61-182b-496f-8b31-42f10c8e1ad4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>primary sampling further details</dd><dt><span>long_name :</span></dt><dd>primary sampling further details</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Further associated details regarding the specifics of the primary sampling instrument/type.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>primary_sampling_instrument_documented_flow_rate</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eaad3c45-4f23-496e-878d-2892c7433cf2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eaad3c45-4f23-496e-878d-2892c7433cf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0c77051-db61-41fd-9e20-022bcf3e17ba' class='xr-var-data-in' type='checkbox'><label for='data-e0c77051-db61-41fd-9e20-022bcf3e17ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>primary sampling instrument documented flow rate</dd><dt><span>long_name :</span></dt><dd>primary sampling instrument documented sampling flow rate</dd><dt><span>units :</span></dt><dd>l min-1</dd><dt><span>description :</span></dt><dd>Volume (litres) of fluid which passes to the primary sampling instrument, per unit time (minutes), as given in instrumental manual/documentation. Can be a range: e.g. 1.0-3.0.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>primary_sampling_instrument_manual_name</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fa5e36ae-4795-4007-89ad-f92ab3f24fbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa5e36ae-4795-4007-89ad-f92ab3f24fbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c6c6ae5-e3a2-41d4-bd6b-93d7cd55ff6a' class='xr-var-data-in' type='checkbox'><label for='data-7c6c6ae5-e3a2-41d4-bd6b-93d7cd55ff6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>primary sampling instrument manual name</dd><dt><span>long_name :</span></dt><dd>primary sampling instrument manual name</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Path to the location in the esarchive of the manual for the specific primary sampling instrument.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>primary_sampling_instrument_name</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c4821f1-029c-4e18-a6be-1c2619f87a83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c4821f1-029c-4e18-a6be-1c2619f87a83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c7fb4eb-0643-4b54-8c71-90c34885fb4d' class='xr-var-data-in' type='checkbox'><label for='data-9c7fb4eb-0643-4b54-8c71-90c34885fb4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>primary sampling instrument name</dd><dt><span>long_name :</span></dt><dd>standardised primary sampling instrument name</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised name of the primary sampling instrument (if no specific instrument is used, or known, this is the standardised primary sampling type).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>primary_sampling_instrument_reported_flow_rate</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-830ecbda-cba9-48aa-8a8f-2fb067ccff2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-830ecbda-cba9-48aa-8a8f-2fb067ccff2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96a43ce3-c00d-4ffe-ae39-7f0c972165dd' class='xr-var-data-in' type='checkbox'><label for='data-96a43ce3-c00d-4ffe-ae39-7f0c972165dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>primary sampling instrument reported flow rate</dd><dt><span>long_name :</span></dt><dd>primary sampling instrument reported sampling flow rate</dd><dt><span>units :</span></dt><dd>l min-1</dd><dt><span>description :</span></dt><dd>Volume (litres) of fluid which passes to the primary sampling instrument, per unit time (minutes), as given in metadata. Can be a range: e.g. 1.0-3.0.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>primary_sampling_process_details</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3daf445f-5bd9-4546-a715-4ad052bd9bb8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3daf445f-5bd9-4546-a715-4ad052bd9bb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3663e5c-6934-42df-9592-b4feea8edf02' class='xr-var-data-in' type='checkbox'><label for='data-f3663e5c-6934-42df-9592-b4feea8edf02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>primary sampling process details</dd><dt><span>long_name :</span></dt><dd>primary sampling process details</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Miscellaneous details regarding assumptions made in the standardisation of the primary sampling type/instrument.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>primary_sampling_type</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ae70e6a9-407d-43d6-bb10-7fd5c0c6887b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae70e6a9-407d-43d6-bb10-7fd5c0c6887b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7eca0263-2fa9-49c0-9709-882b3f1c64f6' class='xr-var-data-in' type='checkbox'><label for='data-7eca0263-2fa9-49c0-9709-882b3f1c64f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>primary sampling type</dd><dt><span>long_name :</span></dt><dd>standardised primary sampling type</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised primary sampling type.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>principal_investigator_email_address</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4f6d8816-95c3-4a96-b650-c1da13b479bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f6d8816-95c3-4a96-b650-c1da13b479bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d62ea0e-37ad-45d9-8a2f-f4fdb89781cb' class='xr-var-data-in' type='checkbox'><label for='data-3d62ea0e-37ad-45d9-8a2f-f4fdb89781cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>principal investigator email address</dd><dt><span>long_name :</span></dt><dd>principal investigator email address</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Email address of the principal scientific investigator for the specific reported data.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Brent.N.Holben@nasa.gov&#x27;, &#x27;Brent.N.Holben@nasa.gov&#x27;,\n",
+       "       &#x27;Brent.N.Holben@nasa.gov&#x27;, ..., &#x27;Brent.N.Holben@nasa.gov&#x27;,\n",
+       "       &#x27;Brent.N.Holben@nasa.gov&#x27;, &#x27;Brent.N.Holben@nasa.gov&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>principal_investigator_institution</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b1794fb9-5e28-4e95-a3e2-92ad8c8e42df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1794fb9-5e28-4e95-a3e2-92ad8c8e42df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb4543f9-ce13-416f-bab9-959d7d510a46' class='xr-var-data-in' type='checkbox'><label for='data-bb4543f9-ce13-416f-bab9-959d7d510a46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>principal investigator institution</dd><dt><span>long_name :</span></dt><dd>principal investigator institution</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Institution of the principal scientific investigator for the specific reported data.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;NASA&#x27;, &#x27;NASA&#x27;, &#x27;NASA&#x27;, ..., &#x27;NASA&#x27;, &#x27;NASA&#x27;, &#x27;NASA&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>principal_investigator_name</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-93fd8011-3257-485e-b9ca-35b309e8928c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93fd8011-3257-485e-b9ca-35b309e8928c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e952cb71-a97b-4475-8577-912c95be36e9' class='xr-var-data-in' type='checkbox'><label for='data-e952cb71-a97b-4475-8577-912c95be36e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>principal investigator name</dd><dt><span>long_name :</span></dt><dd>principal investigator name</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Full name of the principal scientific investigator for the specific reported data.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Brent Holben&#x27;, &#x27;Brent Holben&#x27;, &#x27;Brent Holben&#x27;, ..., &#x27;Brent Holben&#x27;,\n",
+       "       &#x27;Brent Holben&#x27;, &#x27;Brent Holben&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>process_warnings</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2221547f-d533-4f50-8efa-ba1babfb65c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2221547f-d533-4f50-8efa-ba1babfb65c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4b2ef37-6ba8-4d3a-a2f0-5fd50f359521' class='xr-var-data-in' type='checkbox'><label for='data-f4b2ef37-6ba8-4d3a-a2f0-5fd50f359521' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>process warnings</dd><dt><span>long_name :</span></dt><dd>process warnings</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Warnings accumulated through GHOST processing regarding the data that should be considered.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;, ..., &#x27;&#x27;, &#x27;&#x27;, &#x27;&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>projection</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e5710933-00ac-489f-b31e-b09fc014c63d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5710933-00ac-489f-b31e-b09fc014c63d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a43ddfc6-e1bd-4c80-8c44-20311a4259d4' class='xr-var-data-in' type='checkbox'><label for='data-a43ddfc6-e1bd-4c80-8c44-20311a4259d4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>projection</dd><dt><span>long_name :</span></dt><dd>projection</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Name of the projected coordinate system of the original provided station position x, y coordinates. If the original coordinates are not projected, then this is set as &#x27;geographic&#x27;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;GEOGRAPHIC&#x27;, &#x27;GEOGRAPHIC&#x27;, &#x27;GEOGRAPHIC&#x27;, ..., &#x27;GEOGRAPHIC&#x27;,\n",
+       "       &#x27;GEOGRAPHIC&#x27;, &#x27;GEOGRAPHIC&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qa</span></div><div class='xr-var-dims'>(station, time, N_qa_codes)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-df64f2df-a695-4081-9aca-803121ef66b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df64f2df-a695-4081-9aca-803121ef66b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42e3fe95-63d9-4415-87a1-8dfe80439080' class='xr-var-data-in' type='checkbox'><label for='data-42e3fe95-63d9-4415-87a1-8dfe80439080' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>qa</dd><dt><span>long_name :</span></dt><dd>qa standardised data flags</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>List of derived quality assurance flag codes per measurement, informing on the quality associated with each observation, determined by multiple scientific quality control/assurance checks. Fill value code of 255.</dd></dl></div><div class='xr-var-data'><pre>[20291040 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reported_uncertainty_per_measurement</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e4613ef2-4f0b-4f88-a853-14cf7547d588' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4613ef2-4f0b-4f88-a853-14cf7547d588' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3710950a-8028-40df-b45c-e112632cb4dc' class='xr-var-data-in' type='checkbox'><label for='data-3710950a-8028-40df-b45c-e112632cb4dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>reported measurement uncertainty per measurement</dd><dt><span>long_name :</span></dt><dd>reported measurement uncertainty per measurement</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Reported measurement uncertainty (±) of methodology, for a specific measurement. In principal this refers to the inherent uncertainty on every measurement as a function of the quadratic addition of the accuracy and precision metrics (at the same confidence interval), but is often reported incosistently e.g. being solely determined from random errors (i.e. just the measuremental precision). This is given in absolute terms in unitless.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>representative_radius</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9823e079-5973-4187-8333-a3c1d758197b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9823e079-5973-4187-8333-a3c1d758197b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb307955-cc42-43e5-9d8c-162aab4e213d' class='xr-var-data-in' type='checkbox'><label for='data-bb307955-cc42-43e5-9d8c-162aab4e213d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>representative_radius</dd><dt><span>long_name :</span></dt><dd>representative_radius</dd><dt><span>units :</span></dt><dd>km</dd><dt><span>description :</span></dt><dd>Radius of representativity of the measurements made (i.e. for what distance scale around the sampling point would the measurements be very similar?), given in kilometres.  A quantitative version of the &quot;measurement_scale&quot; classification.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>retrieval_algorithm</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6b1426c7-a156-48cc-a5d0-c97f9225170b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b1426c7-a156-48cc-a5d0-c97f9225170b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e69c3a6e-9779-40d0-94c3-884db5b66291' class='xr-var-data-in' type='checkbox'><label for='data-e69c3a6e-9779-40d0-94c3-884db5b66291' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>retrieval algorithm</dd><dt><span>long_name :</span></dt><dd>retrieval algorithm</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>The name of the retrieval algorithm. Remote sensing algorithms are used to retrieve the aerosol optical properties (as aerosol optical depths or single scattering albedo among others) using remote-sensing radiances for multiple wavelengths from ground stations or on satellite platforms. Each algorithm is particularly designed considering the characteristics of the sensor and other ancillary information.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;directsun algorithm&#x27;, &#x27;directsun algorithm&#x27;, &#x27;directsun algorithm&#x27;,\n",
+       "       ..., &#x27;directsun algorithm&#x27;, &#x27;directsun algorithm&#x27;,\n",
+       "       &#x27;directsun algorithm&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sample_preparation_further_details</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-70b4c5fa-3288-4a28-8d96-7599c1e94fee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70b4c5fa-3288-4a28-8d96-7599c1e94fee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ece9ac06-c8c1-4883-8074-a808690dfc9a' class='xr-var-data-in' type='checkbox'><label for='data-ece9ac06-c8c1-4883-8074-a808690dfc9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sample preparation further details</dd><dt><span>long_name :</span></dt><dd>sample preparation further details</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Further associated details regarding the specifics of the sample preparation types/instruments. Multiple details specific to different types are separated by &quot;;&quot;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sample_preparation_process_details</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c7b42a87-e432-41c4-833d-b5fafb9a799c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7b42a87-e432-41c4-833d-b5fafb9a799c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2e61f0c-a80a-4218-b85c-cac60822d23a' class='xr-var-data-in' type='checkbox'><label for='data-e2e61f0c-a80a-4218-b85c-cac60822d23a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sample preparation process details</dd><dt><span>long_name :</span></dt><dd>sample preparation process details</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Miscellaneous details regarding assumptions made in the standardisation of the sample preparation types/techniques. Multiple details specific to different types are separated by &quot;;&quot;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sample_preparation_techniques</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-da7b04ec-e432-4563-a84a-30abd5268ffe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da7b04ec-e432-4563-a84a-30abd5268ffe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e1cbf9a-ea27-413b-9d92-b7929ce75128' class='xr-var-data-in' type='checkbox'><label for='data-8e1cbf9a-ea27-413b-9d92-b7929ce75128' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sample preparation techniques</dd><dt><span>long_name :</span></dt><dd>standardised specific preparation techniques</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised sample preparation techniques utilised in the measurement process. Mutiple names are separated by &quot;;&quot;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sample_preparation_types</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed728401-d697-4044-98e6-d61e1bbba72c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed728401-d697-4044-98e6-d61e1bbba72c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e69a8c1a-a875-45fe-a5f7-fe7135d7e71e' class='xr-var-data-in' type='checkbox'><label for='data-e69a8c1a-a875-45fe-a5f7-fe7135d7e71e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sample preparation types</dd><dt><span>long_name :</span></dt><dd>standardised sample preparation types</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised sample preparation types utilised in the measurement process. Mutiple types are separated by &quot;;&quot;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sampling_height</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a7a2cbd3-1673-4199-8cc5-ba9114d6ebcb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7a2cbd3-1673-4199-8cc5-ba9114d6ebcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8be04a38-bc2b-42fc-86c2-7f97363175ab' class='xr-var-data-in' type='checkbox'><label for='data-8be04a38-bc2b-42fc-86c2-7f97363175ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>sampling height</dd><dt><span>long_name :</span></dt><dd>sampling height relative to ground</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>description :</span></dt><dd>Height above the ground of the inlet/instrument/sampler, in metres.</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>season_code</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-18d19c9c-7f68-415c-806e-05a8f6a073ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18d19c9c-7f68-415c-806e-05a8f6a073ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cfaa21d-be5a-4204-8c2d-c37c8d2b8498' class='xr-var-data-in' type='checkbox'><label for='data-9cfaa21d-be5a-4204-8c2d-c37c8d2b8498' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>season code</dd><dt><span>long_name :</span></dt><dd>season code per measurement</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Code decreeing if measurement was made during the Spring, Summer, Autumn or Winter Seasons. Spring=0, Summer=1, Autumn=2, Winter=3. The classification is made by evaluating which season the local-time of a mid-measurement window timestamp falls in.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=uint8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>station_classification</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-86e2c222-8d55-479e-88fa-6bc5635831ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86e2c222-8d55-479e-88fa-6bc5635831ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a370854-5a1c-40ce-bb79-f17c80d16d4d' class='xr-var-data-in' type='checkbox'><label for='data-3a370854-5a1c-40ce-bb79-f17c80d16d4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>station classification</dd><dt><span>long_name :</span></dt><dd>standardised network provided station classification</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised network provided classification, categorising the type of air measured by a station.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>station_name</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9fe0009d-99ba-4ea0-b7bc-d930648b0eaf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fe0009d-99ba-4ea0-b7bc-d930648b0eaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7f41129-f024-4086-9454-681947796675' class='xr-var-data-in' type='checkbox'><label for='data-b7f41129-f024-4086-9454-681947796675' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>station name</dd><dt><span>long_name :</span></dt><dd>station name</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Name of station where the measurement was conducted.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Aoe_Baotou&#x27;, &#x27;Arm_Graciosa&#x27;, &#x27;Arm_Oliktok_Ak&#x27;, ..., &#x27;Zaragoza&#x27;,\n",
+       "       &#x27;Zinder_Airport&#x27;, &#x27;Zvenigorod&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>station_reference</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2eb330ab-eb5a-40d3-b5fa-d6ae8a461c49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2eb330ab-eb5a-40d3-b5fa-d6ae8a461c49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f3d1eb6-23a1-4076-9f7a-bb4b10abce7b' class='xr-var-data-in' type='checkbox'><label for='data-9f3d1eb6-23a1-4076-9f7a-bb4b10abce7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>station reference</dd><dt><span>long_name :</span></dt><dd>station reference identifier</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>reference ID for station.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;AOEBaotou_P-D&#x27;, &#x27;ARMGraciosa_P-D&#x27;, &#x27;ARMOliktokAK_P-D&#x27;, ...,\n",
+       "       &#x27;Zaragoza_P-D&#x27;, &#x27;ZinderAirport_P-D&#x27;, &#x27;Zvenigorod_P-D&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>station_timezone</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-bfb7ab8b-9106-44da-8307-6ff6a092bad8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfb7ab8b-9106-44da-8307-6ff6a092bad8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adc7c77e-af49-498b-86f7-0d3aaed5c3bd' class='xr-var-data-in' type='checkbox'><label for='data-adc7c77e-af49-498b-86f7-0d3aaed5c3bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>station timezone</dd><dt><span>long_name :</span></dt><dd>station timezone</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Name of the local timezone that the measuring station is located in. This is automatically generated using Timezone Finder Python package (taking longitude and latitude as inputs).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;Asia/Shanghai&#x27;, &#x27;Atlantic/Azores&#x27;, &#x27;America/Anchorage&#x27;, ...,\n",
+       "       &#x27;Europe/Madrid&#x27;, &#x27;Africa/Niamey&#x27;, &#x27;Europe/Moscow&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>street_type</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-94fd4e04-cff4-405e-8ece-43e773b4fbdc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94fd4e04-cff4-405e-8ece-43e773b4fbdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b9377b5-5708-40fb-b29e-9b4897a51c40' class='xr-var-data-in' type='checkbox'><label for='data-0b9377b5-5708-40fb-b29e-9b4897a51c40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>street type</dd><dt><span>long_name :</span></dt><dd>type of street</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Type of street where measurements are being made (if applicable).</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>street_width</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4a53e779-290b-4f7e-988c-6feaf624a9cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a53e779-290b-4f7e-988c-6feaf624a9cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ea47606-2211-44ca-b9e9-13f3fb8172a8' class='xr-var-data-in' type='checkbox'><label for='data-5ea47606-2211-44ca-b9e9-13f3fb8172a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>street width</dd><dt><span>long_name :</span></dt><dd>width of the street</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>description :</span></dt><dd>Width of the street where measurements are being made (if applicable), in metres.</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>terrain</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-938a3c14-12a6-4786-b15b-1c109d1ccef2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-938a3c14-12a6-4786-b15b-1c109d1ccef2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46ca05c3-4bc6-4fad-9cc6-a77188618dea' class='xr-var-data-in' type='checkbox'><label for='data-46ca05c3-4bc6-4fad-9cc6-a77188618dea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>terrain</dd><dt><span>long_name :</span></dt><dd>standardised network provided terrain type</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Standardised network provided classification, describing the dominant terrain in the area of the reporting station.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;, ..., &#x27;nan&#x27;, &#x27;nan&#x27;, &#x27;nan&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertical_datum</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-80c57f85-002f-46c2-bec7-008cd1580454' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80c57f85-002f-46c2-bec7-008cd1580454' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6182f08a-8543-4c56-9a64-94f916008aa8' class='xr-var-data-in' type='checkbox'><label for='data-6182f08a-8543-4c56-9a64-94f916008aa8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>vertical datum</dd><dt><span>long_name :</span></dt><dd>vertical datum</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Name of the vertical datum used to define vertical elevation on the Earth. The datum is a surface of zero elevation to which other heights can be reference against. If not explicitely stated then this is assumed to be &#x27;tidal - mean sea level&#x27;.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;TIDAL - MEAN SEA LEVEL&#x27;, &#x27;TIDAL - MEAN SEA LEVEL&#x27;,\n",
+       "       &#x27;TIDAL - MEAN SEA LEVEL&#x27;, ..., &#x27;TIDAL - MEAN SEA LEVEL&#x27;,\n",
+       "       &#x27;TIDAL - MEAN SEA LEVEL&#x27;, &#x27;TIDAL - MEAN SEA LEVEL&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>weekday_weekend_code</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5561cfef-288a-4e95-b070-3bf98d2f89fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5561cfef-288a-4e95-b070-3bf98d2f89fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79e0bd96-c2b4-4aea-b840-d26cd6873181' class='xr-var-data-in' type='checkbox'><label for='data-79e0bd96-c2b4-4aea-b840-d26cd6873181' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>weekday/weekend code</dd><dt><span>long_name :</span></dt><dd>weekday/weekend code per measurement</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>Binary indication if measurement was made during the weekday or weekend. Weekday=0, Weekend=1. The classification is made by evaluating if the local-time of a mid-measurement window timestamp falls on a weekday or on the weekend. Classification is 255 if cannot be made.</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=uint8]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3520856c-30ae-42b0-9c09-bc5471e56650' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3520856c-30ae-42b0-9c09-bc5471e56650' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>Surface aerosol optical depth at 550nm data in the AERONET_v3_lev1.5 network in 2020-06.</dd><dt><span>institution :</span></dt><dd>Barcelona Supercomputing Center</dd><dt><span>source :</span></dt><dd>Surface observations</dd><dt><span>creator_name :</span></dt><dd>Dene R. Bowdalo</dd><dt><span>creator_email :</span></dt><dd>dene.bowdalo@bsc.es</dd><dt><span>version :</span></dt><dd>1.4</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                                                           (station: 366, time: 720, N_flag_codes: 190, N_qa_codes: 77)\n",
+       "Coordinates:\n",
+       "  * time                                                              (time) datetime64[ns] ...\n",
+       "Dimensions without coordinates: station, N_flag_codes, N_qa_codes\n",
+       "Data variables: (12/180)\n",
+       "    ASTER_v3_altitude                                                 (station) float32 ...\n",
+       "    EDGAR_v4.3.2_annual_average_BC_emissions                          (station) float32 ...\n",
+       "    EDGAR_v4.3.2_annual_average_CO_emissions                          (station) float32 ...\n",
+       "    EDGAR_v4.3.2_annual_average_NH3_emissions                         (station) float32 ...\n",
+       "    EDGAR_v4.3.2_annual_average_NMVOC_emissions                       (station) float32 ...\n",
+       "    EDGAR_v4.3.2_annual_average_NOx_emissions                         (station) float32 ...\n",
+       "    ...                                                                ...\n",
+       "    station_timezone                                                  (station) object ...\n",
+       "    street_type                                                       (station) object ...\n",
+       "    street_width                                                      (station) float32 ...\n",
+       "    terrain                                                           (station) object ...\n",
+       "    vertical_datum                                                    (station) object ...\n",
+       "    weekday_weekend_code                                              (station, time) uint8 ...\n",
+       "Attributes:\n",
+       "    title:          Surface aerosol optical depth at 550nm data in the AERONE...\n",
+       "    institution:    Barcelona Supercomputing Center\n",
+       "    source:         Surface observations\n",
+       "    creator_name:   Dene R. Bowdalo\n",
+       "    creator_email:  dene.bowdalo@bsc.es\n",
+       "    version:        1.4"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xr.open_dataset(path_obs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:69: UserWarning: Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\n",
+      "  warnings.warn(\"Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<nes.nc_projections.points_nes_ghost.PointsNesGHOST at 0x7f510014eb90>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_obs = open_netcdf(path=path_obs, info=True)\n",
+    "data_obs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rank 000: Loading od550aero var (1/1)\n",
+      "Rank 000: Loaded od550aero var ((366, 720))\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_obs.keep_vars(['od550aero'])\n",
+    "data_obs.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2. Create shapefile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>geometry</th>\n",
+       "      <th>od550aero</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FID</th>\n",
+       "      <th></th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (109.62880 40.85170)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POINT (-28.02917 39.09109)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POINT (-149.88000 70.49950)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POINT (-97.48562 36.60518)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POINT (40.19450 -2.99600)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>POINT (-114.37625 62.45130)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>POINT (126.93479 37.56443)</td>\n",
+       "      <td>0.152831</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>POINT (-0.88235 41.63340)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>POINT (8.99023 13.77668)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>POINT (36.77500 55.69500)</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>366 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        geometry  od550aero\n",
+       "FID                                        \n",
+       "0     POINT (109.62880 40.85170)        NaN\n",
+       "1     POINT (-28.02917 39.09109)        NaN\n",
+       "2    POINT (-149.88000 70.49950)        NaN\n",
+       "3     POINT (-97.48562 36.60518)        NaN\n",
+       "4      POINT (40.19450 -2.99600)        NaN\n",
+       "..                           ...        ...\n",
+       "361  POINT (-114.37625 62.45130)        NaN\n",
+       "362   POINT (126.93479 37.56443)   0.152831\n",
+       "363    POINT (-0.88235 41.63340)        NaN\n",
+       "364     POINT (8.99023 13.77668)        NaN\n",
+       "365    POINT (36.77500 55.69500)        NaN\n",
+       "\n",
+       "[366 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_obs.create_shapefile()\n",
+    "data_obs.shapefile['od550aero'] = data_obs.variables['od550aero']['data'][:, 0].ravel()\n",
+    "data_obs.shapefile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Providentia Interpolation (before error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1. Read data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_prv_before = '/gpfs/projects/bsc32/AC_cache/recon/exp_interp/bug_1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
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+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                 (grid_edge: 877, station: 366, model_latitude: 181, model_longitude: 257, time: 720)\n",
+       "Coordinates:\n",
+       "  * time                    (time) datetime64[ns] 2020-06-01 ... 2020-06-30T2...\n",
+       "Dimensions without coordinates: grid_edge, station, model_latitude, model_longitude\n",
+       "Data variables:\n",
+       "    grid_edge_latitude      (grid_edge) float64 -90.5 -89.5 ... -90.5 -90.5\n",
+       "    grid_edge_longitude     (grid_edge) float64 -180.7 -180.7 ... -179.3 -180.7\n",
+       "    latitude                (station) float64 40.85 39.09 70.5 ... 13.78 55.7\n",
+       "    longitude               (station) float64 109.6 -28.03 -149.9 ... 8.99 36.77\n",
+       "    model_centre_latitude   (model_latitude, model_longitude) float64 -90.0 ....\n",
+       "    model_centre_longitude  (model_latitude, model_longitude) float64 -180.0 ...\n",
+       "    od550aero               (station, time) float32 ...\n",
+       "    station_reference       (station) object &#x27;AOEBaotou_P-D&#x27; ... &#x27;Zvenigorod_...\n",
+       "Attributes:\n",
+       "    title:          Inverse distance weighting (4 neighbours) interpolated a4...\n",
+       "    institution:    Barcelona Supercomputing Center\n",
+       "    source:         Experiment a4s2\n",
+       "    creator_name:   Dene R. Bowdalo\n",
+       "    creator_email:  dene.bowdalo@bsc.es\n",
+       "    conventions:    CF-1.7\n",
+       "    data_version:   1.0\n",
+       "    history:        Wed Nov 30 18:53:34 2022: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
+       "    NCO:            netCDF Operators version 4.9.2 (Homepage = http://nco.sf....</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d000f329-acde-460d-9123-61399b307980' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d000f329-acde-460d-9123-61399b307980' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>grid_edge</span>: 877</li><li><span>station</span>: 366</li><li><span>model_latitude</span>: 181</li><li><span>model_longitude</span>: 257</li><li><span class='xr-has-index'>time</span>: 720</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1109c9a3-73b6-4002-86d6-cb1ee5af3411' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1109c9a3-73b6-4002-86d6-cb1ee5af3411' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>2020-06-01 ... 2020-06-30T23:00:00</div><input id='attrs-b0d4eb30-d651-46e7-9b16-81bad677341d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0d4eb30-d651-46e7-9b16-81bad677341d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8b7dddd-48c5-4667-9674-7032aa19656d' class='xr-var-data-in' type='checkbox'><label for='data-a8b7dddd-48c5-4667-9674-7032aa19656d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>description :</span></dt><dd>Time in hours since 2020-06-01 00:00 UTC. Time given refers to the start of the time window the measurement is representative of (temporal resolution).</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>tz :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-06-01T01:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-01T02:00:00.000000000&#x27;, ..., &#x27;2020-06-30T21:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-30T22:00:00.000000000&#x27;, &#x27;2020-06-30T23:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c291967c-a481-4b1c-81cb-67bc30f2e833' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c291967c-a481-4b1c-81cb-67bc30f2e833' class='xr-section-summary' >Data variables: <span>(8)</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>grid_edge_latitude</span></div><div class='xr-var-dims'>(grid_edge)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-65ddc6f4-3e2d-4f87-970c-0bbf850f402a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65ddc6f4-3e2d-4f87-970c-0bbf850f402a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff2a0ee3-b4cd-46b1-bccd-df5e83160b19' class='xr-var-data-in' type='checkbox'><label for='data-ff2a0ee3-b4cd-46b1-bccd-df5e83160b19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>grid edge latitude</dd><dt><span>long_name :</span></dt><dd>grid edge latitude</dd><dt><span>units :</span></dt><dd>decimal degrees North</dd><dt><span>description :</span></dt><dd>Latitude coordinate along edge of grid domain (going clockwise around grid boundary from bottom-left corner).</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-90.5, -89.5, -88.5, ..., -90.5, -90.5, -90.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>grid_edge_longitude</span></div><div class='xr-var-dims'>(grid_edge)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cab5897a-12dd-420d-bcd0-02550218ea26' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cab5897a-12dd-420d-bcd0-02550218ea26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-815be4ef-e510-4060-84fe-27f1ff4a7e97' class='xr-var-data-in' type='checkbox'><label for='data-815be4ef-e510-4060-84fe-27f1ff4a7e97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>grid edge longitude</dd><dt><span>long_name :</span></dt><dd>grid edge longitude</dd><dt><span>units :</span></dt><dd>decimal degrees East</dd><dt><span>description :</span></dt><dd>Longitude coordinate along edge of grid domain (going clockwise around grid boundary from bottom-left corner).</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-180.703125, -180.703125, -180.703125, ..., -177.890625, -179.296875,\n",
+       "       -180.703125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b684848a-dd94-4f54-9bc3-db5ea877a229' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b684848a-dd94-4f54-9bc3-db5ea877a229' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-402d0600-f6ec-443a-a64d-2aeadf325d1a' class='xr-var-data-in' type='checkbox'><label for='data-402d0600-f6ec-443a-a64d-2aeadf325d1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>decimal degrees North</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>description :</span></dt><dd>Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum.</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([40.8517  , 39.09109 , 70.4995  , ..., 41.6334  , 13.776683, 55.695   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49c77913-4285-402d-aa5f-7681bb14d91d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49c77913-4285-402d-aa5f-7681bb14d91d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9bf767e-f81d-4aa0-9fc6-160b58e050e4' class='xr-var-data-in' type='checkbox'><label for='data-a9bf767e-f81d-4aa0-9fc6-160b58e050e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>decimal degrees East</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>description :</span></dt><dd>Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum.</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 109.6288  ,  -28.02917 , -149.88    , ...,   -0.88235 ,    8.990233,\n",
+       "         36.775   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_centre_latitude</span></div><div class='xr-var-dims'>(model_latitude, model_longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d9bcb966-a9d3-4690-90a1-61018a74f00c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9bcb966-a9d3-4690-90a1-61018a74f00c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40c0b875-5b74-4ac7-906b-e56c49542184' class='xr-var-data-in' type='checkbox'><label for='data-40c0b875-5b74-4ac7-906b-e56c49542184' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>model centre latitude</dd><dt><span>long_name :</span></dt><dd>model centre latitude</dd><dt><span>units :</span></dt><dd>decimal degrees North</dd><dt><span>description :</span></dt><dd>2D meshed grid centre latitudes with 181 latitudes in 257 bands of longitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([[-90., -90., -90., ..., -90., -90., -90.],\n",
+       "       [-89., -89., -89., ..., -89., -89., -89.],\n",
+       "       [-88., -88., -88., ..., -88., -88., -88.],\n",
+       "       ...,\n",
+       "       [ 88.,  88.,  88., ...,  88.,  88.,  88.],\n",
+       "       [ 89.,  89.,  89., ...,  89.,  89.,  89.],\n",
+       "       [ 90.,  90.,  90., ...,  90.,  90.,  90.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_centre_longitude</span></div><div class='xr-var-dims'>(model_latitude, model_longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e3898e38-826a-4110-95d1-7ac19607f67a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3898e38-826a-4110-95d1-7ac19607f67a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed7efcf6-24a2-493d-ad3e-4de1aab148eb' class='xr-var-data-in' type='checkbox'><label for='data-ed7efcf6-24a2-493d-ad3e-4de1aab148eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>model centre longitude</dd><dt><span>long_name :</span></dt><dd>model centre longitude</dd><dt><span>units :</span></dt><dd>decimal degrees East</dd><dt><span>description :</span></dt><dd>2D meshed grid centre longitudes with 257 longitudes in 181 bands of latitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([[-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "         180.     ],\n",
+       "       [-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "         180.     ],\n",
+       "       [-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "         180.     ],\n",
+       "       ...,\n",
+       "       [-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "         180.     ],\n",
+       "       [-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "         180.     ],\n",
+       "       [-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "         180.     ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>od550aero</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5ee5a8de-241d-4e08-a37d-c969d2c2409b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ee5a8de-241d-4e08-a37d-c969d2c2409b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-816711ac-f02b-49b6-9ec4-7b3552058ed6' class='xr-var-data-in' type='checkbox'><label for='data-816711ac-f02b-49b6-9ec4-7b3552058ed6' 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>aerosol optical depth at 550nm</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>standard_name :</span></dt><dd>aerosol optical depth at 550nm</dd><dt><span>descrption :</span></dt><dd>Interpolated value of aerosol optical depth at 550nm from the experiment a4s2 with reference to the measurement stations in the AERONET_v3_lev1.5 network</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>station_reference</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-037615f6-453f-447b-a030-3fcbb887d35d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-037615f6-453f-447b-a030-3fcbb887d35d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9337a83-af1c-4a11-bb3f-a5f7ab64e22b' class='xr-var-data-in' type='checkbox'><label for='data-c9337a83-af1c-4a11-bb3f-a5f7ab64e22b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>station reference</dd><dt><span>long_name :</span></dt><dd>station reference identifier</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>reference ID for station.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;AOEBaotou_P-D&#x27;, &#x27;ARMGraciosa_P-D&#x27;, &#x27;ARMOliktokAK_P-D&#x27;, ...,\n",
+       "       &#x27;Zaragoza_P-D&#x27;, &#x27;ZinderAirport_P-D&#x27;, &#x27;Zvenigorod_P-D&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0686ec6a-bb48-4b6a-8ed6-75dbb9e7ae62' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0686ec6a-bb48-4b6a-8ed6-75dbb9e7ae62' 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>title :</span></dt><dd>Inverse distance weighting (4 neighbours) interpolated a4s2 experiment data for the component od550aero with reference to the measurement stations in the AERONET_v3_lev1.5 network in 2020-06.</dd><dt><span>institution :</span></dt><dd>Barcelona Supercomputing Center</dd><dt><span>source :</span></dt><dd>Experiment a4s2</dd><dt><span>creator_name :</span></dt><dd>Dene R. Bowdalo</dd><dt><span>creator_email :</span></dt><dd>dene.bowdalo@bsc.es</dd><dt><span>conventions :</span></dt><dd>CF-1.7</dd><dt><span>data_version :</span></dt><dd>1.0</dd><dt><span>history :</span></dt><dd>Wed Nov 30 18:53:34 2022: ncks -O --dfl_lvl 1 /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.9.2 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                 (grid_edge: 877, station: 366, model_latitude: 181, model_longitude: 257, time: 720)\n",
+       "Coordinates:\n",
+       "  * time                    (time) datetime64[ns] 2020-06-01 ... 2020-06-30T2...\n",
+       "Dimensions without coordinates: grid_edge, station, model_latitude, model_longitude\n",
+       "Data variables:\n",
+       "    grid_edge_latitude      (grid_edge) float64 ...\n",
+       "    grid_edge_longitude     (grid_edge) float64 ...\n",
+       "    latitude                (station) float64 ...\n",
+       "    longitude               (station) float64 ...\n",
+       "    model_centre_latitude   (model_latitude, model_longitude) float64 ...\n",
+       "    model_centre_longitude  (model_latitude, model_longitude) float64 ...\n",
+       "    od550aero               (station, time) float32 ...\n",
+       "    station_reference       (station) object ...\n",
+       "Attributes:\n",
+       "    title:          Inverse distance weighting (4 neighbours) interpolated a4...\n",
+       "    institution:    Barcelona Supercomputing Center\n",
+       "    source:         Experiment a4s2\n",
+       "    creator_name:   Dene R. Bowdalo\n",
+       "    creator_email:  dene.bowdalo@bsc.es\n",
+       "    conventions:    CF-1.7\n",
+       "    data_version:   1.0\n",
+       "    history:        Wed Nov 30 18:53:34 2022: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
+       "    NCO:            netCDF Operators version 4.9.2 (Homepage = http://nco.sf...."
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xr.open_dataset(path_prv_before)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:69: UserWarning: Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\n",
+      "  warnings.warn(\"Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<nes.nc_projections.points_nes_providentia.PointsNesProvidentia at 0x7f5100fcc7d0>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_prv_before = open_netcdf(path=path_prv_before, info=True)\n",
+    "data_prv_before"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rank 000: Loading od550aero var (1/1)\n",
+      "Rank 000: Loaded od550aero var ((366, 720))\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_prv_before.keep_vars(['od550aero'])\n",
+    "data_prv_before.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2. Create shapefile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>geometry</th>\n",
+       "      <th>od550aero</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (109.62880 40.85170)</td>\n",
+       "      <td>0.227307</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POINT (-28.02917 39.09109)</td>\n",
+       "      <td>0.004195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POINT (-149.88000 70.49950)</td>\n",
+       "      <td>0.006696</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POINT (-97.48562 36.60518)</td>\n",
+       "      <td>0.005329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POINT (40.19450 -2.99600)</td>\n",
+       "      <td>0.059810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>POINT (-114.37625 62.45130)</td>\n",
+       "      <td>0.007441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>POINT (126.93479 37.56443)</td>\n",
+       "      <td>0.082251</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>POINT (-0.88235 41.63340)</td>\n",
+       "      <td>0.008346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>POINT (8.99023 13.77668)</td>\n",
+       "      <td>0.446823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>POINT (36.77500 55.69500)</td>\n",
+       "      <td>0.006547</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>366 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        geometry  od550aero\n",
+       "FID                                        \n",
+       "0     POINT (109.62880 40.85170)   0.227307\n",
+       "1     POINT (-28.02917 39.09109)   0.004195\n",
+       "2    POINT (-149.88000 70.49950)   0.006696\n",
+       "3     POINT (-97.48562 36.60518)   0.005329\n",
+       "4      POINT (40.19450 -2.99600)   0.059810\n",
+       "..                           ...        ...\n",
+       "361  POINT (-114.37625 62.45130)   0.007441\n",
+       "362   POINT (126.93479 37.56443)   0.082251\n",
+       "363    POINT (-0.88235 41.63340)   0.008346\n",
+       "364     POINT (8.99023 13.77668)   0.446823\n",
+       "365    POINT (36.77500 55.69500)   0.006547\n",
+       "\n",
+       "[366 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_prv_before.create_shapefile()\n",
+    "data_prv_before.shapefile['od550aero'] = data_prv_before.variables['od550aero']['data'][:, 0].ravel()\n",
+    "data_prv_before.shapefile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3. Plot data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig_1, ax_1 = plt.subplots(1, figsize=(20, 7))\n",
+    "\n",
+    "gdf_model.plot(ax=ax_1, column='od550du', cmap='viridis', legend=True, vmin=0, vmax=3)\n",
+    "\n",
+    "gdf_earth.plot(ax=ax_1, facecolor=\"none\", edgecolor='white', lw=0.5)\n",
+    "\n",
+    "gdf_prv_before = data_prv_before.shapefile\n",
+    "gdf_prv_before.plot(ax=ax_1, column='od550aero', cmap='viridis', edgecolor='black', lw=0.5, vmin=0, vmax=3)\n",
+    "\n",
+    "ax_1.margins(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Providentia Interpolation (after error)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.1. Read data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_prv_after = '/gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:                 (grid_edge: 877, station: 366, model_latitude: 181, model_longitude: 257, time: 720)\n",
+       "Coordinates:\n",
+       "  * time                    (time) datetime64[ns] 2020-06-01 ... 2020-06-30T2...\n",
+       "Dimensions without coordinates: grid_edge, station, model_latitude, model_longitude\n",
+       "Data variables:\n",
+       "    grid_edge_latitude      (grid_edge) float64 -90.5 -89.5 ... -90.5 -90.5\n",
+       "    grid_edge_longitude     (grid_edge) float64 -180.7 -180.7 ... -179.3 -180.7\n",
+       "    latitude                (station) float64 40.85 39.09 70.5 ... 13.78 55.7\n",
+       "    longitude               (station) float64 109.6 -28.03 -149.9 ... 8.99 36.77\n",
+       "    model_centre_latitude   (model_latitude, model_longitude) float64 -90.0 ....\n",
+       "    model_centre_longitude  (model_latitude, model_longitude) float64 -180.0 ...\n",
+       "    od550aero               (station, time) float32 ...\n",
+       "    station_reference       (station) object &#x27;AOEBaotou_P-D&#x27; ... &#x27;Zvenigorod_...\n",
+       "Attributes:\n",
+       "    title:          Inverse distance weighting (4 neighbours) interpolated a4...\n",
+       "    institution:    Barcelona Supercomputing Center\n",
+       "    source:         Experiment a4s2\n",
+       "    creator_name:   Dene R. Bowdalo\n",
+       "    creator_email:  dene.bowdalo@bsc.es\n",
+       "    conventions:    CF-1.7\n",
+       "    data_version:   1.0\n",
+       "    history:        Wed Nov 30 18:53:34 2022: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
+       "    NCO:            netCDF Operators version 4.9.2 (Homepage = http://nco.sf....</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f90e71b3-e8c9-45ff-add8-3d4d490a4ba1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f90e71b3-e8c9-45ff-add8-3d4d490a4ba1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>grid_edge</span>: 877</li><li><span>station</span>: 366</li><li><span>model_latitude</span>: 181</li><li><span>model_longitude</span>: 257</li><li><span class='xr-has-index'>time</span>: 720</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b7053572-0ef5-4bec-acc6-9f5fa7b12ad0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b7053572-0ef5-4bec-acc6-9f5fa7b12ad0' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>2020-06-01 ... 2020-06-30T23:00:00</div><input id='attrs-8234056d-5d2c-49d8-98d2-728ae0ccebbd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8234056d-5d2c-49d8-98d2-728ae0ccebbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7313da53-14ca-4d29-a739-9c36f3761b0c' class='xr-var-data-in' type='checkbox'><label for='data-7313da53-14ca-4d29-a739-9c36f3761b0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>description :</span></dt><dd>Time in hours since 2020-06-01 00:00 UTC. Time given refers to the start of the time window the measurement is representative of (temporal resolution).</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>tz :</span></dt><dd>UTC</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-06-01T00:00:00.000000000&#x27;, &#x27;2020-06-01T01:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-01T02:00:00.000000000&#x27;, ..., &#x27;2020-06-30T21:00:00.000000000&#x27;,\n",
+       "       &#x27;2020-06-30T22:00:00.000000000&#x27;, &#x27;2020-06-30T23:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f90b6eb1-d146-48a6-8dd0-f8ec62fc029a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f90b6eb1-d146-48a6-8dd0-f8ec62fc029a' class='xr-section-summary' >Data variables: <span>(8)</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>grid_edge_latitude</span></div><div class='xr-var-dims'>(grid_edge)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d3c8fd4b-9e5d-46d4-8e2d-b1df5cb26bec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3c8fd4b-9e5d-46d4-8e2d-b1df5cb26bec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbe1cdba-633c-4339-b74e-9e65b205926f' class='xr-var-data-in' type='checkbox'><label for='data-bbe1cdba-633c-4339-b74e-9e65b205926f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>grid edge latitude</dd><dt><span>long_name :</span></dt><dd>grid edge latitude</dd><dt><span>units :</span></dt><dd>decimal degrees North</dd><dt><span>description :</span></dt><dd>Latitude coordinate along edge of grid domain (going clockwise around grid boundary from bottom-left corner).</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-90.5, -89.5, -88.5, ..., -90.5, -90.5, -90.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>grid_edge_longitude</span></div><div class='xr-var-dims'>(grid_edge)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1f64b8f5-1c4b-4eaa-9af9-d8860fa95654' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f64b8f5-1c4b-4eaa-9af9-d8860fa95654' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a6ed0f6-0e97-4236-a677-186b294129af' class='xr-var-data-in' type='checkbox'><label for='data-7a6ed0f6-0e97-4236-a677-186b294129af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>grid edge longitude</dd><dt><span>long_name :</span></dt><dd>grid edge longitude</dd><dt><span>units :</span></dt><dd>decimal degrees East</dd><dt><span>description :</span></dt><dd>Longitude coordinate along edge of grid domain (going clockwise around grid boundary from bottom-left corner).</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([-180.703125, -180.703125, -180.703125, ..., -177.890625, -179.296875,\n",
+       "       -180.703125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-161a5aca-8d9b-4d26-9b8d-d13d0999a366' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-161a5aca-8d9b-4d26-9b8d-d13d0999a366' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-653b70cf-be2b-4cb3-bc64-2bafd0333a38' class='xr-var-data-in' type='checkbox'><label for='data-653b70cf-be2b-4cb3-bc64-2bafd0333a38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>decimal degrees North</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>description :</span></dt><dd>Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum.</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([40.8517  , 39.09109 , 70.4995  , ..., 41.6334  , 13.776683, 55.695   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3c070fc2-3a38-4d47-b200-365072cefc1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c070fc2-3a38-4d47-b200-365072cefc1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e747ffdf-167b-46b6-b64b-707cb1d23725' class='xr-var-data-in' type='checkbox'><label for='data-e747ffdf-167b-46b6-b64b-707cb1d23725' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>decimal degrees East</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>description :</span></dt><dd>Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum.</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 109.6288  ,  -28.02917 , -149.88    , ...,   -0.88235 ,    8.990233,\n",
+       "         36.775   ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_centre_latitude</span></div><div class='xr-var-dims'>(model_latitude, model_longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9b24127b-045b-40ed-8c1e-22792a9d4d5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b24127b-045b-40ed-8c1e-22792a9d4d5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fac7e2a4-835b-4d5e-bd35-8965e83b6d7a' class='xr-var-data-in' type='checkbox'><label for='data-fac7e2a4-835b-4d5e-bd35-8965e83b6d7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>model centre latitude</dd><dt><span>long_name :</span></dt><dd>model centre latitude</dd><dt><span>units :</span></dt><dd>decimal degrees North</dd><dt><span>description :</span></dt><dd>2D meshed grid centre latitudes with 181 latitudes in 257 bands of longitude</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([[-90., -90., -90., ..., -90., -90., -90.],\n",
+       "       [-89., -89., -89., ..., -89., -89., -89.],\n",
+       "       [-88., -88., -88., ..., -88., -88., -88.],\n",
+       "       ...,\n",
+       "       [ 88.,  88.,  88., ...,  88.,  88.,  88.],\n",
+       "       [ 89.,  89.,  89., ...,  89.,  89.,  89.],\n",
+       "       [ 90.,  90.,  90., ...,  90.,  90.,  90.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>model_centre_longitude</span></div><div class='xr-var-dims'>(model_latitude, model_longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f8bcb540-6633-4531-b9b8-f842a5408dac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8bcb540-6633-4531-b9b8-f842a5408dac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cf0dbd1-d26f-4f7f-89fb-d5f4de4c2b48' class='xr-var-data-in' type='checkbox'><label for='data-7cf0dbd1-d26f-4f7f-89fb-d5f4de4c2b48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>model centre longitude</dd><dt><span>long_name :</span></dt><dd>model centre longitude</dd><dt><span>units :</span></dt><dd>decimal degrees East</dd><dt><span>description :</span></dt><dd>2D meshed grid centre longitudes with 257 longitudes in 181 bands of latitude</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([[-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "         180.     ],\n",
+       "       [-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "         180.     ],\n",
+       "       [-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "         180.     ],\n",
+       "       ...,\n",
+       "       [-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "         180.     ],\n",
+       "       [-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "         180.     ],\n",
+       "       [-180.     , -178.59375, -177.1875 , ...,  177.1875 ,  178.59375,\n",
+       "         180.     ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>od550aero</span></div><div class='xr-var-dims'>(station, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-33478ae1-b8bc-4183-bdac-70c5826367cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33478ae1-b8bc-4183-bdac-70c5826367cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98550edc-7124-4351-a7e9-3b405c8f6002' class='xr-var-data-in' type='checkbox'><label for='data-98550edc-7124-4351-a7e9-3b405c8f6002' 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>aerosol optical depth at 550nm</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>standard_name :</span></dt><dd>aerosol optical depth at 550nm</dd><dt><span>descrption :</span></dt><dd>Interpolated value of aerosol optical depth at 550nm from the experiment a4s2 with reference to the measurement stations in the AERONET_v3_lev1.5 network</dd></dl></div><div class='xr-var-data'><pre>[263520 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>station_reference</span></div><div class='xr-var-dims'>(station)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed0c55bc-d623-4a2f-9caa-917861f8b6e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed0c55bc-d623-4a2f-9caa-917861f8b6e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-022b9ae8-15d0-4bc7-88d6-bfc0d40df99f' class='xr-var-data-in' type='checkbox'><label for='data-022b9ae8-15d0-4bc7-88d6-bfc0d40df99f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>station reference</dd><dt><span>long_name :</span></dt><dd>station reference identifier</dd><dt><span>units :</span></dt><dd>unitless</dd><dt><span>description :</span></dt><dd>reference ID for station.</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;AOEBaotou_P-D&#x27;, &#x27;ARMGraciosa_P-D&#x27;, &#x27;ARMOliktokAK_P-D&#x27;, ...,\n",
+       "       &#x27;Zaragoza_P-D&#x27;, &#x27;ZinderAirport_P-D&#x27;, &#x27;Zvenigorod_P-D&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-11fd5a4d-8dc9-402b-b542-ef447c3c6433' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11fd5a4d-8dc9-402b-b542-ef447c3c6433' 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>title :</span></dt><dd>Inverse distance weighting (4 neighbours) interpolated a4s2 experiment data for the component od550aero with reference to the measurement stations in the AERONET_v3_lev1.5 network in 2020-06.</dd><dt><span>institution :</span></dt><dd>Barcelona Supercomputing Center</dd><dt><span>source :</span></dt><dd>Experiment a4s2</dd><dt><span>creator_name :</span></dt><dd>Dene R. Bowdalo</dd><dt><span>creator_email :</span></dt><dd>dene.bowdalo@bsc.es</dd><dt><span>conventions :</span></dt><dd>CF-1.7</dd><dt><span>data_version :</span></dt><dd>1.0</dd><dt><span>history :</span></dt><dd>Wed Nov 30 18:53:34 2022: ncks -O --dfl_lvl 1 /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc</dd><dt><span>NCO :</span></dt><dd>netCDF Operators version 4.9.2 (Homepage = http://nco.sf.net, Code = http://github.com/nco/nco)</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:                 (grid_edge: 877, station: 366, model_latitude: 181, model_longitude: 257, time: 720)\n",
+       "Coordinates:\n",
+       "  * time                    (time) datetime64[ns] 2020-06-01 ... 2020-06-30T2...\n",
+       "Dimensions without coordinates: grid_edge, station, model_latitude, model_longitude\n",
+       "Data variables:\n",
+       "    grid_edge_latitude      (grid_edge) float64 ...\n",
+       "    grid_edge_longitude     (grid_edge) float64 ...\n",
+       "    latitude                (station) float64 ...\n",
+       "    longitude               (station) float64 ...\n",
+       "    model_centre_latitude   (model_latitude, model_longitude) float64 ...\n",
+       "    model_centre_longitude  (model_latitude, model_longitude) float64 ...\n",
+       "    od550aero               (station, time) float32 ...\n",
+       "    station_reference       (station) object ...\n",
+       "Attributes:\n",
+       "    title:          Inverse distance weighting (4 neighbours) interpolated a4...\n",
+       "    institution:    Barcelona Supercomputing Center\n",
+       "    source:         Experiment a4s2\n",
+       "    creator_name:   Dene R. Bowdalo\n",
+       "    creator_email:  dene.bowdalo@bsc.es\n",
+       "    conventions:    CF-1.7\n",
+       "    data_version:   1.0\n",
+       "    history:        Wed Nov 30 18:53:34 2022: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
+       "    NCO:            netCDF Operators version 4.9.2 (Homepage = http://nco.sf...."
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xr.open_dataset(path_prv_before)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:69: UserWarning: Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\n",
+      "  warnings.warn(\"Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<nes.nc_projections.points_nes_providentia.PointsNesProvidentia at 0x7f510000f610>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_prv_after = open_netcdf(path=path_prv_after, info=True)\n",
+    "data_prv_after"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rank 000: Loading od550aero var (1/1)\n",
+      "Rank 000: Loaded od550aero var ((366, 720))\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_prv_after.keep_vars(['od550aero'])\n",
+    "data_prv_after.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.2. Create shapefile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "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>geometry</th>\n",
+       "      <th>od550aero</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (109.62880 40.85170)</td>\n",
+       "      <td>0.504757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POINT (-28.02917 39.09109)</td>\n",
+       "      <td>0.007563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POINT (-149.88000 70.49950)</td>\n",
+       "      <td>0.013467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POINT (-97.48562 36.60518)</td>\n",
+       "      <td>0.011034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POINT (40.19450 -2.99600)</td>\n",
+       "      <td>0.002979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>POINT (-114.37625 62.45130)</td>\n",
+       "      <td>0.014462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>POINT (126.93479 37.56443)</td>\n",
+       "      <td>0.184404</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>POINT (-0.88235 41.63340)</td>\n",
+       "      <td>0.018486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>POINT (8.99023 13.77668)</td>\n",
+       "      <td>0.900909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>POINT (36.77500 55.69500)</td>\n",
+       "      <td>0.013143</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>366 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        geometry  od550aero\n",
+       "FID                                        \n",
+       "0     POINT (109.62880 40.85170)   0.504757\n",
+       "1     POINT (-28.02917 39.09109)   0.007563\n",
+       "2    POINT (-149.88000 70.49950)   0.013467\n",
+       "3     POINT (-97.48562 36.60518)   0.011034\n",
+       "4      POINT (40.19450 -2.99600)   0.002979\n",
+       "..                           ...        ...\n",
+       "361  POINT (-114.37625 62.45130)   0.014462\n",
+       "362   POINT (126.93479 37.56443)   0.184404\n",
+       "363    POINT (-0.88235 41.63340)   0.018486\n",
+       "364     POINT (8.99023 13.77668)   0.900909\n",
+       "365    POINT (36.77500 55.69500)   0.013143\n",
+       "\n",
+       "[366 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_prv_after.create_shapefile()\n",
+    "data_prv_after.shapefile['od550aero'] = data_prv_after.variables['od550aero']['data'][:, 0].ravel()\n",
+    "data_prv_after.shapefile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 4.3. Plot data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig_2, ax_2 = plt.subplots(1, figsize=(20, 7))\n",
+    "\n",
+    "gdf_model.plot(ax=ax_2, column='od550du', cmap='viridis', legend=True, vmin=0, vmax=3)\n",
+    "\n",
+    "gdf_earth.plot(ax=ax_2, facecolor=\"none\", edgecolor='white', lw=0.5)\n",
+    "\n",
+    "gdf_prv_after = data_prv_after.shapefile\n",
+    "gdf_prv_after.plot(ax=ax_2, column='od550aero', cmap='viridis', edgecolor='black', lw=0.5, vmin=0, vmax=3)\n",
+    "\n",
+    "ax_2.margins(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "masked_array(\n",
+       "  data=[[-0.27745032, -0.33557633, -0.39707237, ..., -0.02371369,\n",
+       "         -0.02375146, -0.02365999],\n",
+       "        [-0.00336807, -0.00303048, -0.00253099, ..., -0.00627814,\n",
+       "         -0.00588607, -0.00548861],\n",
+       "        [-0.00677101, -0.00678938, -0.00670995, ..., -0.00168319,\n",
+       "         -0.0017733 , -0.00185721],\n",
+       "        ...,\n",
+       "        [-0.01014002, -0.01006637, -0.01026233, ..., -0.04439881,\n",
+       "         -0.04458728, -0.04401928],\n",
+       "        [-0.4540865 , -0.4427302 , -0.4316749 , ..., -0.5309974 ,\n",
+       "         -0.52981824, -0.526042  ],\n",
+       "        [-0.00659596, -0.0059174 , -0.0054107 , ..., -0.00239577,\n",
+       "         -0.00239077, -0.00250021]],\n",
+       "  mask=False,\n",
+       "  fill_value=1e+20,\n",
+       "  dtype=float32)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diff = data_prv_before.variables['od550aero']['data'] - data_prv_after.variables['od550aero']['data']\n",
+    "diff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. NES Interpolation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.1. Interpolate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\tod550du horizontal interpolation\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_nes = data_model.interpolate_horizontal(data_obs, weight_matrix_path=None, \n",
+    "                                             kind='NearestNeighbour', n_neighbours=4,\n",
+    "                                             info=True, to_providentia=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'od550du': {'data': array([[0.5048964 , 0.63574161, 0.79751828, ..., 0.5958503 , 0.55814504,\n",
+       "          0.54268227],\n",
+       "         [0.00756333, 0.00732328, 0.00696368, ..., 0.01266789, 0.01230407,\n",
+       "          0.01215834],\n",
+       "         [0.01346751, 0.01355801, 0.01349525, ..., 0.01335663, 0.01338057,\n",
+       "          0.01345622],\n",
+       "         ...,\n",
+       "         [0.01848428, 0.01945806, 0.02101376, ..., 0.02690235, 0.02741421,\n",
+       "          0.02839378],\n",
+       "         [0.90109581, 0.88706459, 0.87328311, ..., 0.84196177, 0.86970553,\n",
+       "          0.89764859],\n",
+       "         [0.01314365, 0.01134606, 0.01024394, ..., 0.00430861, 0.00383769,\n",
+       "          0.00348033]]), 'dimensions': ('station', 'time')}}"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_nes.variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.2. Create shapefile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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>geometry</th>\n",
+       "      <th>od550du</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (109.62880 40.85170)</td>\n",
+       "      <td>0.504896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POINT (-28.02917 39.09109)</td>\n",
+       "      <td>0.007563</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POINT (-149.88000 70.49950)</td>\n",
+       "      <td>0.013468</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POINT (-97.48562 36.60518)</td>\n",
+       "      <td>0.011034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POINT (40.19450 -2.99600)</td>\n",
+       "      <td>0.002980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>POINT (-114.37625 62.45130)</td>\n",
+       "      <td>0.014462</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>POINT (126.93479 37.56443)</td>\n",
+       "      <td>0.184427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>POINT (-0.88235 41.63340)</td>\n",
+       "      <td>0.018484</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>POINT (8.99023 13.77668)</td>\n",
+       "      <td>0.901096</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>POINT (36.77500 55.69500)</td>\n",
+       "      <td>0.013144</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>366 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        geometry   od550du\n",
+       "FID                                       \n",
+       "0     POINT (109.62880 40.85170)  0.504896\n",
+       "1     POINT (-28.02917 39.09109)  0.007563\n",
+       "2    POINT (-149.88000 70.49950)  0.013468\n",
+       "3     POINT (-97.48562 36.60518)  0.011034\n",
+       "4      POINT (40.19450 -2.99600)  0.002980\n",
+       "..                           ...       ...\n",
+       "361  POINT (-114.37625 62.45130)  0.014462\n",
+       "362   POINT (126.93479 37.56443)  0.184427\n",
+       "363    POINT (-0.88235 41.63340)  0.018484\n",
+       "364     POINT (8.99023 13.77668)  0.901096\n",
+       "365    POINT (36.77500 55.69500)  0.013144\n",
+       "\n",
+       "[366 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_nes.create_shapefile()\n",
+    "data_nes.shapefile['od550du'] = data_nes.variables['od550du']['data'][:, 0].ravel()\n",
+    "data_nes.shapefile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 5.3. Plot data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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z6r3gLdg4jycnz+soyRvEJJuIJZp+IrLO9my7FOxLXLAVDVsAG6UF35ne0ZbIceH0GxaCSi1ZrAXlJRWcORhH/NFkpt89Gb8wH7yDPZAkCVmWST6dxr6/DrPnz0OWum/djvNQwXv2h3+j0anJScvD3smOnNRcvp67uHM676B7hBCChxfO4ac3f2NXyibCpKF1Ps+SU9GixVl4dEj/ADZ2OryCPAgM96NPZCCSSqKsuILUmAxSTqWZy2XozCUzTCaZ7ORcq7zYt5pG5rykktDaaHFwsUPvaItao0alUWGoMpCfWUh+ZuNJRCRJAmFWjPoOCyFyfDj2znYYq41kJmaTk5pntmyeSmtZNlMrzROVWsXt82ZiNJqorjRQlFuELIO7nyv9LwxFq9Pw5JRXrNJXbS66YjgzHrqME7vPEHMonn1rDrctZrIJnNwd8ArywDPIHa9AD3MCGUnCUG0k5lAcRzYf77B4wfOWrnhedOR7lRXG09uVt0GDtfKvq62jvA0I7BrlTYl5U+hR6B1t6RMZgH+YL+7+rnWKGY+6egQH10ex54+DHNl8nJzUvM5LOd8OJCHhKLuQLMfgRwgCiXx1Fl6hbpiibDukz2lzJhEY7sfmn3ea66t5u6B3tEXvYMOQiRGEDApk2/I9pJw+6yYpSRKDJ4Rz8ZUXcM3Dl6NSm899enwW+9ceYfcfB5QXiy4iLyMflVpFelwmZUVlRG21fkxRZ2PnpMfVxwX/AT6oUzX1PnfFk1TicKb9ypve0Zb7351NTsrZZENCgGeQB3aOevIzC8jPLMBQbcRkNGFjpyNwoB/VlQYkSeDTxwv/MB+GT4nkgwe+4OD6o+2WyRqYjCYqSivaVCrCZDr7EhhzKL7BuLmuwGgwWtxPNVo1jm4OZpdXAcsWrKKsuGOsVXtWH2TP6oMIIRg4Koxbnr8eZNmy8GUymV1ETUYThmoDZcXllOSXUphTTE5KLqkxGc32UZhTTGFOMacPxNXZrlKrCBvRhxkPTUPvYMOhTdEc2tA95piCgkLnoyhvCt0WjU7DBVMjGTCyn8W1p6y4nPioBA6ujyInNa9OBjEHNwcO/B1F1NbjXSVym/ES/hTLBSRwChkTUyZP4aJhF/Nz1Aqr9qPWqLhn/q2Ejwpj9x8HEJJEzMF49qQfbNZFx2QycXhTNIc3RdfZPnBUGKOuuoBpd05Co1WDgJyUPA78HcWO3/fWKe6t0DFUVxn47MlFXS2GVSkpKOXwpmgix4SzaocKzgkvKqUIW8xWaZNsJJMUyuRiNEKLD0FohK7FfZUXV3Bs+0n8+/lQmFPMvjWHST6VRkB/XybfPLbmPiMQwqzo2drbAGYFr7K8mqSTKexbc5iP//2t9eqeKTRLdZWB3PQWlCGxIrIsE73zFNE7T1m2RYzpz9BJEWh05leq1CNpHN99BgdXe5zcHZkwcxQJx5PZuWJ/m/o0Goyc2H2GE7vPADB19nhunzcThMBkNJF4IqVdMXgKCucbRro+EVx76H7KW3d00+vu9BI3MSEETu4O+PTx4oKpg9HaaNi9+iDfvbS0bm20loy3FSn/uwsOwhkHnAEYGBzZeJbJVl4jGp2GkMhABo8Lx8XbCVt7Gx4b92KTMUEVchmZJGGQDTgLd1zxalSe47tO10uW0GdwIGOuGcncxY8SGO7H5099z5ZlnZQs4zzEUGXA1du519XSWvHxGvQOtvQJ6UNubA5O1WZXF4NcRbqcQJgYhlE2ckY+gp8IwUcKokIuI04+TiBhLXY5lmWZdd9tAcylFIZPHczEWaMBs4K2/be93bsmXkvud+f5s1WSzPXrhCQoziuhqqLtSrbOVsuQSRG4ejtjqDJwwdTBZCfnYqg2IgQMHNWfbcv3WupbHt16nKm3T+CVFU8TOXYAd0U83q5r9e9FZ2MnJUnwwPt3UF5cwb41h9vc5nmLta6L1rxbWPu6bM97TUP9d59oqg5BRlHeFBRajRCCvsOCmTZnUp0MWyaTTFFOEVnJOfzy3h/KKmI78Ary4Ip7L8FoMFJdaSD+WBLrf9hKQXYRYSNCufb/LmfFJ2sbtBIUyDnkyOkEijA0Qksu6cTJxwg9JyavKeKikoiLSuK+BbdRVlSuKG4dSHBEAE4ejt2ipERHUFZcDie1lMtlZMtRCAT2LvY8NecZVn2wnlQ5Dn8Rir1wAsBG6OnHYOLk4/StmbOege4ER/hj56THaDARvfNUo4XjC7LNdcEC+vuCLGM0mNrkdqjQOLb2NtjY6SgpKOsUS6Wdk56FO1/j5J4zFOeX4urtjKO7A/+57PUmj9No1QyeMJCBo8LqXF9jrrmQjYt3MHRSBBkJ2QRHBLBk/goSj6fU6dM/zIcBI/vh6u2EyWhi/fdbee/uT61absJkkikvLifhWLLV2lRoPc4ejviEegOQHpfZqhISCgqtRVHeegM9oLixRqdh2JRBDLw4DCHgzKF4+g4L4d9jX2h9Yy0Zb29bcW7lGOyc9BiqDCx6eVm9z2z0WsJH9ePON25CNpn49InvWfXp2fpHWXIK/cQQy8uKO74YMFAk5+EoXOu11xhzF/+b3LR8Xp31fqtkV2gd1zwynYUPfFEnRslqdKNrx1sEgqjJvFoIiYfSuPvNm/nk64/QnnGqs68kVAjZPH+DBvozdfYEti/fQ3pcFmqtmmlzJnHmYBx7/zJbKtQaFf0u6MOgMQNwcnfg2I5THPg7ioD+vsiyjN5R36lj7RC66J6o0aoZMW0ooYODUGlUIMuUFVdQXlqBg7MdGp3GnCX1WBKbOiirbWlhGf+Z9johgwLQO+oxGozERTVcxL42z//8KDGHElj/wzZkWcZQZcDJw5HqymrsnfX88dnfHFx/lPKSCnS2WkbPGEHYBaEIAUW5JaTGpLNl2a4Of5H//tVf+deTV6HWqusUEpdlmbyMAjITssnPKiTpRGrvdettSRbqRpAkQdiIUEIGB2LvZIfOVktxXgk7VuwjJzWv2feqgP6+vLrqWY5uO44kJEZeMZz1P2yhvLiC4vwS/vpig/UXo1v7rteN7uXdAZPcsxc7FeVNocMIv6gfIy83Z4gzGmUObTzKonlLLbV2rnloWleK12vR6DREjO6P1qZ+kgeAS2+fSE5KHkvfWYmNnY7QoUE8+P7tnNwXy5ZlO1FXaepZcdzwJo14HGleeVOrJd5a+zzbf9/H7//9yypjUmiYRz+7j3XfbuoYxa2bE7XlOFFbjpNOFr6yPRqhtXwmyzKoZMJH9mPq7PF8/O9v67gJxx9NYvLNY5n90kzAHFN05lA8f365Ab2DLfe/N5u/F23h2PaTDJs8iNnzZvLijLc7fYzdETsnPS5eTngGuuPb1xtnDyckyXy/MFQbkSRhUdJk2XxuD64/yuK3fm/UVdvZw5GHFs5hz+pDHZZwJDs5l+zk3FYd8+Yt/yViTH8ixw0AQKVRUZxbwqpP/6a0sAw3XxcmzhqNd7AH1ZUG9q87Ut/NvxOorqzmx9eXN/iZu58rXkEeeAd7MOrKC1BpVGQkZLH2m82dKqM1sXe2IyjCHxdPJ2wdbHByc8TWQYdskslJzSMjPovkU2nkpuU13xgw7a7JTLtjEoc2HqU4v5TSwjLCRvQhKznHrLwBbj4uBA70R6PTkJuWR+zhBMvxyafSeHDEM3j4uwKCgtwiKkvNyppXkIe5cL3iSdRtUNwmASFEf+DnWpv6AC8CzsA9wD95k5+TZfnP9van0ADdtDzA9Hum8L/HvuuwhzFQX1Yh1d/WTa2RFho630LU/LT+u7hn/i38/t+/SIvNbHC1fcFd/6vpQtR5yQi7oA83Pj2DDz6Lg5y6bZZTgg3NWx/snfXMX/cCP7z6C7tWHWi17Fbhn3PW3b93ayDLdVy1zsfVVS85kAT5JH0ZhKgZf55tGtOnXo6T7MCnTyxqUGnY+NP2BturLKtkx+/7CB0STFC4Pyf2nGHl/9Y1uG+3x0rzwTPQnUk3jkHvYENRbgl5mQVkJ+ey8/d95GcWWu4jKrXKUpi7ISSVhKu3szk7Z38fPPzcEJKgorSSjx/5pkOeFRqtmltfuB6job5MWhsNXz23uEFlyzPQHf8wH4QkyMsowCPADU9/NzwD3AkdEoTRKFOQVcD+dVFkxGdZXW5rkZOaZ1FAdvy+D4CrH7iUUVePYNfKtiVQ6Sz6Dgth9NXmLOwmowmNzlySo7SgjIToZNJiMykrLqcot5iywlIkScLd3xXvEE9GXXUBnoHumEyy5frPSsol/lgy8VGJGKqNaHRqqiqq+fOLDfy9aCsOLuYYWaPByG+1Fh6dvVxYkvo5P7z2KwBDJgzkmxd+5ui2s1l9y4orSDyRBsAXT/9YfzCddW8+D58B5yPtVt5kWT4FDAUQQqiAVOA3YA7wvizLC9rbh0LPwzvYAzsnPTZ2uo5V3qxNKxZjbPQ6nl/6GLb2tpaHf2V5Fb99+Cf713Z+4LjOVstNz17LrpX7zYpbM5z7wnL6QByn98dQIVdRIHJwFubkEEbZQKl7Lo/d9yQadX2rXO22pt05ic0/78RoMBI6NJiclNw6cY0K1mH8DRdz5f2XYu9iz4MfzuGzJxZRlHt+nmcboceHIGLko0iyChNGXMo9ObUqBUhp9vhzMVQbG1Xszhe8gjwIiQwg7IJQ1BoVmUk5rP9+a7OZHY0GI2qNCr++vrh4O+Mf5mMppi0kgbHaSH5WIWmxmexfe6TVlrA2IQSTbx6Ld7An17jOQaPTsCz9c3549RdSYzIs967AcD+GTRqEs5fZBTc3LY/4Y8mYDCYQkBidQnZKJ8jbCexdc5hLbh3f7ZW3a/9vGgvu+hRZlrHR67B1sCE/s3EXVJPJRFZSDllJOURtqZt1WgiBdx9vgiP8GTltKJJKorK8Chu9ljr50GTZbHFLyaM4r4SC7CJ8Q71JiE7GzceFiNH9Wffd5jqKm0LPQkZgpGcrudZ2m5wCxMqynNhbg+e7Dd3YqnDB1MEMGtOfN27+0OpFTNtEa2ICW+jt4uTuyJLUz9mz+gBPTHzJst0ryIN5vz1FQXYRMQfjGm/A2qtjsomI0WGknE7l0IaoRvdpsP9zzok/oaTJCeTIaQgEMib8s/uy5PXfmhVj8RvLzXXjfJzxCnRj8PiBuPk4kx6fxZaluygpKG3L6BSAiDHh3Pifa3B0s+f0gThev+lDCnOKcfN14f737uDXD1bXceU5n7AXTvQTQ7pajF7BU988yNFtJ0g8nsIPr/3aqBWtNm6+LkyaNQYHVzuqKw2kxmaQl17AvjWHO0dBOwdzyRJBdWU1NnY6jmw+zicrvsPe2Q6dXsvGxdtZ/Nbv2DnpGX/DxQwY2ZfE4yls/XV3k8pBT8bN14WgcH+cPR0ZOCqMxW/+3tUiNUvMoQRue/EGALS2Wg5vPMaRLccbjttr5pkqY7ZCBob7odaqiT+WxOn9caTH1V3olCSBR4A77n6uuPm60GdIEONvuJgPH/ySMwfiqKqo7nS32LODaHtcn0JdlJi3utwILK71/8NCiNnAfuAJWZbrLdsJIe4F7gVa5Jal0H2xc9Jz2R0TKS0q47t59RNl9CaK80pYNO9nBk8YyHtbXkGWZcsNXa1RY+/UuXP58run4NfPh63LdrW7LSEEfoS06VhDtYHs5Byyk2v8LmseKj59vLjyvqnYu9jx3UtLe2/QvJUJ6O/LbS/NxCvYk/TYTD59YhGpZ8yF0230OibdOJrQoSFEbT1O/NHmEzCcjwydFMGQiRGYjCYkSVBaVM4v7/3R1WJ1Sy66YjjHd51mzdebWrT/yOnDGDwunLyMAks2265m7uJ/k3IqjaqKavqP7MtvC//i3bs/pf+FoQydHIEsw9Zf9vDEl/dzfNcZju04ydZfel823CETIxhx6WBCh4agd7ChorSStNgMHN0cWPzW7+Sm5+PTx4uRlw9DZ6sl4Xgye/881NVi12HNN5vw6+dDVXkVabGZRI4bwG0vXI/RaKIgq4iTe89wal9sg8c6ezgy6uoReAa4IcvmKASjUebQhqP8+Nqv+PXzYfD4cC6dPR6VRk11ZTUl+aVUVlSh0aixc7ZDrVEhBGxbvodj20928ugVOgol5q0WQggtcDXwbM2m/wGvYj5PrwLvAneee9oRAkAAACAASURBVJwsy58DnwM4CtdeXl2ihXRjq1pTzHhoGhqdmuUf9v7QRpPJxOI3f2Pxm81boyx04OqYd7AnX/7np7ali++EOjPpcZksmf87Iy4bwoCRfRWXk4ao+R6cPR2Z/dJMQocEU5hTxJL5K+rU0bNz0nPD41dSXWlgz+oDHZahr7dQXWVArVFRXZMoyUbf8uLdPZJWZpV083EhYkx/+l8YyukDcaz+fH2T+4cOCWL0jAvR2Wo5uOEoXz77U+vlaoV8reX3/65hyISBqLVqjm07ScKxJCLHDSByXDg6vY7ivBKyErNZ+NBXlBaWtV/Wbvi8vvaR6dy3YDYl+aVkJuXw0Ejza5lGp+HxL+6jz+BA4g7Hc8vcazFUGSjMLWbGA5dyZn8s+ZnN15+TJInrHp1+ThZWgckkk3omjRN7YtofByibeG3lM6z9dhPVVQYm3TgahGDJ2ysoL67A0c2eyHHhTJsziSXzV5CZmG05dNjkQby68hmWvbuKrb/sbnBhKyE6mYTos+UVhBDYO+vR2moxVBkoLSzrHp5D0C3nmELXYk3L2+XAQVmWMwH++Q0ghPgCUJY6ezk/vbGc11f/B68gjzo30p6EkHrmakxsVCKzX5qJySQjhIykMr9opJ5JZ/vyvd2mTtWRTdHct+A2kk6kKLFw5zDr6asZc+1FlBeVs+KTtSx86Kt6+4Rd0IfJN4/lx9eXU5xX0gVS9jyid5wieseprhajW2Kj1/F51AKenvoq237d06Q7mGegO1c/cClJJ1P56Y3fGs0c2RbcfFwYOX0YHv5ulm2pMels+3VPvWLawREBjLv+IstClRBQXWnAv78v2cm5deTSO9py9YOXcWL3GZa9u6pdhbl7CpIksWnJDh78YA5OHo58/+qvls8MVQaith7Ht48Xs+f9i5hD8QjAztmOk/tiuOqBS+u0JSRBRlwmG3/aTnWVwbLdzklPwAA/3r/3s9o7I0mCgAF+DJk4kEtnT0AILPFksixzfNdpDm081iJXXIC37/iIyTePRae3wWQ0YWunw85RT1lROYU5xWz/bS+hQ4LqzdtDG49xlcNsvII9CL+oL5NuHEN+ZiErPl7TaN+yLFOcXwr5imt/70dglHu2q6k1lbebqOUyKYTwkWU5vebfa4FjVuyr53EerJx4BXlQXlzRvRWgZuLfCrOLeHfzy5aHQXFuCTGH4zm2/STRO09hqPUAazXWqrPUgNxbl+1mK/Vdf0KHBHHrCzcQfyyJDT92QRKGc8ZcXWXgq+cWc/dbt/DVcz9RVtRJmUi70/XXyHdvMpooLShFZ6vl+seuYNqciRzddoLty/ciJMG0OyeTmZjNp08san2fbR2/FSwjrb4fdPL3VrsuVq+ghd/ZhdOGMvySSD76v6+ajJX0D/Nh2pxJ5Kbl88Orv7Y95Xkjco255kL6DA5i7bebyUo6m+Y2OCKAWU/PqONNIATkZhTUUx7VGhXP/vAI63/YQvKpdNpEF14jVpGjhnHXX8zzPz8GwN2RT5AYfdbqJMvw1xdNW1bPJTgigFnPXINKrUKn11FVUY3JaCIvo7DetWoyQmJ0Up0+/0GSJAZPGMjsl25ANsms/mKDJQtmgwiJzKRcFr+1okn5XH1c6DssuM7cMY9VJiM+i4z4LDYt2cn4mRfTd2gwpw+cE4veneLIWvPdK3FubUYGTD08YYmwRuClEEIPJAN9ZFkurNn2PeYslDKQANxXS5lrEEfhKl8kTW23PF1BtVxFOglUy1VohQ4fglDXqjvUrV4em6MNNwWNTsPCHa/yyOjn66zQNcWCDS/y5JRXWt1XYzy0cA67Vh3g4N+NJOw4lxZ8Jz4hXgydFEHYyL749fVGpVaZD5VlMhOyOb0/lsObokk8ntxMS+fQzDmefs8UHF3tWTK/gQdXG9yPJt00htDBQexYsY8Tu8+0RtKW0ZL5XUtGB1d77nj5X3z70tLOsSB1p+uvhdeXV5AHo2eMYMK/RlOQWcjrN33Q4murHory1ii9TnlrBp2tlttemsmRzdHsW9N0VtxJN43BJ9iTJW+vaLG1pDVc+3+Xk5ueb5WYs8jx4Tz84Rw+e/J7Dm442voGmptrnfWybIU5/1Pip3gEuPHs5W+wf62V4tiEVK+8TB1aIbeNnQ3XPjKdxW+1L2mKm68LU24ex/Cpkbw264MmE2K5+bpw49MzGDl9OBt+3IbJJJObnkfC0SQqSivMNQmrDWQn55oXKM5j5W29aekBWZZHdFgHXUz/wTby5ysDrdLWxJAzXXKurGJ5k2W5DHA7Z9tt1mi75UJ0cq2zWv1VyhXEc5JgMQAbSU+FXEqMfJQ+8kC0opvFV3TABa93sOXut27mtZs+bPvLZUfSjrmRHp9Jenwmf329sc52SZIYcFFfIsYM4Jbnr8OtJh02AkryS9m35jBbl+6kqDHFpD2rfQ3VMWtmjJt+2saWJTu4/O7JjL56BKf3x7J9edNuUi2W49y/W2BhLM4r4avnFnPXGzfxxX9+pKK0AwqYdieFrQW8tWYuNnY6ctPyWfXpOg5viua3hX8RHBHAB/d/0bLvqguUNKtY2hvrvxVytVkO0f6X0cboUMWwDZZ8J3cHZr80k5/e+K3ZtP/T75lCVlIOmxbvaI+UjeIV5IGkktquuJ3z/RzdEs28a99hUexHHNkSzcG/o9i75nDLs/52F0uGFbwybg66n/l/v8ibfz1HYXYRNwc9QFVFVfvkkk3UuwW18fxVlFWhtdFY/tfoNAy/JBKPADdMRhOHNx5rstyN1kbD5JvH4u7ryopP1rJ0wcpG93XzdeGT/W/h4ulERnwWXz37E1uW7USSJNx8XQiJDMTN1wUhBGqtmtEzLsTGzgZJJZF8KpUNP2xDtnZyi+5i5T2PURKWWJnhl0QSOXaA5SZhMskkHEtiz+qDVFcZ8OnjhV9fb9LjsyxZ17qaVOLoKwZZLG02wo4+RJAmxxHMgCaPzZJTKSQPCRUmDPgQjL1w7Ayx240Qgim3jiNwgB+L5i3rFpnGOguTycTxXafrJJL4B+9gTybdNIZnf/o3jq4OLHzoS07ti+kCKetiMpksyQgixgzg9ldmYTKaEJI5pXbMwXj2rTncaWmQy4rLWbpgFVfeN/W8z/43dFIEJQWl/Gfa6/QdGsyMh6dxx8uzMBqNqDTqrktNrdCruObhaS22dnv4u3FgXQu9GFqBWqNi6KRBjJ4xgs+e/N6qbafHZ3L3oMd55JO7mfPaTcx57Sa2LNvJa7Pet2o/PYFnppq9Wt74ay7BEf713QW7EJ2tluBBgdg56dE72HLjMzNY/+M2Tu+PRaVW8VX0e7x+0weEjeiLSiXqKI1CEhgNJnat2t9sVtT3t7zMoLEDMBqM3Dv0KRKOJlo+M5lMZKfkNlm7L/yifjzwwR0U5Zay4/e9vTajr85Wy5CJAxk8fiA/v7MScpo/picjy0rMm9UJv6gf37/yC6aaFUtJJdF3WAi3vzKL4ztPcfNz13F81ykGXNSPz576/mwgeievSDi4OeIV5IHWRkPywRjUldo6n2uFDZ4h7txx501kp+QSezgBW3sbqiqqycsoIDMhiwxjCrLAUp9Ilk3EyEcJpD86YdO0AF1sVQgM9+PK+6ayafEO1n+/tUtlaZZOdsPKSMiyZKLUO+p5deUzHNkczaJ5S5s/+B/5ZLnmpwkXyZbM+UbGG73jJNE7zqY+1mjVhI8K44H37yDpRAqHNhwlNSajTW03uk8D8mYmZpOdksslt423/jzqTjFvzXxXc169kblXvglAzOEE3r37UwC0Nlr+7793EtDfl+RTaQ0f3M5VXAcXOy68fBgB/X0tlqIl839vMrmD1eNaW2GJrtN3Y+e19j7NWL+EZEJSSQRHBDBgRB/c/FzN5+HcIcrU2VZRUkn0rtOc3h9rzkrX2XOsDc+8td9u5pa513Fww1H2/dX0Qs1Pry9n9ryZJJ1MZcvSXe1O9qF3sOWW56+jvLiCqG0n+OiRb9rnitnI+BNPpFJdafYAMVQb+eaFpR37ftDZXj+tPDZqczTvbn6Zbb/u4e07PmqHYE3305rzUFlWwddzF/PIR3dxwaWDefuOT+q48z944bO4eDnx0xvL2zxH7J31DBo7gAcueIaYNta/PLHnDCf2nEGl0XDp7ROYMHMUv763CpVGhau3M55BHrh4OePoao9Or0OWZUuMZkVpJfmZ+WQmZBN7JIHy4gqCIvyRZbPlUAhBzKH4xhP/dMI7rbOnE9f+3zQM1UYOrj9KQnQyLp5OvV556w10O+VNUkmMmDYUjVaNd7Andk56hACTwUjc0SQeHfcCfv18+PmdleSmNe32YS3+8fN283Fh4qzROHs6kpdRSEZCFtWV1Th5OmBKMiHVutiMspH0uCy+ffFnvII8CIrwp6KkAlt7GyLHDeCKe6bw7kfv4JQaUKsfiSD6owqrYvKFlxB/NInE4ykdEmvQFtQaFf1H9mX4lEhK8kv59IlF3Ua27kpZURlPTHyJe9+5jfl/v8gLV73VfveVDqC6ykDUluNEbTmOh78bw6ZEcsltE1CpJewc9az831oSj6d0SN9blu7invm3NB1P0Yu5aPowMhKyKCmon7a8qqKKr577iSm3jGtceWsHnoHuPPfDI/z19UaST6Xh388HvaNtj40DKzEWUiznoVc54aRyq/e5nZMenxAPvEM88Q72wNbOBiGB0WAi4VgSe9ccJjuliSQK57Q1cFQYs566GrVWDbLMyb0xHNp4tNtmNcxIMCe8GX5JJHNeu5HSwjI2LdlRL9kDYEkuFDwogBsevwqtjQaT0UTy6TRO7Y1p0q2tIW6eex0/v72SotyOzzL7n2mv4+hqzxX3XsK3Jz9gy7JdvHbjBx3eb3fA3lnPQx/OwS/MF2QZlUri+K7T/PHZuq4WrQ7Jp9L47/99TWC4Hx9uf5XYIwncP/wZwJwluT2eVW/8+RxuPs58/Og3bVbcamM0GPnrq404uTsw/Z4plBWXk59ZSFZyHrFHEinOLamXyMfGToerlxNewR5cfvcUAsP9ObLpGB4BbuSm5VOSX8o9b99KeXEF+9cdqbOY2lkEDfS3ZO1093Olz+CgRuvm9TZMPdxt0ioJS6yFo3CVJzpeRUhkIJVllWQkZDdeh6UTmPCvUYQOCWbCzFFs+HEbBVmF7Ph9X71YgWK5gBzSCCbc8gIaJ0fjLQKx+8cFsoFVqThxgj5E1Nuebh/Lhb5jCR0aTOAAP6SaVWSTSSYrOQe9gx4HFzvAnH0rPT6Lg+uPNp25qQ2EDgniounDzS8mNf2f2hfDse0nKS9pf+r5Lk9Y8g+dtGI+ZGIED35wBx8+8EWD7pa1mX7PJTUJS9oX0G0N69P8dS9yaNMxtDoNK/+3joKswpa318LVw6GTItDoNM0mUGgVjVn9OstC0sKx3/jMDC6aPrzOCqwMIMuUFJRRUlDKlqW72L/uSMMNtNPy1ndYCO5+LqTHZTWpoLfG2tZa5a+xthtqpyHLmyzLxJmOoRO2OAsPSuQCCuVcBthcwNSbxuMb6onJJFNaUEZ6fBbp8ZlkJubUv4+pVM3Kqre3oaKkEpPJVOfcCyD84jDufuNGFj78FWkxGR0Ty2lFHFztmfCvUbh6OVu2ybJMRWklVRXVVFVWUVFaSdKJVLPLmCzjH+ZLxJj+BA7w49T+WA78HdUiN8xrHp5GaVEZfy/qeE8NG72W+xbMZsot47C1t0GWZS5V31h/x662yDdEOywuL/3yBCOnD6OssIyZPve2/MAuPg8vLnuC8Iv7AXBqXyzzrnunTe0MnTSIO9+4idQzGcy//ePGd7TWs6uV580/zJd5y59i4+JtqDVqs5VOCI5uO8HB9Y0n2RGSwNHNgQEj+2LnpCc9PpP89AKGTx2Mu68rhioDBTlFZCXlWDJ+l+SXUlpUZlEshRBodGoMVQaLZxvAoLED8A/zITctn4KsQs4cjO/1CUv6RdrKH64MtUpbV/SJ7rkJS6xJeUlFsy+2HY1ao+Ke+bey4/e9fL10MQMv7seil5c1ur+DcMZU4/IoyRImTHiLgLOKWyOYTAZMoq7FrkjOQxRrSDmdTsrpuitPKrUKdz9XyorKzPVIavAN9WL01SNw8XYmMNyP0/tikVQSaq355pCRYE6VW115djU4Ykx/hk2ORKWq/SIkMBlNZmVRCBKik1m+8M8OfwGxtbfB1t4GnZ0OWzsd/zswn+zk3EaLfDp5OBIcYbZYHtkcbdlu72LH8vdXd6is7eHI5mgeHfsCCza9zCf//pronT2j9pSh2sAfn64DBHe9eTOfPv4dleXWtR5GbT3Brc9fb13lrYewZP6KellF9Y62TJw1mmGTBrH2280c295xq7Ixh+KJORTfYe13BllyMq7CCxfJEwB74YST7IbjSJmYw/GsW7Sl3jEtVUYDBvjw9Of3AaCz01FaUIazhyMn9sawafF29q2LYsDIvgyZMJBZT17Fyk//xj/Mh4suH4ZOr8NoMLJt+R4SjrUyI2070eg0BIb7YWtvQ2ZCdoOxPcV5Jfzx6d91tgkh0Om1aG20aG002NrbEBThz4wHL8NkMrFswSr++sqcwKnf8BAuuWUcjm72NZ7eZrcxIaAot4SE4ymc3h/L4PHhuPu7UXG645VZrY2WVcXfk5uez22hD/fqepJBA/2Z9fTV+PXzwWQ0MWjMAK51m9OgFb8788rMdwFYnvsNY64ZiY1e16aSFEMmRmDvbIdvX29+zfqSWf73t6+8j5XJTMgiLirRkrXa2dMRexd7ZJNM5LhwZJOMyWiiMKcYo9FISX4pRbnFRIzuj8kkc3jzMdLjMvHp40XgAD+ObTtJ0slUNFo1Th6OeAa6o3ewxWg0ETjQD72DLU5uDuj0OkwmmerKamz0WtRataWAe9yRRHYcT1HqhvYwup3l7V+D5lBWXE5ual6d1YHOQAiBvYsdl90+kYTjyexfa17pbpWFqBWr/WVyCcnEEiT6o8OWIjmXDJLox+A6Cl1rWLDpZeZe8SYmo8mS+TFwgB9Pfv2AZTwDRvblzy83sHPl/jpuj/8UdjYZGrnZWckHe/jUwTz55f0U5RZTkl9KdZWB6ioDhspqc1KaUG8MldU8PnGeVfprlC5abbS1t+HDHa/x8EXPNepCaTXLmxV4ffWzvHnLQkoKSnH1dua6x67ky2d+qLuTFebGzc9ei5AESxesqrPQ0Ga646o6NHqu7J3tGHX1BQSE+VJRWsnWX3fXW8CxYI2xtTJDaHPKjrVcLWv301yb/+ybqDlJYHV4nZpgADHGKPqqBtcW0vLngIvCuOr+S9jw004ObapZBFLVPifmvteWLuKNOz5hy7K6WREvv2Mij358J8f3nCFkUADGaiOJx1N4fPIrFrlloxGdrZZx11+EX19vS+IFISAtLpOD6492iPv/ZXMm4hXoQeyRBMqKK/Dr64VngDu5afms+GRtvf3tne1w9nREkiRKi8ooyi2pdw1+tPsNDm+O5rI7JuLs4ciPry/n2xd/blQGB1d7QiIDeeKL+/jjs79Z9q4VkhK1cN7f8eqN3DL3ekwmE0e3ncDBxR57ZzsqK6qYe+VbpLfS5bPdWLEMwfgbLuapbx4k5mA8uen5pJxOZ9G8Zbj6OiMJQVZy4wk4WiVTa2lsDC20cg2ZGMGCDS+yd81hXpoxH4Oh5dmUayNJErc8fz03z72OopxiZvm1wgJ5jkxt6b81bX926G3c/dy43vMuy0c2djps7GzQ6NTYu9jh4GJP0vEUqyeE09lq8enjRUhkAM6ezjh7OGA0mFj6zkr+KPm+l1ve9PJ7K/papa2rQ48qljeAS2+fQFZSLi5eTggBpw/EsWf1QXNAuJUZfkkkF04bSnmx2XXGZJIpLSxj/7ojJER3/CqpXtjTRw4nXU6imkrscWqX4gaALNeziiSdTKWqvMpiPZy/9nkObzpWL16ts+LXHJz1rP9hK1/PXdIp/XU3yksqiD+WjG9fbxKO9azsVXkZBVS2tUhvM/z05m8EDvDjktvG8deXG5s/oBfg6ObAxVcOxz/Ml5L8Unat2t8pbmW9BTdfF2Y8NI2PvlwI8edkFAFk6it/U24Zx9PfPIgQgmM7TnH1g1P51xNX8POCVeRkFnHVPeZi6Ft+2cPHO18lL7OAFUtWUSmX46zyQCdsGXBhKI9+fCepsRnMvWaBudi8seFnVGV5Fet/2FZvu19fb8bMuBA3XxcM1U1b59x8XfDr54NfP29cvZzJyywg+WQakiSorjSYk2AlZlv29+/nw1fPLbb8f2jDUdx8Xbjr9Zvw6+cDwJCJA/EKdMdkkinOKyE/qxDZJKN3NK/Wa200yLJZ0QQ4czCO4rwSZnrfgxCC5TlfsWf1QU7sabhuZHFeCdf+3+X4hnozdHKkdZS3FvLtC0v49gXz8yV4UCB3vXkLv36wmgn/Gs2i0wt5+OK53SIDcFvY+stuhk2JRGer4e07PrFsz2lhrGZ3ZcGGFwGYe8Wb7WrHwdWe216aybzr3unSsJuW8Mxlr7Es/Qts7W0sLtwVpZVUlFYiJGEJhemIGOTK8ioSopPrvOtOumkMnoHucNzq3XUrekOR7m6nvJUVlbPqf2stVreI0f25Ze51qNQqEo6bs+DlZxa2u5/L7phIn8FBfD13sdVdwFqDRmgJxDorAC2ltLAUBxf7Huda0avoRhbv1nJyzxnunn8rCceSOLHrdPNZKVtB0slUpt05qcHPquVKMkiiSq7EUbjijjeih9S9kVQS3sGe+Pf3JWhgAHpHW4QQFOUWs2/NYdZ9V9+tT6FxAsP9GH/9xQhJ8ONrv1JVJiggAV/Rx7JPvikLe+HIjU9dzcRZoyguKEUIQeTYAez98xAvXLuAhxfO4YJLInH2cOSVX5/g9MF4Ni3bjU+IJz/FLCQ3N5dh3mNwlbyxlexJM8ShRk2Eqh9xR5N44OLn2zyG1JgMy7Wj0aqZfPNYJt84BqPBiNFgzoD5j+KUm55P6pkM9q85Qm56Pu5+rgT098VoMGLvYkdQhL+5bpok+OG15SSeSOX2l/+FbJKRZRl3fzd8gj04uS+WwRPCqSqv4uDfUWQkZDchYePIssy1bnc2u9/LN7yLZ4AbPyZ8wvR7pvDnFxva1F97SDiWxAtXzwdg3XdbeGf9i9z49NW8PPO9TpfFWnz4wBcs2PhSV4thVaaqZvFr1lf8bfyZqapZbWpj4Kgw5q97gdVfrGf3qgPmuNRugFeQB/0u6IOdkx5JCCLGhePgbEd1lQGTyWSVHALWQElA13PodsrbuUTvPGWJDQoa6M+EmaNw8XKisryaXav2t7nuxqn9sbj7uXLHK7MoLSzjwPqoOqlq24wVis22iKbM+I19VrNdCNF4Gvq29gktHmNBdjGX3j4RSRIs//Av8jIKWieHtWio2LVC0wiJfWuj2Lc2Cv8wHyLGhnP5PVNJj8tkw4/brBIf2dADpEwuJlmOMbsYC1vyyeaMHEU/BjeuwFnL7aWV7XgGujN4wkB8Q73NhwuB0WgkIz6LlFNprP5sHWXF5fXbbo42FGVucXuN0JK4sLauCrek7XP3UWtUTL55LPbOdix7bxVVlWZrl7PkSbmplBjjEfTCAYO2kgnXjOGRRx7BwdmOuVe+ZbZMCQm9o56yojKESsXHjy2yuEoOHhfOK788yt61R/hy7hK+fP5nYqoO0Vc7DE1NDU9HlRsp1afZvXMv045M5MPNL3H6QByfPfUD1dW1zkMr51t1lYG13262/C+ppCZfpLKSchrMEOkd4slDH95BXnoBsklGCHOs9L41h9j756EuyYKZlZyLLMvMfPwqdLZaflv4V/MHtaJ0REu59pHLuX/BbCrKKrnebU73uu+34doW7U2W1x43R2seB7j5OHHlfZdSmFPI/574rtXHX/foFdzw+FWc3HMGGzsbPn74q/Yrbu2cH4HhfnwV/QEn9pxGSObr+cimYwhJ4u9FW8nPLCCgvy+fP3W21mFrXMYV2odR7tnZJru98labxOMplmxotvY2XHzlcCbfNIaSgjJ2rdpPYXYRRbklLUo3nnAs2eKiotGqufDyodz1xk0UZhfzy/u9u2Dw8V2nWBT7EVOlmV3S/5HN0dza52G+PfkBfYaEoHewoTi/lLXfbmb78j1dIpNC66mdVMevnw83PHYlWhsNBdlF7F97hKSTqW1qt6F4t1Q5jn5iiMWl2BVPBIIc0vHAr+2DsBIhkYFMnDUGWZbJTMwmasvxDqt/6OBiT/jo/qScSmt1uvaeTGC4H9c8PI2NP+3g2D9ptWu9gPpIIZhkI9MeHM+1903nqYmv89gv8+q9hJUVNexxELXtBFnJuVw2ezxL3zMnPpLBorhZ+lGHklAdzbv3f4kkw1+l31GUW9JkUqvW0tYV8Iz4LD64/wuryWEtbu//bxadXsjQiREtU946gDVfb+LB9+9A72CLobHaWj2EvsNDLEkvejJhF/ThlZX/oSi3mGPbTnB3xOPNlWWsw+qyHy0LGV8/9yPFBWXkZhR0er6Ehvgq+gNO7ovhkVFz639Yc99KOtG2Z6RC+5ARGBW3SWsjt8ilrLykgk1LdrJpyU4cXOwYMW0ojq4OOLk7AGblbsuyXZzc27xfe3WVgZ0r9rNvzWEeeO92nvrmQc4cjGPb8r3kpXdOLblW09TqXDOWiGXvrebed2azYOM8y0eR48LZvGwXb96y0IpCNo6hykBGfBbPTX8DAJ8QT67993RueOwKTEYT+9Yc5reFf1JR1rkure9seKle0oOOwCfUi8oXGg/2716Ys482NedSz6Tzw2u/AuaYg9EzRjDtzkms+HhtnVic5nDzcWnQLVpCVS8W1Bl34uRoPISVlLc2rOprdBpuePxKSgpK+e7Fn+u+eFjb2l7T3nWPXUnokCA0NhpenPFO25O7tEO+jrS2nXOA5c/MxFxMRpnEE6mW7aJ22AGGWgAAIABJREFUan/ZhAo1YQPDKMurPOsWXquNOvs3IMs385bx7LcPorXRUtXIeTVhREJCrRJ8eWg+J/fF8v2rv3SeJaehfrqp+7CrtzNPfHk/4Rf345PHvm1acbO2te2c9sqLy/jzyw1cNmcSb/w1lxdnzO/6TIStHNsrK55m1JUXcGjjMRa98ksHCdXxvLrqP1x8xQUAzLv+HXb8trdN7aTFZqLRqslKzmH78r1UlFWy548DbRfMiklKVn6yhouvHMHPaV8wy/eedrXVodQeZ1s8shS6hG6ovLWe4vxSNi3eUWebJAmm3TWZcddfROyRRA6uP3q2PlUNjm4OjLhsCIED/JBlGUOVgVP7Y1GpVfQdFsKSpDns+fPguTHwHU6xnE+WnArI2Ap7vAlqXxKTBjjXp/zut25m1lMzOk15O5f0+Cw+efRbANRaNVc/eBmvr34WlVrF4U3RLF2wqtEVc2sihODJyfM6vJ/eTHFeCWu/2YzOVssjn9zNO3M+af6gGvIyCswB0+dgov4DpYqKelaRzkKtUTNx1mj6Dg/htw//PKugtuM6tdHruOqBSy1Fsv/xbjZUG0mPzyQtJgPZJHNw/VEqyyq58LKh3P/ubDwC3IiPSqyX1ElSSWxasqPXrO461izM/VN3siH+u+NV9q+L4onJbasfeeDvYwgh0DvaUJVdjRo1ZaZi9JKDZZ/k6pP4qEIwGEzYOemJ7uLSNt0NSZJ4deXTuPo44+7nxu4/DnBL8EPdIq7n/Xs/5f17P2Vl4SJ+Tv2c6z2aj9vrTrw26wNWl37PsMmDeHrqq10tTpsZPH4g/334S1Y2kPm0NdwT+TiSJHHP27eyquQHPn/6e5YtWGklKdvHfx/+CgdXBybdOKZOQhKF7oFJ7p4LXi2l+ypv7SyiaDLJluDo4EEB3PbiDag1EjtXHsAnxBM3XxcKs4vZt/YwG3/a3mAbhzYcJflkGg8vnNOmIbSWMlMRcRxHz/+zd96BNZ3/H3+de2/23oNIhMTe1Kzau5Taaq+qvWq0tlKlrVF7712zqNhVlCJqSyQkIjJlj5t77/P745IhudkJ/f76/kec84zPc+4Zz2e9P2aUliogk+TEiih8xB08qV6kHqEtM/fRY3In9r1ci4WtOTvmH2T7e7LsqZQqfl36G78u/Q2ZTEbrgU2Ye2gSCn0FV4/9zcGfjmekEf4fw85nq1Gr1CiTlJzceI7o8MKlCM4WhWQk0DfUY8C8HhzMY909IQTPHrygSqNy3P3jYepxMywI0wRhJyvxpp2G5+IxbpSDdxW7vOQy5jHvw9zGjFYDmmBuY8bFvVc4s+OS7n55uJbl6pShWa+GHPjxGGFBGb39Cj05jqXtcS7riCRJJMUnc+/Px1w/6c3zBy/QN9TLcmMgSRID5/fkwt4r+P3zPNeyFCXSe+xy5YV7cz3lCjk9JnVg7aRtpKRk/m1+OjsDjRD43wtk69yDunNH0v8+WUXOGeqTHJ9MjSaVuPDrDdwMq/A0+Q5ylRwDyYg4EYUVdhhhAhpBV6fhLP9zLiN+7MvKMZtzXk9+8S+xhrtXLcWaWz9w7fhNRtSamrfOxZTHCbBm0jbGrx2OQl9RuN633Hhu8uvdkWQok5QEPn5JyTeMoUWC/Obh5gGbpu+iw/CWfDa6LTM6LiLIR0dJFKB8XQ+6TuiAk7sjCj05Go2GkGdhxEbGYmJhQqWG5TG1NCE+Op6bp+/kXZh83HemliZ81K4m03aMIdg/BGWiErVKg4WtGXoGegT5BqOnr0fZGqWJfR3H7sC1fGbVP20AHd+J95Ln9v8w91/Af2GTxQWZXIaRqWGeqF/1DPSo264GTXo04OXTEC3j2OsETm06p7t+Ujr8cbD48q+SNPEE4Yc+hrjLKqUeN5MsscGB14RhjX2Rza9SqhhSZSJWDhZ0m9SRvjO6vjflLT00Gg0nN57j5MZzKBQyOo/rwJLzs0lJVmUIr0uMT2Tzt3uICi1GRacI4KXR5s0MrToRgMVnZ+Jzyz81j/PRdd83BbM/XBiaGNB3ZleOrPxdZ6H17HBm+yWG/dCHgIdBqYqro+TCKxGAj+YOMmRoUFMCN/Qlg8IWPxP0DfXxrO1OnTY1SIhJwGvbxUIl2WnctR5W9hasHr9Fe+CdzYQqRZ0hv/BdJMZlnb8jhGDzt3sYOL8nCTEJ+WYW/BDQbeKnHF19Wlu7MovNlkJfzpiPZxd4npTkFGb3XMawhb248OsNZJIcD8OapGiSUIpknKUy8I7hyNjcmEoNyhV47n8zxq4eSr32NfG55ceq8Vs4vOLU+xYpW5xYf4YekzuxK2AN6hQVvVy+fN8i5RqDKo7HS72XlX8tYGTd6e9bnHzhyMpTHFl5inJ1yrLl8XLO77nMgt7LUs8rFHK6Te5I28HNefYgkGOrfufGKe/U8y7lnLFxtgZg8cCVDP+xP+2HtuDrraPRqNVcP3mbm153CHoSXCSEaLsD13D73F3O7b7Mwj7LMpyzd7Hl4671OPiGO2H0L4Np1KUeG+79xB8H/2LrrH9LusR/+JDxASpvEqUqudCyb2OSE5QIIZAkCSEEykQliXFJHF39ZvOqw3rRZlBTytUuQ3hQJN4X7jO/51JkMok/D1+n06g2jFk5BGWikiWDVxd64cMMyKUlI05Ep3rclCTyQuNLCalMqqfNEjuC8Csc5S0ba+BbQpjFZ2by5KZf5rY5eSmKOO9CpdKwf8lR9i85iqmlMdZOVqnnHFztmHVgIpHBUfw8fG3eyiB8YPkinSz7p4aIjqn/Da6VXFLPNe3ZkGVXvuP7vssLVmi2iNZct31NKtT14Ngar7wrbunutx1zDzBoQW/WTNiKKkVrGXeUSuGYx3Hy1FaSYWZlQtma7pSp5oqJhTEAysRknt55zrbZ+1C/JTooxOv3yj8Ua0dLnTJleTw9spFFCMGh5Sdo3LU+R1Z+WBtqXVbmdz1yXca249F137Twzww5GhLthzYnPibx3UHS/ZnWPivPn5WjFTWaVqJ0ZRcEWgXuj19vINLVbVOoJBQYAu94aYSGUuWcmfHZDzmsNhvkxltblAWD8wF9Q31m7p+AnoECu5I2pCSn0KvUiMwNi1LWAj6D/T1HY2RqyNGY7Yz4eQAbp+1CmVTAHOvcrLeAbI4b7/+MWqXG612vf1Ejv2zT2fR7fMOXKa3nMWblUBp0qsO0HWMIC4okMSYRX+9nDPAckyVrZODjlwQ+fglA64FNqde+JiPrTMHvznOcPRyp2rgibQY2w9rJEjsXbRh++IsIts/Zn8penut1pUP/OT1o1b8JT2768X3fX7JM5QgNDE9V3EAbPrli1EaMzY1ZcfU7Oo1qQ9zreHxv+zO3248gNCgUchp3b0Ds63ie/P2U5ITkYs/3R5LlmNv+vwKB9B/bZGEjJCAcz1ru7Jh3MMtQoM6j26ayQgb7veL6iVtaa2w6OJdxZNfCQ4QFRqQe02gET/72w8/7OSH+ofx5+AZDF/VhzaTtxEbGpbaTJAkDY32USSnFUvMiRSh5yTOqUC9VWYsS4QSJp5SUtPXfYonCGLPshilUPLzuQ/k6Zdn7cj3LR67PdzJxUSIuKiGDghbwMIgbp7yp3rQSM/ZOQK6XNROX0Aiiw2MID4ok2C+UwCcv8bvz7IPw2Nk4a5XR9B+EpITkDMVkH9/wxcHVjum7x/H3KW+2zy08hruCwNLOnLaDmxEXlcCWmQW3LCbEJrJ/yVH6z+3Bxmk7C0HCnFGqQgk6DGvB5cM3OLnxXJqXv4g3y09u+lGuTlk8arrjc8uvUMe2sDWjZosqNOhY+4NT3nJC5YblafhZHa4dv8mdi1lXje0/qxutB3xC79Kj8r3p6Dfjc7x2/MGVY7do3K0uFT8qy+rJO3Ld/+qxv5m8cQSf2w/J1/z/RvwWv53nD1+wY95BwgIjMm+I/yVISUrB+/w9uoxtT5ex7Ql5HsYXpb9632LpRNtBzShVoSSXf/2L0ICInDv8C3DL6x+GVZ3IrxGbMTDSZ1jlCXliA/3nwgOC/UNZdmUBU1vP486F+wQ8DOL4Wq/UNobGBlRrUomll+fz/OELts/ez1+/3SQpIa20Tfm6HtTrUIuHV59QukoparSoyjdtv0OlUuNZy53aravTYXhLQgLCmNgk7zX2EmISGFxpfOr/5x75mg33f8bY1Ag7FxtePH7J69BoHFztsC9lixCCqLAYfG764VjanmNrTheZR9vGyYrG3epTuooLdy78j1fofoP/inQXMrKk1063eTq0/DckScLI1JBSFUowYH4vlIlKQl9EYGhkgLWTJaaWJujpK5DJJEwsjImPTkDzplhp+jIAayZuo9PINtqNvhAIoTU8JMQmYWZlglqtQf6mBlDTXg25uO9q3hS6XGwmXolASuGRIZ/NUrIlTGhDpJQiiRARQDmphu5B3t1c6vKE5bI23Jh601Eo5BwI28Tsg5MRQtDOoBcqdQ51jAqYp5gd3oYN5pT3533+Pt7n7+s8r1DIcCrjgEv5kpQo60Cjz+rQeXRbjEwNAW3IX5YyFoO1u/WAN8Wpc5g/5HkYYxt8w6DverH08nzmdf+RiJeFxIqaj3WaWZsyc/8E5nT9kejw2EKbL+R5GA+uPKZ608p4n7+XZ7lyhXTXusPwlqyZuC3zM14Mlsjja734aukAnv4TgEalIwcnl3I4utlRsUE53Cq5kBCTSFxUHNvyoOTrykUrjnwMmVxGq/6fUNLDiUc3fFk7aXuWpV/K1ixNt4mfIpPL0NNXICEhdHmwdHjhkMtxq1QSlVqNWxUX2g5qyr6lv3F6+x/a8+l+B6ERhKgDiBERyJCjQY2TzA0TYc7Mzxaz+u/vWXhyOtPaLsj7onPzu+b0XBazN06ZlMLvm85xYU8W+eLFZbkvhDWrVGomN58DaEPxll/Nx+9XXJBknN97hdLV3Cjp4cTkTSOQZF9hYq6NEDjw83HWTtqewyAFmz8TCum+UyYpObHei9ptauS5jEOwfwjjP56Bl2Y/i8/Ooo1ez0zeuqSEZP46cYu2hr2o07o6ddpUZ9zaYSiTlGjUGsxtzdE30CPYPwT3qqUwNjemVIWSHIvfQZBPMLGRcfjeeU5oQDijPppWKGue2Unrrd9w/2cAzu2+zPZ5B1PPK/QVmFoa41rRhejwGCas/5IOw1tiamnCjVPebJm5t9C++5+NbsPtc/c4tPxEoYz3H4oeH5zylhsIIUiITeTRdd/UUgB2pexISUqhy7j2XNx/lVotq2HtaEFMZDz2LjYkJSTje9sf/7sBqbWRYl/Hp9Kbv4WppQmW9uaEBUYwdFEfYiLiMDIzos/0LgQ+eonvbf9CXYuSZAwwynRchZJ7mmsYYkxZqYruQsRFBJVKnZpgu+zP+ZxU7mH73AOFWssoN1CKZALEY95SfkpCwk0qj1zK362rUmkIfBxM4OOs84eWnJ2ZX1ELhFMpe5HLZaiUKo683kKf0l/lGPq56ZvdlK/rwZiVQ5nVuQBhWwVEbGQcF/ZewdLeIu/KWw7467dbDFnUh5TklCK17tf/tBY3TnkXi7c9Kwgh+G3dGXpN/Yw/D18nNiKWiFyUKZHJJGq2rIqVgwX2LrYYmhjwwieYh1d9dBIxfajwrOVOi76NObHhLKc2ndfZrtfUz/iobQ1Wjt+CJJNxZOXvaDSaPJUhUOgraDu4GZ8Oa84Nr7u88g/jxKYLOtuHqAMAgYe8GqAlzPHR/IMr5TA3MadMdTd+GLAy1/P/25EQm8iVo3+/bzEKDStvLMKzljvfdlj4vkXJFkkJSlaN24KXWhvdEBUazfU/b/NR2xp0Hd+BV/5h/zoPO2hz3DqPac+ifvlnu57W9jsWnvyG8nXL8kAHA6xKqeLqsb+Ze2QKt87e5bseP2FoYkjVTypQp00NFvX7peDFvfMI1zee1O1z92dQkFVKFVGhMUSFao3Rb+vFGRrrs+j0DPYErsnEGp5fbJm5j36zunLrzN1CGe9DhxCg/o9t8sNAWIA2GT/4aTBVG1cgLioePQM97ly4j98/z7F2MMfK0ZLBC3qzqN+KTLHtHYa3xLG0PVFhMUQGv6ZJj4ZIksS2Ofup2rgCIwvJ2vIurLEnjGDscU49phEaUlBSTqqJgZRZscuEdxW7QmYPGtvwW47H76TvzK7smn+gYAVO8yCTEAJ/cZ8yUmUUbyjhk0USfuI+HlK1/MuQHxRlzokkY2rr+SiTUnhw9Qle6r0citjMpQNXmdf9p2y7Rr58nX1R+sKw6ucC53b/SfuhzXn+4EXeOuYgn0bAuq938umXrbApYcOlA9fSThbG7yA0fNK9AbYlrDPkKbyPuP9n9wOR68kpVaEk5rZm2DhZgRBoNIJXz0KRK+SYWZmmeoklScvCePPMXe5dfkx40NX813x7T5DJJKo1qUTNFlUI9g9l9fit2d/PQOcxbelR8g3BRB5/p2pNK1OpgSdNutbjwsG/mP7Zj4QHRaY1eMfb9hYxIiJVcQOQJBmusvIEa56hn1COwZXGM3b1MEb81J8ZnX7QuXnMER9APltO+GrpADQqtW6GwH/BGt7Fb+u88Fw7nCY9G2rLA32IeHNdqzSuAMCCPss4v/syCj0Fzh5OdBjeklHLB3Js9eniU0Dy8PwJIYglikTiscAaQ8k49ZxKpWZC45l8s3c87Ya2xLmsIyc3nM01uceugDUEPn7Jon7Lc3z2tj5ZAcCUltpyIjGRcZzZ8QdndvyR67VkiXx4JrtN6giA1/aLuZ7mYPgmkuOT8+fl1wG1Ss2jG08ZsrA3G6btKrRxP1xIaIq7Blgh44NT3kb81J/dCw/lm0jk5MZzyBVyjEwNUak0tOr3CU2619c6boTgwt4/Myludi62dBjeihG1vk7bOBTT5s1SsuOpuIcQGuxwIpEEXvCUslTNneJWTOhg0gcvzX76zenOpm92F8uccURhIdmmKm4ABpIhBhiRLBI/qOtTUKQP9ezj9hVfbxmZo5e3UoNyjFg6kBPrzmQ4bmFrzri1wzCzNuX7fr8Q/iIyw3mFvgJLe/NMxwsCmVyGmU3R5WUeW3Oapj0b0GvqZ+z+/nCO7TVCQwyRSEiYY60z3LbLuPYE+QRnVNzeI556P+Op97MMx2QyCQc3e1RKFbGRcRnyNIoChiYGuFVywczalMBHQUXCUimTSXzSowGeNd35+/QdNs/Ym2uvZ2J83tdf0sORDsNbcOvsPQ6vPI2FjRl7lxxHk8tQUCmL/Ah9DFCJFJAg8HEwk5rNYcj3vZm86SsGVhiXZxn/Lfjj17/oNKrt+xajUDF+7XAA9v5w5D1LkjNcK7q8+bckoI0mCXgYxKpxW+g8ui2zD01KDcn7UKAWKny5hwU2mGBGKEFohBpXyqW+m+9efkjPEsMA2PRwKc5lHandujq3vP7JVhmVyWTYlbTBtoQ1B348iqmlCXFR8Trb3/vzEbYlrQt3gflEoy51Abh2LPeFxddP2UGDjnWYc2gyca/j0gxZkKH0RZVG5YmPScDvn4BcjXv16N9YO1ri5O4Avjm3/w/vFx+c8rb3hyOZQ6/yyGKkVgviorXsY1kyU6ZDnTbVadCpDuMbz8ho8dWVR5aTTLqgY35JklGGykSLcJ7xBAOMKENlFJLiw7Bgplvn0GqTWH9nCWVrlmHLt7u0jJQFRVZrfDOnkmQMswgpNcBIZ7hpsaCImd9CAyM4t+dPzK1Msjzv4GrHhA0jSE5IZnqb+cSkI9x5W2ogIvg1Nk5W7H6+Osf5Wsq65V64LNZbrk5ZPu5an10Lfs39OPnA+T1X8Kxdhj7fdGHnd7/qfAajRQSvCMBGckSg4bG4Q0nKYCpZZGhnaW+BnoE+f53w1o6VgcUwn79rERh9NBpBsN87zKL5rCeXE7749nMAfG77ExMZS69pndk6a1+h0m0bmxkx6LueeG2/xPndf+a5f8CDF1RuVJ57lx9luA5Ck3Vum3MZR1r3/4T1U3ejfrMJPL3jD5r1qIfXjsta927qGFl/AzRCg0ZokKW71tEiIvWeUugr+P7kdKydrBhe4+ucF/E+3u2FdJ/c/eMxQiOQyRUF9/B8AN+4WQcnEfYigt6l/h3lAk6sO8PYVUNJjH3DsJruGj7++yn1O9R6P4Jl81sG4IMb5TGQtFEDZlgSKoKIJBQbHDK1H1ZlIp1Gt+Xjz+sxeGFvvmm3QOc7SKPR0NG8LwfDNjHn8BQSYxMJDQxn76LDXNh7hbZDmvPi0UseXnvCR+1r0qp/E87suIS1o2X277W8fuez4hrIYQybN4zZqc9RLt7rh1ecSiUuOZG4k2Mx21Doy99wNkgkJySj0Wh4HRJNqfIluPbbLWZ0XARoCaw8arpT5ePyqFUa3to0nz14gUqpoqSnE17bcu8F/LdC8F/YZKGjKGpy6IJMJqNWq2osG7G+2ObMCpIkYSnZYYmd9sAH8EHLCs/uBTKryxKadG/AyhuLuHPhPlNbzStYGGU2sMCa5+IJlpJdhuMxIhI7qUSRzPkhw8jUkIkbR2DlaMmyEesJyCZE0crBMjUevmx1N549eIGTmy0jlw9iauv5AGzz/QUnd4dUhS9PSlw6tBrQhBWjNuWrb17x5O+nKPTkaQrcO9AIDa9EAJ5SWlF7W5x4IrzxpEYGD1yHYS04sur3YpH73wIDY31unPLm7uVHAHzcuW6O72S1UKEkCQOMMyg3utB3Vle2zTlATET+8iONLYwx02HYyArNezdi+/xftWUe3uTFPb3znMad6+jsoxFqojShKCQ9zLCihFSaJxpv3GTlMcCIKBFOiCYQD6kan3Srx9BFX7Dp2z3/ujzDnKDQV6BRaTIoaR2+bIkkScWeG1RkEFpj18hlgzi/53L+Q16LAS2++Jgp28bw9M6zLL2E9/98jFu60jIfCtSoUxW3t7DDGT8eZKm8qVTq1GiIE4m76DapI2snbdM5/tLL84kOj6WXi9aDOmBeT4Yv6c/oFUMQQmBhZw5oQzdfPn1Foy51adqzIa9Dooqsxp+RqSGmVibYlbQhyCeY6LDoTG1snKwyG+bygHZGfbI971zWka2Pl9F+WAsc3ex4HRLNk5t+bJtzIK3sDeBe1RVDE4OiJbz5wPBfke73ibxYEnVYQAyMDSjh4aQ7fr8gc+a3X2F5drKas4DW1ytHbnDlyA0W9FnGlK0jOancw52LD5jUbE7hKZ1vxlGgwBhTnmse4Sy5o0FDkHiKFbbaxy67+YorZ6mwvXBvx3gn7+fLn/pTpVEFtszck6FY6btytJT3wEu9F1k68gbfN2F4gY9fpipuAP3KjqJV/yZM3jwSgN9T9rJj3gH87j4nKiQ6a4KQrAok6xXva+TB1Sdo1BqGL+7L9nkHSEhX4yuaCGwlpwxKmiTJMMeGeGIwRespcSxtT1J8coYyIcXG+lcY92Yh399vyT42fbuHtoObUb6uB5HBr/H757lOlkkhBAE8QSVSMJJMSCQOE2GOo+Sqcx6ZTCI+OjHfihvA4oGrmH9sCo5u9hxacTKdQOm8cG/L8UkSCn05yfHasjPSu7V91OpM3rZwzSsiNa+wlhxIEPEECX9cpXK4U5FXmgCUIhkzyZI21TtSo7cHp0+eYZv7ZuyEC8aSab7XVViwcbYiISZRW2onh/vE0FgfBzf7tFxVocGlnDMff16Pj9rVpISHEy+fviIlSZtLKckkJEmiX9lReX/f5ZSLnY86YQXB2xCzOV2XUKVRBb7dN4HmX3xMF5uBRTJfQVC9aWUWn53Fw+s+TG/3XdbfAGDtxK10GduOb/eM5ccha7Mst/R+kPkdItAgvc07evPbl63uxqob3yPJJFKSUxBoyXG2zz2Qqb97VVe+Pz0DPX0Fdy8/1Hq834yzZeY+tszcl6NUXuq9fLVsEKvGbcm+YR7et07uDnSf9ClBvq+Ii4onPCiSr7eOZEbHH9JCw9/c04+u+/B93xW5Hju3MDY3ot3g5pjbmrFk8GruXLifbfi73z/PC12G/6CFJEkuwDbAEdAA64QQy95p0wQ4ArzNlflVCDE3u3H/3crbG5hYGNNr6mcIITi78zLP7gfmqp8QguOrTzN21VB8bvuz/uv/P1aHwsCi/ivxuf2MET/240jUFoRGEOwfQtnqpfH19mdEzVyED+UAJ6kUCSKOl+IpEjKcccPwfyjXLSd8OqI17YY25+TGc6yZsDXH9m9ZyHKL01svcHrrBbw0+5HJZfSb3T313NiG3+TKCh1dlIXudeDRdV+CfF8xeEFvNkzdmbpJkZAQWW4URNpGAfj0y5a5+rj/f8TJjeewc7HB2tGSR3/pTn4I5hkWkg2Wkm3qsVfy50SrIrCQbLLsI5PLMDDSK5B8jbvWw9rRklObdTNSvoUQArlMRreJHfA+fx+FvoKSnk589lUrXofGoG+oT3K6HEKVSCFSE4KHLI2cxA4nfMVdPGXVccEDl/JOTN02mpmzZrF+6g2shQMlKEsgPpgLG2ykzJ6E4sKg73rRbWJHnj0IRC6XseCLFTy7l/l72HlMW9oPa4FMJkOVosbCzoywwAiS4pOwdbbmwr4rrBq7Cd/bzz54D5tMJuNQ5BZ8vf0RQmBX0gbnMo48uPqYa8dvUrFBOZSJSs7u/IPwFxH0nNoZWxcbVMkq9I30KenhxO1zd5nbdckHW69ukdcMDq88xcrRG7Ntp9FoGNfoW5Zens8vYzZ/MMqbAcbEiijMJMvUY0E8wy4dUZtTGQe6T+7Exuk7c5V7uNZ7CVNaz+OW1z/aA3lQsCztzRkwTxuZUpie1s5j2mFsZsj6KTtJiE0zKrqUK0G52mV4+JcPoDUcjF09lBc+wYQ8L7ycYjsXGz7uUhcza1NOrD9L2Iv/jVqAhQ2BhKb4inSrgIlCiFuSJJkBNyVJ8hJCvFtQ7w8hRIfcDvo/obwp9OQ4uTvww4CVfD6uPa0HNsnW/Vu3Q23Kf1QWSZJ4/iCQU5vPceX40yy7AAAgAElEQVRILmmPs4prLqL8k7fjmVqa4FzGAf97gTmyydmXsmXW/gmEv4zEvaorES+jWNBnWbZ9MiEPltFfl/7GqU3nUSYkoVJpkMlklPBwYMPdn+g2+TP2LzlaYMupsWSKK+Xy1imv1t233QpQykogkUgcIOVsgc/hmhgYG9BpVFsuHbjKV7Wm5MjA9xZvPW+5mSNDv3Qhk5b2Fux/tSHdR01oL8w741VpXIGfLszlpe8rEmIT2bvocMGZL/OA2Mg4ts/ZT+cx7dDTl4Mk8fTOMzYfWoeNxjHV+6YRGmLFa5zeeITKVHfL1bOUJfKyBl3XvzA8CUXsWQ4LjCAsMPsPv8Y4BctE2wzH7FUu+Iv7OpU3VYqasMAISpR15KWOcKGc6sntW3yUdkOaZ/C4gu76dOun7sDEwphxq4cCsHnmXs5su8j4tUPpPKoVuxemEeCEi1c4SBnDzmSSHEOMKf9JGarUrsCg+b34ovaX3L3ng7PkBhIo0KM0FXmi8cYa+zTPb3GFwEsyll2eR8X6niwbuYHja7wY9F1P1t9ZkmU4tIOrHa4VSnLgp+M8vuFDTGRc2ia4GGQtzPbb/VZibG7ExKZzMDbThuaV9HCkUZe6xL6O55dRG3Gv6kr1ZpWxK2nDw2tP2N/tWGr/TY+WUbF+Oe5fWZLnpRQXZDIZn41sQ9WPyzO8+mSd7QyNDVh6eT4/D19LVKhuo5pMJmPAvO58NrINSYlKkuKSiI9OYFq7Bdn2yy9K4s4zHhMmgjHEmARiMMUyVZmr2aIykzZ8xV8nb3Fyw1ltp1zcJ4+u++XrXbg/eD2RIVGc33OFC3uv5Ll/eij05DTv8zGlKpTA+/z9LL2ih5af0JY4aVcDl3LOlK3hxuZv93Bx/7UsRswMAyN9qjWpiGetMsjkMiSZxMX9V1MNM3Xb16RSg3KEv4jg8qHrhAaEF2hN/x9QXGGTQohgIPjN37GSJD0ESgAFqob+P6G8RYfHsmfRYXpO/Yz46ARObjyXbXt1iho9fQX3rzzm3h8PUSYpqdmyKm0GNsNr+0Uu//oXAGZWpnmqHVRQGBjpU61pJVRKFbfP3sO5rCN9vumCEBru/vGI2q2qUb9jbaa1XUDzPh9jYWuWmnCaGJfM3T8e8jokmgM//4adiw1rJ25j94u1HI/fTlhAOEvOzdY2liTMrU2xL2VL+MtI7cs6/ab77ZLFm7/f3UtJEghBxfqeOq1WD649oe+MLtRtW13nestUd0uTqRDg4GZH3Ot44qMz10cr4elE0JMcQmMlibLV3fJV683QUcHo/uOJuhGOQBCo8cFF8sh3GFVKkpJja06z67uDOTfWgZNJu2lr2CvP/aJCo3NkNKz/aW2mbBsNQH/P0XjWLkO/2d1zTe1cWIgKi0klSpEkCfdqrvTp9QUnzh5DijRApVIRJSJwkTyQyWR41CxNm0FN+WXM5mKV898ItVCjFir00rG9podCXwEZ9ac3ns/scWbnH3z6ZSv2LTma6VyMJpJIEYoBRthRArkkz3Des5Y7S87N4uzOvNF6x0cnsHPBr0zdNhq3iiV56fuKG7/foc/0LqnKm5mVCUq5BYlhGZV6IQR6lnI+m9wCc5k1v4zdzJ17t7Ejc96tgWSEmhQUZH3NihKvQ7Q5NTZO2g1xfHQCAY+CsHG2wsLWnBE/D8DYzAiFvoLkRCVTWs2nRvPKVKjnSaMudenjOqLYZS4MRIVGM62dljY9IUb77n9y0y8DoVbI8zCuHsvaQPvomg/l63mw5Pzs1GP6RvqcXHeGk5uy30sUFzpZ9udI1Fbcq7pl227mwUncOnuXE+vP6FRq6ravyeRNI3h65zl9y45OJYjrNLIN+4PX09agF05lHHTWQs0PJEmiNOVRiRSUJOOIS2p+bPWmlZl1YBKjPppK4OOXOY5laW/O4AW9gbTfO68YXmMyddtpyUuOxmzlC7eRGYi/8oLBC3tz+dB1ft9yIdt2u78/TPmPymJmbcKmb/ZkLHuTBbQh3woqNyrPR22rc/nX6+z94QgpShWSJNF3Zlcq1vdErVJjZmlabCzg/yETbCVJSv9yWSeEWJdVQ0mS3IAawF9ZnK4vSdId4CUwSQhxP4s2aWPl1qJfHDCXrEVdqbn2P0VsWZYr5HjWdqdSg3IYGhtw5+ID7l1+xMdd61K6cikUenIiX0XTok8j5Hpyvqw5pchksXKwoNPINlg5WHB212UMTQwoX6cswf4hOLk7EhsRS6WG5ZjfcymNOn9ESU9nzuy4lFafSGgwNjOiWpNKNP+iMYGPX6JRa7h69AZPvZ/x6YhWtB3cnFObznFszRl+8PqWEp5OrJu8nUkbR7Co3wrdL5JsfofND5dS0tOJtkZ9Uulpc0Q6S/SSc7OZ1Gx2Lq9SzhizcgiXDlzD+/y9TOeynCuLtS0+M5PJLbINNc4EIQSaGlFIty1SP0gaocFHeOMp1dBJU5/1YNrr025oC8ytTdmzKGdafCB1LXXb1WD+salA/glIQJv/dnTNaVaO3sh3v01jYZ/lqfTLCn0FhyI2s2HaTkYtH8yTv58y8qOpNO3ViPAXEdz942GuZC1KCCFQmyZTvXElqtaoikJPgSRpiSpunPImOVGZ8yCFKlDh1wbMX7e0ezG9hyv9cY1Q46d5gJ6+HgZ6Bqj1UjCPsaN5u2aEBUZQ5eMKWDtasPvoTqJvpGRgfY0UIahQYZ8NoVAJDydqtajCsbVeabIIgZ/mPkaYYIszicTzUvjhKpXHSEojJ5mxdzyJ8UksGZQ9i6qudVrZm7P47CwuH7pO4671iImI5fbZuziXccTIzJDEpEQmjJmEbYgrkiQRJ6J5IZ5iijlGpsYICyUz588kOPQFG6fuwVqyzzCvr+Yf3KmYK+KWAiOLOeq2r8nUbaPobDMII1NDvvq5H60HNkOSJPb+cJgNU3dm6jNt51iUicn8OGRNsctbELTo25jukzoSExGrzbl+FwV45sytTflq2UAcXO14/jCI46t/T80dLg7U7VCLzqPbcnH/VU5uOEsJDye2PF6u8zd8Cy/Nfro5DkGukNF1wqdYOVhgbGFC/Q61+GnoGtoMboajqx0zOv2QJVv0Np/lWNpbkJKcgrmNGZ+a9SUpoWjflT2ndMKjpjvzevyMU2l7uk/uSFRYNMrEFHy9n9FuSHNKeDghNBqUySnYOltz88w/SJLE4oGrchxfJpMwMDbAxtmKxLgkZHIZr19FoUrRJsZ6qfcWaJ2Ope1p2qMBNs5WrJ6wLQMRyFsYGhvQf053/O4+5+K+qyiT0hmIsrhPO41qg52LHQnRCfjfD+Tq0awND46l7dE30CPgUVC+ZM+AdHKcEQduCiFqF3zQDxMulc3FhP31CmWsCRW9cnWtJEkyBS4C3wkhfn3nnDmgEULESZLUDlgmhPDIbrz/Cc9bfqBWqXl4zYeH13wyHL+0/xqX0rmyjUwM+HxCrsNQc43arapRvVllNCo1UWExHF5xkpiI2NS6Q9dP3AbA1NKEKo0rcOmgVqbLh65nOV5CbCJXj/3N1ePaAqMGRvrUbVedT7o3QKVU8ffpOwyc34tmvT/myS0/JjWfC0LDPxfv8/WWUfSa1oXZXRbnKf56YIVxrL39A4cjN9PBtG9BLse/GrFEIZ6osEi3OZFJMiywJY5ozLDMpnfhYeyqIXQY3hIomOIGsHT4WiZsGEGZqq4YmhqkHq/TpjrfHZ/O/auP8dp2iVHLB+NZuww7/Fcxudkc2g1rQVRodK4sqEUJSZJQxBty7+RT7p18+l5l+bfBX/OQEpTGMMUEUrSKVaidHx1GtcDKypLft1xg84y9mNlZoCnnT8ijcEwwJ4pwlCRRmorZjh/3Og7H0vaUr1s2NacuSoRhggUOkrZ2lR76mFIdP+k+n9b9nI5ftaJ0lVKUqeaWJctoTjAyNaRV/yZY2pkxtc13hAdFsmXmXsysTPjyx36c2nweX+9nxEbGoSdMeSK8sRA2hPGSylJdrQEmATTxalasWE7plEqEikDMsUIhaXP4ojTh6GNQPIqbDjy+4YuppQk/eM3g65bz+HHIGlZP2EbPKZ10WuYv7rvCnENf8+pZGDvn59/TX5woX7csQxf1oY/byNwbDvOAmMg4vu+7giqNKzD38BTK1SlTKDncucX8o1MJD4pkwrpqTFj3Jd/1+hm1WpPKmqgL+xYfYa33EpLikvj7tDdBPq944fOKms0qM3b1UC7uv8rEpnN0XrN+HmNS//ZS7+Xz8R3y9bzlBXsWHWHe0Sl4qfcihGD9lB2oUtSUrVGaOm2qExoQzrBqk4CMdcxyQuVG5anXvibJiUqSE5SEv4zEwFgfBDiXcUClVCFXyDm2+jQ9vu6EECBXyNCoNaQo1cS9jiMhLolgvxDu//kYI1ND7EvZokpRU7ddDfQM9NDTVyBXyBACIoOjdCpuQ77vzZ5FR9IM7tnA2MyI0lVcWfplzizor/xDc3Ut/sO7kFAXY5FuSZL0gIPAzncVNwAhREy6v09IkrRKkiRbIYTO+Nf/t8pbbiFTyImNiKXX1M9ISlBiZW+OWqXGsbQ9W2fvx9zGjJKeTnmiiJYkiY4jW3N8zWmun8yaNeot4qLidVpdskNyopJLB65x6cA1LOwsWHBiGtvn7ufkxvMZEmkjXr5mSqt5VGlUgSXnZ3Pgx2McWXkq1/OMrv8thyI2clq1h1f+ofj9E8CuBb8WTg24bBAtIgglCAk5GlTY4Fik82UHgQZlfArvvgtkyBAUX7L/27ApgKOx2+loln+F+uSmc5zcdA4HVztW3VzEimsLiQiOxN7Flv4eown21+YrtZR147R6Hw6u2nIOm7/dTe/pXQh5Hvb/ol7M/xpa9m9MyGF/DF+nebskScIkzJZ54xZQ2rwc/ncDkCvkjFg8gF9GbyKFIOKIxhp7jKWcC7VHh8ey7usdjPi5P8qkFPzuPCdShOJGhQztZJIcS1tLzG3NSE5KYdzHM0nKY4HuSg3LUbtlNVRKFS4VSvD6VRQ2zlapm6jOY9qxZea+DIn9lpItFljzUjzHkVIZPOcySU74wziSkx7hLlXmuXgMQiAQGGGCC2XzJF9h4pRyNzKZxMGlv7FmYhqtekJMQibFzUuzH/+7z7Gws8DaUWtcSknKOQdULVS8IpAkKREFejiJUuhLBjn2ywtWXl+IZy331FIn6WForM+CE9Op8nEFRtebXiSKW3oMXtCHlWM2cmZH3sJ08wN9Q31+S9B61d5GMwBsebKCb3aPZ9M3u9i98FC2Y6yfsoP1U3ZkPCjJOL8nd/UUbUta84PXDF6/iuLRdV86jWxd5MqbubUppzado177mnzhPirbfK3c/N5O7g580q0+ibGJbJi2K18yyRVyzKxNsbQzx6mMA31ndiUpPpmwFxHIFdrrmRCTSIpSlcYgmQ4yuQwrewtqt6lGqfIlcqW4Wdpb0OfbzwkLDC9QusR/+LAgaT8gG4GHQoifdLRxBEKEEEKSpI8AGZBt0vmHq7zlk3CisLFj3gH2fH9Ia2Ux0CPudRwajcDY3IhPutcn7nU8Zau7ceXIjVxvLBR6cpSJSshLOF12yIGuPjosmpF1puJcxpEJ64YR7B+CfSk7TC2N+X3zeR5c8+Hu5YcMqzqRny7O5fmDF2mhhzn8DsokJe1NtEpClUbladGvMb/8tYB/LqULndMRmmvnkjWpQU5rjBcxhBAESMiQkCHnJc+5efem9prqukd0FWIu4D1ljhVPxB1sSaOoF0JgVl4fw0dWBRo7L9g2Zz/b5uznZNIujEwM8dLsz78H7s01CQmIoJv9EFr2/4TbZ+4SGpjxw1rCQ7vmUXWnpSp0O+YdoFLD8gz9oS+bpu9Ks0a+R4/Ee0dh0KDntZSBJMPGyYqE2MQccxg7jWyNlYMl1ZpWYNOuDZnO66HP00f+KCUFMpnENt8VXNx/lVb9P8Hntj/3/8w7S9/aSdtp2bcxzXs1YvXaVaT4JmcIwQSIidQaJBOiE+g/uztrJ+eOEVhoBHoGenzcuS6Hlp/g8/EdiAiKJDwoktJVSvH4xlMQGuKi4qjetCLuVV1RGOixcdou+s/pTp3W1bl1928W9l0O7xjTDQwM0CSq0Zf0KCOrnPsFF3bo7DvjyeUyQgPDqd26GidG7KCdUe9su3ttv8TBn44jk0m0GtiUcWuGpeUhprvX3oagqoUKH3GfUvLymMotSBaJ+Kfcw0WUwaiQSiTMOzoFvzvPsCtpzdI/5qKnr0AAphbGqZvfi/uv4V7VlUaf1+XRjWy86rl85uJFDJGEoo8hdjghS5dneXytFx1HtaXz2A5YO1oUuCaYobEBmx8v48yOP3h47QmTNn2FkYkhs7ssZt7RqQQ+ecmg8mMz9BngObpAc+aFUG2d9xKtV3rWPjp91ZqyNdwKNncusPDUN3jWcmfTN7sLRLQhySTcKpWkSfcGHFx2gtjIuNxxFmRxTTQCosNjiA6P4fnDF1w7fjPbeUH7znGv6kqTHg0QGg2hgRE8vObD75sv5Gr+Jj0asmfhISKCX+uUK1cornI1/2IIQFN8RbobAn2Bu5IkvfXWTAdKAQgh1gBdgRGSJKnQZpL3FDnktH24ylshwL6ULY271iMlKYXft1zIcQOjC6oUNaoUdQba3YSYRE5tPE/Hr1oTHR6bQXGTJImaLapQoqwjR1efznK8mIjY1NDI4sLLp69YO3k7P3jN4Njq09w4dZuOo9ry6YjWTG4+h8S4JJYMWsXKv79nUb8VnN+dO2vdW9y9/Ii7lx8RGxnH5+Pas2TIas7t+lMne1x+iEEAgsVz1KjwlGqkEhokauLZsnMT3er0y9eYBYEkyXDElSfCGzucEUC4eMma71aw8osdKJOU9J3ZlecPXuSYpFwYaGvQE9Ba1wukwL2BRqPhdx207EE+wcz+fDG//LWQtvo9Uwu23//zEVEhUXT8qjWHlp8o0Pz/IX9Q6MlZdPpbHl335ceh2eczJScqQQgigqJISIpHgyZD+N8rEYAhxiSTiIHGiCFVJmJoYohapabL2HbEhMfmOVRWo9bw+5YLyGQSrpU9uGpyiZLxnqkGkFgpio8a1yIuKp7NM/ay5NwsmvVulOsoB4WeHBMLYzqNbMP6KTtQJqUw68DE1DwZ2xLWlPR0JiU5hcc3nmLjZMlPF2bjdzeADdN2ceXYdV6JF5hLthmMMibuCqRbOXsYixstZd04rdqHMjklNfJBoa+gSY+GnNme5gX/+ZI2p9fcxgyNRoNGAyfWn2H82uGYW5vqJG54KZ7hqqiAiUwbumcgGeGpVxNfpTdlqVIoa1Alp9Cy3yd8++ki/j59R2e7IytPscN/Jb63/Ll04K98lzN4Jh6jQB97yYVkkcAT/sFVeKbmWZ7ZfpEz2y+yN2gd1k4FN8TN/20aSfHJVKzviZO7PQt6L6Nuu5rMPz4NIUQmxa24YWZlwr7FR7h76SF3Lz3ES72XaTvHsLDP8iKZT99Qn5ted/CoURqvHZfyPY6BkT6dx7RF30ifHfMPpuazFSdkMomRSwewYvSmXJeregvnMg7YlrBOU9z+Q5GjuMImhRCXyRSXlanNL8AveRn3w1PeCtEi8OmXLfH75zmfdKtPVFg0F/flQAmby7llchk1m1ehXoeaHF93JlMdnQ7DWyKTyxAaDa36f8LprdoPp30pW1r1+wRDU0OOrsqs1KWiIJaTHLxwYYHhDKk8gdErh3Bo+QlWjt7INt9fWH3rBxJjk5ArZPwyahNTt4/l/p9PCA3Iew2SDVN34XPTj2k7xnB2x2Wd5AEZ5M1D8eNk4nGU3DIw0RnJTJAl6JOsylzXRpJJIElIMgmhyeL6FIJF3EKyxgxLoghDQsJDqka7zq1pF69V7i1sM2/23g1veiPsm3/z+GLJwrp6ZsclWnzROG/jZIUc7qmYMC1bmeqdeP8g31eU9HTCtWLJtCLA/6HYoEpR81WdqRlCjXQZUk5t0irnI5cNpKSsLE/U3jjhij4GBIvnJBCLqWTBKwJQaVJwj6tIUnwyRqaGWNpbEOSTd2a6VIs1EHQvFAuNHf569zEzNSNZmYyFqQXhF5IJ5zH1O9Ym5HkYg+b3JO51PNdP5mz4elsKIuBREAp9BQPn9WTvD0dIiE1ErpDTb3YPNkzbRUx4Wrix17aL1G1fE+9zd5FJchxw4YnwxhZnBBrCRTBH1+9jz9TfuHX2bs6LzOJ5EUIQLAUQK6JQkoRAgxGmOOCCuVSw/FhJJhH4KIhKDcrhpdmPEAJJkpiydRQbp+1kz6LDVG5UgRWjN2q/QZKWdtypjLY23bRd45jeYRGSXuatgVKpTFXc3kImyZHJFEiytHexUKe9BzJ4PrL6Zr1zfbx2XKJW62pM2zGaLbP3cXyNV+Y+bzC0ykR2+K+kw5et3hIgA2DtZMnrV1G89H3FshHrUane+Q3eyBEjXqMvGWnLPaBVRk2FJU/FXTykNJbkny7OxtDEsMBGsA7DW1K1cUVaybtnOP73797s/O5gKiFUoSObfY1CIWPy1lH43XmG9/kHBPuFMHiBNj8LoL1JX36L386KkRuIi8ofs2N2qFDPg15TO7N19j7CX2QMK8wt0/enX7bExsmKU1suphWizsJznHeku6d1lS9Jd/92Gdeeo2tO8/zhiwzeuNyg17TOHFz6Wz7lJO97mKzaZ/jO51+U/1A8+PCUt0LEzvkHadKjAQo9OU17NiQ8KJL7fz4q0JgdvmyJQylbbnr9w6pxW1IJRmQyCQs7cwbO68n1U7e5/Ot12g1tjqWdBZb2FlRuWA7P2mU4+PPxVGre9wW1Ss2Z7Rf5atlADv50nLndfyLgwQuUSW/YliQZkSHR/HxpLo9v+HJ8nVeeawBd3H+NsauH8fWWr1icAzNcXiEhyxReBWAoGZGsyp93tTAgk2RYk1agN6ucDZlMxrany3EoZcfn49rTrHcjts3Zn+0mJb9Y1H9l4ShvOWDxuVlcPpQV8y38MnoTI5YOYN2k7Zk3Uf+hyPGW1Sy3G5i41/FUqlIRw3+MCBNBhIgXGGOGu6wSADY4EksUQfhRkrIMWdib7XP2p74HCwILmQ3DJw3i8KqTJMQnISWkyaxOUdG0RwMA+s7sis8tvww5nlnh83Ht2f39Ieq1r0Wnr1qz+/vDxERo372etdy58bs3kgRfbxlFRPBrNGo1GrUgIvg1xmaGjFw2kNUTt2MebU0U4chRUE6qTvzzFBad/jbL5zs3eMZjrIQ9zrLSAMRponhFABEEgxCYS/n38Ixt+A3Dl/RjUIWxGTyho38ZzOCFfeg3RyuzXUltyPrb/LK3OX+7vtfNbitHTopIRi9djpsQAk0h5vVePXaTyOAonMs4ULtF1WzfiyOXD8Tc2pRvOyzMkimwcdd6rLzxPX7/PMe+lC0nN54j7EUkwxb1wbN2GRq4NsUhsGSGPjJJjnNpZ0777kGSJEIDw7F1tiY5qeCMi2e2X2Ls6mFUaVSBu5czMvJGhWZ/LxcVOo9tR9PuDYgJj2HET/24e/kRiwenfa+VSUpeh0azzWcFI2pPLdSC0gB2Ja0B6D+7Ozvm5S3PS5IkRq0YxO9bzvPkb9313jqPaoOhiQG7F+Vc+Du/qNO6Oi+eBPPXiVv56r/0y/UMWdibtZO2FrJk/yErCCEVZ9hkkeB/WnlLik/i1KZzPLsXgL6hfs6KWxbeCyd3B4Yu6sPzB4FIksRfv93i+OrfM3Sr+kkluk7oQJnqbizos5ymPRviWdMd9ZtE1k+61iP4WWju63DkJla/gN6ie5cf8fCaD5M3j2Td5G0ok1UZ5r1y5AZXjtzAs6Ybn0/4lDErh/J9vxWp7HC5iaMf2/AbNt77mR8G5Eznmxe4Up6X4hkeUtXUY0IIDOwVmBlkz8aVAVnILZMXnSu9TpvqfLt7HGu/3s6J9dpCpPYuNux8tor+s7tz68w/LPxiRTr5pNx7JtPjTVsvzf7CETyHueOi4mnUuS6Nu9bLFBYqhODQshMM/7EfO787lL9NygdCs18g5HcN+chzS/szs9dDkqW1zcoqvG3Ofqp2cyciOIDE0CRSSEZgkurBATCTLAnRBKYGgrhWciHyVVQeFqUbBsYGJMYkZyqv8eCqlhX4lzGbKVfbnQUnpjOiVsbyLTZOVqQoVegb6tHws49Iik8myOdVRov2m2uVokxhyMLeOJdx5M7FB3hfuM9HbbVlPao3q8zn4zrwOiSKdXcWY+VgwdRW87l/5Qm2Jayo3LAcoGXjex0SxeoJW7F1tuLQshNp3uesfjdJhlJo12YppRU3N5VZYqAJwwYHXuKPOflX3h5cfcLYht9mmBNgxejNrBq/jZNJWkKMnlM+o+eUz1KbvVXmXCuU4O7FB1l6zxyFC34p9/DQq4FMkiGEIFD9BFvsM9yDklyeqa9uZKzjZ+VoSYmyjhxZdZqV47Zkvo5v5ilT3Y0qjbQENzI9PSRZZrKVP379izsX7uNY2p6nt/2ZvGUkqhQ1Qb4heNRyp92AlpyadwWjd7ZBwX6vaKXomfp/Q2N9Vl5fyCnlHtro93x3mlzBxtmKpZfnEx4Uif+9gHyNURTY/+NxBs3vxfopu9KMt++gu9MwvNR72fTgZ148CWLkR9NQpRT8nSzJJMavG86Bn46zZ9Hh7O+Vd+6D8nXK0OKLjzm62ouAR0EZ7rl32yfGJ2sdSTlEkOQka9bQzlutaWUtOco7cub0vrV2tMTG2Yro8FgMTQ3fz7fp/2n+m/o/5e3Dx6Prvnnu03lMO6wcLIiPisfa0YK5XZdkOK+nr+DLn/qTlKDE55Y/8bGJPPV+RsV6noQFhmNoYkhygjL3dbreA9QqNdvn7qfLuPYIARunZ1Yun9z0Y2GfZSz7c76WZCUPCHwcjFql5veUPXS2GZTvgprvwkQyxVAY8UzzEGfJHTUqAoUvQzoPJOZhwTxvxuaZPXoFhUIhY/quscgVcj7gP3MAACAASURBVO5dfsi1Y2nJz6GBEbSU92Dooj50GduOT7rVR66Q8+i6L38ezrosRE4wtTThUOQWoOAlA96FobFBptzRrvaD8dLsJ1kHW12wXwgbp+5k6A99uXzoOg//8skza+B/KHqU/6gsasNkTh33wjGuNHYSIEGkJoRgnuOMW6Y+K0ZvolGXj+g+qWOWRbfzCoW+AmMzowyMuACfj2/P1DbfcfNNBMDsgxPpO7Mr2+ceAGDooj64VS5FWEC4Nrf04DUiXurOH3nlF8L53ZfZMnNv6ublbXj7u7BxtmLpH3NxdLPn4fUnnD94mVUjt1CqQgnaDGrG9J1jSUlOYdjifpzadDbbemlJxGNCZgOTmWRJoohHoug2FBqNhtb6vXAp58SYlUPx9fZn+9yDzD4wEQNjfSrU9eDEurM6+xtKxpTADd+U28iQo0GNDY5Yyex19skN6rWviZGZITfP3KX94GYAWsUtG6y+8T2zPl/C1WPZezpiIuOIiYxDaEQGw9jhX04y6+h49q85jGGYW6qxIE5EZ/AsgpZYy7G0faoxNj9YeWMRpzadY8uMPfkeo6gQFhRJu6HNOLxCN8v0nkWHiY+Kx62yC0ejt3Fy0zlWjNpU4LkjXr6mUZePaNmvMcqkFGIj4/iylu6auhXqetCqX2PCgyJZPXF7rnIdT20petbjlOT8eWZ7TevM/SuPqVDXg7WT0lIoJEnik+71KVHWkdvn7vHg6pPCEvU//I/gw1XeCtvargvprA6etcvQsFNthABlUjKbpmutlHXaaOPfPWq6E/Eyksi3BR4liV9/Pk5E8Gsu7Mkhib4wrBt5tcLnAkE+wWyYupPe33TBo4YbPrfSwg9+i9+Br7c/YxvOACQsHSyBgNzLArQ16sOP52dx5PVmHv/9lFF1p+dJPl1wkcqQIOIIFv7IUeCGJ9Ur1eDSw8yEIEIjtHTeOYR2tR7QJG8FtXOBKo0rMGrZQLbN2c+fh28w9/DkLNutn7KT9VO091u1JpVYfGYGZaq50W5IcyxszQl7EYFrRW2Ijy6FrNe0zgz6Tsswp0pR0dagV/4Ff+c+atCpDl3GtafaJ5U4v+cyG6ftolSFEjz1fpbqdZm5bwLtjftkOVxSQjJrJ2+nQj1PPh/XHgNjAx5d9+GlbwgBD19kDrsryuc/r2P/L1gmc1hziy8+xsjMiC17N2IbWzJDerW1zAFfzT+px+JENIZSmpHj8q/X+ern/kiSRA4EWTni9NYLrL+7hEsHrvHb+rNEhUQRF5XA1ln7MqxFoa/Ayd2eId/3RmgEtVtVY/bnP9JuRFN+Xv4zaqFCpi9Ro2Jt6n9UH3WKmmvHb6FnoKDjiFYYmRpRumop1txejEatISk+mRunvNmdRdhguTplsXOxYdKorzm48jgKSQ+lSCL64WsCvg5m3dfa53b1ze9p2a8JPw5dl9b5HWu/sTAhnGDs3iliHi0iscOZ1+QzLC0br8JbEpK33rSABy8yFLUuX7cshsYGzOn2U9ab4XRjm8qt8HzrGczNc5GVx0KSMLU0pn77mkxaOxSNRkN0eCwJMYnI5NoxW3zRiLO7siLM0no6NBqBLL23JYd8unfzrh9cfUIPuxEYCgtemDzGysqaV4EhKCQ9XPHMMMzyK99x6cA1FvVfmfOadTxn1g6WBU7ZyBXy8a5aOmI9g+f3pPe0zvT3HJuBmO0t3hp2FQoZlw5e59s943KtvOnyWklyOQMqjAdg9PIBdBjWkoSYxCzbGpoYsN77Bxxc/4+98w6Mqvje/uduS+8JKSQhQAi9VxGlSUeqqHQVBEGaoiJVUakCUqUqCkivgiBFQEGKIL0lhPTee9298/6xYbNLOuWr/l6ef7K5O3dm7t25c+fMOed5XNiz7DDJ8akMmdkPcwsz1j0ULa+AZ608OWwl4lHvmiSVHDZuEglR1AsnSXD56PUi+Y6O7va06NaYZWPWM3h6P7xqeWDjYM2pbX9WjNTk/8K76xlAAPL/UOftWeDfa7z9jyBJEtUaeNN2wAvk5eSzceYOXDydsLQxw9zKnJzMHKzsLBm5YAgpsSlY2VmhVCnITM1C1sko1cqyG/kP4PT2c/T/oAcDPu5Ffo4WJL1hZ21vxY7Itfw0Z0+F894eYnJ7/UJh9t6POK7bwe1z/nqR8CeEpWRd5EX7uLCwNqffxO6MafrpU6kPYPzKd/Cs4cGkl2YV+0IsCddP3zYJ2zHGcd2OoseMwiMvHb3GtG5zKt7ZMtBpaFsattXnPUXej+G7O0vJzsjB3sXW8OLq6/h2qXXk5eRz/fRtrp++Deh3URu2q0vLHk3YsfDZ5SM8R/Gwd7GlWddGOFSyw69pNRa+8y11u/oRta8ocUK+lEe8iCJRHUV+Xj6W2JBFBpaSNVqRx54NB3l7zpsEXHrA2X2P5zEGvedt+8IDNHi5DvMOT9V74dKyyUjNQpubz/EtZ5AQ2DrZEBcWT3piOgqVgq1z99JxSBu+mDMbd6pgprBA5Av8b/sTFRZFDZc69Bz1Ci/0asrBNce5+cddYkMTkGUZc0sNbtVcWX99UbHG2+y9H9GjaV+Srmbhq9CzKgoED8RNLLBCI5kDEHYvCt9GVUu/PkmNWqiJkyMMBlyyiEVLHhEEPpFOXHFzQ0JkEs6VHRFCcPPMHdZO3sSqSwvIyczh8rEbbF9wgA2fbmXc8rc5u7f4vNUngbmlBhsnGxb88il2zjbEhiaQk6mfC4NvRzC110KunNTPBw8ZR17u34KP140uwXjT46NXvmDJyc8YUW8yWRk5aCw0RD+IrXD/bCVHbLMcORC7kSE+40lPLMq0efGXK9RrU6vCdYN+47f/Bz3JSM3k0q+la7r+U1BrVNy5EEDvpl3xa1bdMD8XB61WZuLqkSUaWRVF3Rdr8tX+j4kKjGGA+7sl8gHkZecxrecCYoLjTIix5h+ZxtfHZxB0I4yf15wgMjDG8F2zTg2o2cKXv4/feKzoq/JCCIFC8XhGUvi9KGq3qlFkbCRGJbNj4QFGzh/M1ZM3GfDhq/y85hjdRnbkznl/rpwoB1nSc5QC6T8fNik96U7p04St5ChaKjrp/3lGO+9NOzWgZnNfQu9E4NukKj71vDm94xzx4YmE3ApnwOSeZKZlY2ahwcnDgaSYVGq39GXOoGUmE5a5pcbwEnpq+F/skpSSq/Zi3xbYu9gSHRSLrJOJDIwhMSq5WBHKMtmKSoBvIx/mHp6Kg6s96cmZ9HN+p+T6ygOjNiesGsEfuy9w7VTRl8+i32aVaTDOPaz3Ck7rPvfx+lIA+0q2fLH/E87s/Ytdj4SSfbH/Y+6cDyAjJYsTm38vNtG+NPyc+iO97IabHFNpVBzJ1u8+FiFReApeJgsrDT+nbWb2a4uebIFXwvgYt/xtjv5wmvt/l6LZ9G/DM/CCP5U2TYoX3Vl8uOPr4GrHoGn92L/yVxQKPRNrYnQKA1d0YemI77DOK8y70op87DsouPn3HVxTqxYYbPmEiLto0WImmaPBnCyRTutWL9K6eRsOrCo5BKuk/nnV9OCrg1M4sfkMm7/cbdJf71oeZKZl029id6ztrVn9wQ/68F0hszdhI5d+vcr+ffu5uP86trKpfuR9+To1FA0BqNbAm24jO+Ls4YiNgxUKpQJZFkgSeNZwx8HNnvVTfiL4ZhhzD081eOKrSrXxVTQwqTdXZBNLON5S4QbSQwNqwVurOLH5jxLHQIKIIYk4cskGJKyxwQMfzKTHC9s+pttp6OuYZlNIKogQeYgmnRqw4OhMstKz2bv0FySFxODp/U3qMJ47SmIINslnK2CkbNKxHkOn90NtpqKSlxN2ToXMukIIwu5FcudCILaOVjRsV4f+rqMK69BoCjtQUHftFtX45ug0MlOzsLKzBODAmuO4ejvzWX+9xm3dF2ow5+dPyMvJx87ZhiMbT7F0zAaTZ0TodExaPZK2r7XC3MqM+1eCmTdkOVEPYun9flc6DnmJqnU9EQKig+Mws1ATcDmYJe+uNgkBX3F+DjWbVSc2NJ4h1caVen8exXHdDiIDY5jadQ7RwXEPTyix/BOjAnNE+zdfZMS8QQRcCiToZhghtyPKNb8f1+3g3IFLfNZvUcm5YOXwgo1fNZI2vZuz6sNN/LHrvNGpBUyNRnmXJh4sozHoU8eDvuO7ceT7UzTr1IDAayGcP/g3blWcGDi1L8vf/45hnw1g34ojz5QUZvD0fuz+5rBecqUU1GxWnQ4DXyQrLQtJgr9P3OTG73dKLK9QSIb1ap3WftRvU4vzB/8uaow+ZW23E/LOv4UQzcp9wn8MHnUdxIjt7Z5KXV812P+P3Kv/r4w3Fy9n5hyaysXDV/Cp68XMXvMxszSn5+hOeFR3JTkujVPb/zShvVaplcVrhjyLCfgfNt46DGpD6J0IHlwLqVg9JdRXGpp3bcTcX6Yy49X5XDx89akYb+NXvoOLpxOz+nxdpNhx3Q5+mrNXn+NSCpb9+SVTOn1ZYaPqIXqN7Uy3ER2YP3RlsfT4Vep40qJ7E8ws1LzcvxUfdZhdoq5ScTiu28HZvReZPWBJkePwbIw3lVLiSN52/jpyhdn9F5eY2P44dQMoVUrav9kaNx8XthQs2v/1+I8bby5eTnR5q73hfhsWTELvUbISdjjiSq46kxznFGr61ST8TAJWws5QV7B8F1c8sVQULtaDxG2+WPIZW6cfMDBdFgfnyo607t0MB1d7qtTxpGp9b47+cIq0hAyOfH+ySH9LQpqcSLXOHjg7OvPL9qP4SLVNJEQAHsi3qCbVNQmJThPJJBOLBgsqURmlpDdE1l77mmr1vYkIiCYvJ4/RjT8pqOMm1RWmWmay0BHCPapJdU2O70v8Hmt7vU7Y0845LQnrbiymaj1vZvVewPmDl/UbOjn6ULfsjBzMLDWE3g5nVMOPDOcc0+1k3/LDXP/9LnHhCQReCTZ8VxHj7WimPlfnwY1Q8nO1LHn/O0LvROoLGW38ffrDGJRqJXMGFWqGFWe8AagUoDbX8OKrTWjeqT5t+7cEICkmBY25mujgOFZP3sTtcwGMX/EOvo182DZ/P0Nm9DNsNnrV1Gv4jW7yKVmpGeyKXo+ZpRk6rY7EqGQu/PI3mz7fZeLt6TO+K/0m9uDLN5YQeDXEcPxA8g/cvXCfKV2+KvX+PAqFQsG+xO8xs9QQfCuMBcNXEXIztMTyT4xyzBHL/vwSZw9HQu9GsuL9DYVGZTkx7LMB9HyvE7P6LMSnrhc1mlRl2/z9pjmm5TDe5h6ehk9dT+xd7OhuNdRwvEXXhlg7WtOgTS2iHsTqc2lLMN6M6xayoMfIDjhVdkKlhMo1PJg3dDmvDHmZ+1eDy7eueUzUauGLvas9Fw6VnoM5dskwvv1wU5F+PzGeG28VgntdB/HOtvZPpa65Dfc9N95sJUfRUur4VOts8koDmnRqgLmFhtycfH6ctYPaL9TE2t4Sv6bVUCgVHPvx9woLzFYYFVnI/UNxyiq1kjFLhvPDZztJL8ugeELjDeDd+YPo+k4HrO2tmDtoGb/vKkOHr4x2LG0tGDl/EM06N2TD1K2c2XOxoKuCag28+XzPRwyrMaHUqj/bPZntCw7gf6liYRaVa7jzycaxBFwJYtWEjWWWd63iwlcHp/Bp1zmlEis8iuO6HSRGJfGm1xiT4+uufU3V+t7PxHgD8G1SldWX5vPL+hNsm7u3/JTRZYwJ79qV+XDdaHRaHZ413BnsMxZtvrbUc/51eAIWs8eur4T7Wha738OFgtpMzaTVI9kwdSvJsalFzkuRE0kVCTjbuTBw+JscPn+A/MvmJmUC5RtFvFF5IpcQs9ssX76c8JsxnDtwyUBDD/rwy2SHSBSSgvTUTCStRHWzuoxd9BZdR3Sgp/VQisOjCxwhBMHcwRJrnHEnV5lNsPYeTrjhrqhiUtbY8/bwfwuscZO8yBZZRPIAb8kPS8kGhULB0fxtRZ6j+/I1qkn1TQzDaBGKJTbYSY4PO8kbn/TmjSl9CLj8gKadGtLLdmiFQqafBH0ndGfs0rcZ13Iqg2f044VXm/Oq9ZAiBEMPcVzeRTfzgSY6gIWspCUYb0bHHy6ixy1/m1dHvYL/30FM77uYTCObXeQUtu3XpCpLj02lu7Ox501dWNgo9EzkG1UiCzoPfhEnd3vsnaw5uuUsQTfCCoXdAHMzFTO2jqdZpwZsmbePzXMeCX8tMOhcvJwYNqMvi0etK3JtDzHlh7F0HNSG7haDDPImx/K3M6vPQs4bkU1VFC27N2bs0rewdbYl6HoI9y4Fsv7jzUXKyUJHDBFkk4EKNe54G0JznxiSgl3R60hLymBE3Q/LfZpKpWDO4Wk4VLIjKz0bC2tzQu9EEhcej6O7A1VqV8bcyhxtnpZbf95jzYc/llsaZkfEWiyszUlPzsDG0ZqEyCQi78cQH5FAz1Gd6Kx+E0llNE5KmBMf/pYWVmasvjzfkE+n0qgYNqs/G2ftLPU8KHn+NBknxbSvVCkZ+Gkftnz1iNxBwfOkUCpQqfVlfpqzF21e4fh+KsZbAexdbOk2ogNqMzXJsamcP3iZxKhkfS5yBd5NJ8Tu//PG2/BtT8fWWNBwzz9yr/7TOW9WdpY069KI4BuhhN2LxM2nEh6+boTeiaDX2C5IComM5AzO/3yZKT+OY//KIwyb/TphdyMJuR3BuQOXnzjB/v8StPk6vpu+jRFzBrJvxREiAiouvFsRrJ+yhfVTtlDnBT+W/TmHzLQsLh99/LyA6o188K7liZtPJYbOfM1gvHUb0YGx37zFz49IPBSHuLAEfOp6Vth4m3d4Kh+/8mWJRk2iiCFJxOLo4oB7jUqM/XIIC95aVSHDDSA1IQ0nD0cUCoUJucCoRh9zXLeDnVFred1jdIXqLA/MzPU75D3efYUe775i4lHYeG8Znn4efDtpI/uWH65QvaO/HsrikasJ949m5LyB/z3D7T+K/Nx89q/8lc92T2bSS7OKfG+vcMIeJ3x9fchOy6Gyd2XuXgrDQrIyKlV0oaNERWZOJiNHjaCBY0tGfjSM37adJfR2BEIIgsQtfJMboJLUuADZUgYBOTewsLUgO73sPJpMkUYs4WSLTJxww03hDYBK1lBHasZ1cQ474YilZIMsZCJ5gINUyIYYLYdgiQ2VFdUAsJE0+InGBIob+EmN8Kzprq9PozIxarzw5b64hruogrlkRQJR5JOHu2RqKHrX9sTGwZqjP5zm0wIvzbOCnbMtmWlZaPO0OHk40KyL3kD9+If3qVJbT2608LdZzO6/yGSesa9kx66YDfy68aSp4fYYaNy+Lq+OeoWTO89j52RTsPgt/p06YEIXcirIWPwQx34qyH/TFRMFg967OL3X1wya1gfPGu4l1hMfnmgw3ErCgre+pcPAF/l0ywS2fLWHkFvhxITE897i4U9kvF08fJWLh6/i7OnInIOf8vrkXmycutUkh0sWOgK4QWWq4iFVIVfkEII/nqI6lpL1Y7f9ECqNit+2nqXPuK4VOu9A6iaunb7NqEamhFvGxo7GXIOdiw2Tvn2XX7J+oofl4HIZcG94jsbWxc6wYTz/yFQuHrmKhZXeS1pRZGfmkhBVKPit0+oeq56KQKfVYWZpVux3DdvWoUW3RmSkZJKWmF58JNdTQu1WNbCwseDmmbvEhSXwUr+WePi6EXQjlFNbz1D3xZrcvRj41Ji/n+Ofw7/OeLN3sSUjJQtJIeHgaodPPW8UCgkLa3OcKjti52yLSq0iNzsPjbmaY5t+p9Wrzekw+GUUCgmFUkGDdnWxsDbXa5ehwMrOkgXDVxIZGPvkcc+P60H7J8KsHgNZadl8+8GP9J/Ug4SoJE5uLYFFswzvQHnDSkAvR6DT6mjcoV75jLcSQj87DmrDiS1/MKXzVyaGzdBZr3HjzF0Dm2NpiAyMxq2qS9l9eARxYQklGm4JIpo8cvS7/4mQk6DjzY7DqCrVQSMVP+GXhNdc3+W4bgdH87cxsv7kIqGZDq72Fe57ecbm7XP+dFK+wfLzc6jdwpdRXw9j02c7yMnKxdPPA4A/dp/H/NEXWEGomlRMQreZhQZre0vC7+lDrKKDYmncoR5XT96q+DX8k/gXPLfFopRn9NX3OuPq42JgHSzpGe0x6hUOr/+N1z98namXpqEO06CS1AghyCYdrchHJRXuikeJIKpTF41kzp2kK8yc748q34wsOQsQOCgqGcoLIUgRCWSTwbc/riBe16/Uy0kW8aQQTxVqEsI9XKWiAss2wo5EYomWQxEIXKTK2CsLc+Bi5XAaSK0fOU9h2MTbcHMx6z7ZXMSoMVdY4ycakUA0aSIZR1ywkmxN7qu1vRU1m1cH4P2lbzN18wSQ9KRYEQFRvF1rYqnXVx4cl3ehzdcRH56AezVXk+/OH7zE7m8O8ULPphxae4wtX+5h6k8T2Ba+FkmSOLTuBMvGrGfM0reJj0hk8ci1+jFSAitjWVAqJBq1q0teTj4LR39XWIexN80ILp6OvNNkGpJZ8aGSlET7brzBWtJmawFL5bavDzFuyVDenj2AjZ8Z6VwaX5dcOgMgwMAq7/PhulFM3zqRKrU9yUjJZNJLs8rt2S4NCRFJVK3nzbmfL6HVCZPfIIZwPKmGtaQPTzaTzPEV9QniNr7UL63ahxdk3BkAvjjwCTWb10CS9EQYN8/eo6fNsLLrMsKiEav5+Psxhk0Nw30wai8vJ4/48ESmvzqfSWveZVfMBvo4vl2st1bfvcJ7lZ6UgXt1V976bACN29cjLjyJWs2rs3PxIVOvGzwyZoy9Zkbj2ChEWqFUoC4YkwaWR+M+GY1Xk/eU8XFjT5nRBqNxXp7/5QfUblWTuxf8C+sQMi/2bsq3H/xYrmiJJ/XCnT/4N7fPBTBx9Uju/x2Mp587cQXyKS+/3pqmnRrQsF09ft99gXavv4AkSeTl5HHnnD92LrY4V3Zk3/Ij8HT4aP61EEjI4jnb5FPF0M8GcOS7k/R6vyvBN0IJuPwAJImoB7FcPnodcxsLmnVpSCVPJ3KzcukwsA26fC1CllGo1Vz57SZXf7v13KP2BNBpdexc9DPNuzZi5LxB7P7ml2ea7KvN03Lt1C16v9+V9VO2PHY9siwTGxpfhO56Vu+FfHt5frnqiA6Kw69Z9cfuQ3FIFnEmYVsKSYkPtYghFO/HYMvspHyDiavfZcPNxVw8fJUZr85nS/AqgGc+7j98aSZvTu3L0JmvMWDyq6Ql6vNFooNjWXxqNkIWxIQa51CUPEE6V3bk5h+FydpHNpxk9OJh/z3j7T8Iv2bVWDHuuyK7wHVe8KN17+b6OVVAxINoKjWzZeqQ2aToMgjnEubCEpAQCO6LGzjjjgVWxIkI1JIZtgVhhHVpSUDadYSUj1pSo0RFrByBGjMcFZUIErdxktypr6xGxmnBotNryZLT8FYU/0wkiCjDc6RGQx65mFGU4MNL8i1x2EkoySUHC6xMjmvRL87unA9g1MKh9BzdiZzMXNTmapw9HFEoFZhZaHhwPYTYkHiSYlP4fcc5er/flTGvTSBWhGOntOWjKZMZ+cZ71G1Ri/GtphEdrGdAPC7v4ri864ly4AbP6E/QjVBGN/qo1HJrJ/9o+PxR+88BvZdu7bWv6TlKnxv7y7rjbI9Yw4px33Hxl78fywO3O3Y9ljYWzH79mzLLrvx9JmmJGaTEp5kab88AKz/czOydE2n7Wkt+3/14BEtJMSnM6LWQo7k/ce7gZb4ZtbZEJsSKou2AViRGJ+HXtDq/5urDljNSMjm05hhLVi7GQ/IxKa+QFEgVYMar36YW5tbmdB/ZAe9alUmITOINj3f1Xz5mSkb7N1uTHJta7nHyzah1tO7VHL+m1bh/NbjUsmOXDKN2qxpUb1iFzV/uYdPcfUQGxprkS1YEVet7kZFcyJgrZIHG4tmOOYAbf9yj/RsvmBhvbQe8wJ0L959528ZIS0zny9e/wdreClknGzQz75wP4PrpO8RHJDJmyXCunbqFm08l/tx/kb4TulP/pdpEBsZg62QNRdP1/89Bfoaamv8LPBXjTZKkECAd0AFaIUQzSZIcgR2ADxACvC6EKDNGbMW473j5tVac2X2ems19eaFXM0MCsiwLEiISuXrylp5UpCIT0T+xO16Ch6jEnZYytGpM8Czz4gravHTkCrfP+dNvQncyU7M4tPY4+cVN3sX0pVw7SEbnfdp1LnN+mcqB1E2c2Pw7K8Z9V+JpueQiEJhLluWqOzUpE0mSTBOdeYTNqgCJUUlY21kVOf4kUFBUTsJMsiBfzivNtikVy8asZ+ein9kUsNyEIryIxMBTzsfSamW2fLmHLV/uQaFQ0GnYywTfDCPgciB9J3QHYF8pYq/GmLBqhEmuacN2dfQ6g89RIZToDSjl9946dx9vffEG2jwtqQnp2DpZG8ThN32+k/w8LclyPHFEYIUNaXIyVaRaeFODWCJIFYl4SzWxw4kUEggWd/GlvklYpSRJ5IhMvGiApaQnNREIHsi3UKJCjRn2ChdDWXvhQqR4QK5OnyNmJzlSSVHoXTN+jtzwJlT440sDwy57tiYN89zCOUEr8gkV/iZzkRo14eI+NWhoOC9VTiSXbHRCy19HrlG3dU3mDFxGwN9Fx+J7i4fRb2J3Lh+7zvyjM5g09kMypFTqihYoE1QEHopi8KFBNKKNye6/EAJJklh1aQHjW04tl7iwMfqM70bV+t54+nngU8+bdm+0xr2aK6G3w/n1+5MmzJKPwtbRmpf6t8TSxoL0lExy0nPoMaoTy8Zu4PM9hYbgivHfcXDNcZNzXau44OjhSGRgDO3faE3gzQjuFjDd9asygS5D2jBp9Ug+MVNz91IQC0dvIMXIyJFU+iVGtXpejO88X2+4qYpfdpjkuZnkBBl5VIwJTozHfUFu3UOvyOf9v2Hl+S+4fuo2E1e+g42DFdN6L9KTLZk8L0YELFLRd1ZCZDKN2tVlV/R6Rjb4mLCCKIGS/ToxcwAAIABJREFUYMKVUwwphU89LyauHsXrHqMMhpDGXIOnnxsfrBtNYN4drq67XyQiQ6aU8SLpSVEkSUKlUXH99O2CnLQIZr+2xFDmcTF770fYu9ozuOr7Rb8siXhDyHw/fSur/ppHZ7XRO8mEeARe/7AnfcZ1ZXDNSSRE6peHklqNpFYjhFGIbUnriUd+S6VKSf9JPchKz8bWyZr05EwEEhH3Y/Co7kZUSDHRMUZGotAWrm9MPHIWRjmHSqNxWjDuXLyckPO12Drr5zkzCw2vf9SL0LsR/L6jkEmzuPvwrNamj2rHAYbooHUfb8avWXXu/RVI2wGtyMvJ5+T2s9Rq5ktCZFKR857j34en6XlrL4RIMPr/U+A3IcR8SZI+Lfh/SnkqunbqNj3f68TZfX8RcivsKXbxOSqKrLRstny1h8q+brwzdyB3L9zn0q/XnkkS/vQe83CvWokf7+vpnPd8c8jk+xyRTSj+WEo2SEhkyul4STVMcgGKW8h2GvoSoE+6LisGPyowBjtnm1LLVBQyMrKQURhN2OkiBYsnzGGIfhBLJ+UbrL26kJT4NI7+cPoJe1oxyLJs0qYQAqWqbN1DW0drFhyfwdXfbpnkyNk4Wlc4B/A5Hg/RQbGs+2QLKrUSCxsLE4IiSSEhCx2xcjh+UiPui+vUkpoajBEPfNCJfHJEJvYKZxxwIYs0RDELTBVqg+EGeiPNC1+CxB1D3hnox859+Tq1lc0N5Azxugii5GA8FHrtNJnCzRaNZI4rXvhzBXt7e5zcHWjfvTVfLPgcgK8GL2XDtjX4SLUM9eWLPPzFVSQUBIhreoMKCQvJCj+hv84f5uh464vXeWfOQD7tWlQvcc3kTaz58AdDn29ykQZSK8MmjDPu5IlcIgjCi0IPflf1mzTqWI85h6ZyVLuj3B44r5oerL2+mPB7Efy+8zyntv3JtK0TsXGw5vrpW9RoWo23vxrI4Q2/ce7nS7h4OnFozTHD+T1Hd2L8qpFc/e0mr7u/W0heUjAXHVqrN9ZeGfoyk9eNZvyKEQgh+GrgUgZP70+1+t7EhSfi5G5P8O1w3m/ow/nDVzmw9gRvfNCDe5eDOPT9adr2bUFCVBLb7i2mmxEhyUPs/fYEK49PJTwwlk1f/8KVP+6RnVE8kcrTgCzLLHh7NVsfLMf/0gMsbC3YE7maqAexmFlpsLSxID8nn8jAGD7pXnJUxpAaE0HI9BnXldWX5zGm2VQTA27w9H50fasteTn5/LzmOP6XHhAXllCiMd1/YncyUzNNPFh5OXkE3QhjfKvp5Ioc8qomowquZHhfRIkQHCk5lH/gp30wtzLjVdvhT5zD+CiadKxPi+6N6WY+uMLnHtnwGz1Hd6Z1r2ac+/lyke/9mlZlxJw36GY1HPkpRbA1aleH84euUrl6JSxtLUgv8MD9vvsCPUZ2ZNvXP5dRQ8Xh6G7PW5+/xoOrIbTo2hifOp7EhMSzb/nhf60hlJ+n5fY5vYdw79Jf0JhrqFrfm183nCzjzP8bEAJ0z8MmS0RvoF3B5x+B05THeJMUpCVlsnXO3rJb+Ce8aRXxjpWAktiNivVW/RPMk8W0GRkYw9qPNlO7ZQ36TexOUmwKR8p40IUQZJCCUAhscUCSFGV65KKD43i71kS+u/1NEeMtFH98pYYGxjcZmQBxjVqKJvqFpQBZKxe0UbjQc3K3JzwgivzckqnLHyInS59LWVG4+rjQpl/LYnVyPCQfHoibVKEWGsmMDJFKpHiAn9Sowu0Uh/dbTmPOoU9Lzk98iGc8loQsUJSRE1LJy4kvf57CvCHLi2zMVKriQkpcGkqV8pknmP+r8QzmtZKeO22+jvSkjCKbHskiHhfJA4GMStKYeJEAXCVvHlhcxy1HT9jhijeB4iZ+NDIsOpNELDq0hMr+WGOHo+Sq9w6gJkdkkSzisMMZ0BP6VMLThFXPRelJgPaqIRTYVnIkRoThiheSJGGBNdUb+WDh70z+HZkLd+/x6veDMW+mI/hOGOYqCzRyYX1qSYOjcCWBKLTkUZvmmEuW+muToLqoT7Z7AknRyVw4dLnM3yGffCwp6qV3xp0ArpsYb7Isc+X4DV61GcqRnG3siFqPSq0kNzsPCxtzBriMMCGueIhw/yh0Wh2jjYgi/txvKoK+NXQNVep40nHwS0gSTPz2XdIS0xnT5BMmfPsuw2tO1AtYl3I9Jzb/QVxYPPOPTGdkg4/4YO0odi46yMltf6J0NTIcVEr6v9uOL3d9wO7v/yBbUpGWksW4Dl8C0KJLQ0bPfYO10/TRAA+jGzbM2sV3s/fyYs/GdHyjFWO+6I+Tmz0L3tvAqd0F12McJmc03kzykLTaUssYh42H3omkn+soveyLkHGv7oqZhQadThB+T+/xX/3XHFac+Zy5w741Ffl+5F7tX/kroxcOYd3VBXw5aBmj5g9GqVTw58+XWfDOWhzd7Hi5XwteGdwGGwdr5g1bif+lQt3Kh/lYS0av49fcbdR+oQb3LgYWeS7NJHM0ic7YdJBIjk4lLy+P5l5NmLHkU9TmavJz8tGYq8lIyeLeX4Gs/vBH2vRriUqtonWvZvyx+0LRH/cROHk48OaU3mSmZrN7yUEyUkomr1hwbAZLRq0tuAajOaDgGTeJXilmfM0dtJQV5+dy/pfRRco8DLlf+vssJrSdXXjSwzqN5xxl8XlzJhAC79qVObv/Mn5Nq5GcmKn38gpBWnIm1g6WhTnYaqN3vJFnVxh7fDONEr+MvXBG3l/LSpb0ndidmIhk9q44gkqjJDcrl/0ryxd98iieZv5bBRolL1eL/+Ugw/8lcA79n8LznDc9BHBM0scdrBVCrANchRDRAEKIaEkyovwygiRJo4BRAOaUEgb3HP8K3L14n7sX7/Pqe50ZOus1zv18uVj9lCyRTpi4j4PkgiQriBahuCuqYItjmW14+LoV8eBkiXRssDeh6lZIChxxJVUkYi/pF4GKYogxqjf0YVT9yeW+Rvkx8saG15jIuutfk5ORw+Vj102+s5Js8caPKILxa1GV/DvZ1EhvhEIq20tVbvwLJlttvo66L9Yq8fs2/VoyeHpfPu/7tSEXyBgnfzpDk04NqP9ybSxsLNCYqUlPzmDz7F3F1PYczxKW1pbkZOcgaRXIQlskvDeXbOrUqktSQjhylBoUMtr8fGKcHqCRNMjoCI0PphJeVJIqkylSuSUuUIsmxBGJg+RCkhyHg6ISdpIzWSINN4VPkX5IkoSMDgVKXCUvEkQ0Nu1l8jK1BP0ViOc1X7SSPiRRCMH1xEtUOVqLPECDBY+mNZhjSWWqkkIiFgpTw0staVA6W/N2rYnliixQoSKPouWySMeK4r332jytwevWpFMDrhy/wStDXuJQ1k9IComU2FSu/HaTxSNXG7wof+67yI8BK/i442ziwhOK1DmoynuGz4Nn9Kf9wDYsGbmatdcXkZeTT2Jk+bzZGjMNyXGpRAfH80nnol7Hh9iz/jR71p8Go1AyUaCLeWjjaYZ8/KrBeHsUfx66yvWz91h2dCrrZu0sNNyeEYz1Og3GmdEm1pgW0xnwQXd+uLWI91vPJPBqCG36NKeStyN7lx0xqSs+PIFKXs68MqgNXw5cxoPrpptPZ/b+BUKmScd6zNoxiaSYFDyquZIUk4Kjmz0xoXH41PEi6EYo9y6WzGYsZWjIOA0+Neow5PN+SBIsfW+dCQOySq1i+vYP+NF/GR6+bkUlYoqBxlzD/F+nIYTg52+PYmljwfyjM8jPzUehUGBuZcaysRu4dzGQhcdn0LCdXr/w3IFLZdZdEiLvR5OdkYOtsw1pj+QMRj+IpbNmEMfytj52/Y8i7F4UPnU9iQqKxaGSHbFhhc/LrT8DaNi2Ntd/v/tU2vKp7UGPt15mxzdHqN7AizGLh/HrxlMEXQ95KvU/x3OUhqdlvL0ohIgqMNCOS5J0r7wnFhh660Cv82b44n8QE/xYeIZ9MQjl/q92XJ4AB9ccQ6lS0uPdjvg1rcaR74yEdYUgXNzHTyrchXfBgwD5GjZKB5PwQcM5RtfcYWAbAL67s9SgRaNDoChmuCpRojPysj3MJXl4LzfeXUrUg9hya8482hejg4Wfi+n/6r/nU6WOJ/OOTCv2RVrNrxpvdutH95EdmdL5K5IySs5R+U9CUvDrxtP41Pdm1cW53LkQwKqJPxi+nrBqBM6eTrzffBqyrvjQnqSYFE5s/sPw/4ztH1Q4N+g/jYfj6hnMMRWdW8wyrYmziMBO64xK0pAhUg0MeLKQSXGIYsbYJbzQoylDW42BfJg8dhIJEYkcWnucAHEDH2riUpCzZiFZYSecuSUuICNTVVkbH2oSJYcQQyhaqxzUmWa4K31M+pEncskkjWw5iyRiUKPh6qk41hxcSrt27XnVtpA1L0UkkEU6odxFgwVpJJEv5+OlKPSAJRNHFakmKSIRWehMNlCEEATdCMa3nOHMCkmBQqhIEvE4SnrvlFZoCcGf+rQq/WRJwZUTt0BScGLLGU5sOQPovSL9JvbgSM42ts7dw8YZ25k/dAU7otbz2uSefDvph1I96B0GtmF865lkpWUxuOo4U0rwUtju1l5ZSH6eli/fXIakkJCsCw1b4VQozC6MWfyMIhkkc70hN/ijnnR1HlWYi2Q83hQS7fu34PXxnZk7Yi2B18NMPGkmXhyphF1x4421MrS3Sh7rhe1IColdSw7x4HoIn++chIW1OZIkYWVnSdi9aJp3rs9L/Vpi52xDVloOs99cVqZcwJXfbjHE11RPtMvwtgyc0psRDT4iJjjeSE9PLtrXgmuJDIhkwbAVxbah1crMfm0xAJ9uHseumPUEXH5AQnQy5/b/haRQIOtkOg5qg1etytg6WpMan8bGmTtMNhePfH8KgJHzB6Ex19BrTGe+2P8xOVl5vGoz1MT4rRCMxtqikav5at9HWNlbYetoTWZaFvOHreTuxQc4eTgghEClUho2KwTlj7p4NG+9bitf9q06Ro932pOdka0fLwVj6eLR67z9+Wt6460EyQmTHDqjMsYeOYTAztkGaysNybGp5OXk4VvfmzVTt+svvQRGTdOO/3/0XvsXQs82+ZywBCFEVMHfOEmS9gEtgFhJktwLvG7uQFyplTzHfw46rY6fVx+jbuuajFo4hKAboZzcepYMXTo2kqmRJkkSripPBszrQqvmLxT1FBm9lLXWOQzsMRSFSqL3vLa82OwlkCTGvDsGESRMQriU3lpWb1iFWq3BubIjNZv76vWiCspozNRozFQs+q2olpWlrSVmFnoxS2O4eDoWW740aLU6Iu5HM7v/IpPjGnMNKy/OxbmyA3uXHebAqqOGfIg1VxYwd/Bywu6WngT/KOycbXCv7kpMcBwpcWkVOvdZwtLGnM7D2hJ4NZjGHfS01gqFgkUnZ3H52HWWv18yCU1x+OrNb3hvyXDUZupyhbw+x9ODQlLgkOXOXXEZO5wJxR8hwAxzBAILocbZ156k+BTatn2JE1vO8MPM7Ybzs8mghtTQpE61pEEjzLGQrHCS3ACoLOnz3qJ5QJIyGithi63kiCx0RMiBWEt2PJBvYYMjtRRNDXVNePVT1v66jOO6HeTl5DGm2adEpt+lRlhDbBR6uQwhBPfE3yTL8VhLdoSJAKwkG1SSGle8CMUfH1HbMJ9kuMYz8rURXNl2hzSjHMDSUJNGBHGHKBGMAgU6dNSikUmEQEVgnPN57MffDZ//2HWePuO7kxiVzI6vDxY5r90brWndqzlIEnVa1eDyseslajn51PNCoZAIuhHG8vNzUKmVSAoJR1d7vGtVJuJ+DNmPubYMD4zl299nMvONFSTGpGBpa84X2yaQHJeKp587UUFxTOo09/GNgmcEY4PL1ceFTfeW8vmuD1BrVLzb+GPC7kY9Udj5ic1/4OnnzsffjUEIgZ2zLRpzNcP9JpR9chlYMHwVds422DpZU6WuFy/1a4m9ix3xkYkcXHOcu+cDWHlxLu+3nFbs+ZW8nGjcvl6J3z8prp26zfjWMwB93rl3HU8+3/MR2Rm5VK3nxdb5+59arp6FtT7Pzc7FhrRE/TPc/vUXqN7AG51WJuhGGK8MfJHfiiMRKSfcq7owdHpfbvxxjyunbjPk015smf/0c+me49lC97hscf8SPLHxJkmSFaAQQqQXfO4MfAH8DAwH5hf8PfCkbT3HvxO3z/lz+5w/NZpUZdTXQ7H2MmP6O1/CI+sfnVZm3Sdb2KM4XqSOhztUcSKSHLLwpBoSCjYe3MUSsRJ3yYcEkUYEUbhRBQmJeBGJc7g707rNN6kDyhc73nZAK/yaVS+i/yaEQCAjBEQRRC45gIwN9lTCE0lR/MJs4up3mbJpHNb2VqTEp+HXtBoKhYKl762nedeGNGxbh1M7zrEnbgMZKZl4VHejdssaFTbeFh6fiUKpwM3Hhb6Ob1O7ZY0Knf80oVAo6DzsZbqN7IhOqyMtIR0hICU+jUUnZ6FSq8jP07J17r7Hqv/QmuN0fac9B1cfK7vwczxV2CocsJbtsMAKe8kJy4JQwERiiE0O4512Y8lX5CILgQ+1ihAv69CiwjR/VEYuQtMPoM6wQhYJZCpTiZMjkJCopPAkRg5DgyVVpdom5b2oweThU7kSfQ6NuYYNNxfjY+FHZUWhzqEkSVSnHlfFGVyFJ7nkUI06AFhLduiEFn9xFTsnG3xqe5N93ZzKDl5MjZtMRnIm79SeRGqCfnPEwtqcA6mbAD3Zy96lv3Bg1a8FbdR9ovv8KB7OXZH3ow3H/C/dR8hdcPZyNhxzreLCK0Nf4q3Zb3DvUiBhdyOwsDIjJysXa3vLIrlMHQa1oc/4bmSlZlHJ25n05Ay0uflMfFG/sB46awADp/SmWecGLHj/h8fq++j2c+nUrymfbR5LiH8Und9szdx31hJ6L5K46FRysvJMdLL+TegxsgPjlr2NQqng9M5zzB2yksOZm1lzeQEfdpjNvb8enwlXq5XZMHWb4X9bR2sWn6zYBmFpSE1IJzUhnXD/aM7uNQ1FFbIgLTEDv6bVimVQnfrTBJaP+/6p9aU0aAsMqGE1JjB92yQi70fzw6ydRfLYOg15iRd7NSUyMIb107aXUJspqtX3JqRA91Sp1DNPCiGo2bQqa6ZspUmHetRsVg1La/MyaiqjnXrexIbqwzGbd6rP6inbDIzoz/HfgOB5zhuAK7CvYPdSBWwVQvwqSdIlYKckSSOAMODxBW7+7QLXFdyRK0vo8x9JWn0KuH8lmNSEdNr0bUFybjx2opAxSwhBgojCT2FK0pErspFQoEaDLMvEEYGt5EAScTjhiqNUiUyRSrQIxVXyJFUkES7u44YX1aiDSlGYPFxcQrX+Y/F0xkmxqfq4fqOxEysiSCMJFWpSSMSPBnoxXiBRxBLOA7yFb7HtLBuz3uTaXhnyEm1ff4EJq0YQF56AfSU7arWogcZCTWJUMkG3QrkSeYFs33hy0nNJiU/DU+eLWipdk2bl+O9p3LE+8eEJtOjRlJHzBhEfnlh84fI8F6WMX6+a7szY/gFmlmZkp2fj27gqd84HkJ+nJT0xHRdvZ0PYyxcDVhAdXNTBvvj05+XvyyOICIii64gOFT7vP4mH96ek3+MpzHHlEWE2fkbMJSuUCiVWQv8MZItM0kmmrqKloUwmaYRznypSzcLzhAXh4j5VpTqGY6lyAhrMyKIw9yVXZBMi3ytgpbQmSRFNFV1dLBU2RMkPSJeTsJUci4RaKyQFZnYqalo0ID9HH96kQy6S46ZCjaXaigGdBvL6V90Y3Wc8luEuqCR1QY61oG+7Nxgx9w2mfzyLdT+tZfUPq6jeyIfFF+Zw748HeNWqjHNlR/Jy8tGYqZnwwnR+vL+CcStGIMsyXVRl5xuB3js2fdsHxEck4uLpxJ5vDrHGSJMN4LPdH2FfqSBM0eiaB814DVknc+XYdRAyL7/Wipk7J5OZlkV0cByJkcl8/fZq3vy0D9/8PpsH10LIzc1Dm6vFxdOJ7IwcAq+F8HHHL/R0+UZ4KIK8Ze5+Dm2/yNzt4xFVKxfe6+jCuUUyJgwxovzvPqgVGSlZ/PHzFY7vuczxPZdZtH8SR346y5nD+jA9Aw17SaGSJqGDxYe1lRmGVsF0C2Ek2D1u+Tv8sfs884atKqhKoofNMEbMGcjiE7PQ5mvJz9Uyo+8i7l0IKKzEuM1ytD996wTsXWzZ9OWe4jv1BB4+EykFw73Vcf9qEK8MeamI8ebbpCrVG/rQvGtDuo/swLmfL/HmlD7UbuXHbz+dYfGINaWGrpuuVYqXQ3rkBMPHA98eY/K6UTh7OpEYnULj9nUZt2w4malZhPtHEXAlmFbdGusLG/3uJiGMBeLsLbs1olbz6mxdoPeAhdyNxL26K25VXDj/yxUArp66TUZKJkE3w0zkJ0xCcY1DJU3Gmv64mYUGv8ZVSI5NJTY4jiNGbMsGEhSj58L4eSlTFkpfqPjjz/Ecj+CJjTchRBDQsJjjiUDHJ63/Of5biAtLwK1qJXzUtQjIvYY9zihQkizicFdUMSzCMkU6EXIgFpI1Mjpy5CxkZJxwxQl3MknFX1ylOvVxljxIIhZryR5ryR4H2ZkEoh87NOkhbv5xF3NLM7ZHrMXWyYZjR4/xxbCvcU1rQIZIxRxLg+EG4CS5kiIS0AktSqnsR8c4lwUgR2SRaB2Jg4sdkUEx5JJFVakuVpILFoC1yEVXJ4VamuY8uB5acr/P3uPm2cK00uBbYXywpig995PAvpIt391aQnpyBnfOB/DDzB34NqnGhYN/I8syXjXdUapVhNwKx9xSw4wdH/LugsF8YSTYa26pYfr2D7C2s2T5n18RcjuMvw5fwcHNnuTYVO6c8ycnK48X+7bg+I+nS+xLblYu5pZmhTTnz/E/gzPuBOiuYi3ZoZRUxBKGJ74mZawkW6LlEBNSk+rU4w6XuSNfwgpbsskkjxzq0YoYQonWheCmqEKwrNeHe/g8yVodYY630cpanFO9sJUcUaIiV+RgZsREmStyiA6OpYa2ESj0mm7XxNkiz2Y0oYx+bzRtW7/MwfmnUIfZEIa/fsNB0lBdqk+rLk0Z9+4kMv+QsCrg1YqKyGCQ/zBGvTmGJSMLF7CHMn9i4YmZ9HEYDujFtzsMeomTWwuf8+Lw2e6PaNSxHgCDvN/j5ddaMW7lSPp/0JNp3eegy9cx79cZnN55jouHrzB/2EqT89+pNRGVSsmehO8RQr8Z9vErX3Dt1G1Ar8W19upCPHzduPXnPaICY6jZvDpfT1xjILooj+GelphBXl7FQpSHf9ITF3c7VBolXQa/yNcTNvP+3NdRqZT8vP50her6J5GXnYuFTVHR9+9m7OC7GXoCloVHp7H01CxGNviYCCPPaEXgVsWFnUsOcWbP4wmIVwQqjYrpmyfh5OHA9zOKerACrwRzcM0xdPk6Tu88x6vvdWbX4kOcO3CJnu91Yl/i9/x9/LrJvP60cPv8fc7su8S0zeOwdrAiJS6VBW+vJuCynqVz+raJ+ry1MtCkfV0cXe348YtCY9itigtHfvgdpVLJK4Nac/OsP7IsCLhSumB4aWjaqT6dh7fj7L6/8KzhzpWTtwzG43P81/A85+2fxb9FKsDk6ydwxRZDWFBSff9mj9z2+fsZPGEAW+epyCAVGRk/qSESD71wMuFyAH6KxkZaNsGYCUscFfrFk4ZKWGNHOPexxcEQsgVgqbAlTw6FUqQHjL1tpWFk/cmG+53jl4RNqjNIem+CDfZFyltgTS45WFIxjTad0BIi7lEjoyHKTCU1FJWIl6NIJcHATKeWzEi5m0fjubWYuPpddiw8wJ/7i2f6cnSzZ/KG0SwfuwHvWh54165cbLmydoKFEOSRjQq1YdFr52zDwmMzyUzNQqeVcXC1p9+kHqyZvMlwXri/fuHSundzhs7sz6bZuwyJ/A/H7KJTn/PTV3u4/vsdRs4bRMitMKKD4rh9LgBHN3teeq0VdVvXwsnDAY9qrvz4WfEMdZa2lv9/GG4V2XE3EcctYce7rPpKENg1rkMhK6hKHYLFHSQhkU4q3kYeNqPGTP5TKcyoL1qTJOKIJgQFCqywIYjbeFGDTNK4q/sbK2xRKgpfQwpJiUWSPTlk4aCsRIqcgAseBMu38cQXa8mOdJFCkHSLOvktDM2qJDWVRTVuiHNUoRaWWBErIkgnhXOrbnJu1U0ALBQ2VKMeMSKMdJFCilMU3+9Zy/3fQ/FW+Bn6oZY0xAVksu6LjSY6dT2tBnNc3sVPIasZ7DOGL19fzMydkwm8GlRq+PPepb/wYt8WDKk6FoA/dl/g7N6/OKrdQVJMCiPmDebm2bvMG1I8QQWSAq1O0NvhbVQalV4SBWH4rT7rtwjfRj7M2j2Zz/stIjUhHTtnG77+bRYTX5xBTlaeITTNvVolYkMTkQt+c0ldeP+FiwPmtpZo1EryHhJI2Bnp9KUWek2btK3Fe7P7kxyXxpQBywHoMvAFVh2fgmMlO6a/vpzgq4WeHoPn7WmROFSg/KPj+1j+doMHMjc7j/ycfMwszdi3/LDeG/iImPRDfNJlLuNXvMW6qwvobf8WeXk6TAhQ1MZLKiMB8IIy7309lPw8LX/uvQhCfuz3uHGYoXGbCkeHwmtOS+fz3ZMIvBLMyg82kRidUux6YsPUQrbHa6duG/r064bfuHX2HuuvL6JKHU9CC0IS9e2X3r+S5hOT9oXMxoI8WVOyGgUv9W+Js7sDH7T7vGjlRr9N45drU++FGmz6cq/JeFApJeScXEKuB/OXnTnDZ/Rl48ydBimJ8q7T/JpWw8xSg0tlB6rW96bBS7W4e96f7fP3FehEGoWFK3QFdRf2T5jMwRWTv/mvRmD9VyD//57z9hzP8SiSYlKwtLVAkqRiDaBkEnCWPExCobJIx02qYlJOLZmRI2eRQiIOuGCGudFC6ulPZvFhCXhJeikDa+xJIQFr7EzKZJOOG54VrjuWCDwV1VGZ+tc6AAAgAElEQVQavdBdFB7cl2+YrHvNhTWrpn7HfrPf2HjnGz7bPZne9m+ZUJd71XTn+ztLyc3O482pfanTqgYrJ2zkzSm9+XXjqXKTmKSIBOKIwgIr8skDAT7UYvm5r5jV52vDy9rS1pIZ2ycVW0fzro04tun3Igxs7d9szf0rQZw/+DeVvJ3JSM7kl3UnDPptybEpPLgewvVTt+n1fhda925u8mLNy84j6EYoVet7c/nXq+W6nud4NjCTLPCVGgCQKpKIJQx3fAzfa0XxnhpJksgiDU+qG6Q8dELLfXEdP6kxaklN1qOJsYAKMyT0O+7ekh8P5FtYS/bEEs4D+Ra+datRLcsXdahpeLGb5EXrIY1wcnPi/t371Eh7kfCzRan1o0QIGsyooWgAyZB8VEsaSeiEzsSbb4Y5eeQVEbDppBjAcXkXfk2rMXOnXoLku9tL6aQYQLUGVQi6UdRrfvPsXVLiUll4fBbvNfkYB1d7WvZswr2/7vPJD+M4u+8v9i37pdj7+Cgekjs8ugDNTM3CytYSBzd7UhPSad2rGVXredNzdGd2f3OIvuO74uhqT3JcKi5ezmRn5OBRrRILR20wPHuePs5cuxDE3t8/JSw4gblTdxF5L6ZIHz78ZjBW1ubMH7uRoNuFRuvRbec5uvkMfo196DH8JQZP7oY2X8e+b48/EeX800S/Sd25dfYuH7afjY2jNelJGfR5vyv7V5VPm2vF+B94uV9LPlg/mgXDv61Q243a1WF0k08ep9uPBWcPB2b0+rpcZRt3rMcLrzajda9mpCdlYG5phqWdJTmZubTo1sjEeHvWGP7Zayx4u/R7qzFX06JbI9Z+Upizrtao6DK8HWoz/dLWq6YHUQ9ieXA9lOoNqxBYjKxRSXCu7EifcV0IvROBrZMVoGDhW6u4evLW41zSczzHU8V/z3grg7L9WeOJPGuFlZT/+3Lsjv8bd2XsXGxRKBUmoqkPoRNa1JIpkYGEAhkdSqMhGS1CMcPCIGYdSRDmwhI1Zgba8qeCgvstZ0vkkIW5ZImVZEOMCCNFJGAv6fO6YonAHGsUCqO+l3M85pCJm/AuopeleCRJJ4lY3PBGm6dlVKOPGTlvEOtvLuanL3cbaJ2VahWyLDiz5wKrxn9P826NmbBqJDHBsTRoWwdLGwtUGhWbv9jNxYJ4/0f7lydySCCGGlJDA9telsjAuZM5HtXdTF7U2jwttVuahso9xMpxG3jvp4H4xDtwZttFKgkv3p05jGZdG/Fh289AyMSFxnHh0GUGz+jPps93mpwfcjuc5WM3FKnX0sYCr1qV+W3LGeIjSsjn+/8BTzm6oOQclbLnGQB7nEjVJRAq/HHCjVxlNgmaCKQsFZHiAW5UMXhwhRDkimy8FIWEOkpJhRtVSCAaF9xJtY1DpJkyyFr4SVgIewjSl/dR1CJUDiBFxFNFqonlHTcCxQ2sC0S/HyKZeLr1GkTf1/ry68bTdHmrLZIk0dvhbRxc7fh872Sq1PakfYMuqO84mPTJW9Qknkjc8DaprzqFOXuP4vVPepOTmcuNM3eIC4mnTd8WvDqmC7f+vEfg1WCCb4TS+JUGjJw7mOiQWK6euo2dsw1zj0znj10X+Pv4DQ6vP0nXd9qh08pkpGSW651W3DtIUkgsOD6T3d8cJOyO3pg68v0pLp+4xdhvhrN7Sm/WfPwT+1bqiX+kAo22IZ/24ot9kzGzNMPWwYqIyBRCg+LZvPQ4fYa/yOdfv8GoDvMKGxKCyUuHEhuewJZvjuqPKRSQU+gZF3l5+J/3x/+8P0KrxdxSw8SV79BvbCeEEJhbmhFwJZgVE34o81qhlPdbcfeqhOfloadKqVQyeFo/3qql34x6qD22b8URFEb5YkJrJNZcjMNE1snc+KOoXpgxGYtkJOz8sK+nd11g+bmvmNJlLtkZOcVGiBQnGwCgtC6M9pCcnQyftW6FG6R5NoVtWmsFlk62dB3fg2O79CQmcnzhXGqcOzZx1Ts4ezgSciect2t9UCQvUn8JFVj7lBAZIOSyyWrqtalBZV837l9+UHyBAl3Htz57rdBTWtCmm48bjTvUxdLWAnNLNX3e78z/Y++sA6Sq+jf+OXd6NmZnu3uXki4VWxEbuwMxEQsbW7HQn4hiCxggBgaKIhImIKJ0w7LdvTsbsxP3/P6Y3Ql2l3jltd59/tq9c+65555b51vP43arFO0spWCrwzuubkoqvdfp7Ilj0Jv0rPrydw47shezHvyoS8bjvaUKYK/r7pdWKbpZy3V1/I7z6cF/B1KCu4ewpAc96IyGqu6jP+Eimlx1K2GaKO+2aBLJlzvIEJ66EJd0YpN1AQQnKfRip1iHwW0OIEc4VEgUGeyWG4kRiYSoYUSb4tjTup0gWY4AwonBIiLIk9txSxcgMWAkkYyARWRXCCOKGsqJIt67TUpJm7YFl9uJgoYaXQnCKTAIT91Fa5OdGbfMZuWC37jttetI7pvIrPveJ39LEWN0F+OWLkrJY+cXm1i8diGWpiho0IBQOOLMoTz++d00VDey4OXFZA5KJTzOSnhsGFdm3UoFRSSKTI8IsnRTSyUSldLCVpa892Pg2KNCCA4LYtDx/bw1NuBhTNMNa+PJcdMJa4siVqRQIHcSnK3njqMfwuVSEUIw8vQhDBk9IEDDbX9osbUGiNL24O+DFE0vWt3NVMlS6lyVZLkGYFKCaZY2dsuNZNIfbbtQt2YvJw2AmRAaqQUExoZQdrORWCUZIRXsEXUElVqJSo6kMqUEW24bDbKGVNGbDNGPKkrJYxuRIp4SYw5x9jQ0Qkuz0kjiseGMPXcssx/4gA+eWcDz177GQx9NYtoPj5AxMBWAa/rdQeG2EjIUa8CYgkQoeXIbVqKQSEplHqFYKWR3e2RRIBCkkI22/ZyOOe8Iplz0PEU7yzh/0hmEhAdz78lT0Om1jL7qOM56/RS2r97FDUPuprq4hrE3n8aZE0ZTuK2E0MgQxj1+EbXl9ej0Gqbf8OYfvi4FW4s4f9IZjL1pDHOf+Iwda3IIiQihrryeiYc/QGWxR4Yga0gaI88chupW0ek0vHjvR7S1OmhqaMUd5zEKNPXNzH/rB56b5xEAT0yP4sRzh9NrUDKRsRaev32Ox2g7ANhbHEwd/7pvgyr5uOjVAzbeDhVCwoM56pzhHrKlA5SC6A5anYZvZn1/0Pt9+NxCCneUcOvL45k67uCidgcLu93FlRfMYMarV3mNtw4oisLhZw+ntcnOmCuPJbFXHBNHTAb+GmewoijeWuoXfnyM+09/qtu2Or2W65+9nKVzfvKyPnZg0PGHUbC9mF7DMnhi4X3MmDiLoDAz1mgL+Vu7jxwOOq4fA47ti96kx+1S2fX7HhKy4giLsvD63XMP2Xn24O+Df3rNm+gqMvJXIVSEy5HK6P9s54OJyHXn0RDd1I500+ZPwYF4a/6GuPqJi3nn4a5rmABPvZesJlpJQsVNuVqIWYbQRD16YcQm60kikwglNmC/el0lIeYQ6urrqJPVKCgIIUgkE70weNsdLKueb5ubGipoppHH357M+7d8hb25zXvdd6rriSaROlGJlBIFBYkkXdk3XbiUkhy5iQgRh5UonDgolLtIjk5h8Dl9aGlu5Zgjj2HkyJHcNHxyl32ce/vp9BqWztOXz0CVbnaLTVjVKO9C2KFrIdqZTKQSB3jq4ia9eQO9R2R65A/cKuFxVjav2MED9z5I0y+SFmyUynyiRSIKCuWafCLdCV4drg489NEkhowewHmR1xASHsT1z11Bq87Gw+OeIkb1RSuklGiGN2PcGUVqvySOOX8kq79ay/rlm/d7Pf7n8Z++t7rpo6v6t25raLtlWfPvInDfPe4tJPsZM+Ah5amgiFhSaKSWCllEPzEiwLlRpuZjpwUXDvQYsdPKCReP4oTRx/P2tQu8/bmkk51yPX3F8MD9ZQHJCSlcfMc5zJ//MXqzDq3DwNlnnkN63xRGnj7E27ahuhF7cxuh4cFeMooU0Ssg4gxQpZag4sYh2gBBNAkUyd0kkIZJeKQNHLKNPLbTSwzipunjGH3lcaxZtI5eIzJpa3Ew76nP+PHjVQAcf8lRZAxKZddvOfz0yeqAa9BBvvP62qk8cMYz1Fc1etOJu0OXtYzdfPOyhqbx0Ae3M2fKp9hqGvnt240MObE/fY/MRqfXUVtRz8KFW9FoBE6HG0eSz5DV51V5/nC6GH//WRx/zjCqyhuorWhg9bJtrF26kfvfuAaNTsMnLy9h1TftTJKtvrRuf/Y+f1a/jlogo0nHnB0vMHHUQ50W312im0hUl/fpXs/InD0zCAo1Ywo24narKIpAKIJTTVd0aq+YOpOV+J+Df6Tq8+pZCEVgq22irtKjE1pfZePLN5axdln7u64bpsFTxx9HQlYcM+/zk6jxf279a9gS4mi0V9DqbMDitmDUeO5Fd1KMt03tAF9ErinRTwO1netjxd3XsnNLCW888zXleyqIjLUw+cXLqC6oxFbXzOpFG1jjl6ERGHUMrEXzjqt9jP7nFdD2AN9VcRkxnPPAaJb/tJSKoipuuX8CA/oO5MKEG7rcxRpj4bIHzmX+819RUVDV6fdxj1/Eu494atuS+yRw1oSTmTvlU+q7cSYLITj3ttNwtDn59u3vGX3FMZ7U582FrFm8sWsW5/1EdwPYJv0Fux3dzOuBoCNi+CetAZepH6+VUg77Uw72FyCyT6Q8/d2xh6Sv90bO/kvmqsd480eP8XbIcPWUi3jnkY/32cYlndTIchQUwmUMqX2TWbZnIdgVLETipI1EJSNgn2pzMeffeTqfP7cEqz2mvR8HOXIz2WIQSnvNyn9qvHXM99VPXMx5t5/BGcGX094hNllPpSxGEQrJZKMRWmyyjj1yC/0YgU4xdO7Pv2spqaaMRlnbnj6WjFH4Kmpmbn7eQ6CyD0z74VHuOO5RhlyTxdezl+BQHSSThRAKDtlGjn4DJ6SeTvnuao45/3DcLjePfnoXLbZWFs/+zuPpN+j45edfWfHzChqddWSLQQEL2d3qRtJFv06Mmq+vm8ry939m9BXH8sbd7/H5kk+JV1LRicDzross4alpT/DjB6vZ/NO2bsWCe7AX/iLjrVltpMS9B6U9dTlcxhAuYjq123vfHNdmMpTDOrXbqK7CIsKxEk2TrKeKEnozFIMwUSPLqKCYcKKIJpEaynHjwhiqZ8KUa/lw0jfeflSpks920kWgY8QlHZiHQExrKmNvGsPnMxZ7NdFOuuIY7n1nIhOG30dOO7NcaHgw4fFWXl87FY1Ww3DlRGpkOSmiFxq0NOvqiTvWwhVnXM2rkzzU/Q7ZRpnM7RThL5eFBBFKiPCkqvU/ug+jzhnB9x+sYOdvndO8nlr0AA+PnYrL6aL3yGyGnDSAiHgrjdWNqFISHhPG6q/XsenHbQF1rfuc+/0Yb+/tnM7CN5cx//mvvG2Ou/AIKotr2f6rJ5qtJPkIjroz3l748g4mnTUN/OnnWz0WgdGs58Yp55PaJ4EPpi9m9QK/yM5+jDfpdjPxhStZ880Gfluyqdtz9uI/NN5MwSa+qHubtcs2cf8ZU7ve7z803joW5ZaoEAYc1w+9SU/GYUkccdpg9CY9z133Bhv8HFb+x3xz/VRuPfKBQKHyLow3l3SyR78bizGWIF04tS0FCJdKqnnAARtvFx8xkOHuUL6c+wvj7xyDQa9FKII5079l45c+tkv/dM8/03g77bFRzH3hYyx1MQgUanVlNDubSNX07tQ8PM7KpZPPZubkeR6HahcYdupAduZtp3R7BcGEERRqZtzjF7Lqi9/Z8IMnayR9YArhMRYGHNsPvUHHis/XEJMSSVr/ZBbNXE7pnopO5xuAHuPtH4+IPlHytHcOjfE29/BZf8lc/XvSJg8i/727h/JgmNoOSe0b+/8A/V3r3PY3lu58Av5ttYqOGJEEQPbQDO7/6GZ+6PsN6YqHHGGXugGHtKNvpwhvk63UNdXw+ayvsNqTfP0IPfGkU7lXzcr+jt8dtHotR59/BG/c5WNXRKq0ShsO2ugjhno3hwgriTKTSkpIkKm+9l3cP0IIoognSsR3+u36Zy9n88+dayi6wiX3nc2qvO9pcjeSKQZ4DS+9MJDi6MOAK9KY88Ar3va1FfWM73sHzfXNAf20qDaMBNFGCwZp9vYTKeKpp5oIPNG35L4JXHTXWSgahWMvOJK7R0+hoaoRAyZaZBOWvYy3msoapl46w9vfeZPOwBpjIX9r0UGlTv4p+Dvp7exP5+1Q9O1HmINGQ5tspdiVQ5Z2EIrw1KiWyD3UuioJb6fN93UR+Ox4hOxVhN94a9RyoogjXqQBYBHhRMo4dsr1BMlQwghHj4FgLOSwmVhS0AodFY1FvPHqG6xx/8JozUWcMv54jGYDz7+yrRM3USvN9M/uT6+ow5h53/ucdt1JwGAWzPiGZXN+4sK7zmLC81dy5/GPAdBY20RjbRNvP/Qh1zx1KanRaRgqDBTJ3ai4iTXHM33GdJqcDdgslbjqJFp0GDpRlXjYZgec2ouKVTYURTD05IG8/cAHtLXuVSPUPicLXv6Gqx6/CNWtUlNax6cvfBXQVghBv1G9uPT+c3C2ufjqjaXUlte3d7Hv79HewsYduKrPJJ7/7mE2/7ydHWs8BuXKL9Yy8aXxmCzBrP9hG9LkM8hcQb5lQMsRHqPOsrEWh0tFWkJoTfXVF5u3eoxkuxum3/E+eqOWJz+8hcSsWD59bXl7h351Ta1dGKRCULijlBueuZScTUXeqMgBLWj9WRZFZyPMv76qtamVS1Nv4o31zx5QjVFAvZrJJ0vRodUljL73XIdB11DbzM/zPZHV5cDM5xZz2Ig0Jr16LdHxYcx66CM+m/EtuD3XPGNQKi6HG3urC4TivcYi2CdcLwye4xQ1rSXNOgKj1mOcWYyxlNm2U+soJbzA936KavIzvg/z1cJVD1AY1TeF1XuK+G6Ai++Wfo11l28/S4rvOyTK/KJYrX73naNz7Zvn/Pf9HQ0wWroxllXVxStT3iTN1c9bCx7pSqCZndjdLRg1vucva2gGp1x9PG/cPQdHa9eGW41azgeLNhEuonFKByUyl9TGPrw66V2OPGsYVz16IZGJ4RRuK2LM1ScwccRk2lodnHHDaEpzylk+b16X/XY+uX1zEgjZjUHbXf1bNwZyD/676GGb7EEP9oLyH2ifRCVFMO3hlwlvifcK7aaLfhTInThkGxIVozCTLvqRX7wDq5IUsH8wFqopPRTD586ZE9j803YWvr4kYHso4dTROc0nXMSQJ7cBnmgByE6iwvtD5uA0Hh47lUpZTKOsbZdVkCSKTG8NHHh0mH769FfW520GRKdauyARysolq5lwUzONdU38vmQjMybO6nQ8Vbpx40allRrKaZY2IoknXETjwknWYekMGzKCrMFp6AxaPnnhK/K3FnPkWcNIyIyloaqRGCWBsvA9BNeGeVn6GmQNOlUfMK7wOCtup4uWxv1r9vTgz0Wpcw9pmr7e+1UIQQIZNKWXcsUV57enmSlIVcVW14zRbEBn1OFsc7FlRzaLFy4mzpGGEAIpJcXsYYA4IuAYBmHCLEM8UTqpUi+rKaWAbDHYe5+ki37k7NlETU0N1029jPXLt5C3uZATzzmGTZ/nECo9LLCqdFMq8giLOpoZM18kPNbKvE/nkm05jAkvjOP9Jz/j+oF3cdP0cby4cgqPXzCNmlJPrVf6gBSEEGi0CiYRRBoe776slzz91FP8vnAT+rpgpKkNXYYLQ6OAosD5sulrGX3aaAY90R+9UcfUK1/ubLj5Yc03G1jzzYZuf5dSsmXFDras2IEp2Mgl953Nxh+3UbK7jLQBKeRvLaK8C+H7/WHh60sJCfdR/DsdLl66/V0uvedMyguqKDtEvj+H3cXdZ7/APa9dzV0vXUHO5mJSMqOxRodSW9GI3dZCnxEZTL/5bfK3+VgpF765nPGPXUBYVEi3KW2HAtUltYRYgwm2mGhq+HPeP1vW5HHN0U8wfEQyk169xmO8tcMUbOwy3a8ruHF7DbcOxAb3IqfyJ8K1sd3sFYhnlv3EV9dfwbw1G3HtQ2z7r4CdFkKUzsRj4cRQTw2x7c4Ta4yF0ZcfzYxbZnfbl1M6aNTXkOXyOH5RIEomkKNuopdmMKu+/J1VX/6OolE499bTqCuvx+10YQ41kT0snSX70BntQQ/+jvh3G28HGWE7mGjagdaFHMy++/P2/xXRtoOJ9nW0HX3lMaz6smta6H31ZzaYcAuf91MrdGSIw8hTtxFLirf2RO2CKqqeKkJFuLf/fTHmdcJe864zaFn+fmfRXaMw4ZKdF2o2WY+JIHLkVg+tgRC4VSdJZGJUDkwPTqNVyG/OQUFDZnvksYNWPYuB3hTG8HgrVUU1vLnwZS4490JkSyBTX5NsoH5DK9cNvAvVrXq9+HujgJ0kiSzvnCJgj7oZswymQlfImIST2bMxnzWL1tFY4yvuX7t0EzdNH8fwMQMxBhv5fPbXrKr+EQFIJAbMJO0l4vzWPXMOaA56sB8cCBPZgei/dUCVuKXLG9n2thWCirwa5j75OUiVOrWKYnUPQgGd1GMhknhtMkEWM/37HkarpZG8HflUldeg4vboj3UeGNYYCyddfgyLfhFsX7G7k+Mh1p3CxLPvpH6NA7fLzfEXH8kJlx7F1Z9eT6UsRiCw00yrbOX1l94AICjXikkxsKxwEUEfBDG/7E1m3vc+r97+DmPGHceHRa9zUfx1XHTv2ZxwyVEA3PPuLTxw+lNeyv06qvh1XjkR7jhPBMAejHOLg1LysIkW4mUaCgplFIJD8Oatczn8jKEcfuYwinb6OYz+oNe8tcnO7Ac/5NRrT2DCtKuYP20hZ004mdR+SUQmhBOXHkNNaS3XD76vSzZA70yrksxBqbx1X2AkQYRZmP/2Sq65/ywe3b3Bu5iPW+VLazZUed6tbosJqdXgtpjQ1/mOJYN80RD/6/fsbe9z0nnDCLYGsXTeSsrzq4hNjSI0PJgFb3zHA+/eyMcvLGLFgt9BSgYd2wdziIm8zYVdn0QX0ZrL7j+H8yedzu9LNjF1/Ou43J7tWr2WsKgQqkvqoAuBcafDxTVPX8qLE97yjFsTGH32wr/us4uIoX+EJLAPv+hdfYP371+/WkveTScz+e0beepyj+j6GdePZulcX/aBaE/VFDp/vbD2/kTn760qVdzWEFD8Ujz9xhq6o669C4UXH7kGl0vlslc/RLaoaABHiF9kMslnOJn8M3ArfMalfzSyS3SjSSv8U239UzLbpzClbyK52/Np1NfAXrdyC56MEAEce+ERZA5OY/aDH+xzfaQkOYksSsD/xaMRGgzChFM60AnPeFS3yicvfIVWp2H6iidYt3wzc6d8isPehdzJgZTidLU9INrWjSBeNzqCB7rGOpC2PegeElB72CZ70INA9D+6D0vfO7j0OGebk6EDh/C2fBcr0d5IgEs6ceLApPjSSiJFHHlyO8lkeZjmRAPVlHklBf4ovnhlMU8teoAzO+rd/BBBLOVqIbFKcvv4HJSRh4JCqujjXQi7cbNbbqCXHLJfJkqAFpudFm0j6W5fDZFGaEkkg0qKiSOVXsMzaW1sxWF3MPLkocx4bxp3XfogyY5eKKI9BU7mkGUbSHVT7T6P55augDkFSBSZbJe/E+tKZumSZV7iE3/Ym9uYdp1n4dzxIcnSDNjrQ/L38vD2oHsYhZlm2UiQCPVuU6WK2n4N69VqCtSdxCrJ7TVr5TRSi+IURNXE01JtozqkBAuRxOiNVDpK2CO3kSX6e/trUhtow875d53BFzO+pS6/DYfovDh24WDnylySQtK4ZPKF5G0uICzaQm/rAJrqW2hQa8hjG70ZSrASilu6yJGbCVdjCNdFsytvBx89+wUDj+9HQ42NTT9uY+2yTby35xUMJj0Tht2L0+5k5pZpfGP/gNHKBYDHeEt39Q1Y+OmEHoM0ESnjKWIPEpVI4ggVnhS1w47qzUs3vYXqPvT3+jczv6M0p4JL7z8HU7CRaTe8yWu/Pc25Uddw2f3n8tbGZ9FoNTjsDiwRIXz77o988uIi6vwcNdt+2cWoscNY+cXvgXPsdLNh5S4euOEENhaU8u2m3Qc8rsvGHcW5F47gvRlL+erDNZ1+X/ap51jS5nH21JTVQ/u7746Tn+aNXx4nMSOGoSceRkN1I+P6393lccKiQ3nx+0doqG5EqpJd6/Mo2lHKyNMGc0nqzVz+4LlM/+ERTMEG3C4VrV4LUhJsDUKr1dBY20RQqIm7TnqcW14aj06v5bXbuo/a/Dcx+bSn+SD/Fd5Y9wy2umaSsuOxxlrof3QfakrrUBUN6YclERweRFlBDWuWbmbL7wWoqopemGhqqyLY4GNlLmreQkRGf7pLMtEbtVxy3XGMPLoXc37YwKKV22mK/3stUuPSopm5+XkAbrxqIpvm5hKE5/3jkG20GBu46JyL6HdEL77/cCXff7hqnw5Yc6iJlCMSWVPUFSmW57ukN+o497bT0Rm06PRabHXN3HPylJ5skP9h/NPZJv/Vxlu3ETZNgKujy33/4+jYQdeh/jO9J/uKbNWV1xNsDTooOuac9Xlc8fD5zHjnBWa8NIPC9ZWoqDhlG6miT0Bbq4jGqJgocO9ESsmFN5zN+pmhSD9PSreselJSSyUNVGPETAxJaPbSWtv803ZaGlu9rHD+iBVJVMlSdqsbPUyXCJLIoJqKgAiGRmiIII4GWUUYkR2D6vb8VZc7QMC7A0GEUkkJ1z93OefedhpX95mE0azny9e+Ja1fFk+98hhz3plL/tZC7PUOMkX/TkQjvnNXqaGcBlnrEeXeCxq0hIlIzISisUhkQ+d7s9uP6D9Vk+bvPu5DpWvZse9e0bs4JYXKiFyolgQpFtqknTzXVq82W766g/6aw733lFUTRZ66jWpZQSQJFLGHrKZBXmdLpEhgq1zDbnUTofXQt/AAACAASURBVMJKi7Rho54MTT8++nYuo04+icqZ1UgVHLR5GWKlVCmXRWSLQYydeAqfvbiI+soGeo3I4rzbT2fd8i18tPIdUl19CVY8Cz2N0JLNIDbInwlyhpKU7lnkRidFcuvL1/DSzbP4/oOV5G8uZOzEUyjdXcac3FdorLFhtpi5+aXxjL35VG6+9la2zCpElS40aP3YMyVmEexNrzSaDZxz22l8NPULzKFmj+G2j2tiiQxhyOgBhIaH0NrUiinYQHislRWf/cru9fn7vFwbf9jKpp930Gt4Ove9ezNPX/Ey9hYnsx78iFkPfsRN067ku49W0mJr4+YXx/FB3sucHXGNh/REqqxcsIbLHzyPsvxq8rZ4cj+l1ZNGuXptIQvfa+bqMcPpExrBhfccSUpKJBqNQk5hNc+/vASt3Y1UQDVqSA4ycf6Vozj9Ag/NfkZGFDMX3oZGozD+SE9dYUC0wd9Z1b7d5XZTUVjNSZeO4uHzp1GS004KsVeELS4jhjfXTkXRKFyZfRsAJ1w6irETTubrmd9hb7Yzc7InoqhEhHc6plrjiTzFZcRw39s30nt4Jj9+shqHU/pIe/yJSbpJ8fev45QddV9uP8IO/z6C/P42+NX/lldiDjFSll/FHSc94d2c1iuGmJQoopOjMFhNfL/gd0qb3GT1jeeEK47hthdS+GX5NpZ+kcbyFZ8iWvZg0lqwOasJCYqjf3gqZ8waTVKoBYNGiyhtQwjYtm0Ln8z/lNdef4E3nrZgIppwwGryjclt8Y1VcfhF1XR+tYRmX3RV8Y+atflp+XVctwPIBhCKQKvXEhEXxv1zb6H38Ezef/IzTrh0FI9NeZSb8m9j+4YdaBQFa7SVcSOuIW9DIT98uKrz8ej8Dbry4Qt4Z8qHlKmFhGh89X9u6cKlcaKTei5/8Dy+fG0J1SX7dmoeLPab1eN/f/8RnoSual670ebswf8G/tXGWw/+GmxesYOhJ/Xnh49/OeB9akrruHnk/QDc9vIdBEUb0el0hIZ4UjuEgLT+yeRtLkRKSUFRASajieioGKSUrH1jyn7Xtk7VwRb5KybMJJGNFCpb5GqMBKFFgw4DCaRz+yvXozNou06lAKJEfIBeW4u0oUPfqZ0eIw4OjGmx14hMkvrFIzcGpkHa9LXcdM91nHzlcWxbvYsbnr0Ca6yFz6Z/zYybO7zJOmJIY1/1t6pU2S03Ekk86aIfO+U63NIVYOiVU0AUCZRTyH0T7mH+M4sOaOw9+OdCI7QMsh5J1tUxrFu1gYEDspl3zwwswWEYgvRkGDqzjsaLNLayBpusI1REBNR3CiGIIQmt9FB+WImiiByCpIW8H0tRij0LsjTRh3y5HQ1aFDxR4ySRiSIU6irrCQ4zU1/ZQEtDC2uXbkRv1CO0gjA1ImAsQgiMMohM0Z+clYU8vOpRhr12BBfePZbFs7+nSfWwXU555EnueexO3C43F8bfwFL3R4y9+VR+/nQ1P85aQxWlWInEiQNVuokijmB9KDghIt7KW5umYTAb2LMpn/FPXkpNWR0fPvM5lUWdF4MDj+vHEWcOpaKgmvXfbaauvB5TiAmNAiU55dw9eyINNTbWf7eFDd9v7VL8twM7f8ulPL+Snz9bgznUjCUqhLI9Fbx6x3veheO9Y57koQ9u44va2TTW2Ljt6Icp2V1G7uZCpiy4i6njXmPzih0B/apS0jcpmpOHZDPnle+Z/n8els8zLhnB1MfO44F7fUzBU98cx8wXvuXD2T9RvdVXBPhN4XQumHgS819Zts97rANrl2/hpEtGERTSmdXRGmPh8U/uIHtYOgAf/d9CtHotLoeL7z9cxfd+C/n9Idhi4rnFk4mIt2Kra+K1O9494H0PFRLSIjl+/JFodRqWzlsZ8FvupkJyN3nSRTXtBqiMjaSirJ4Vy7dhDTEyYHgaV95yMrc8fDYNjQ241BYKdjZx+LH9qK228eKOjawq8VyL7DnNlFStRyKJix6ETp9MGetRmitJCurMBvtn4bLJYzn3llO5KOEGvqieRUONjaKdpVwQfwNRiRGYQ838MP8XbKshwe1xFtEIP+b8uu+O90JzYwv2BgeRShz5hq2EusNx4aRJNHD7TbcTFRlNW6vjkBtuPfgHQ4p/fNrkv0YqYL+02PthCNp7+/7wv+7p2JfHSQjBba9ey/T2GgN//JF5+7/vHuH642+nnHxChBU3LlpkE2lKX/TCsM9oaZ2soowCEvFID5SQi50W4kkjmgSEELTIJorIYU3eSq4fcOc+qbt9J+th6NstN3bSjtqjbiGZTG++fcc9KKWkiBwcshWBAARDEkdy/euXcOelk4m0JaMVOpqUekp1e+hlHYC+wUx0ciQGk4GcDfkHPXflsgAzIYQKz2KhTbaSK7cRSSwmEUyZLECDgqLVYNGGc8+UO3jr3vc79ROQc3+wdMc9+O+juzpfTfcSGopG4bL7z2HOlE8BzzMaFh3K2U8dy73jH6KPCGRBbpXN7HCvQ4uGCBFPnEgJ+L1alqFFR5iIpEIWoceIVURhly3UUE6CSPe2dUknKqo3AieE4MK7z2LU2cM9moFahcljnsRW38JGdSUZ9PdG3jqwRV3NYcrhABTIHcSSwplXjeG33FVsW7WLaFciLpxUmooY1HsINRuaWKrO58EznmbF16sp1u8h1dHb++y2ymZas6tZteFnGqpsWCJDMJgN7F6Xy03D7gWhYAo2cvKVxxIW43EuabQapKoSmRDOT5/+yppF65FS7jNyeu5tp2GJDEFn0FFZVM2eDQVkDk4lxBrM7nW5xKRGE5MSRVxaFOkDU1EUQVRSJKV7yln4xjKOOmc4/Uf1xt7cRl1lA3Fp0Ujpcf401TdTVVzD7SdM4bLJZ/PBcwtpjfKl37UmhHD5RYeTl1/F719vDRjXfY+fzQmnDKCxvoW8PZX0H5zCmX0mo6oqtPki9k+8dwM6vQZrdChVJXV8NP0bNq3aHfhe8EsrlU4nh58+mHMmjGbJnBWERASxaNYPjHvkPM679VSev+Etrn3yYgxmPUU7S8kanMb0m2bxzds/+O7VKJ/xXj/KR1planITH2vllWcuITQ8iBUL1/OkvzC43ziE2c949BMal00+Nl5h8HPGdWTqBFDh+0dU/CJyYaFcctMJtDS1kbu5kM1rcjun1nZRT+dO9RGQNKX40tlNFZ5o16AhKfTLjOWTD3/B5VBxm3w1cmX9Bc3fLCU7+viAPvPylpOq7YNO75ce738OOh0tjga2lC9CK/So0kWQKYY+QSN9bfyut3T4/93ucOhmzZTYK4E3fn+a3etySR+QgsGk5+u3f2TGHZ7650kvXcVLE2fhcnrGo3bDark/aHQa7px5I79+tZZTrz2J1pYWvp6/mF4DMpl3/0LAEzFva3Xwh9a6B8Kf0OUAu6l588fBSGH442Czxg4C/3apAGvvaHnC7PMPSV+fjXqtRyqgB/8O6AyenPLu4JJOitiNKj3kBsEijBiS9lsbZg4zUEY+vRQfS51LOslVt5Kt6b7eTZUqlRTT128RGiwHs521xIhEX/8iGKuMYumiZeiN+gMz3uiQAEggR24innQ0aCingCAR2mVEroAdRBBHiOLRinJKB9WRhRw/+jhOPGoMX33zJa1qM8ZgHVGNyVTba6iSm0nZkY1JHBgBipQSG3U4cRJGBM3SRqziW2QbhIneDGGb/I0IYtGZNAwdNpShfYcTHRPNt+/8cEDH6cE/H13VbUWdaOSFiW+jlcaA9EaAfHU7WZqB2NUWCuVOYknypplJKamQRaTSmyJyaJQ1GISJWllOm7QTTWJAhNhf4Bug/zF9yN1UwNdvLKWpoYVgi4nzJp3Bu4/OJ52+7GEz/eQItELnYbeUOUTgq83sYKr78p1FVFBMisgG4UkJTmzNYt2O30iWvXnwrGd44qvJTJv6Ap/e5wh495hEELaaaj6ZtpCwaAuVBdVc/cQlHsOtHa1Ndr549Vv2Rgfr5oHgsxc9kW2hCOLSY0jIjOWHj3+hub6ZrGHp5G4pprKwM7vtCz88wuUPnMtH/7eQmrJ6jjv/cH5esAZTsInNK3eybvEGjhw7jMVv/4DQ61jw6lImTL2MPaWN/LB4Mw21nnfz3I9WM/G64zsZb888vIAXn1lEa7sOWZhUPYbbXnjwkpdJzIwho38SuN3c/sIVjB/58D7PuWB7Cb1HZLLt1xzOuXkME5711BWvWbyBgcf0QavTYDQbsNU189SVr3D/exO5/dVr2LZ6N5OOfzygr1OO6sPiFdtZNe8OVFUigbenfM5nry8/oPk/1LBGh3LWjaNZ/sU6inOrwNl9RPVgsWFdAZtXddYRBGirLsOqT+i03WJNwVZTRbg+qIu9wOV2srlsIYeFnUiQLswjE9KynU21SxkQ/p850UMjQ3hywT1kDknlusH3UbyrLEB2AUCn12JvcXgNtz8C1a3y2YuLuPiesbx59xzytngimkMG++R89i596EEP/g34xxlvB8QU2YWI6cEKSB6U9+LPqpv5AzUv+/MQHZJIYvv4Rl9xLD/O7zrNRUpJjtxMmuiLQfG81GtkBSXsIXEvlsK9sXjRt8SIQCNPK3QYhAmHbOvSUAKwUU8YUQHbXDi9RdL+CMXKtu3baKjunr66WpZRRxUKGlpkEzr06NChQUudrAABsaR4BLj3ujfc0oWK6hX5BQ85Qu0WJ0cYx2AVUSSQQbHIIb2pn1c2wUqUV4i8O7RJO5UU0yZbqaUCDVov8YSJIFzSgVb45kgIgV4aiRXJ7G7dRM0qO0tWdmbZ7GG46gIHwvzYjkZZSwXFKGhQcRNFPGFiH3WQB/s+ORivsH/b9t/DYy202OzeKHKlKKF44U6S1H6EGsLZ49iEXtWjlwZqqSJGJBGshGEUQVS6S9gkVxNLEgoKtVSiF0ZKwnbjqpfEKile4hundFAWksOo0Wfxy+e/o3ZxLzXXN5OQFUtTgyfduKm+2SM9IlVChIU02Zutcg1aqcMp2ogjlRg/2ZBW2YQRE3VUEUkg4Y4QAo3dgAM7W1fswOlwMffpTwghnL0RFGJGAmU5FVw15WIAgq0hNNV375RCqr6ATEf2Rzd6V3tfm7LcCspyK7z7bfslB6EIXzu/65a7pYjE7HjmP++JLCRkxDDspAFMOu4xj/CzIvh27gqETovQaKitbGTGHXMISY1j7JWjKC+s4dtlO4iKtVCxvQJNrc03kPbvpEOv91bg2vwMN9niSwMXQqF4RynFO0qRDgd6jcKUeTfx0KWvetuoe0WZ7E12jGYD7z7+KXOe+Jw7Xr+G+IxYRpwyiM0rdtBU34zOoKP38AwGHdvXu9+T415D6HXEjcxk5kcTAVAUwcM3nUp9s52jnvRE2aLXOWCgx0mlr/SNVWn2jUOa/fTa/L4lir/z0P/6dFw3fwPWT4gZITj9yqMwGHWsXbaZ4nYDIkDvzr9v/333MmwADHV+enP+Ith+ET6HxddHeF0IjW25nfppaakhOjgp4HyFn8G0PW8BqcGDCNKFtQ9RkBjUlxp7EWqrHUVR9lo3dVG/5ffzra9ey6gzh/L17O+59URPjZ/Q6wKMt4xesZx21dEs/eCXAN084c/0eRAZHVKVnHDxKJ669MWA98mP81dx7m2neR0kfxjdRNC7+h4GPNvdabgp3d1r/mva/UThuvkGdVvvvx+G9P+1b/s/PW3yH2e89eDvj58+W8O4R8/nvcfm01BtC/itjkoiRRwGP3KPCBFDjlqBiuqtn2mRTZRTiJQqZhFMDMnYGppQuiD1ECjIdiPFJR24cQdoo2nR4iTQ+6ZDTyudCVUcIS0MHDSQ39nZ5blVy3IcOMgSAyiVBQQRSgyJCCGwyxby2UUvBneKIpbLImyyDoFCK800ywaC/DRujO4gGqgBoqimjJi9xMYVoUGPsZMB1oEaWU69rCaeNHLYRBYDCBNROHGQK7fQQC1b5K8M5Cjv2CplMRYRQa2swCSCDipq0IOuMXT0ALKGpvPhMwsAaJaNVFNOJv2981vAThSp8TIX/pXQG3Vccs9YXnvgbXa7NtMqm3DjJKE5A7SgFXp6GYZhV1uodpcS504hQuMxiurVKsJEJE7RhlkTikTSWwxFCIVaQxFlspRIjc+A0gk9YS0xbNq5npPHHcfi2d93Gk/upkKOu+hI7/8arSagJixUWBmIR0cuV24j2M8J4pQO7CE2omzxuHDSRgtB+LTOAELjg3jxg6cYfPQAXpzwJqaGUMopIpksbxtVurEmhFJbUse5t5+OEIIVC9bs23D7E/HhswsZecogPix4lbqKet6cPI9nvp5MxsAUtv7iYY9MyIyhsbaZpkaP0XLWDSeSOTyLypI6HG0e46ClqY3YhEN3Dy55fwWnjT+OsdcezxczO19bgLqKBjb8uI1vm9/j9jGP8ODkh0nIjsEcauTCCy8gNjUKvVGH3uiJyF6WcQs11Z55n7ZkMn1GZDLzlWXMn/sLIWNTyaus8ayPuw4u/WmIjAvj3alfBRo4fxIM5jBaaaLFXofZ6LmerW0N2JtrMEf231vj3otWtZEwfWdGYbPOgkO2YjyISb179o1o9XouzvCQzLDX9y+jXyIjT+5PS30Tr9z74SFlaS3YXkxsWjSleyq823b9nsux5x/R803rQZfokQr4k+DvvTygOrYOb8ehirD9XRjpDhXz3EHgYLTvOsZnq2nk8xnfMPL0ISx598eAJi00dfKIA+iFETcuFPTUy2pqqSCZbLSKjkZZx265kUePupO3nn0bi5+nXJUqrdKGRmjJkZvRCk8ErEXasMhI3MKJmSDqqSVOpnrFpFVUHNgpkjkkkuGpE5ENRA0MZcTwEczi405jBA+teJboj5SSZhrIEgO8vxmFmUgZQ52sJNwv0lcui9Cg8baVUrKbTaRKn7RAvaaKMHc0AAoKKp31dSRqu3h3IFSpUisryFIGUi3LiJWpWBVPXzqpJ4oEmrFhp4Vt8neiI6KoqC73jBkzQcJCopIR0Ge30ba/y7PwV6ObeRh5+tAA0eZyikjDV08lhCBZZpPLNkL5C4w3fw+t1sDYm8bw/rQFbG1YS6Z+MDqhp01tpdC5nWg/B4JRMeN02Qlvjxi2yiaKZA7RMpE2jZ0qtRSjMBMiwtCg4KqFIENwgGcewOQOQQY5WbesK1pvz5rPYPI5J6KSo6joInUQ8KRmyhzK2h02LdiIFtEU6Hegdeix0YBFRngJV1pkE+HxVhrLm3nv0Y/56s3lmJVQNGo1+XIHkcTj1jqwR9p49/lZbPpmJ2uXbcZW9wunX3siI08bzK+L1nc/t/vRDO3uXRrAftydd97Pg19dXM2Hz33JrTPGY42x8PRX9+F2uZn23cP8ungDYZGhaHQaMgemcEro1Z656pPAB09/TuHOMk/XlhBa6xqpL6omNS2S/B0e7nkZ6knLFi7/2ijfMkEE+y3om3006x11ZJPOfJ7Js67nuAtGMn3yfPJ+9l1n0R45uvf0qcwvncGa4p8Iqo+n5TcDTVLlqbXTeGTGPYwyHYnBbEBv1DHp9euITo5CRSW1VwLTHl3At1+sRwA76mvoSLZoTvWMt67Jd+80H+/7O3Kj7743VfucAcLp265v8FsO+T/fXT3rflG1lKxYGivqwd4WWBfm6kYjzc+wEXaPca3xi6ppKn1ZH+4Yn4OvzeqLVLnMvvZhPxXQR/Ylv3YNTuECAdo2SZapP9TUIUJ8qfb+UTiDEkydo4xYU+C7v9lZj07okHI/Gm9AXHosl91/NmFRYXzwzOfoNdDW6gi4TwxmA1feewZPTZxFg60Rgy4IoRdg65qN+kAjQapUaaSWRllLeX5n4fOqklosUaHUVzZ0sXfX+CP6vQelJ+v/XjwADbmOKFy3c3KArJ89+PfgH2G89eCfh/K8SsaM8xRQS6lSKytxYMcozdSLKmIJJDlok63e+pcqWUKmGOBd8IYKKy4c/L72N8KIYrd7I1FKAi7ppFqWkqRkk6duJ0XJRi+MntRMdRMuHEQSSxMNKCis52fCZKTHuMOGQKGUAuy0oEgFI0GYV6Sg1XVfZKy0G09uXOgxdPo9FCsVlAQYbzbqA3SvhBCkyGxKZR4p9KIppJYjTxvGpWMv56nLXiJaSaQlrQK5x8c86ZQOXNKFRtG2z6mvpk0rtV4ikgZZQ7ro5xmjdJMjNxIp4hnIkdTJKhrCKsg2DSRam3YQV7MHB4oN329B2esjqQjNXv8r6LQ6urDP/3S4XW52VmwiRdfXS6xjUExohJY6dyVWjccJ0KjW0qo2UUIuuD3OlnRtP/LV7fQ2jsSkBGNz17GzbQPZmoGoQU6EW7I32WqtrKB5tQlLN/WtIeHB3nrZAcf0YdTZI/hm1nddtj3n5tPY/utuVm9cSaWzjEFyFMLm6bcWj9QI/ZvJ2VwESLToMK/rRWlOGaGRvohcgpJOm7RTJysYfvQQ3lo6g+b6Fn61bWTulE+54M4z+W3xRia9eQMXJ974R6b7kKGqqIaG6kauOewu3twwlSBLEBqthpGnDOKJy1+mNLeCV1dNAaDXsHRqyuq9hps/NDoNmYclcOQpA5g3ffEhGdvTt84lMtbCvS9cCs6zeOHm2RTvCjz2PTc+iK4wEr3ieYcqQsFSl8xN591JtmagZ9zD0xl9xbFMu30uLoeLj3dP46elWzsd76+AVqchs38SA4/qxTFnDWXSqVP/0vEoQkO6ebBXYw8FRDeSMR3oE3IU6xq/IVgbTrDO6qkhbd6KUCWKsm/n8DkTx2Aw6Wmqb6Z0TwXPX/8Wd711HedPOp1Hz59Gi6uRPPtGXNKJpkXHXbdvx9bcjKNZ0OpuxGpIIIrogz7PNtmKgzZc0kklxUSIWL6eu4RKZylJIjNArzIy3npQhlsP/rfQE3nrQQ+6gMvppnB7MUdcNIjZ82YRIxIJFeFUiVLqZQ0GacaqRKFKN/lyJwoKEolAoKDplHZoJZpfV68mUsQRLqKplZVohJZeYjAqKhqh8UaxytVCIoWvrsgoggiRVoplDs000oYdDRoECr0ZhEX4GMzSB6RQXVrX7XlJVFTpRoMWB50LoeupIYSwgG1KF9EyPUYaqCVXbuPN+S8yaMgg7r/tIQwjHQSHmxl59Hm8+fQ7KI06TwqYbPXq3bXJVrbLdegxoEVHCzZM0kyMSMIoTLTIJoJECKXkkiSyMbeTnESJBIIaLGxs+I0UTa8DuYw9OEis+uK3gP81aGmT9oA0Yad0EGINhs7O4j8dO37LJSjGgLkkML0wTdefda3LMSgm3NJNhDaWBE0GFbIIBQ2Nai217kqCNBaa1DrPM6axkm4cQK5jC9GmaM484zS++vhrImyJKGiop5oGtYZMv2j13miotrFjTQ5XPHw+u9flMvvBedQ116KTOq9xqdVpGX7qIE687GiiMiP4cstHpDv6B7wzwkU0mkQ32ZqBaIQveoEKX7zyLWPGHRdwXIMwEitSKPqhiklHP8yNz19JdWktZ9wwmtOvPwlziJGnr3j5j0/4IcKaxRsICQ/GYXdwcfJEQqMsRCWGU7C1BDcSU7CR0twK5mx/ntLcCh44+/ku+/ntu20cd+ZgjOau64VHntCH2uom8naW4XIdeNS9uryBuy95jRiT5PaXx9Pa1Mb677fy5ZseMpGln/xElmlIwD5CCDSKlrm7XyJ/axEPnv0cO3/LRQnx3Jt520qY/8O9VJY1MH7sSwc8lkOJiFgLM1c9ysqv1rN6ySYWz1vJRy9885eM5Y9CrzVyWMjxbKv7Ho3iYZsMxsIA87Hd7jPplfEYzHqyB6Xy46e/sumn7ez8PRcpJaoqefyi6TSoNRS35dMn9Ci0ih67u4nNu74nLWgosUGe+tS8pnWYpZ4gTVi3x/KHW7rIldswCCN6aaKGUqJIJFLEgcuj/bpLbiSbQQghiM+MDUij7EEP/CH550sF/O2Mt64KtAPSSroqJGb/qV3/qej2/woONqS+r+L7Diyd8xNtvarJVgZ6U5eCNKFscq2iihIK1J0YMJFAGm5c7JLryWIg6t65VkAzDYzMHEnNEk+9UCRxID3HVqUroBaumQZiRWDNmEGYQMIAcQRt0o6KGyPmTkaiotF0Lb4rVZpkI000sIU1ZDOQUMIplntIIN2bdllPNVkELk5V3KhSDdDDqqeaOFKIVhKZeuZb2FJKMeREoBcG7NLBOz98gKnZgkmYPQp0im/xv1OuJ759X/BE4XbIdTSqtcSIZLayhv7ycBzSjlkJZKc0i2CcMtDo7OrZ+K8WLx8E2cff9vgH2EcCaeRpt5PoysAsgmmVzRSwi/TKvpw1cQxh0Rb0Rh0fPfuFT9S+u767295dm67Scfbatv23PSQOTaS4qJZgJXAhFaK1Eq1NotSZi0WJpMpdTJZ+MIWunYRqI4jWep6xancxBc6tpOoPw6QE48JNWGMqv3+QS7A7jgKxG1WqhGjCydYO9TFTdkMi9dviDaxduokapYxqRwVBwkKbaEVKN6cdOZZTrjqe7+at4IEznqFCU4Bo1XZJVlRX3sCe4vxO5x+bFkNbazdMgEJh19o8QHDZA+exe+0ePnvxa444YyjjHr+QxF5xfPX6Ur9h7/s5kaqkTlZRKyvQKBqiRRJBIqT7VKkDeA93HFMIgcFswN7iwFbTiK3Gk26nSpUd1Vs4KvtEFK2Gw4b3weHQoyh+n3u75x2QvzGft9fs4sr7xyKbmjHrFd5a+QhV5Q1EJ4RTXlRDUIgRk9nAl+/9zKev+UVB/cca8I32nVt5SS33nfMCA4/uxWPzbuGm/7uc5R+u4tar76JFtWFWfE4DKSV9j8xg/vRFXP/MpZx142i+eHWJNxVxwhEPIaIimLPyQW645USmrF3r3ddZ5Tm+24+81G3wXZuGdD86f5evUVCxX2i41ZcGKpt922U7UYnQaKjNt/PjJ7+y4M3l5G0ppv1C0CX8t++v7qq23ve31S9VnNM6ZQAAIABJREFUMtyX3aGz+e5Z8y4/z48fq2UAs2PH8e1+7/tgXy24jLIShJXh8WmIGl+ESvqTzPg/o1ot5lAT82d8S3l+NY21TZ7z0moxW4MZdvJAxow/kaffeIxBoWO83zujJpjDQo9jY/0ywo3xKEJDkrk/+Q1r6WeJxd7chvBLzfWmnvq9EwrkDpJFluc7LiCWJPLkNuzSilGYEUIhFCst2EiKTebcW09j5n3vI1V5SNIFA9c7h7h85SDKYQ6IPOxAxtTFNytgnnqWxX97/O2Mtx78u1BTXEd8B7Me4JB2gkUYCoIUegUQixhlECXkEiKsVMhiL42/Szoplns468ynWfNq5/SUjjodn4EkOhlLgJfUxD8K4o/opEie+fYB3rxnbqffbLKOPHbSl2EIFIrJpZZKggghhy0oUmAkyEtM4Y8E0tjNJlJkNgZM1FNNJaVkC096UIkzl7CcKD+tK4WolmQP8Uh7GwCnbKNA7vKMVfFJHAghyGIAG+UKLDICgcJa+SNatF7dJ+8cSInstoS9B4caOqEn3dWPcgopkwXoMZLJYWiFjuCwIJa88wPNDS1ccNeZzH7gg79kjDFKKhuM69HadRiVINzSRb5zKzHaVMK0UTS6aylx5ZCtH4YLF25cxOh8ac9R2iRsbfU4VDuK0GAQPqdIsCaMDIMfQ2oXRAUGkx6BxBhkoLmhBYfdyTn3j+atV2eTXT/Y265Z2li2ZjFHnj6MuIwY8rYUUVFZgRsXNZQH1NK6pRun00FXwvVbV+2k1/AMHv30Tras3Mkn074CICLBSlOrjQVl76JtZwSUqoreqGfYGM85hEaEBBhv+0O+3IFJBJOh6Y8bN8XqblpkKFGaxP3vvBeikyM5Zdyx5G8rZtsvu8nbUsTdsyfw4FnPBrTLdW8hTkklqF0Pr3ZNE2TVcPuEu/ns5W+7zCyIT49m7PUnMmHqJSz7eDXPP/CZ70eXi5TsGMbfdyanXXIEqirZ8useXrz9vQMee2VxLZXFNSgaQXxGDEOzDyePTThydegVI6p0Ux9WyDP3P8zUs2ZjNOvpd2Q2X7y6BKNZz20zxmGNCiUvtxqXw8XYq44KMN7+LKiqZN5zC5nw7GW89+QCcrcU7X+nfxFevnsex503gsHH9CHIYmbr6t38ungj9qY2fvpsDaYUiVaj7/TtNWlC0CtGattKiDQmoxFaYtMimHD/xaz7bhsmk5bvPlpFW0vXem8qasBaASCBDMrIJwVPFokqVYQQjJ04hrfunYu9uUcioAfdQ+3q4/APwt/KeBNCILTtXrFuImz+nqDuom1deiT+bRG2v4C85KCiMe1jamhsIN7PKduMjRCsNMhqDErgy9gkgnCqDpKVbCplCbvVTd6Uw3RNP+JTY3luuUdHqGNxOLCdTnqU9nR2qeuJUZIJxkKh3EWq6O3tu0YtJxgLXUGr1fDM0ofQm/Ss/24zS97pggWPHfRjmLcuL43eJMp0trOWAeKIfU6FWYSQLvtSTiEO2gjFSrYY6I1A2P+fvfMOj6ra2vhvn2mZTHrvCSWhg4CAWEHFrtg79i5iudeCXfws2BsWLFeUa1dsiILYAOlVSgIhhfTeJ1PP/v6YSeYMyZCEoujN+zw+Die7nLP3KXvttdb7SisWxT9sTQhB+oBUGndVU2ktRQLNNDCQ0eTTMe9DLwzopB4QOLEzRjmWWrWSUpnvJ4pcRiHRJPw1UhhdeYL29zPaU09VT/rvQRt6oSeFvh2Of/rM11z/7OW88a85BFk0Gwr7cH4Bd5nb6mq9L14PSVxyHEfWT+LnTd/jlHaMIphEQx9CvCFNEfo4WuwNKEKh0d1AqNKRWj9cF0OTWketu5yk4IGIYI/H12Q2Ep8Ry8gjswiLsHjo9FWJEOCyO1H0CkFmI62NVhprmwmLCgFVZUXer4TXJfoZXxYRSmOogs3qoHBLMbGHhRD0q4mEpgx2kYNLOokliRaayGcbAzik4xh68cWL35E1ph/jTz+Uz1+YT6maT50swWA0Mzb9GC679QJuu2safYen03e4x1AtzasgsU8cA8dmkr16p7fpwHPSKls8i1Sdp74ehQzdYLa71xOrpBIZH4HD5sTabOOos8ZgDgli4dylAGSNzODq/7uA2OQoHHYner2O5MwENv66jb7D05n6wpWEx4SSMSTFb87tshU9hnbDDcAsQijKKeONx97nxgcvZ+emXWxZk4/qVsk8JJ2MwSnEp8dSVd7AEzfN4bdv14NOM2Y2O4WbdvHQxbPa6fLnrH2M5xfcxZ2Tn8Pl2C15U0uprwje2/AElUU1vHjruxx+6iicTjcPzb0NQ6jCzBlPU5JfRmh0CIYVicw8/S3P/RQTQunOCm6ddRXHX3o0BVuKGHBoP4aOd6E3eu7hc9Rwli3wEKK0DE8CwFjvW/y37vJ5rapG+j5ChmbfGkK3q7L9t7smQLh827Pj9S6Wby/h02e+4faXLmfq4Q/4eY3862nesVqSEo1nsj2qSCMfoIb5vos6m28sDRriHtmoIfvQesc6yVXTCo4LzVzZUn3fQ0OQr4yuVNOPzeeFE0Khsc7K12/9gnS7SRuQSOYhGaBTkMBr935Mjm0lTtXRYdPQrlpRUZHesax2FhDWEEzexkLqKuqYeOMkclblkrepsMP5e3vvcETRsEyrUvXIsUgFl9vlZ7h1FiXUncihbsFv3ans1/b80Mk7TARKzQ+0Fu6ibb8It4MgH/uAQv79c97EwUSjGq5Ey8NMp3j+Ech468aN+T9hvGnRDeNtf4cOdNm295w2OH9jgO6QdnFpm9pKkZoLqPQT/l4qVapslasJIhjwGHNJ9PEY9Yrg6cUPcudxHqFWu2ylKbqKPsNSiYgNIz14AK0VNr5fsBAXTlTVRQtNBIlg7LKVIMzt4Y27Iy41hn+9fSP3nfZEx4WIF5vlKoaKsd0+3iU0c1Yq8wkjihBNbo6UkorUHeiKQ4iRiQghaFBrqaKEZhoYJg5rD0UFaFCraaaRZKUvG9VlZIkRmEUIlbKYBlmDHgNN1BMvUokXqfQIB9J4OxD9dNZfdz6KBziEsjMkZMRx4lUT+fmDpezKLtnntgMyF3rrCqNmIef9HRYVwuTrJmJttfLqa7OIb/XR5gMUOXJodtXR3zQSNy7KXPn0MQ31K7PTvhEbraSaB3Psiccw4pjBOO1ObK1OKkvr2bQil7rKRr8QL72QuJxujzdYE9r19vonufmmm2n5xdCB7KUyMo9nnn2KOTM+YcmOxWQqPgbXBllNPtsQSLIY2Z7ruScdvHNvP42C2lwWfPALcZpQ68bgCgadkMau7eXYWhyERpu5+porOHvKZFS3SuHWYvI372Lu/31Oaa6HuXXksUM5fPIYslfl8uuny9lsXYWi6NChJ0okEKl4SIyK1Z3E6JP5rvx9giwmCreVULC1hNbmVtIHp4CU1Fc1Mef/Pqcou4x7/nMjfQYn8+jFL3uIP7xzGRRs5IP8WSx452fenP4BAA1qDXbZStxunr1atQJpNBBjSCY1K5HBh2WCgO3rC9m1s9JH3W7wbp7uZry1Q2OYjTtxOFfeOxmDSc+s+z5l3W9eeRWNftdJZwwnpX8Cbz74KTi8c+81VMKiQ3C12rB65Qy0TI1RCWF8WPAaAD+8+wt1FQ3EpUXjdMOnLy7g5MuPZvINx7N5VR7TL361c+MtrnPjLWmp7x40b/J5ztxVNe2/DUY9qQOTsIQHkzEkhS3Lssnf4ns+P8ifxR3HzqC8oCqg8eYXFhzIeGsLc9Qab4m+HGxnuG9Tx6TJ4dpb440oX2i0n/FW55vjPRlvbTjkyCzGnDCM9578ut1QcrpslKs7cblcqIpKP8uhCCFwSxdbGn/DodoYEH44je5yIhLMmHfFtn+P2+Y+bWAyR5wxmi9f+Z6Wet81Fpm3k9Datz3vFSBP3Yrn6TJQTRlmLMSSiEyzEVGU2sH7B10bb93Z1Ay4lhL7wXgL3Gn3y+4H422hbe5aKeWhPTrHvxHCB8TLw2ZftF/aWjjhxb9krA4qzxsCn9GmzWcLkBvR5YP2TzbYtAiwU/9nUcO252B00l+kiKVIzUUvDBgwYZVNqELFIkMpIY8UfDTFhTKbSBFHis7jpWhQa8iXWxkeMYa3tzzfvpNpd7eSzzb61Qyl6VdBvaxja9o3vPnVLDYs3uYxwISKlBKndKDH0OmLvA1RyVFUl9bhcu52v2jquKULVbr9FpNu6ca9t1tUmjmLJ5UdciN9GIxJmL0kLtlEuMOIIql90zFciaJZ1hEmo9ksV9JXDiVEhFEjyylnF4PFGAAGMJJsuZYhjCNOpBAjEykgmyCCuzTc2j9uPRBK7TEOtNe4G7lenZ7L3mIf2igvqGTOgx/vW9vdGcO2Mp28Mxtrm3nvUU+YnNVpp0YpI9rgCUFslvUMmpjOAzPe4OJTriTW2he3dNLgqiZc7wmHbnLXERZr4e7r7iciJpQNS3J456n5vg7aDMYgExh9+UauFivo9Z7bWxNOGR4bxo4teSgyiCSN19gpHTRUN/DUla8SMyKUKF0cbRHAQggiRCyp0oVO6rHoO/ey7z5Wn73wHQX6LWTslqfa1NjMpm9LiDWmYQBkpWTmIy+gcxk57NhDKcuv4o3pH3LDk5cw/JhBfP7Cd6QNTOLlW94hdUgSuaEb0NtN9NEPRUGhwl1IoTubdN1ALPFG7n5iKiERFq459B6Ksn1MjFrhYlSJISSY2JRobhjvjTgw6NufTVuri0uzbmNexWyyV+ey+vsNuFrCqFJLiMPfeKtXq0mRA5BOJ7u27KK40GeoaCntRYTXY6c12LQGgcZ4W7l4KysXb+WJj6eSdUh6u/Gmd7s4/MRhXHTbSYRGBDP16EfA6Wo/77avRGNFvT+1vuad82HBa6xZuJHpJz/udx1t76c3Nuzk92/X8syC6Zx/1RF8/LyHKVO7CRCmMVTClvraVut9+V3u3SJ5+o/swyX3nU1tWR1leZVUldSwbfl2Jl5wBOenRpPUL4H8zbuoKqpmwOgMynJLe+wpl373oMc4apNaAFA0oYOmGp9sgJ/Bpm0vgCRB22Lcb36bfbl8pmLN91rDsCwjfVEgokmzedJqIzTSwrk3T2L76lxm3+Mf5q0TOmzOVjIth5LdvJy19fMxKsHY3VYkKnGGdBqaixmQMYgzLj6J/z76WXsAv3S7MRj1nHv7KWxdvp2BY/uz5fccguL1tITXEtscw7qcX0iRmVhEKLWygiDMhBKBRFJJCQJBONE0F9VTJYuJ3y3vHbpeM3bHkAu8id1G59+NNVhPv0c94XEIgG5JkvyPoFfnrRe96AJJur7sdP9BMv2RQsWIiWJ1J1WUYJBGamUFAh1WGgkmBCM2ctzrSVMyCVeiqXdXU1NXg7XRys3j7gWglHz6KkPad+F0Qo9lVxxPPvQMGYNTyN1QAHgWdJ3R+e8ORVG6FA1Nog/b2USWHIEiFFSpsoONnYbD9RQ6oac/wykhD5fq2RW+8o4pfD27I3V3GNG0imaGMo5iuZNtcjV9GcwQMbZ9FzNICUavGtkiV6GXBty46JPWj3EpR7FtxY59Pt9e/HORrh9EuSuf7bY1BJlNjD5qJKcedwax0XEcNfB41u1cQTgh6OKbaWhupnJXLaMOH8Hg+Iks/HB5O82/1vvSU/yxdgv6xhAcioMCdRvRIoFW2UyVLCUzJYvzLz6DlNFx3H/1DNhtPevA5sce2x24HSrC5P8ht4pmUo3+8h5hDen8tuZnyrNr2fDrVhprmnnq6tdJHZBIa7MNe4sNVZUsWjsfu2hlmPGI9vqJ+j4UOLdSI8tJ6ZtIUlo8txz1kJ/h1hlcDhdGsyHg362Nrdxy5IO8vNQTkXBi0CUECQsl7jwSlQwEHvZdvTC0s/Hub+RuKuL8m4+nrKCaX79exys/3EV4dCgPXDKLHctz9qrN2vJ60gYm77HM5qU55G8u4vL7z+a8aSfTUNNMQ1UDt098tL2MEILY5Eha6pppbbbRd2gK5157GQ3VjXz09NcYjTpGnzCClMxEkvsnULy9jGeveb2DIPuOdfl+C3G9QYfLeQA3uA5C9B+exoSzx/DR8wtoqugYYqoIHTp0NLvqGBgyHlVVqXGWUuTeyvCQ49ArBnC6sOjDqa9q7FD/8Mlj6Ds8ncSMODYt2UaDqYrfflxK3M40jESSRF9KyaePHEga/dtTGFpkEzp0tNKCIhRCZSRVlBN/wEekF39n9Bpv+xOS9hAAGSi3rVvt7Ifd9AO5U/8noatY731s3Pe7jT2uE0YmAwbSlQEUqTkIqWCOMHH80RP4+qtvsCih1KqVuHAwRBlDqOIRLValm2x1PQOVkYSICFpkEwCtzZ5dShW1ndyjDYrQUZ5XwdwN7wIwSTmv8/PubF6l6vsvwDXGikSkdPMHy9FLEy4cJJJOtEjY8zh1E3ph8CReCxh25ECGjBnI7OfeIkLx/wQ1yGoihUcfRwpJlIxHL4x+4aBW2UyYiCRNZAEQHhPKeVPPYMFbizu9Nu2Y7PX90ZPwvp6yKR5I/NXMl/sBfru7e+nJbAvlEkAiHg3AS+48k8+f/5b/LP6IOQ98wolXTODEiyaweWkOw48axCETh5DUJ467TnqCFV+t8fMadRaeDCDDfeynLcN897a5zOcRaG50oQsWpDMIu2qlzl2JSYSQQB+OO2siBVuKCQ4z0+CuI0RGt2/iuKSDJupJ1vXdY6ik5h8AmAimRW3AomjCljupaxBBrF++lewSz/tICQ5Gut0U7/IsZKXDAXo9jY5aEnUddRSjdYkUqdu57+K7uXfyM+0bRn5hdBpBZaSkz8AkyovrEdE+Qfc274m0e7xMOzYVsWX5dvoMTQW9nhSRSYO7ijz3FhAQrSSSqI9HGDRGoF9+i8ZzU+9dVJs6lw7ojFnxqDNGsfz7Pxhz7CCmPXUhrc02rhlzH831VqRDw+rpfb78xtaPVdp3+O37PuK6mRejGPQBQ7/UVhvXH3InEy88gulzb2HZvFWccMUEpMvTp1pbzw1PX0Lf4emU5lXgcrhxOV08ffVrpA1KZuIFh+NsdbB9bR5fz/qhy7Ay7Xk47Vpvl7vz3wHfLZq1Tds6R+MxVOwab6SGAdNvzrTzoB0fP8+R9/7SzIFUfUap0Hg9/TZbtALt3ntj4Og+jDw8kzfv/cjvvMVuYZpHDjme5qhS1i1bg1QlUqoMNo5F7wJwEpMQzpR7zuCN6f+lyVVDet8MjjxlHJHxERRvL+XmsdPb6+XKP8hURrS7alNFf2yqx4vXZrhJKdnJFiSSJK92rJ1WDMLU7TXP/sp/80UgdcyD22Pbfsc1c9zJfd+tc91bJtsDGW3Ti/2Og8t468U/EmZhob/OG5bUBBOOmcjCbxZjwES0SMRGc7vhBh5DLFn0IUfdgEs66ctg7K0O0gYmsyu7BEXocEi7nwGnSpWdGwuZfvLjPPjJ7WQMTaNg8679eh0qkmDCUFBQMWIhrOtKe4GwoUYevupJggmlWpYRTQJCCBrVOuqpxi5bEUIhjhQsIow8uYUWGokghibqaZS19BfDSBuUjDHIyIQLDmfBW4sp8ebm9KIXXSEqIRzVrbbns6hu1U8se+lXHqa/hz66lRufuZRbjnxov/RrDjZywlmHc+t1LuyqFZMSTIKSgSrdbHeuIVKJp9hZwrsPfUqKHEABWxEqgEAKlX7KkB73mWzIZIdjHeFKLOFKNC26etxB1g5h0jXOEmQhmLv4ahqFGbu0djjepNZhUiy889/ZjLpmKDdOu46rh9y7x7bGHT+E4rzKPZa5YebFDBmfxTkpN7YfC9fFEq6LPeAhUWFRIYRHWXj+tvfadeCkvXPGwO5i8s0ncMNTU5h6+P1+x4+96AimvXIVZXkVxCRHsenXrYw56RCMQUZmnP88S79YyRFnjeXjkje499THCQ4PJTUribtOfNx/HNxudm4sZOfGQlTnP52ZYf9Ap1NorOs8bFOLM2+YxAO3zUB6Nyai9EkEKRbAs6Fz8T2TWfjLArY1rcFktLC1YgMb3lxFsr2/X2pDK1Y/we02JIg0tsk1lMsi9Biw00oMCTTTSAyJqFKlkO30U4Z2qNuLXrShV+dtP0NK2fXL9EDujv8DvG1deVR6mpy7t9e+J3KTyqIazjrpHN757jWGKYdTxPYO5czCgpEg0nQDKFXzeO3Jt7j/o9u5buRdWJQwctzrGKR42B/d0k2euoVEkc6ahRtZ9tVq9Aa97/qlSrNsoJUWQmUkQbtRDgfyEmhRLovQoSNTeEKpVKmSyx9kyIEdvIDdhSpVdrEdl3TStr2YKjL57MMvSLT3BwUqZQk75R+4pBOBjsFibIf8vX5iKC1qA03UYSGUNHNfrn7iYoqyS7CEW3jn3g9QVdmt3MgDqu/m66TL437x+doi7u7sbP8PoIcetvY51o6TNrpB894NCQ/ion+fztv3f+TXht+ur9d78NhFL/DWH896CC60c6P18mjaFppQM1eQ776rOMyTZ7Ni5lQ+/GwV0aNPpWDzQnS1LQgEbpxMve1OCnLr2LwiD8USjIlgspxj2hnscHSu3RbIMyksnveAXq9nEBOod1RQK+sJNcbRV8lgW9Mq0gwDsOjCqXIUUa9WkhV6pJ+XQmiuWVg9XhLh0CGlpFmtb9fOc0gbJepOBhjG4NoUxtINW/ht0WUo7gjMBt/GVdu4HXr0AC6ZejzWZhv3XfU2arRvISu8Wl2i2dPfqVdPZMPSHKxuBSU0xM948iPT6ITMAkCYNO+vEIt3LDUGWIBnEZeLe2ZdRktjKy6bxrujrdvZcxngGR44PouoxEgGHTYAIQR528oQRmN7Htv1T12K3qjnhlF3ozfqOf2GSTx52SwcNl9/Z0Vfxbzad3hlxRO8fsccnpzyEqrDcWAYANv+HMhhIQKQRfg9u14PlmaMtd42v2dO216gx187toHmra1tbS5cS+fXqDZ7PHV/LNpAbOyRjD1+CCsXbPB53DQ5rJEJETzzwlOYlXASgzM9pFvOAors2Rx7wkR2lG9h5kuP0ZjfSlIbKZILmtV6imVue6QIgBEjdjReRy9aRTMJ0Yk4G9zY1BZiY2KxN9tIT00lL3srEkkqmRj28pu8P75//m1o+Ro0pDXaqdF45/w2GTrxyAVkmAyEQFqDWk4Jr6f6T/n2H0SQvcZbL3rRM/z6ye9ccNdk1pSMQNmsw6HaO1ALV8lS4kUqFhFGf2UEqzev4ObDinBJJw3Ukmk6lEJXLkgVFUkT9aSS2aEvt3SzU24ihAhCRDiVshhVqqST1d6foldwu/f84mqivt1wA1CEQprMopxdpHXSb3dQQDYJpLWz4rmlix1yE4a6oPaPc5xIJk4ko0qVArIDEq9YRCipCamcfM1xxKXF8MUL8yncWnxgCEHwsH2WU4QbF9HEEy5iD0g/vfjz0Hd4GkdOPhSX08WHT33VLZ0kl0tFb+jpiiIw6lpaef0/v6DTGUkfcRqmXfUgVXT1VhSHCZu1pkOddtHvfew7whhPhNFH6DMg9UTK67dS4SwjyhRHlunIbm30JJsH0mAto0aWU+bKBymoUysZYB5DuM5D8mJWQnHnm8lzbSQTH1vtoJFpXHP3qVSW1nPvFW/SGkD3Sot5b//K+Tce58ndVf/cTQxTsIkX//Xf/dLWrS9dSZ+hqSyfv55z0m7q8PdX/zWH++ZOAzy5gPNeWtBpO2U7K/jgiS9Z+sXK/XJevfDgp49/54J/nUZ1SS15bQLlGpRXldDsdhKn9zxDQggSjH3IN67h5x9/IlHNoMiVS19lKFrm/xAlgjJXgV9bemFESkmLbPKI2uMhLFKSXWRUDMXqasGkC+KumTdzwuUTWPblKmZePqs9taIXvfin4+Az3v7sHfR/grdNiy4W6/ttd2UvjQKpSmrL6nnt9jmMvnQUi7b+RiLp7FA3kqZkYcREqVqAUzjbd62FENhLJW57LU3Uk6Dvg1mx0N/oE7DOd2ym0JVDFHF+/RWxg1SyMHtDN8JEFFVqCbVUEi08jHpBFjNOu3OP16TIjn8zEYSTvQsRckvPrmc7nTke4pJYmUwpBR3KN8paQggFVD+PYjuEwsX3ncPb936ITic6JNy3lekMPb0n6mU11ZSTRiZ6DFRSTJ1aTYYyaM8Ve/BMBfKw+e1Q9yRGvzsMl39TT17G0FQS0mMZfcIIvnjxO8ryvJTiXbBuaj0kp18/iYrqcmY//i5Gu09kO6DHQjMPblViCDHj1roDNLu8MkrjNWr19RnxS177b/NQz4KvprSRM885lE+/Xw9AkHdn35EZzi87Knjljav4+cPl/PbNemKSInC22MjbUozD5kRqZBD8+tfIE2jz8hSjkbSsBPqOSKe53sqm5bnYNH/Xu1RSIv1ZKAHc4T7PvbD5vBeKt88Yc38QkmrbLlAVpFAxiCCqXEVUuYowixCSDZkeqQ8hEEEmEtKjueP5KST2j+fGC1+jsd7qmSuv/p/U+8bW5fWOyTjPovb1z1YzctIQPt3xDKdNfh5jlSZks0WzmNWGM7b6jvt5YNo8KrrO59IPqqR4ezmnX3kMq75Z03V5XycdDiX2iaW6pJY+Q1N55IIXOs2tW//jZk/1NhbFzp5/qfLDf35m2qyrWfr5cs3hHjCyetvZZ/SEOVB7KQE8AtoyQnbuTe4sL05qDXp75xsyAWdst+f/k+fmc+3jF1Lz8kIaqpv88ijrmiuxKBF+aVsAepuFSDXKG4IsEJ2KIwu/75DJbGTaZdP4ecMiigqKQIJOryO8JBFVSIJEMKjw9FWvsSu7lOTMBMadcgi/fPx720AEuqL9ix4wJwfyyHXLC7e38HsP+p7zQAzu/0voFenuRS/2EtvmFRHRx0Jh3i4iRCzb3RtoxUocqfTRD/YrqwSD0Wrag3ikoK9+CPnuraiq78XkkLZ2w60NMSKJPPkH0XiMN71Bh9u15xeYiooqVT/GxBBIAAAgAElEQVTPVz3VhBK5h1qB4cLZKRNmEMGYMNMQVkFYQ5zHcJWtlFNAVpvo8G6whAdz6QPnseLbdZ6dxwP4MpbSQ8ucyfD2BX4CaRTLPFrURj9x4F4ceCT0ieO0646ntqyepL7x/gu1bmLS1UfwxoezcNV4iHMaZS1xIrVdl6wrVJXUcuMTF/HK9D1IHnQTt94+l5dmXcaKjQUUlfkz2lmtDmytTr7/cDljjxtCTXkDwTGhnH3DcYSEB1NXUe8rrI1Y04Y2esMITWYjQZYgdm0vZ9vGIiKiLZx743FY3ZIv3lu2z9cRY0olxpSKdLrIa11PvK4PkQYPQUujq5qdjo30M45AMcCMuTcRFR/Gkzf+hztevdJjuPUQjz/xDU8+fj7PPn0RM256v1seu/0Bh83B915h8X3B0wvvo766ibOTbuj0762ymR1VGzlq4CTCDwuh+PdqYknqtGz6kFRWL1jPzIUPcPcJj3Zaphd7ByklHz3zDSdeMZHPXv7B728hukganBWE6qL8jlvdDSTiYQ6NVhKokMUkaGj8bdKKDgUhBCERwVzx6IVUFdWw6bettKxUiEIjb9PJevv7d37i9tnXd/kN70Uv2iD/ASLd/2zj7W+6kx4Q3WHp+7PQRf+B8qi0x+2tDgx5ESSregp028gUIwgimGx1HW7pahehdkg7Mf0jePzTJ/jt29U8/dTjhCm+D4RbunBIKwbFRJiIorCk0NdHJ3HzEolLs/OldsPzlEQfcvmDNJmFiSDqqaaSUrLEiO6Pv2bMjARh3Z3nHKimjDT6c90TF/P2y++ya2sJOvT0Z5jPcNS0c9Q5h5E5ui8fPDGPplpve/tyP3TxnLhwYsLcIXwshgSqKcciQwLU7CF66iXs6pp7+uwcLB75LlgTKwqryNu0i6R+8dRXNVBeULXH5nb3Vggh+HLxZ8TUpWPQeTxXCTKdHeoGwkQkejS5a1r21cxkjjtvHCn9E1j52w6uvusUXpyztF0GrLGvzzsVVOfr01zqM0wUjcfHWOnzFP/n9V94fNqpzH7jZ1bVe8qEueHuhyZjszpoaHGy6OsNnsJuNyuX7EDRKRg0mzZd5f6oqoqzLU8ryERJQRVb1hZw5LnjuPyWSVhb7LQosGpVHqVl9Rgrm3xjpsnbkyZN21rPn5exz+FsRqC0G24AYfoY6lzlVJl3ct8jdzL/k9Ws/GkrAFFxYTz88qW8+MIPVJf79Mi00Nm9/bt945dbU8+5189mxJAUvlh2H/O+38Bzby0muNQ3f7oKX3tSex9rDf62ORGB5Qm0bcx9+hvueu1qln27rv24cGre/ZryQt9xudHm9TOZjUw7+hFP2KcQfh4Dp3RQqOYwwDAapVCHsxBComzEDw2jarnXg+i9ntBwM6ffcAKqKnnxpjd9XrqeRpz82WsHP/3aAM98IGeSX06nlpilk/PujoBzgEiHNsd6Y3UToZEWkNIvvzJEtVCpNNHsriNE59nUrHYU4XC1ovNumkTp4ilwbSPXvYlYkUyzbKBZ1vHvaXcSEx9NXWUjX7wwv/vkWlLF5XASlRDhZ9j1RM9tX+a6Wx7drtpwB/DCaV/VeynS3S1Cns4ieXpx0OOfbbz14m8BsxJCqCuKeqpJUNLIEAPZ5l6DQRjRY2DgYf249sppTD/vJSZfM4G7/u823nj5TUSVGZfqokGtIkN4QvYc2AgLC0dVVW54YQr/mr6eJms9ocIn2FomC5hw3uH866Y7ATAEGZjz8Kd7PEeLEkaGHEy5LMSJg1AiyBIj2vNtegohBDEykZ1yMyn0R4+BcnYhUAgSwcTHxGOt9yxMJCpu3P4LaeCUa4+ntdnGO/d+8KcZ8Dr0uOhIDNFKCyYOjI5UL/aM795ajKIIjEEB6N33gNNvOZ5XXnmZNI3khRCCRKUPle5ikvQdKe9PvvpYLnv4fLas2smyb9fTd2RfPnp1Mfsr3WrD+kKmTX2P5164lFW3vMewoSm89Nwl3HPrXNatyu80IEp1q9g1xBV+dOf67odQLVm0hSWLtmAJDUKXHM7jj5/H8t9zUVrsVJTUs3VDIUVlHY2qQUNT6JcYTnR8OOVFtVTlV7Dx9x1Y3Y0EKx0Fw0N10TQ0VfLZQ0s45rKJPPzGFYSEmykpriUvr5JbbzuRqCgLGzfsYvarizvUD4SNW4o5/+a3mPv8FTz3Vvfr7QtkV2GSAXDKlROY9sJl1FU2YLKYAubrlauFZOgH+zF/WppiWPbbcrIMI/3KNtY2U7itmK9e/cGPHbUX+xc5a/IYeGhfstfm+x0fFjWOnKqNlLZ6SMhCiUAnddhlKyYvUVi8Po18+1bswkoYEWRGDCLIZOad3QiSuouWBiulO8s57NTRLPlsxb5dWC/+Z9BLWHIwootcj78VeuoZOEgQaOcr0PFk0ZdaWUmu+w9iU6MZHTaG5i0uFCFwrNBhvlHPxJOH8vGMTxA6HbH0JT9kPdGNaSSIUQghcEgbGWMSmXTORE6+4Djqqhphmo5qUUq1LMVMCM2yAT16MpL78u/jZnR1Eb7fQsEoTH6MWJ7DPWBq3G3OokQCFhlOhdyFGzcxJBKqRGKVzdwz7X7Cy5MIV5JwSjt5cgt9GIyCghs3YcHhxKbGMOehT/7Ue0ERCjqpp1k2ECI8i1K3dFNBkS+sc3/sWgdgngyU87bXuZw99WAH0gn8i6GqEpt1t3yWznbzteOq6AgKMeF2qh1SRBQU5G6ZMEpwMAPH9OX216/jyhOfprzIE9b401ZPjp2+yZdHVT3KZ8hHbPN9ZsylmgY1+TKqRoha1+LE2eKkqc5KRFgQt9x4HOee+QL1dVYw6hDhwZoL9/0UDk0uWqWG2ESb96HN7wrzhPi643yhvvZoz3m3As/ccTLffLGW+d+sR9fiICE5kmGjMzhtUBKqKsnPrWRXbgVHTBxEbk4Za3/Ppbq8gaT0GNJTIrjpsfPI276dF597iXivBlUbalylJJsH8trvj1BW1sCsx7+lorQeZ7Tv2hSHylnnjeH1t65mxeqdvPveEj8DWdEkQQUX+zyXNU1ucnMrMda7cIVoxrVMY9AGyn/xelL89LsC6IsJnY6mehuW8GCe//5ufp23hi/f8DcYtUyWwuK5tgVFLyJViVAEd578BCddfgyfvTDfv57GS+cQNoKEf+g7eCRl2nIY2/I3hU5HaV4l016+mrLcCtYs3Oi9nq4Xal169bvzrtjfuXIB3qV++mHd0pZrOxTA2xag/0B5dqu+WcN1My+hoayW0p0V7fWaKxwk08fv3RGjxFHoykFFRafX0WdQGlmbhqMIHSERFi6afiY/f/Q7+4KCP4oYd+qobpfvTJN2b+p2hv2hFdexzS4YT7szr/4VenJa/0D8eVIBQohU4D0gAc8Xa7aU8sXdygjgReAUwApcIaVct3tbWvwzjbde/C0RJeKIEnFQAtfdezVvTf8Aa6Mn7HHm5bOYteoJ1v34B5XFtQgEKU2DKZTZVMlSQIIK6asG8MAZM5m16gnmPvYFp155PJvez0fnbtOEScSeVcOlV1zC8pf2rLH0Z8AkzKSJAX7HymQ+GeWD0Xl3mg3CRB+GsE2uJlxEo8NAramYbz7uOrTpQCCdLIrIpVzuQiCQSPowKCATZi8OTpiCjUi7x1u9u6ZZuVpImn5AhzrN3nysNsPtQGL17zs45eQR1NS2EBVl8RhvfyKsVgcFmjDU8pI6ykvqWPjdHwAkpUbRv38sc9/8BbvNieLNMyvOr6L4j0KWLdhIVcN2VKlS5SwiRp8CQK2rFIdsxSXt3Hj0DJ7/8T7iEiOoKK3vcA7zPl3NvE9Xc8ZFY3njtat4YuY3FGzbc0iZy2Xnt98W0jfDQW6BgqIcuM+8qqrcfvJTDB6dwTUzzu1gvO2OF7/9F7Pu/hBrUysrfthEc3ktm37b5vljACbPMBFFvVpJpM4XeiqlRBWdL0AfPv8FUrISeW7R/bQ0tHDz2Om0NHYMn+/F3sPpcPH6nXO56O4zWLlgAzvW5QcsqxN6+gqP/uLQ8QMJjw0lp34nI48dxqnXHseSeSvJXR+4fnfwydNfsW7xJoxBRj/piF704iCAC/iXlHKdECIUWCuEWCSl3KopczKQ6f1vHPCa9/8B8fc23v7XFot/RZ7bX5T7s/6nzYyeNJwln/vonmec9xwPfnoHUw/zGF1GjGQqw31hO95TLcrxbO/f9PwVPHNtK8tCVtNQ34gOHQOO6MvEIafRZ2AanaI7DGFaxr6uGA+7OU9BwSbiM2Kpq2iAKtFuuLXBKEwEy7D28FBZJ1ndtJwMObg9N3Cv0cO5FELstTzC3mC/eNi61dFePl9/0TPikDYcOLCIMIRQSM5M4IgzDuWz579F3T3fpe1cdjvXjCEppOsGkONeT7RIwICRasqIMCVgMoQhgj3erMi4MO567Wqi40N56vb/IjvRD3JG+bxGSb/65ska4yva2N+XE2kp8d23To2HyGXx3PvfbcvnvZmXUV5ez47iWvDml0mNhpOhxudxElo2Re3ut9nnBZRRob4+wzzHXSG+83AbfPUSE8IpyalAsblRNIt/NcwzJmUFVZRpcwy1Y+5lazQHRWNxNyOR5No8wuZhuhgMwozJEIpbCuZ/uZ6b7z+d6y583U9/SYvPvlvPoqXZPP7g2Sxbl8f7n69E36rJoa3xeD2r6ndQV7GLd98YAqiUVW4kMfoQwi2JAY0jLYSpk9BbrW6boeOGUUxiBFOfm8I9F7yCMJtBK6OpYe/ErRKdEM43b/rCGbUeNqnR6dOOQqwhlRzHGnSKnjARjUPaKXBtJdGc2aGdtjaKs0s5P2Mql9wzmffzZrHl9xxGHTuUlsZWpmTe2r7A9/PAaF+5nXqt9kEbs6v3STfePd3RD/NPlOqqje4gQD9C4nK72bE2D5PZAFIN6P3RHt+2PIdJlx/D4/PvYefGQt6f8Rn9Rvh7pTtro6sIJIlg2qxr+OHdn/nmtYVdX1agHMP94F3181Z2obXXXXQ6b4HyFwOU6YU//qywSSllGVDm/d0khNgGJANa420y8J70LGZXCCEihBCJ3rqd4n/M+unF3wVLPl/JqOOG+R2rKKxi/ps/cvecm/2OCyH8CDRam2001jZzzbA7+OGdX4htSKUfg0kjk/lLv2Ll3E3YbU4OPcEnNZA+OIVZq57gse/u5cr/u/DAXpwXF997NlfMuIDLHz6PyVNPImNoKidcfgz9R6aj7qb86pIO9BojTQhBrCuVbXItrbITWYBe/GPhli52uDdRJgtpknXkuNax1vkLi4u+4p2vXwez74M96LBMTrt+UqftBFlMNFQ1YVZCGKiMxkQQKm76B48m3uBbUI2aMIhH597E+89/z3WTnubnr9Yf8GsEiIkKob6hhalT3/tT+tsd5mAjjY37phsVYoik0rmLcH0smcFjyAweQ6g+BqdsJbyPjme+uoMTThvhMdy6QENjKzf/+7/ERFp49oFzMOj9P99ut4NmRzGZ6ScQFpJMWEgqWX1OpdmRQ1i4OUCr+47ZSx/is9cW01gb+D0UFGxk8hVHERbZMfyxKwihcMdNd3LGncdSZtlOiTuXNP0AwvTRHjF0dx317irUThaqHz83n5YGKyMnDuHr139k6ZermVf1FmdPO7lDWSnlXufv/a9ixDGD2bw0u9vl3S43408/lO/eWozBZCBjSCrfzv5xn89DSsn6nzdTU3rgIwJ68feHxMM2uT/+A2KEEGs0/10XqF8hRAYwEthdhDIZKNL8u9h7LCD+3p63XvxjcNQ547A2trJ20ab2Y+t/3sxVj13E16/+QHVJLQDfvbmYcaeOZMxJh7D6+w0B2wuJCKYkt6L930IIdOhQ3Sov/f4oM857jv/7+m4+eeYr5jz0KS8sfZT3Z3yGTie4aPpZ9Dskg/tPe7JH19DvkAyOOW88eqMet0ulub6FktxyNi/Jpr6q0a9saFQIaYOSeXLKyx3aGXLKCJbXLCOkKA5F6HBLFzlyI+kMQMUX3mbASBiRlFNIqIwkxqtb14t/NgrJIVXJJEiYUaWbBlFNPzGYiNYYWnNaWO9eSj9lGCFKOEeeNY668npiU6OpKvLlgSk6hUvuPYv/PPgJ4Hk+wkUMiX1iuX329ZhDgjCYDKhSEhETxvUTHsXq/HMTvLfnVVJfZyU6OoSqhj83ZBKgpqqJiKhg6mv3vm8pJaG6SIodOahebUedMJJlGUdsVhAPXDKL5siILlrxx/Nv/cRho/rw+ouX8cKsRWzJLgGgtqmQyPDdcnKFQCGKSaf3ZeHsOqxN+1/EOG9rMXe+OIVTpxyJqqqoqkSv19F/WApGk4HNa/JR3SprftnGxWMf3qs+TrnyOKYd/TCJrqz2VYtNbSHfvolwfSx6YWS7Yw1xMplIxaf1efkDZ9PaYuOyyNvbj3056wdOnX44jG6mrqSRqooqWhz1WHThKFJBqIIMsvY6oiE4LJhJU45m/ps/MvXlq9HpBM9eO3uv2jqYMXh8FttW7ehRnYHjMqmvaiRjSCpPX/FqxzzdfUDZzor2dUIvevEnolpKeWhXhYQQIcDnwG1Sysbd/9xJlT3uJP09jLe/gvL/YJEZ6IHQJ/C3DSUdOLY/bpfqZ7z99ukK1i36gwvvOZMlX6xg+xqPqO/z183m7jlTWfXd+oDJwW6XisvRkSb3RONFPPjJ7Tz4ye3cdtQDnHvHafzg/JCtK3Zw8lUT2bxkG2dHX8lDn/2bG569nNf/Nadb53/WtFNAKLz74Cft0gNBFhP9DsngxCsnEhwaRNH2MuJSogmPDWPJFyvJ21jYaVtbvstDJy3ky60gBW7hQjU6qHKUIPGEp2SIgVSwi0SRgUmY2aFuJJqEDhT++wU9fRb2x/MiOg9R6lHIajfa7vHz1Vkb3anXnecyQGijXxHp4R4N8jK3lctdJCv92tlUzcLCUN14NrtXMGXidWSvyiUlKxGdXg9CQSiCoUcM4IjJY5j36iJaW52g06GEBHH5fWcy8qiBvPbw52xfv8vToTbsx6ghGAn1hcO5LJ7jzlBfWWOj79kz1frC4XQaUWvcvm+TURutpAkBWr5kO8dPGMQnH6/U/N1XVjVrwvI0v7WkJtKoCc3TiE+XTPCETRo15JHOo3zf1NUVFQy8cCjfrtxG1FZfGzqrRvRbIxvgxybiDaEUThcSSb9gfzKFRlnJ5t+aidClg853f7fLAACqwXeuqlHzPEhYuTaf7TvKue2647gq/CiEG9auWc7slzuGjLndDr56fzU33Hk6s5/4FrdLRYRr9BibNR4zL1GJ1BwTsdG+v2sIX9pEf/910ky+r32TB//9MU0NrSj1HlkFvVGPSysS3hZCqQ057KPR8Mr25T0pZs/9ndwvjofev5HFn6xAGgx+RCoF9g1kWca1G1mxxnRyrCuJsKSgEwbQKZx4+TG8dt8nKNE+Pc6NuZv5+aofyQoZRwxJxIQNpMi6lWA1mCh9Iq3uZvJtW+kvvALtPXifHXLsMJ78/j5yVu/kuqenUFNWR0JGHCB49to3AlcM8L4LJLPjH0rexbtoP0kM7U7ycdRZY5h919x2b2VXoXtBliCu/L+LEAI2/LTF33DbD9+M797UePB6cp17+w3oBvy+Ud0Yby0xSY9JSHrRfUg/HqsDDiGEAY/h9l8p5RedFCkGraAhKUBpJ+XacVAbb2dNO4U+w9NZ/N+lpGQlMn8/uNd7cXBi24od2Fv9E40j48M5/84zcDndTDj/cFoaWinZUUZ9ZSNRCRHc/9FtvH3fh5TnV/rVG3XcMJCSI84cw7IvV3foa8b5z3P2bafy6uonkVLisDmY8+DH3PzilYTHhvHY/OlExIUz+sQRXRpvEy44nAFjM/n5gyVsX1fg9zdbi50ty3LYsiyHkAgL4TGh/F7ZQN/h6UQnRfLZ898GbNciwugnhqFKN9vlRoY4x6N4Fy52WtksVxInUtrpl4NFKDasmOl5WFIv/m7wLeKsspkkxZ/OXyd06ISeo84ex/y3FhMWFUJ5QRWR8eEcfc44gkKCeOOuuX6G2ZCx/Rg4ug+3nfSkH0PgXw2ny01Q0F9DzOOWcr9shkQYEii25ZBsykIIgc3dTLFjJ4NDj9undusarDz09DcAGJrcSKlSUrWFEEt6u3fe5bYjaEK1G/lyzjIuvuk43n9p0T5f0+648YJXmfXB9WT/UcJbj3xOdVlDp5tnPcU9s6/hgfNforyw2u+4Q7URpAvx844JIUg09aPKUUyCyfNMvHLXh9z31rXc8xpcdMi97CzJZpcrm0PCjvdrL8U8iO1Ny4kiEbMuBL1iwikdGIR/DqDeqO/0usJjQnll5eM4Wh3cMPpuCjYXtS+4x59+KDO+uhub1c7km0/ivtOe3GPUyN8Bk6Ycze9fr+l2mGmrbMHltuOwOagsqkFv1DH8mMFs+nVr15V70YsDALVTZ9f+h5dJ8m1gm5TyuQDFvgamCiE+wkNU0rCnfDc4GI03ze7EtpW5OJ1uBo7pR2K/+I5l9/MO/z8CB5LU5AC2vXTeqg7HdHrPCz6pbzz1VY2MPn44jV4h6sj4cBb852eunHEBz137hmcXz3t+Vz1+Ic31LWxess1zrJNz/eKF+XzxwnwWuT+mPL+SDT/9QXJmItPG30twmJkrZlxAn2HpvLpmJjcdeneHNsKiQ7nkvrNZOm8Vb/z7/S6vr7m+hZZGT/jVlt9z2o8LRewxibyGChJEmh+To0mYCZURxJLUfsyBDQM90PnqybPzV+/6BfLCyR6SBnTR9n4Zk5542HoIoQgEAtzgkk70woBO6DpdZComSe76fCbfeAKrf9jIjc9OoTSvkuULNlBVXAt6PUqET3/MEGKmtLDGY7hpvBuuVB/biDvI97mwR/p+q16SD6fFNx+Ky2cY6jTEGroW3zwpWlY4jYNGsfuMNWtFI2nxYehafGW1njSt50todrm15Clab5Yt1meYWoo9/2/I8p1T3Ic+QpWgU5xYqlxEbLeja/R5CYTGwya0C3mtodfmoVIECea+1NhL0A+tJXtdIQadmQHxxyIUz3Xq6jVhmc2+3/5UB75QQHuUZ64Vzaa+LdrTVqzxGLblLiREF4UqXbiaGrjv3nsIC4vktLNH89K9nzBp8iEsmqcR1Y7ShG02NXs71/S+B6mANhRs2MUVE2dy8gVjeeXbf3PhEf/n+YOGsES1eH4Li49ARjt+IiKcu165jMriOipL65h0/mH8/OVaKsobfUQqbfp9qtvvnvGDXgdGA60GBx98+TEfff05515yEW/+9gDHHz6ZUHuMH7Oq57IEQjPiEVGRXH3P+Rx5wuEk94uncGsJrS02+h+SgcPmICwqlNYWGyu+W0d1cS3n3HoKd534OJt+2dxhfFZ8t54zo6/is/LZbF+zk8fnT+cEQ8/zqgPTyHe+CBU6z5gFFGru6XvI+27rP7IP4TFhLJzza5dV7KqVAnKIDIpCkXquv/p6Jo47nhPOmERzbdMB+7aYQ4KYcMHhLJzzK25XDyI09iXiqiux6260J/cymGS/wO/a/7rT+AfiCGAK8IcQom3X5l4gDUBK+TrwHR6ZgFw8UgFXdtXowWe8aZC9KpfsVbl/9Wn04i9CdUktz137BpOmHM3cx+bR0pb3IlVikqO49slLqK9q5NIHz2XnxgJ+/mAJAA+eMZM5ua/QUL17WHFHqG6VqERPSM3K+Wu58z83M+O8Z3n3wY855oIj2LWtmPfzZjGl3y1+9S68ezJzHv6kXcrgQMGFk2BCOhzXY0DFDRiw4mGzS0iJ54gzx7L6+/U+7Z1e/OOQrmSRq24izpyMxRbODnUTg5TR7V6iIncu0cYEinJK+emj33l11WM0VDXR75AMjjrHwz4spfQw9HnXfeFRIRRsLfmrLikgho7K6A5J4oGBEPttbTmo3zCmPnwJu3ZU8MHLC7G27rtXqjOYw+JIOP4cnC0NCEVH4laVrz7ayPBR6Zxx7hjW/ZrDmGMHc971E/nq3SU47Pv3PBZ8vIpbHjlrr+pefvdp1Fc3E50Qgcul8uaMeWxd0zmFvEkxY1ObO0pcOPLoHz6eEms2DmwkBg/ArTp5951X+OrTb5BSwaSz0OJqwKL3bV44VBuK92GQUlLVUEZKUirWxlaWzlvNhPPHsyu7hMlRVwGQNjSNYUcO5NaXrqBkZzlTx9/PzgBh8BFxYfz7rRupq2jg9gkPM2vl4yx0frRXBtxfjYi4cI675Ehm3zm3W+ULyaE/Q9HZvUyx5ZLvf/2OTV/nHhC2vGFHDaKxpongsGAGjPXk1y3/es0B6KkXf2dI/lS2yaV0ntOmLSOBm/dUZncc1MZbOw6inf92/NXn1B3sb0/ZX+Cl3LmhgJqSWq5/+hLmPvo5lbs84TPVJbU8edkrPPzFv3lyystcO/NSmmqaWbNwI7UVjUhVskj9lBP1F6B6d8qNQUYQSjtNdHx6LPZWB4qisMD+IXqDnm9ne0KKakrrWPvDRo4+dzwup/92WHComfrKxg6GW2c7oN3xCO1J1DNaJlCq5mHBl6MipaSOKppkHaqUuHUO0pLTOfrcsfz6yQrGnz6aM246ge9m/8iubM2CfH/Q32uxt89AN3IwuiPMfVDiT3gvGEUQA5RRtLgbcOgbsDqaWO9eQhBm7NgwCwupjlFsXbGD4y4+gpmXv0p+oZeFTWsJhfo2BUaNy+C+16/imgfP5I23l7UfV42+sXebfPOjc3S8r4Xqa9ulKav9RhoC7Kf4edA0BoVewNql2xEuTS6IxskoDZ1TcWtz2xRN/pbO5vME6e2eMnFrfNcSXO5z5+jsbnQ2J4ZGO4pdQ2Ov7VOzqy81OYE4PeXbKOwPO2kEz9/6Pm63m3Ovnch7L2hy02ya3B/t/Ggo+g3lvoFzhnhy0NxBmjHWfMkdYUBYOBKw1bqwAcsKyvnw9Z+xxEfy88KtxCZFcvldp7L291zW/aQJXfNKQwhvOKLerAAAACAASURBVFxMQjjVNRrPoPb8tNereS6FECzY9gQnDbnPLzeybY5FoybHzivjMG7CQPqN7sv9t/4XAKXCxxooY335aqKuqf13umu4J8dNH49eGKhxlhIXloWq95CZ9Asf6ymogwFhh7O17leMejN9ww4lp/ZXEmU/IvTxNLlq2N68koHGMbSqTey0byI5KIsnb/oPLy++j43Lcph44eGs/WkLxtBgDp00nA2LNrCu2cqyLwcTGhVC7ro8dofQ6Zj+3lQyhqbSUNVIkCUIl1Pl+pF38XGxN/+ti/esnzizX76v73CgvLi28opB4yXfRy/cObedwpyHPuk8XHK3NmyyFTMWP9kbIQTB9RHUUU60SOhWnz05vyPOGkt1cQ3Lv1mLogiiEnpGBtStfrr6Nv5F0jF7jX9a9Fm38OeJdB8o/D2Mt178T6O+qpE375rLmbecjDHIyMr569i8LBspJS/e8CaXP3w+L930FmdOPYnUgckUby/FHBKEtamVm1+6kpenvg3Aty1zEULgdrkp3VlBcv8E7FY7Vw68lUGHZfL7V/75cY+c+wwzvr6b8acdyswf7ufuEz2hQLYWG8FhB456WwuTCMKImQKZTTypuISTUpmPEwf9GYZFCQMJtcWVPPL8wwwTY/n2jUXo9DqmPHQeP/13ib8B14t/BIQQhLgjCCGCeH0GTdThlk7CiUYRCsYgA+fecToup5vFHyxDFxm+x/Zcdhc/z1tDbXkjr75/LbNfXMSGNQV/zsXsATq9jqxhKSz9bftf0v/+oo7/Y/kOJl14GNs3FFJRVMvAUelkr+vcU3Og4HC4mPbImbz00JeUl9Yz+6kFHDZxENdOP42VP21j08qd7WUNJj03PHQWYycO5rVHv+L3RVu6bH/Y2L7c8n/nAHseN7vbiipdBOlC27ej3W6VqNhQrpk2ibe6mZMXrA9lkOUImt21uISbAebxKOZgylu2E2v2zwMVQhBmjMHqqqfV3cjA6AlUWvPYYVtLk7OGPhFjyW5cSVJaAvM//pTE2BSWL9hA+oAkbnvZE8E06tghnDDlKHLW5jFl+hk0N1ipKKji02c7z12eV/kWS+at4vpRd3fYxKurqCciLoz6quZuXetfjVGThhPfN5bvfphPQdMOokQcRhG0xzpnTD2BD9/9GHZTkFBQcHWhR7e3eP0OT4760CMHAuyXvMte9OJgxD4bb0KIVOA9IAFQgdlSyheFEA8D1wJtKqb3Sim/67LBHu5OXDT9LMJiwvzyjkYeO5TsVbm0NmuC4nsovqxFp+xPBwsb5f8ImupaeH/GZxiDDBxz3nhGHT+M/z72BbUV9ZTlV3DM+eOZ9/ICbnrhCo457zAeveB5dqzP553Nz5F5aD/efeAjqktquTj9JsadOoqRxw5lyecr2/PPfv96rWdOd5vLB8+YyQ+ujxl1/DAWuT/2GH55FdR4KYl7ItYacIc0QJk2JIkMbNJKDWWMO340seXh5G7K8xhuXkSJOKpkCTZpJ0iYcLvczJ3xGdfMvJTX/9UDnSzvfd2T8+sxAnnbNH0KQ4BXk/ab71e3M1HdHp7rX/1M76VnVEiVMCI8gRne42ffeiq/LNhMRVENuujIdg/RNY+djznE5Amx1Cyw47MSKS2u5ZNFW/l6VR7T7z2Dsy4dz6NPf4vVSySkarxpeqsmv8wrcu02+uZPDff9Dq7yjaVi9XmThJagSMtiGOLZGElIjmTQyDSuO/k5MPvcbULDWKn1grnDNF4erWC2Vjvb5jtvs/fLpGi8ejpNOKNid6JYHSgNVj8Pm9Dk82m9ZsKlzX9ry3/xjHH+lmJueOx8ImJCmf3Q51z7yLmU7qigsa7F79rRiFb7CWJr5kpv9ZRXNZ4vRTOUlnJf2ZZ437lOOG0E2zbu4vzrjsHtVnn7ue9Zt2IHq37NZtThmVz30GRmPfIJUVGRTH3sPEaP78/7s38hc1gKvy/05HKpGhZPQjW5a27JUx/cyIpfc7j27Fe856/zG3uHtJNf+hsmQyg6nZFm+xaSwkcRGpLIis3FrHv0Mx696wysqSHoo3ybY0HFGjpQvWbsg0wIIJSkdlF03CoGTDjcLUAsWrjcNtJNQ9lZuwahN2DQGUEIhsYcj0lnoc5eQlzDUJ6+6iNOuGg8SX1i/QhrMganAHD/xa/6CYojJbowr/i7t7xer+CwO3l+2hxPHqk2YkBK+g5Pp6HetseoC9gtB0rzTuqqHoDq9dz6vVcDMVlqoxt2e29e9fhF/PjHfN6ePYsYNZFQIimUORiliXThL02hRVRoNPpY/p+98w6Pomrb+G9mW3pPSChJSKE36Si9CAgooKgILwpieVHsFUVFFPFV7I0iTbo06b33TuiBBAjpvW422+b7YzfZWbILAUL7zO3F5WbmzJkzZ87MnPPcz3M/SIX2wj8ZJBNBA8CiMt1xUDumj5mP0RkreBM4s+88weFB7LkdLpP303zvRr4p99N1VQLu95SOlcG8GYG3JUk6IgiCJ3BYEIRS09n3kiR9WwnncIpjW0/h4mb7sKvUSkZOHMK8CUsJqOFPfnYBW+fvvkYNVbifoNcZ2PjXDqJbRND5yXZsnreLNdO2MGbOaLYv2ktxQTGf9P+G/CyLe01v1yFsNC3k6/VjWTd9MwD7Vx9h/+oj1zqNHb4Z/hsF2YW8O2MU//y6nv6v9rbLmXUn4CK4UYNIkjbnoWpbgrfgX66MB94UkoeLVdzAaDByeGMMbfo0v6HrrcL9DVEh4uXvSZqDMdqiUz1+G7sEoMydGMBczZvz51IB0Gr1jP14Mc0eCOPHb59h9drjLF91ZxJzy5GalENqYg5tOtdj3/7ybmn3G2ZPXEmtaIvw1u41x2jQKoJ9G07csfOfOnKZ/kMfZGiP/9HniVY8/3ZvBOCXz1fwz7rlLNmjReXnTb7yMn/OKqJO/TF07dmYmN2xtO/dhCM7z+GMJ1p37HMAPrO6PTrCxeSdRIR0RKWwLPokSeJs4jqiI3ojikq0Oj0uGhVfvP8on32y7Kav009TgzO52/FzqYloVaMsMRVhMBXjonDDV1UNL8+aeKj87I5TimpMkpGs1Dzmf78OgOSLGQx7rx/r5uwiMS6dEWP7V6gNH/z5EvvXH3e4b8qhr8opK9/L+N+Er0nIj8cDHzS44YUvPoI/ydIlcqQMfIVAh8ctmLgcb+9qXPGOxbXAG9EskkUq/gSXqYRGNA3HzcvN4fE3i6fee4yD646xac6OSq23Cv+/cKdi3m4XbnnxZpWzTLH+LhAE4QzXyQxemTizz+pOY7UwGPRG3njoIwx6I52eeoj8rEK7/RFNwuj8ZFumfzS/fGVOGAFHjImddep+83G+T9DwwbrUrBPC+pnbyu1TqhQU5WmRzBL6Yj2Lv19N12ce4p9f1zNgdC8EUWTB18vRFZVQXKjjlTZjuOLMfVAy07hjfd6ZNor1M7cyb4J9Go5Nf20HQWTmp4sY/EF/Ui+ls23hHuuhN2e+qQgL5/A4SSL3aAk5pOOJvT9/PtlEUtNu28G1R3jxm/9w4XAcWal5OISDWDO5JVY+jiWzA3bhFmAX2yZjEgSV43gaO8hzHTmJAbn5ht1kbqDbGT9QQWYwonEop/bG2qsEWs2M+blaTlhzuJkCPMt2K2Isz4YSMIRYXCyDg7zYuu0Mj/ZpxnNDH+KVd+aSmJxraYrR1seuGRYWQhJtRjRJ1lR5Xja7vGgGGXshs7oLebYDPn9uMhMXvsr+VYdt++XX5Wpjf0Sd7XNm9LVNCIvDZS6jsmdN72FppCbXttuskX0SBcFCHAkClMji0gyyi5MzQXLmoPQ8Msbh1KGLNGoXjX91X84dvMCLnw/i6JYT6OR54+QxZfJnQMZEl6pnamxhYXZmZKObLMZJxpZO+XkTjVrWJjtfR15mIQ+0jQBJoH2/avy1Po7AiIfKyh6IjWHpipUMeqI/U6dsJ6peCL1HdMLVXQOSZDmdUqSgQMfpk4ls3XKapUsPQZAHLgV6RIWAUqnAzduV8KggTJKOhL/dyxZupdca7NOAnOTTBHlFI+oMvDn4D36b/zI+ejPaQkuf5zewLbK8dssETEQH98HTHQGo7dKe2Mx9KFEjSWZQiETU6AyiGn+vRiRmHMKjNCYOy3u12JCHRqkBowmzNU5y/rjFDHuvH72GtufJiNdo1aU+3618G6VKgauHCx7ebuiK9bzWYwL5WYVlY7NJ+zo8Gfmm7Rm0vqsmrnoP3yBvHg18wTI2rtoPFWfErodSb4SKxAk7UzkMjAzgaNx2ImiAN/7kkUUsx4mWGhNCGPGcxtfKcPoF+9CqVzNcPFz45xfL4pc8BTWlOuSRhQkTETS0i4FbM3WTfX62SkBYg1pcPp1IfMyddUu+Z1A1H/1XoFJj3gRBCAceAPZjkcd8VRCEYcAhLOxcjoNjXgReBHChciwwBquf8/ZFe8vtMxlNmIxVg/h+gHegJ94BXg731WsdZZcr5/Secwwd+zgJZ5KY9dnf+AR58+y4J5n8zl98NfQnfj0wgaeqv2TvSmtFWINaNHqoHiU6PbWbhLIkfTqHNx3nu5F/2CUSXfXHRlb9sZEPZr/Klnm7Kv+Cr4H2A1rTY1hHUi9mUJBdyAcT3iHblI6fEIQkSaRwGRBQi+pyL+lZnyxk6CeDyEzKYeNf22+7QmYV7i46PN6GjX/dutV58NAHWbTkIMtXHMXLy5WP3+3HvoNxzJy3pxJaWTEYjWZUGiVjp41k7dw9HNp6f+eFWjljB/95tw9TPl7E+rm7aduzKdv+3nfHzm8wmHjprZ507NYAN3cXxo/+izX7VxFQzz7vmV+Nxmzato2mTdsRFhHIhbMpXDhrn3bIrFHi6eVKo8Y1EQSB1q0jadYsFKmgBLNJwmg0UVSs58qlTPLysnB1K/99Vyo0lEj2RqWdG08z6u3efDtu+U1fp6vah3qBXTFLRkBElC2ENUp3XF39iMs/QLBrNEZzCcnac9RURZer56O5r1KQU8SgsFdBEHi/3zeWHbKF47qcaVQPDywzFEc3DaUgR1uuLoAHOjekj891VcDvOhp3bEB+ZgGJygs0kFqW5RP1IQCN5EoSF6lJJLUbhdKxfluadmmESqOi94iubJy9HU8/DwqsaX0EQcCHAIfnadq5IX7BPmxdUHneUX7VfYluUZt9MoPP7UC1sEAyErMwm6rmk/cbJKmKeSuDIAgeWDKIvyFJUr4gCL8D47Goco4HJgEjrj5OkqQpwBQAL8Hv5k3mjqwKDrZdPpXArE8Tyv6u0zKSwpwikuNSK3gaSxMrFOt0t2No7nVch73Y849zf/VVf2zk2XFPkpdZwM4l+0i9lMHE//zCMx/258LRS+Sm57F90V7++92zzP9qGfMnLGNpxp98OeQndi3dX1bPxwveoNOgduh1BhZ/t4IZHy8A4I3JL/Hb4a8ZUf+NcudOiU+rsNtLRcZJRSyqsYcu4BPoiSgKBIYG8t34H5j0y7ecyz0MArgUelJfbFF6UnnllBTr+fPDuQSFBjD4g/4cWHOEE7vsc81d/VvOgsmt+nKbnuTEWuwMZXUrZQybLA+UsxxSkrkiLNcdVqF09mzfbG4gp7vlee3kDGh567xSpWD4V0PYsfwQycn59nGDVoZKUoiYHYjtSDLGSZVimUxnJ+eyboEt/+KiKdsZ8XIXfv/fM3z+9UrS0iwKiKVMkFJra5/RVZbzTS9rt3XSW6jLIK3gNJJkxselOgHqWmWskzHExrQYvFS89fkyurWN5tHXHmb4uMd57clfMVoNcCZ32/gpDrGxOiaN7fwGN9tvhfyxtXZhiY9trMnbCiA6ylOgkNOKst+ig99XsZ/aQh2b/t7PwP92Z8lvG+k55CHyMgs4tuOspYw85k32W1LIng3rtYsmx8+c/KMuZsti9QwmPn9jLi+82ZPcrEKGdZiAJEnkGQx4XjUOBUGgSGvg03FLeWVkF/buiEUQBU5eSafIGrcomiRyDXp2HYln1yGZW6usKnWGbRGTnZtGoIcZQXauzPyzbD+4iPwsAwqViKeXK9VCfFi15FDZ+8Asi6W06xMfm1pqaQ45RXK2rayHG2Jpb8hzCpaUUF0Ip0QdRKpnHgqVhrDAAbidlyUCtzJvP78+k/dnjuKHLR/zeufPbf0jigx6vRfPf/YEVy6kcu7IZb5a+haLJ2/hxU8GMHHUDPv+lI2DCSvfo27zCL5+YTJ7re7IFWHHnHtDSNfefhPzkO+2jSPpQipdH+hBqFDPbp+r4I5B0pMoXOC1Qa/gpw5k6/xd9Hu5J293+RRtfnHZwu168K/uW5aqp7Jw6WQCofVqXr/gLWL8ig+YP3EZW+ffWUPuDaFq3ukUVWqTgCAIKiwLt7mSJC0FkCQpTbZ/KuBYkukuY8Brj6DX6fn+xcl0HNQON09X1k3fcrebVYXrwKA3Mu3DefiF+NLvpR4c3hjDiZ1nOLD2GD2Hd2bd9K2cPXCBhDNJPDf+KRZMXM7ufw7y58nv6KF4CgBRFOk0qJ1dOoFS/PLKNFZpHcdvmCvDLe8GkZ6QyarJNhU2USHyzIhnuRiTYMmFWAFvvfSETP78cC4jJw4hOT6drORyRHgV7mM8+t+HWffXLpKukeNPuMWkadP/2EpIDV8+/WIg+w/EMWvOjbNw6fmxaEuyqO3XBlFQklkUT1zefqJ82josX1ioY/XKY6xeeYzefZryxmf9+fbjpQ7L3g+IO5lIzfAABr7cjd/HLOTxl7uTm5nHgWP7yNNnIwpKQlyjcVM5ZituBfk5WiZ9sowPvhxIn2fasmruXtxET7R5Kbh5h5SV0xVlYsg34u4uMfmnTTRtHobJZObpQW04F5vKrr3nb/jcNQNbcPbKOoJ86qNUqMnVXuDjL15k+ZzDLJ+3z058RrgDagIahRvBtcqzbXLkpucTe/QSPZ6xuJRKkkSWKRmtuYg6XZ9j65L9dLHmTnygYz2qhfqTl1XIhROJTus8f+QiTTvUJzjMcazY7YIkmbkinUePDgEBMxK1iMRFsGdEVWol62dtY9LrP5FVlEEtqa7de8MsmcknG28pgBWfb2bgG48QWr8mXw398YbbdDs8WBZ8tazS4+gc4cNeX5CT5iQUoQpVuM2oDLVJAfgTOCNJ0ney7SHWeDiAAcDJilV4nSz1lYy10zYTWMsi/mDUG23SsrLzP/X+APavOcKlk1fKtt2wcuCdYuHu15wdN9nu7JQcZn22iP6v9sLFXcOhDccZ8YUt+am2oJiZYxfy/FfP8Otr0zHqjXQf2oFNc3Yy/Iun2L38QLmFG4DRaEKhVDBy4hCmfTDXXunLyQT4RpQnbwThDWvRokcTPHzdy+o2myyM2rPjnkTjpubcwThmf/Y3xYVXK5iVj5mYPW4xL337H6Z/vMCpC6WglsUvyfJNyZkEu14QHF+vnbXYyliI7rIPq13cipPXkfx5kasSGg0OCt8F3Oiz7URh83rvPmdjqrSPq0eFkDz/AEJpHJjGxko991E/mraKYPfWM2UKgHa51fJket7WNhXnawl0UZGVbs2tZZ1cp11I59VRsxgxoiO//jCUCZ8sJS0lD0VJeYYNsN+uEMgpTqSef6eybYEekehMReS763F3CyKjhXvZvoIwyzWrCtQ0D61Oz94t0WbpmLjwJQQgLbuAz39fh9Fspl79EF55vD1uLmqMRhMzVx5gd8xFJPnEUyFjkUsvX3YL5MqTkiAgKRVISgWCl43lkStP4sxlqvT65XF9Mte9bYv2MvT9R5H0BpZN2Yw+NAmFmwdRLg0wSXou5R3BT9Th5xZqXx8gWHPO2T9/MmZO/j6TLYJ01W0xju8u3cr0jwZzUKHlSlYQSVtX4VNUCy+fMIoLk/BW56GLakuBQolbmpF95y0xkQfjUxj6VFsSU3NJPGVzoxRlKp7ynHjI+tNT9KZeUDeytQkgGhjzwfu44c682evKZbCV1+F1TiasEyCL85U9dopSN0UP27vFjq2Us6UypUiNXMlS1ldyj4DVM3fw9Ft9UHmqiMneTTVVbUKUwXzw1NeEtvGhw2MtEFVKDCUGxr84nUtnU0ChIFefSprxMgpBiclsIFCoQU/3YQgKBa6errz01WDysgrYsmDvVXHHsiZZGTkXNzWLU6eiUit59aGPOX9YFvtXARYOIEE6hz/BeAiWGFCzZCKWGOpITRGt5/cJ8mbIx48z+9OFxOadQkRJInHUIqqsnsvE4kMAvgRiNptZ/N29ZZfPzcgnN8NJQslKRFbKPWr8rJT8q5XTlHsZVWqTlti2/wAnBEEoDUIaAwwWBKEZlmFwCXipEs5V6YjZYYuhuDrPVyk8fd1x87wzeb2qcHP459f1vP7bSJp2bsiRTTF2+7QFxSz7aQ0vf/csr7cfy8T1H/P21JdRqpW8222c0zrf6vgJ3+34nK6DO/BM2MuAZeF2IwIjVyOyaRjNuzfh70krr1u2RlQwfV/uQXzMZTbM2kZBTlG5Mn9PWomrhws9nu3EhDVj0BYUM/mdWVw5l+KgRgv0OgOzPvub4eOfZskPq0m9mH7T11OFewMNH6zDsW2Oc3F1HdCcoBAf3hjyB5KzBbIDpF7JJqpBdbLSzzncP336DmqsP8FH4wZyaH8c05ZcP3ZLbyzCVeFZbru/WyiZhVdwdwsqt89Dreb7wb1RKkSem7kY1Qlb3GqHVpHM+GIoRcUlRNQK4OlPZ5OeU4iXWs3bQ7owsn871uw/w4Itx8rVe7ehLSimekQQZy+eQxtvJMTTwgIpBQ2RPm05l7Pdtni7DXht0hK+Gf0oqEUY/TQxR4+wf/8h+nYbwjfLz9gvwmSY//d+Rr3QhRN+Xuze6XhsOIMoiAR4RgAw74/ddOvThNp1grkYW7GwhbuBjESLK2ZszlEiXVugES1zAXWJKwnbs/jp298ZOKobSpXSsnADCvWZZJlTqOvRDkEQkCSJi9rjKMwKvBVB/PzGbJq0r8d7U160LN5k8K3mze8HvuTKuWTqNI+gMLeIgBp+HN9xmoPrjvHzrvGICpGMxCyGRIyu0DWYJTMGDGULNwBRUBAihZFJCl0e7Erbfi0pzC1ixkfz0RYUU0wR0TQijtPoJC0iCsyY8CWIbNLxonLdHatQhTuJf33MmyRJu6Cc4Qzg+jnd7iRugfma9uG8st8aVzWD3u6Hi7uG7NRclv54E5d5vbbcr+xZRXCT+facsg6l6p+SxB9vz0YQBYeiJImxKSz5fjU9hnViZKO3+PXAVwTU8OPYNucCCCd2neGlZu8wYsJglmXPxMPHwghsmb/L1l4nqqTybT2f60xRnpb1SzdxKH03p1ccIU2TgZ8uGA/Bx+Fi0CfQi0dH9WTah3MxGkwO6y5lXHTFBlb+voGVv2+gRnQIb015ictnkvj5tZn2jKHS1tbC/BKmfDCfV398lq0L93H+6EW0+cW2WDd5m+RWfxmbI2fBnLIA8pi20oWDnLmU57iS5cyS5OOhAjGl8pgRh4qwFcANqX46Zd4V5Qtfs7xdoXJlHe0He0YzqJYfPYa25+fRM8BDxhBpLPcyN09HUWGJ5T7K2AjkMVN2ZkhLmYTzaYSH+3NAa2FnzT62RVdpbFjKxSxeHT2b4SM68PukoYz7egWpafko9Lb69D4ysYgiT0oyy4s5FOqzUAcFYfRWUyTTKv5vjwfoVas+Xxzcwr60BKgJbkW2MbUq8yJHF2ZhliQm9O1OsVaPIEGhVs+4qesRRXhhcHvmfPIMaw6f4+/FtjhaU1mzZPFxHjKlRoUAKhHJRWnXV3Y55K56n9WoHUhU45psX3KAcpCxcIJaxZp5exjxcX/ef3sXNTyb2BUVBAGV6IpJMqEQlXZMZplLock+nq2sSbKyupq28ZBfSxbXqJUo0uoZ9fViu/NGamqRkqHmy2E9Gfu5Ra5f7y2PvbP8f9LC7bzWow17N1ren/KFnl1bZONNso5Heft2bj3HoGcfIj4+E0EeQyt7R4hyo5VZxnrK30XF1ne+Xfyu7FmUeQ/YvVuSnCwa5e8ohYL4E1dw9XFBo7c34noK/vwzbw0bD67AZDKX3eOUgrNEuj1Q5qUhCALhbk24oD2Et9oywBVKBSf3nCOsXg2OHz9MuimR5l0b0bR9Lc4eukCd5pHM/HQR4Q1r8ce7c9DmWeLIFn2zgr8u/MyGWdsrFCsHIGFG6WC654IbUd1r4uKq5ocxv6Axu6EWLP3qijsmTIQQSh7ZVCccgATOE+JWE7HY8buuCncIt+LN9f95jlkBSAhVi7d/EzSuat6b9Qpn9p4n9XIG4Q1qElqvBr1HdiXu2CU232EFwirYQ64MKYdSpeCtqS9j0BvJSsomOzWXwaH/Zb1+PuNXvM/Yfl85rTM+5jIf951Y9reblxuPv9mHeq2jLLFmFYBCpSCnOJsU6RIRyY0QBZGaki/JrnE8+lwvQvyro9QoSTidyDarQmqfF7qzftY2QiKrUVygq3BeuaTzKbzT40v6vdSdH3d8xoG1R1k3c5vD+DajwUSdFhHs/ucwT73Tj9SL6aybW5UT8X5Dpyfa8udHCzAZTQ5DH9MSs/Hxc3ew59qIP5PII0PbV6jsjOk7WbX5FJ++/ygHj1ziTyfqcaKoRKlxJ6c4CV9XyyRWbyom3XCZKL/mZeVa1qzO5726szHtLI9tmI5J73yykZRrcZG6nJpL4+jq7D1xqWyf2Qw/rdrNT6t280qfB5k5fghrdp5m0Ybr561Tuyh5ZmQnevRtZm+cMDl2S0QCn0APNBo1jzzTDrB8M7QFOsLrV+fK+VQ7dyRBFPCr5o2rwpNCfVZZf5TCaNYjCnd2gpyUksucv/cz5duh1ywX6OeB3gkzdyMo0RnIySqkdftoDm4/e8v13S6cO3IRjZsKrtKpMksm8rO0qDUqpn1hr4559b0TBRFBNpKWTt5Et8db8+ALddn92iYihSYU7BRZvWM3ex/az2cffc7Sn9Y6ffINkAAAIABJREFUbM/scX/z7vRRhNavwfqZ2+yUlx1BISjRSyVIkn3C7BxVGgmbYjnAYTzxIYs0kCCMuoRTn3McpSGt8MKXdJIxYiCsQQ3CtPVJu5xRka6rQhWqcBsgSPeQ46eX4Ce1EXtY/riXVHKsVgoXdw0jv3qGU3tjuXwqkeS4VLoMfoiLMQm07NkMdy9XdNoS1s3YSnpCptPqKjMe6p6CM/bsNlqFKhRvCLz603BKtHpCIquxac4O9vxziGadG/DN5k/pIQ66oeZ5+nowbNyT/Pra9Gu21dPXHRc3NRmJWcRJpwgX6pclJwXLh/+idJq/ls1k7oRlNO/aiM3zdpGZlI2XvycPDWiFUW/EO8ATL38vkuNScXHX4B3obfcRLi4oZsnkrbY+sTJcnr7uDBzVneimoRgNJrYvPcju1cfQ6wxIkpmQ8ADe/PFZfh+ziEunkxjybh8MZojZc56zB+NsfSlnDFxllmc5a2Z2wpTJVSutsSbmYlmcnTzORBZnZ6d2KZtsSMU2VtUs+11RC/TVuBUX2LJz34XnWZQpSb787TDqtYqwpEi5Wq1QkhCVItqiEj55dY6dKISolc1E5TnKrHWrXZR89vtzfPDyLADMrrZ7YtbIYh+tixmDh2X/8//pQIsWYXz2/WpS0vJQGGz9o0krRpIkktMPo9VnWyazahWufbqj0LjiolQy/r89USuVvLZpFSa/bGuTbPc3L8nm+qXMs7VjQEgdujaO5L05azHLhk/Njba4pqJQD/77n460aBLKgh3HWbnnNOpC29h1ybb1w3MD25CZns/6lccQ9TKWWb5okbHFdsqSRVZ20WwmOMyf7LQ89CWmcmUbtoqgRt1gvv/9a+r6dURpzZWXVZxIoZBLaGArACS17L5anzVBL8+5aBvH8ti2vEjbMyXKmu2aJYtRk90fl/RivLxc+Hj843wwcjoAJpk6qagzoHFR8cIbDzPtmzWUFFsrlb/6Ze8COctWBpkHQGlMXK8nWlKUVcCuddak5fLnWSt7X8gZMVmOPymvoNxp5Gqr8veG/J1kt92Jx4AI9B3RiWwhkZVTd+BL9bJ9KZzjzw3fERlWh2cavFdW98WCw1RXRaERbTF4BnMJCbpTVJfCSDHEY8SAq+BBSBMfxFP+dm3PJpVxf7/FDwP/srX1qnecq4cLE9d/RFCtAE7vPUdWSg6/vW6vclmK6OYRBDTzYtnSpfjmhKBCQxqJ5JBBENUJEGxiNblSJsUUESKEkS/lkEg8rrgjqCR8grxpV7sTodE1HOZfrcI9DGdzMgdzmE3mRYclSWp5B1p1V+ASVUMK+1/lRHLFPv7pXemrKubtBqArKuGX1+xfjutnbKPPC92Jbh7Oxr92cHTncS6rz5AuZiLpJRSCiiixMSpB7aTWKtwJTH53Dr1HdKFmdDDjlr5btvj5cdTUax5nkkxkk4YE+BOEQlCiK9Kh113f6vz4m32JahbOx1ZmT75wA6tlVhLw8PUgKykbF3dNWc6Y/KwC1k23LciUKgV+Ib7oikrIz7a4EZUuPJp2asCwMY+x6Md16Ipsk8mCnCJmffkPAHWahfHoC11o2b0RaQlZnNwbS+sejfli+GS6PNGGPs91JKxedX58byGhdarx8vhBrJy5naS4qni4ex0dBrZm0aRV/PPbBkQ/m6CD5CPLkai8cTcZvc6I4iaO+/Ovnazbc5rvP3mCZ0b/WW6/IAjUqNYSo7ftnZip0dCxQW0+GNiFsfs3sTXBIshwI5px647HMrr3g7iolWglo9Nyv/+1A6VS5JVR3RjUuSkzFu9h95GLTsvfKlIvW1lzB0JHpw7G0/aRB4jyfZBLBYfwDfQiMyMPN7dAavnenbnToKEPsmmlcyZn6IudWThjp23hVglYt/gQfQY2p8ujD7B1xfVZ0TuBwBp+dH2iNW4eLuxbd5wze2MpMuSRIV1BJbqiNxfTrUc36kbV5/nWY+2OreneiPN5ewhzaYS7wgetKY9LxTGEqKOJL4ohQtkEjeBCrjGT8zGXqKuwX7x5Sf5sXruV3/ZPoFp4AJfPJPFmh0/syhQX6niv+3j+88njbJm3i74vP0z9NtGc2W+vBOrp60HXZ9oz+Z3Z+Ek1SCUBIwb8CaaIfPwJtivvIwSQKVncSb0EX+pLzdFTgsKgQJms4qHXWqPXGRAV4h3LcSYIAkqVoiyH783Cv7ofPkFexB27VDkNq8L9iao8b/8SXCMuzWwys/KPDaz8YwMD3ujFseK9eKYFUF9ohSAK6KUSzpgO00jRpkzR6Wat/fcUY3c9Nu0OMacV7RNDiYEVv2+g70s9WDt9C9+9MJlVBbNZ9ccGp8fkSVmkkUgQNREQiOc0/lIwfvogjm05Qe/nu7L2TydpJQSRRd+sYMhHA4huHsHFw2fRSyVl8QQARslA8+5NSIlLoSC7AEOJwV7JUmYRMxol0q9YWIjSeKdS63DMgYskZekYMmYAl2NT2bzmhK2OAkucROzJRL59ZwGtuzWgQcsISoxw5lgCQeHV2L7iCE+/0YsPnvgRs0JJ0uVM9m87y4gP+rJm7h6S5Qs4NxnzJlehlCm4SSUyNqLAlu9HMpT/8Mot3HbxcTJlPrkVXpLH2dnlQCuv9Fmh58wJc+pIJdNSXLSeQxZ/5iQG6rr5l5w2yfEYsNsua9+BDTFsXnIAwUWDOdiWI01SOHC5k8e8ye+Zm63vzW62RZVZKWLws9xz+XXK46BKhUYLq9u2ndXns/1UPEumvcyWrWf4bfo2AHIa2mKw8qJsbXmuXXO6RUXQfcZMxKgc3KzzyfY1LYuqHQmRtnaLtv4zudraZFKLfLp8E1NfepzhXy8s214UJosDtB5qNJr5bvImXNRKxgzvwXN92/DztC3EJdlygAomE5LeiFhiQJAba+TjXsYW27HPpYqUcgZZzkLJy4oCGo0nUZoOdHm6HfHn04g/n4akEByKvgnWeEOjzBVW529rh87Pdh5Voa0Gt3Tbs+OSZFPjE3LsWavGDWow8+05ZQ5+ymzLNYgKEbMoYszTUpKYhSBnEp3kYrR7nzma6MtY/TUzttO4XRQj3+1N3JF4ti09iNlktnsWzcW2eElBPn5L74mMybdTyZV7CTh4DwF2z8YDHepRv3UkK6ZuoVAWc1dTFY0kSRjNehQKJen7ChEVIgqN0hJvZ31XqVBSx7s9KcXnSNbHoTYoiFI04WzhQRqobHMBb4U/KcaL5UJl88xZbPkjmVDqcHLXWaYe/5beI7uwzvocSWaJ92e9gslo5sy+WOJjLvPnh3MZPGYgolLBqd02F9TBYwYw94slAKgFDaHY0iNkSMnXTR8iCAIaLCynp58H+VkFZCZlUz0ymMTY5GseW1kY9tmT1KxTnS8Hf39L9fQY1pG6raL4etgv6IrKx8b/v8a95M1WhVvGvztqsZLx6/d/YMqHYDG07IWoFjTUJJIMKekut64KAIc2HOe3N2YC0NfDeVyHJEmkcoVomuAj+OMt+BEtNCGTFMySmYPrjiEqROq0jHRah7agmOIiHa6eLjQMakpWQAIlkuWDoZdK0NfJJsK9LpNG/sGXq8dw+XTiDcsP128dyXNjBxBcy5/pX62kuKiEjo80QalSEFLLj4ZtImncLop2PZswbuYL+Ph7otIo6dy/BTqtnkZtIxk/7xVWz9ppl7/ObDIzfeIqBjzfCfGqRZDWkEdK3ilydEncS27X/1asnLaV1g83uX7BO4xJf+/gq3lb6Ny+LqsXjOaNl7o5LPdJzy40Dq7GkAWLMTpLyF5BmMyQ6sCFzhl0eiNfTFrNxxOWM/zph/h60mCeHtqOXyYPp3OvxqQkVizW9FZglAl8bNtwkkf6N0d5E4znraJll/qMn/0ymSm5Dve//r+neXFsf5IuZlDoJMXIreLE3gtMG7+chNgURnwykF7/aX9T7O+toGnHetRtUZt536yiMLe8uI4gCCgFFYIgUFKs5/cPF9KkXZ1y5ZSiilrujYj2bkstZR0UVlu5KDfICAK+YiApxJW9S3WSljTTZQKEYFZP2YS2oJi5Xy7hle+HA6B2UTHyq2dYP3MbZw9eILi2RaG1y+D26Ip0JF+wibB0G9KBfasOU5hbXqkYwBMfsiV774p8KQdXHMfIdnyiLTsW7+Py6UTCGtz+RNil+OeXtcz+bOH1C14HCyYux93LjdG/PF8JrarCfQ2pkv7dJVQt3ioRWqkAFeXdI70EX7RSxScUVagY1C4Wi/ADXRsx4svBFTpmyQ+rGTr2ieuWKyAXHwLKWSX9qUYelgndhpnbePyNvihVzgnsOZ8vJmb7afoN70UjrxakSpeIM5+g5X+jCdaGc3SFRd797P5Ydi93oFDnAHVa1OaV74fRb2QX6rWIKHMpAdizLgZJgidGdqJhy9qo1EoUSgV+1bzZuvwwMfsuMH3CirJt//y5nfVz9zBhYXnJabPJzIHNp6nfsjaZxZc4lrmWA5fnciV9P55Gd/TF+ZzJ3ILR7Fgopgp3BhdPJVK74Z2bSN0Ith2P48kRk+nz9M+0al67bHvL+rX4pGcXlgwfjFav581VjoUZbhQd6oSx6Uzc9QtehZxcLe9/voQ/ft3EwCdas3DuXl4c8DOnjiRc/+BbRMOW4XzwwzM0bBGO0Whm6YL99O7f/PoHVgIatwjnsxkv8N3yN3mod1N++nAhX42a6bBsfk4RU8YvZ8uyQw73VyYuHE9g2qeLSbuSRbvezW77+QA0bmqGjulPYE1/FnxXcRXpzX/vx9PHnagm107rYMaEQSj/rvQVgzEE5ePV2Uiqx3mS9Ofxl6qRJ2UjSRJz4n7hkZHd+LCPxf1+4GuPsHneLo5tPcWqPzby54dzeeyVXuSm5TLn88XkpFkW3+7ebtSqV4OY7c4VlYOoQR7ZXJZiyZEySJDOk0FymbLk1QiJqEbqxXQyrmQRUNPPYZnbgdyMfK6cqxyWb8t8S1z5/QBPXw9e/+PFsgV6FSoPkiRUyr+7hXvPbfJ+oXYdBHx6CN7kSOUVmLKkNDwFX8dy79dx7brrrpJ3435U8Jy/H/6aX0ZPp/vQDrhWMA9fVnIOe/45yMuThnFo3VEObTjusJwCJSbKu9cYMeKKxcXMoDey8JsVvDn1ZY5vO8XWeTvLfPJVagVdn2mPl78ngbX8ObTuGFnxuYQJFgvt8MEjePP3T6xllTR8qN412y0fJzkZBehLDGRnFLB71RHLfplr0cG1R9mtM1iYNOvi89jeONCoadSqNnWa18avmjcaNw2IIq26NSAgxAfJYEAodVe0uoMdPXCRVGkPGdoc6nm0RSGoSNCeJE13iUiP5viaQzD5JeCeEYqHjxuZSTlIOieLOUdj3UlCYTtXSW0FrPx27oXl0zg4S6Au7ze5SIogE0NwmM3T5CRRs8mJK5msjN0zf7NCK/J2u7ly+mgCHZ96kM2xaQ7Ll0q2KwttrmamAJuwhVltq8/kavssaCUj6mhPcgt06L1k/SlrttHaVXqbjgglAbY+y24qoBRF8BD5dcIgon0DWHcxlk2p5xi3bT1GsxnP+rZ77ONm+x2TZRGHCPGxiY4kmmztMOTY3D1VhSKRfn4s3XYCQdb1Bjd5wnCZeEqO5VlVam3PeUJqCtvXn8RUVGKXYNoOcndUueCF3F24dCzJx5HcEFRic+mbOmk9sScSefG93kSlF7B87l5q9G+OwiyVseFmjUx8o1RyX16dj+0aTRrbDpccy40SRainVBNRL5iGLWpTq5YPIeGBxOyOZerHi2yxraWuy0rZ+DIY8Q3yQqUUoUj2LMqTldu5e8vdmR2ko5Bvk7kz2r03rC6mJ3fH8vjonjZ3aXl5uYuk1RVSuIYhzVa4/Le7WecGtOnXksW/rCc7Nc/u+SpLLSB3lbZzgZVYO2sHQ9/vx/mjsthJmVunoFAgmgUUooIk4wVqKKOsh5q5aDyB6YqRcfPHMe2nmSxbuAyFoKRE0nHWfIS53y+ixxPdqFbLjxMmE1HNwlk7bZPddXgHevHPr+vsLnPAa4+w/KdrL0IFQaA29SiRdBSRTxA1cBEcR5o+9mqvMlXL/KwCvPzL52u8H7Bu+haGfzGYqAdqc0F+v+5BGA1GSpyoaFca7pc5dxXscO8t3u5jBFGDNK5wyXyWUCEaUVBQYM4lnUQa0vpuN+++xYOPtaJxxwZMfnuW3fYvn/6ehLPJPNC1EbkZlytc3+m9sZzeG0urnk0YMeEZpo+ZV66Mu+BJohRnybNklXw2S2arOpdN0js+5jKTnv+d2o1DGf3rSFIvpuPibpkANe/ehKJ8LWN6T8BktJ+ka9w0PP1+fx4c0JoFXy0jM7HilsCMxCymjllgv8DAEpPy0vgnyEwrQKVWolIrKMzXsfqvXfgFeTNi7GOkXclmy4qj7Fp9lE2LLUnp67eMYMP8PY7PlZ9Aak4Gjbw6l22L9GjBuYK9lJi1aEQ3MvOKeOGN3jTv0pBV07exYbqTOMAq3Dbs33iSZz/o53TxdrNISs+jfu0Q9sbc2iTHaDbTfs5UUEks6PMUH+7agOB6a+IDjuDl6kJydr6TbHv3JmJPJAIw5X9rGflebwDWLTvMsJe6MPP3m3+Wgn09GTuiKx7uGkxGM/lXckhJyGLXhhOcWHOISWve5/vXZl7TfuBXzZuhbz/CheOXWT1jx0235WZh0BtRa27vNKX38M7otCVM+XjRTddhNktkJOXQvHMDjjjJHfrE671JmnwSTb4bsYYj1qTXZqqJ4eSRgVeIByuXrSRabFp2TADB/G/iN2ydvp8px7/F08+T7Yv30nVIB5b+sLqsnFKlQKVRYbAqobbr15KctFxyM/LLtcMRNIJLWVybIzTt3JD8rEKObztVtu16sXL3KkRRpEZUMGENat7zi7fiQh1/vDXr+gWrcMO43yM+qhZvlQGr5UJEoKHQmovSaU5I+xAlAQ+8aSC0dPqiu+PM2n1oZSnMKSI3Pa/c9vgYy4Jt+kfWxdcNJp48uD4GBJGRE4dw6eQVDq47Rl6m7WMXRl3iOImL5IaAQDFFhFGn3L00myXijl9m6ntzMOiNZYHQ0z+az7KcmazTL8BsMjPuiW8wWZP9RjePoGbd6iz63z+8N+tVnosu77Zof69klnCrHLxdwmxRxMVDQ3pKHkv+sE34Aqr7MmhUd5ZM2cqFmCu4e2qo07A6JjMMfrM3SqWClIQsXD1dETQaqof502VASwTJTEmxgRl//4Z3frVyTQtxiSZHn0KwSyQmo5F1M7Yy+J2+BIcHcGTtYZp1acjmebsdi2bYiQrIRAfkYiTO0gbIq5FZ6u1SKjlyZZAzVUpZAmAPWWyHXGZfxoxIjlIiOLPwy+XGZdZ5p0+5rF0mychl4xlMVqVEteBCqFgHwcG4trv31t9mSSA3yjYBk7NMqiJLuxUylkmZaYuDETxs9el9bH0fn51DWFQg2y/aG0d0Mo8po7ULS/xs4zUk2uaBkFtkY8WL09wBAUwC7aNt7o3uStu9NEu26z2bZ3EXyiy0iY6YzbL7q7Gds6C2hMlVoqC2hPd5Wxl1gWOGSFFifW/rZcmudXowmhAMRjvJezuRErmwjjOUitzIx26htvx+sCQBB94bPxCFUoGkUnD5SjaFZhMuNbwpKNChKLbdt5JAy/lV+bZtBjcZs6yE1tE1ead/J7795B/i4yz3QiVbgJsLClj352aGvduH6dZk3CBjha391LHfAyyZuo2ki5Y67BJc62yso53Aj5yVcsS8yb55ds+WHZNmey+Igk0IxI41R3bfrM+jIHuG7eqWP9uy8/R9sRsFOUVsX7zf5nVgOantt87K6sneSXZiKFasnb2TEZ8M5PSBOIsSp0p2T0wmNszZRd3mkeTvUBCgsKUbuGg4RbAilA3r1+NvrG5XpygocMENFz8Vb3Ucy4+7v2Ro7VHkhgfSf3Rvlv9scTde+fsGnv38KUq0Jbh7u3F08wlWT9lUro03i6adG5aJnpTifl28mc1mJKB+2zpsnrvzbjenCncBElVqk1W4CkpBRbTQ9PoFq1BhxOw4TcwO5377t4KD645xcO0RwhrUpOuQ9kQ1rc2fY+aSnZqLi+BKHZpaRUYkNMK1XTMLcgrLbRvg+xx1W0Ux6ofnaNnzAQwlBiSzROqldN7p8hlplzNIOJvEg/1bs2Zq+Y9ti+5NaNS+HrPHLym372poC0twdbe3nmam5lFcVIJCIbLgl40A1HsgDKNJYuvyw5jNEnqtjiPbz/Ls+33JSstn0a8bMRTpULuoKCg2UGIuH+yuNeWjFl0pNGcR4BeAYBRIiE3Gw9sNTRMj89bMweRhwpAvUVNZxy5Ivwo2KJQKGrSN4vS+C5zTHiJM1RAXa26oQlMO8caTRCorJkZSGTnrrsb5pCz6tLm2S++N4v7+ZFY+GjcP473PB3LhXCoJlzLRaJSUlBhZs+Y4A/q3YPZf9gnPl0x/mTWbTuClUqNQiCSn5LDkShwZeUUoRRGNi5J3B3TmP98twCPOeaz16imb+HX/BJAt3kohKkRcXNX4h/iULdzuFi6dSSKsXnUun608ZUNRIfLcp0+wf90xTu09f/0DKoiN8/fQaUArNswr78lQmKvlxaee46u4LzFeUaERXMk0J+MqeuAquOPm6WZZM19l6cnLymf4+KdZ+PkKvn72Fyas/Yh3unxGv//25OC6YySdTyEjMYtp789BFEXMMqODi7sLZpMZva78YvNGoFQry3mP3M8o0Zagus2MbhWqcDvxrxi9fsE+NGhXh13LKiYIcUu4TvLDCh9bhRvHLfTf5dOJXD6diJuXG0+//xj52YXsWrqf1IvpaATn7iQVOWdE41AatKvL2H4Tyc+2LPCimtcm7bJlUnRsy0l6jehSrj7JLFKYW2QnVW3ZYf26y/2dShksq7U5JDyAxm2iCA4PQFSIGIymMjbp7L7zdvEq6+btpX2fZiz6dRPF2bbJnr6ohABzOMn6LRQbC3BVWmIcTJKBxOIzeIsBKAWR0Nh6pAjp/P3davbGbyVtfzHVJEtsX4Eim3j9caLUzWysh53F1gknJY950zmJi3P2rDlg++yYPDnbJk8oXFB+8X31sWVMirMYH7m1Xz7ZMTn+PWzsQM4ejKPXaw+S/H0cLrLMZh4KXzJMiRiUJkuKCScJitGo8A30pKCwxC75slnGMJbGd4klspghWVyT2UWWuFjWleeS03nevxWSAkpkMW0aGRFusJJiykBbPFTrQBtTd1isVfY7SavGTaNC8DSQWGSrMKvIdk/CfGyKqwpr8JrJ7HjJV7uWTSlPF6JC7WakWsN0UlWBZdvVBbbr9Iq3sV/KDMtYl6dJkFzUoFQgqZRX3WMn73I5c+wtH1el9clSKiht7KFgvQ8TZo4kLKoan70+l9jTydRvHkrz1rXZu/0c+blaSkoMBPq7k2S0MJOD+jQn0N8TpY+aI3Hp5BZqqRsWxMcduuOuURPk7U5Rbgmz/9qD+oIO9SXbwsssY4sEpeV+Xz6bwqOv9mLl9O2oXVSISgXBof48PuphLsWmcnhPnH38m6wf5MyWUw8SR75JckZTnkjb4DjdyKkdp3igayMuHrvoNI1G2XGy+2GX4kP+TpAkhozpz8Z5u0k8n2pjyBVO7nFpG+X9oJQZ8mTPeXGhDoW13NVJtQGmvLWYnSlbWDx7KZNen0yoqh4qQYNkMDL31TWkmC/jgU8Zq2WSjOgkLat+2sRD/Vvz6+vTMRlNLEiaTFGulvjjl0mJT2XbQsti0XyVWuvon5/nSmwS3gFe7F99hGNbTzq+xmugbqsoh3nR9Do9ahf1LS8M7waSYlMsMd9V+HdCwrGHzn2Ef8XirdeIrtRrHX1nFm9VuK+hzdcy/aP5ePl70vGJttSoU53Te85xes+5G5bxL8Xa6Vvw8PNgYcpURtR7g5SL9nFJT777KEu+X023IR0Ia1ATlYuarfN3EXv4ImcPXODsgQuIFQnEB4xGEyPGPEpifDoxe86zYcnB6x6Tl13I6r92OdynEjVEqppyOn8HGoU7AgJ6s45IRUPc8UYp2Cb9K2dvIEuVRA2pbtk2T6Uf2aZUdOYiXBQejk7xr8bp/edp2LYOc2fOw8XkWS7fk6voSYlZi1px7YlGx16NiawfwqSeDYiuHYSXpyvFOj0vjZ3PpVtQVivU6fHzvJFU2ddHianykjvL8WztzqTpHMvc36vwD/TkmR7flP199kQiI0b3oH6TWhQYTKxYcYQhQx9k8ZYYGtarTofWUej0Bn5YuAPRunY6cDKB4v2HAfBwUVM7T0NCUsXeVT+PnsHkoxMJrO6LQW/EYDBRmKvl+/cX2qUOuZtIu5xJYC3/6xesIPq90JUTu86ReD71+oVvEG5erhTll08vIMeQ6NdZkvQHe2adIi7GpmaqLzJSTQwj1nQMb/wxYaBQysOPaqw7vIJNMWtJkzLZOM/AiC8Hk5mUzfyvltJjWKcyCX85gsODiNl5mvUztgIw6sfhnNx1FqNV3KWiSbbbD2zDzLELym1393a7LxduAE06NSDpQuXf/yrcP6iKebsPsGDicpTqu3CpVUzafYv8rAJWTd6ISq0ktH5Neg7vgkqjYv3MraReTC9Xvm3fFiiUCg6uO2b3QXP1cGHc8vcQRRGlSsljr/bij7dnERJRjTHzXkehUmDSm+g/uje7lu5n89ydCIJAr+e70n5gG66cTeLs/vOMmDCEtX9uIbh2IKtKhQOsVuYHOtcn5uAlatevQcy+OE4fv2JrmJw9kLNMTmJXyg6TWaq9XYNp7hpsseqW6BEU5S1WftW8uZSahUrvBleFqHkqfNFShKvCu/Tktp3yuDAnygl22+XHOmNDrOXlDJvg4lg90o5tk6tNuskWLBoZ81ban/KYF/kkVx5bI4+zk1+DzFJ/YPNpDm8/x9MfPs4XH0zAWxFgdykF5hyquUSAoLCL50GjpkifQ2rhWcxFJnJKQojXQyejAAAgAElEQVRs0Iof5+/k1LkUtDo9UVFBzJn0HOfi03hzwC+WdsiVy2TtVsr6ROFp6wfPRCOF2cW0dPHn9Hkbi5MXbut7pXX4qN1tda/a3Mp2Htlw6RZdm7RcLUKmhnhdNYdlTmTb7ptksuxQyMRNzAW29iUpfAAYFN6Muuo6DF+2DAhEobdVqK1m++1xRTbeSg0isjxrkqsKSRCQFKL9OPFwohqpshkkTF42NqaU9ZTHGAoG+/Ht5uGC6aoFklGtZMpky2T7lfd7Ex4ZxJbtZ5j903CWbz/BsUvJvPnBAgs/KzvUPaX0eS4hMzO9jL+VvGx9KcrjOA0GQmoHMuCl7kx6fQ6nDsZb1BtLx6y7jFmSt1Ehe1foZe8TJ8+unSpjWR0yxjff9vzZsWOCPWumUCrt94N9XFppG52xbQoFD3SuT8tujTi48QTH95y3f1avLi9HqeJnriyxuZPk654eavLTcu2Yw6tRmGVhfDv0b8WFw/GArf+88cULL4qkfBQoccEVLYUEXI5EEARCcOcCJ3i766d8t/Vz5lz8jaG1R9Hzuc50eLwtO5fYFnBRD4TbCXLoikowGoyE1q9J96EdCG8Yym9vznD4PZPDaDBiMBjIJAUtBXjhhy+B1zzmXoaoEMnLLCCqWW18q/mUpVaoQhXuJ/wrFm9m8637fFfh3wmD3kjc8UvEHb+ExlXNo6/0wt3bjeKCYmK2nya6RQRBoQEc3nCcovxiho59nMTYFDbM2gbAe7NeZcFXyziy+YRdvUc2xZCXUcDDz3biyZAX7PZJksTaaZsBCG8UyvNfDSGyaTgv/G8IV84ms0aWUNvLz4NREwfzybOTadgmEld3jf3irRIhiiKSkyD1sXNHs2zqOmbOmU4Qtez25ZkyqaGKvi1t+v8AlUaJsQAEQUGq8RLVFKFISCQZL+Ch9EMUyk8qs7QJ5OqSCfNtiUJQsWLmPqb+PI/qXWw5DGMvZdD+6e9Y+usLPPFcexbP3EWTtpG8OKYfn74wnUatIkiKT+fj359j66pjmKzS+EZv20LFrISQIG/OXUgD9c3HLapFkfpBgYzp0JkXVpSPsboVtA0MZ0B4U55Y+Pct1fPkyI50eKQJnp6ubN144+5lN4r/ju3HnJ82ON3/088beeW/3di05RTnE9JZv/8spy6mUlk8aK2oYPauPcbJA/GVVOPtQ3FhMb7VvMlJKy9cdT14+XnQe3hnMpNzmDr21sbI9aAtKKZZx/oc33HmmsxlUlwqMz91rG4pCBahM4BEKY5owRbzqhFcqC7VJubiUSY88wPf7xiPKIqsn7mN3iO70bZvC/atsrCwnn4e1GkZSfuBbagRFcKqyRvwCfJm2KeDWP7LWtx93ElPyCx3fg8fd7uk3kXaQs5xjBDC8KcaOWQQy3EKCwbg4qZBd7ul7CsZZpPZwlg+2wntdVjSKvw/RhXzVoUq/DtQUqzn729XAOAT5E291lEcWn+c5Dib+0XsoThaPtyUQe88yt/friCqWe1yCzew5HZr0K4OwyJfveY5L51MYMp7fzFp6zjSziZRXKjDXBrXoVCQn5HHC60+QnDRsGKK1R1THqNlx1TJFl7y2BAHeZnMssTbgjP1ONlxP789h+T4NFSChjTDZYKUloS12UIKgkKJxtXbFiMjj8uQ1W1nzZaryjlRjbOzlMvZsVIWwElbpcLyAixwFdvmIfstN/yUWtSdxNZQEQU2OTsgCjR8sA6nD1+ktucDZOmTSVFfwDfQG+8rwXjLYrdK49wkSSKjMJ56gV3KdvloalFcUkjxlYt4+Vj6XlFi6YcXh01hyfI3SLuSzX8/7MuinzcwY+uHHNpxDk9vN/ZvPcPlVNuk2FRoi13Te4pMnLEJvYeIKLtkv3O2e5JVX4mrWslHYYPR6g3EZ2djDDcyuF1TCnV6srMyKcjLJSCkBl8u30JKXB4uCJjyHMfZGZKSKTp+HMwSYvUAPJq3AI1tQakpkN1LvQcTHn6UXnNmIsrYNpOrbYyZlXIlxPL3R7AyUg/3b87no+dwJd6qrCgbm4YAG4Nl0tjqk6tA2o2x0nhD+XpXPjZEgeiGNZn04WJQ2grJYxK9fdxo2qgWS/7aiwqRr1/uy7OjppMSZem3wlDb+I5Y6ngSKhTKmDJ3N0SFSHSjGrTrUp+iAh3L/9xmHyNa2j/O3Onkao7Och3K43bl7Fbpc+zsGZa3+6r7tHraVp544xFmfSZb8JjsDrA/ByC4WtjD0T8+y/RP/yYl3sowOTmn3fXIF17G8ikt7BVoLWUDa/nTqG00F45cZNiY/ui0Jexaup8rsSl2MYH/ZE8nPSGT/JJMUrmCYP2vhhCBCg2ZUhJF5OOFL6KDpBee+JBBMqf3xqLXGXjkhW6smryRtdM28+znT5Ut3tb+uQWFUmEnNCIqRA5viiEoNIDJb88u5zbp4qZh/IoPWP7LWrYvssTRLVu3hEgaWuJugQBC0EiuzPxzFn0H9mPTnDufQuJW4OrhQp8Xe2A2mykpvv+M+oIgoHFV33eL5nsLdzfBdmWgavFWhSrcBHLT88o+klfj0IbjGA0mnnz3MSQH5h03Dxc6PtGOR1yfcVr/Kz+O4LFXeyFJEoIgsP3vvRTlFaO0JlNu+XBTVG4uaNzUZCRmc+Y2sW0VxcWTlvOHahqQZUohznScyMa16NKoHXFbc0nIuoDOWICvIhg3wf06tf274FfNG79q3jRqE4VKrcRYYmDRj2sxKh27opkxoRLLi+j4u4eTkB1XtngrhbZIz1MPfsHUNW/hF+TFP9O28s+0reApc/nzt/02eKoxm02kp8VQYMxEVKnxj2iBq9qnfNt93Xi+bztaR9RkyuFDuKqUvNOpPcsPnGbYL/O4tGkVaqU7KlcvtLlJqGuH41m/sdO+0J47C7EphNbpjqhQUpSdROo/K/EZ9KhDafIvuz3MolMn0BqN3IqO5dDXH6ZEqy9buN0J5OUWoVQrqdO0Jl9PH8mK+XsJrxNCVL0Qtm88xbQpW5kxZRv9Brbgr4X76PxQHbp0qMe8uDM3dB6VWsmDDzcivH4IgiBw9lgC83/aYJu43geS70V5WrJScgirX4PLZ5Ju6NiEcym2hdttRNenHiSsQQ3++W0Dx3ecwdVdTYfH2/DwsE6kxKex7e99FOVpiT0cz9JZy0knkQjqIwoiRsnAeSkGCahBOP4Ek0smRZRXCy0gh8eG9kV7woyLu4adS2Wx/Fd9bq5WiDSbzGVeHY6g05aw7KfVnNlnU+DMzsjBWwixK+cp+BCXcpIGD9Yl6UIq5w5cKCeWcq8isFYANaKCWfD18rvdlJvCo6N60vChevz8yjSHCtdV+HegavFWhXsbpRbV+yx+8NjWk/gEeXHp1BW75Km16lZn+pkfMZnMTDn+LROG/MSlk7ag9b4vP4yHtxuPvdqLRz3/g9Fowqg3giDSqH09XD1ckEwmGj1YBwSIaBzGsW2nOLXnXFkdgiz/WUXykTmEPN5H9lG2j0uR1SHbHqAOI4AwXh89FP9QD55cNAx/oSa+6hAy9FcwYyLCrZmlsNzC7ozhkzFydhZvOeOltFeTK9c+O6U9WZ84+y0/vyx+pSyWRW6xNlVg8usoPseKjXN24eHjRonOiNFgslyLQomgUNq3ycouipIKY2F5q2uhPhtPhRuaNOsHXXb9xWlZrJm5g34jOtmuRaYwaZKpImoDFFw5sIrgoOZU826O0VBMwsn/Y++8w6Oo3i/+md3NpveQQkJoQXoH6U0QFJAivUkRbKiIiBRBERABEQUEpHcQpYTemzTpvYaENEjvbbNtfn9sspklu0koKv6+e56Hh2T2zp07bXPfe973nBO4l6uNo0cAAA5lnZk9qgsVSnuyYOdp+m86gd4GFDIZA6rXYvHaP3l87TC+QU1QOrsD4EldYq8eQSH4Yevsidah8HVLu3afsvXeMv7u6OGPR3Ya4qlQnAMMqbfKzILrV8/Vl1kzD+CFgCAVIpXUTNknFlCGWjvJc1LKsIggz7albc+GzP4mGNHGAisjqfPU2hf0rXYuEJJRSnzX5Kq8SbOFry1RIcO/rBdrDn+Jm4cjg7rMY+y33UiITqFcRW/KVyjFT4uHcPNGFAf2XqNlhxocO36HmtUDcD1iOI5LZMGY4hoVLIh4Xyxg4ZwUAiPGdWL3pr84tf+6MTVWtLcFG8PzZOJnpzXDWktq5ZAoQpr4GEreBxMvNul7Yq5GVfJeiBpLpu2GMe1ZepjhM/qxbMLGvGNKfN7M2knmnSui6feGxRpaC+9xMcoGolaDg4s9gVX8uHzkhnFc2WlZHFhp8Nz0LedN5xFtyUzL4su23+LQSkd5qhoXJBSCDTaiLYFUMjJcnviQK+YQ4/AQn6yyyAQZKjEb18ZKvpo1Dk9fg9liWkIBa65Ra1DYKIyiJM+CJ4VPZIrC900n6gCBpWPXUbt1NQZ904uE6CSzdjcvE2xsbegztitXT9zixsmnWwR5WXD12E0cXR3ISrOmfD4XrGmTVlhhhTkc33yG22fvM/jb3mhyteh0OnqN6cKsd+ZzeP1JvtszgUUXZ6HKUvG251DWhS7E098DuVzGiJpjCqVFNOpYl7VT/sDWXkmpAA+O/HaG+MgkdC/piqdcIWPimMlUVNZDIRgmimXtqxOT+4AUdSzuSt9/eYQvDzJTsy0LJjwBQRCwl7uQlBWBp2NZALS6XHLtoxg9ahJv9W5MdnYuwRv+IiY6ySD3DgSvOEaX4a2L7T8l+hbeXjVwcfUHQGFjT/mgDoSG7iOgnMECYuucd5m/+U+2HbtGrr0ksNHrufcogTWT+9Ou+15j4JaPUtWaYpd0h6UzR6JQytHq9KRmqfhhyzEi45JRKAozii4+FYgNO2sM3vKxcHxPSnu54uxgS8ZzpBDVrBuIKlvNbYny3z+B+JhURg9cYrAnAMZ9uBYhuyBQ0no6MHpsR2rWLsOW7ReZNa0Xl69F4O7qQEoREzeZXKBv/6Y4OdkiV2lZ+eN+MlKzQftyfk+UFHqdHlEUkcmEp1LC/CfMpAdMfJuy1QLYPn8/rXs34a89l1FJ0o9jwuL4bVYw73zTC71ez/Vjd6ggVDMdJ4IxcMtHaaEcd7IukkUWgihQu1lNpsyezKgmk4iPSmRD+GKTurPE6GS8AjyKFSF5GtSsWpNzEZfxIcC4LZoH+BCAKkvFuT2XObfnMr3HdsHL34PE51C3/bvRd3w3vAI8+GPurn97KM+MfFsjK54DotWk2wor/l78xxi3JxEfmcjy8RsAQ/pSbraagMr+9Py8M7MHLyQtMZ396t94/Z1WuHg609Gun9l+fMt5kfgoCU2umtpt6qK0V5IUnYiHtwv+Qb4m/kYiktVxnURBUWahNsvM6rPFQEK6XRo0Sn3ZRBFPPzfU2WrSkjLxeCLlxkdZgbCcK7jZeCPopeyZ5Gfpqn5OwSQI6YqyjY3Z7fnsmIknlZR5k9YEShk7KZOXI6kVkjKPtnmTK+n1k/ZhwdtNzJacg/R6S2uI8p51QSY5LzvzFgFlXGvzOPM29xJOYOdgS/3mlZkz9leq165EWko2gl5k5IROJEQn807t8WCjIDs1G51Wz+zto/my53wTdcT0cgXHyT4fjV+ZdibHEwQBR1t79iz/kOzsXEIfJ7Hl1DX0clDkGM4n185wTab8cZjfP+lHoK8HT67/CzIFVcp48eHkTWhsDO0rBnjy84i3+GPrebbeK2xurE6N5bOenWnf9k0UChmiXk+NWoFo1FpmTA1m8ajuLJp3iFs3CiY0WseC6yqtSxMl91jjarjOch9HZEo5OZ52KNOkCqGS+66TKHNmS+6rhJFT5BRsl2cb3kHBglDWkBGtOLvnCkJKBkifjcCCd0VvK+fH+Qf4ZnI3vIK8+GT2Vv74aRhHlv2J+n4CWdUK6iF7V62Gt6czdkobhIYaDhy5RfSjFGxj8+o77ZTITFQyC85TlJa/avKee5nkmZb6r0m/Z0wYLEsUYzHMm2RM0jo3E984SR9/7blMg/a1Obf3yhPecrpCxxDzlCzFXNN7YOIFJx2KJQ+5fAbPgqJmmVdKE3E7Gk2uhqg7kXw8bzARNyMIuxFVqI/85X69qENENAkstWiMafL50It6lNhTUagOQOYZPV80/9b4+a0zdylbvQz3LjwAIOlxMp6lX2zw1rJxa/7ad5EQriNHgQ4tHvjgJLiatNu1+CB9x3dn1aRNL+zYLxIuns4o7ZRE3I42yXaxwor/IqzBmxVW/EPQqLVGwRPP0h68NqA5pfw9ibgVzZvD2zKx43cW93X1ciE90VD/ULl+RW6cvEP4rWjCb+VNWJ/WEF7at6czlRuUx87RlnsXH/LWe69xYO1JcnPUxEclPVOfrbo35PTuy2jMFPvrRA1ywfrV8zwQBAF/Z8OEzq+iD5981o0Vs/aTHr+dBq2qUKFqaa4ev42tvZIvFg3lr0M3Ob37CgPqTWJv1Lwi+1Y6upGTk4yDg6m3lreXI9duRTFzwX5CtIVrcaRY/PspAkq58SA3G4VtgfhLVsQVKvfoxr2TBXVLodFJzJ23nw7ta7Al2Jm0yBu4BRrq4rSqLLIir7N3pzf7dxsWQXq93YALf4Wxce1pAAYMbs7Muf2Y/vU2zp0NLeEVLMCYj9tz887jp97vWVG9biDd32tD31oTStT+22nBTJvdm/ZNq7D14FW+XTaUYa/NQhDAydGOoX2bsv/Kffaeuo1OL2KfWHzKnCAIVK7hz8OQOIaOeQO12rBPyuNkgleceK7ze1HwLO1ORnIm6hyJYfeZ+wyZ2ptze6+UuJ+/m3kLqleem6fv4V/Jj5xMFZ82/SrvwJa/k30JJD0gFqdob+SCHI2oRkUOsUThR0HNahQP8MbfYj+v1K9IQlSBYmRybCr+lfwstn9W+AqB+BJYKLiUIidTRUJUIlVeDeLu+QcvfAzPi/SkDO6eC+HW6bv/9lCseBlgTZu0wgornhZJj5PZPm8vAA3aX6dK40rcu2B54nnvwgNa9W5CyOWHuPu6ElS3HMHz90paFO+R9iSb5lfBm9cHtUSTq+X6qbsMn9aHW3/dp0rDilR5NYiYhwn4lPUiLS6NR2GxaHK1pCRnER0SS26OBrleh0wuEHYjyiT/3sZOSdUG5ek4pCV7mu4gU5uMk8LD+Hmk6halbYJAp0cUpPUx5plBaY2ayfetlOWSS/rJmzSZeNlJa/+kbJY0uJQwDCZ9SxnG/LFYYMRM2EAJBKkappTVM2FD8va1l6QOSlg9qU2DIGEaNclpePu68NHEzujVGu5cCOP4hhPIbG2ZufUzjm05z4Ql75KdmYtWq0MQBD6d0x99nndZp851yMrO5Y3+8wEo5V+HqKt7CarUEZncMKb42Ot88n4nFv5ymLjQJOR+BeeTz0o5xBXcg0sR4dgrgxCiT6B29kWtcEKT8JC327zK4d9DccCUBQu5HUrIqVA8KI86KYSYiJ0IcjkKrYyynk2xTS5gp4KXG9Tt8q/M3SuRlCtfilFfvMmg9nMMn2VK2Aupx6e84F7mehjObdyCXSya2BvXCq4kxRQEpdIaunzfNgCZVsLCZUnYtiwJu6vR0eqNmrR9sxZzvtxsSFvMUaFQyPh0Ulc+a/0t2Y8N4igyiXCMtL5Uer+//XkPWxeOICM7l8sXHjJ+6VDCHyeTmali547LPApNJJ9HFRVSVliiZKkSqdMkiIiQWCpU8qFei8q061Cdus0rMf2jtZTyc6NZ26oIag2izPzzL1ryNywJ8t9HS+lKku8nmahh0KS30Wq0bPo+2JiKp9fpyUjKxM3bhaTHiTwmnFwxB0TwkQXgpC8Q1RFkAhXrlOPxgxiL9XRPqloWGisFLKAlNtA/yIe/dl7AxlbBV5tGc2bHeY79droQG2lrrzSKejgJrlQMqkl6nQRCLoXiU6YUP/f5mdNnT/Pn0T+xs7VDrVXjlOCKs1BYKAjgp5PTCgVqDs72qDIL+3Y+D5SS2tjiAuHdSw7x7vcDiItIfCn9004Hny++0X8ENkoF32wdy+4lBy2Kp1lRFKxpk1ZYYcVz4OLBa8RHJdF3fDfWT9tito1bKRcyU7LIychBrVKz+PPVz3y8ao0r0axrQ2IjEvh9zi7K1QhkwtqRZKZkkpWWzSfNv6FK8yq8N603Lu6OePq4Mu/TVWSlZeMeWIrASn64eDiizcpBrxfpOKw1TnmeQSeDL+Dg6ogowqYfduGrr8RDzXVi1Q9RyuzJ0aXjJffHXuZU7DitKBkSH6dyIvgS6+bsNbFBqP9mXQCWfbuV2RO3GjZqdUz+dTANW1dBpdXz+FEK06cFM35iF5o1rMjpC6HIbezwq9aGhw+OIYhQMdCDr8b2JCCgFn9sLblCW45ahkOp1vw84TXG/biJeat+prRXKfqcml/kfj5uVfChyhNbLU9If/luF03bVMXL26XEY5Piblgcf10Pp1/H+vyy4vgz9fEkVu0ejZ2Dkoy8VNV8TFwyjO3LjxN28+nUYed+1YOJc3Zw+VY0o3s1Z9eOy9yKLFDGLKpaUmmroEadQFo0f4XTB2/yRu9XiXmYQHamiqwMFVn7b9CiY22unL7PvC82Gsy6/2V4+nvw4Go4B1YfZ/CUXvy55S/j4taBNSfo/H5bvv72KwJkQTjJXdGLeqLFB6jEHLwkadqtejVh9eTf+Lsmam6lXNDkalCrDAs/h9adIPRquNm2XUa+wcHVx42/h56IZE/2BnYvOcTi0atZdWEzAF99P5mw6xE06liPmYMKvyvj131C2wEtEUWRTg4DTDxsSwf5cvOUZWZJFEVSSCBNloSN3hZfyqAQzC84ATTuXJ9Lh64XdQkKYccv+2j+9qvsWmzZw9AKg4hN10/eZN+yw0TeNVVQfWdKb2OgnBCdRExoLA9vRJKaUGAUr1FruXn6DuG3/l2laSv+HViDNyuseAkQeSeakMth9BvfnWt/3iIxKpn4vHQYRxcHhkzry4qJG8lIzmTp2HUl71iy+tthcBv8g3y5fymM5ePWodfr8SlbirErPsDO0Za75x/g6GyHqFZz49gtRreZSuUGFVDIBDLyFM2SQmNJyvO1y2f1rhy6jmCjwCfQi64fvE5KXBqzhi2m7mvV+eC7fohiX36d/BsaMRdbwcG0hkxuRiUSnqgpM+MVBSYqlPq0gj9qxto+qZ+UQ0F9lwnDJj1OVgG7I2XtpLVzxrFL65Sk/nQmKplK8z/bWGiffy2ktXfSuispYydhQMQcFYJOZwjc8hjGKRtGorS341jwJVLScguO4+TAtFEGtT5NqYIA2qW8GzO/epsm/X5E7SwDN0+8/TvhEq6me9d6VK9emXHDV5E/coVrwbXNV190i1HTuk1Vjh65hV81X9b+Mgy9XmT/nqvIo93Zv/UWzZoGIc/IG7ukXkwmEeow66sn9VCTXHvBx4t5f4xEo9aSGJeOwkZuUGeVBEwyyfMmrfOTqwzb9Qo5a7efY/bYbixffcqYQpj/OZjWvCklLKBMwjiL9gX3eOWP+6hcM4AqtQPJzmdCnBwoW6U0U0dtNFUrlbCyWneJv6A03tDquX4+gsrlS1G9ZhmSUrK4GV2QLqdINxzDp7QbXYc0JzdXi8JGjj7e8F5cPfOAFWPXkJWWg69rGx6cuc+RFYZUUZMa0PyxSK+3xrxSo2iJeTNhrUw9DZ/s25I/XHxEIt5lPFHnqFk2bgP9xnfDxtaGW2fukZ6YxpkLp/FWBBjNrGWCjEDhFe7rruKFn3F82lwNOq3eIsMmmvOKK9QmX4GzcF3fKw2DuHr0Jh2Gtib8ZiTn91422weAVqNFlVWQAtrlow5kZ+QQcsnUJH3/yqOMW/MxBySBnhRtB7RkWu8fC6lCAngHehEXYd7uQhRFHnADd0pRTl+FHLKIsr+PT05ZHITCi2mCIFDntRr8+vkai+dkDomPkvHy9yy+4f846r1ei0sHrtK6bzPWSvwLZTIZSjsbY628Z2kP/Cv58tG8YfwwdCGaXA12Dra8O3MA2ek5L7S+8X8K//4a1XPBGrxZYcVLgnN7LtPwjToM/34gmlwN7r5uePq54+LpzPrpW8hIfjZPF7lCzvCZAzi2+Sx7VxwmigdoRDVyG4GOI0Yw7+OVqFUakh4nkxBh+EMg2MrRanTcOhti1qTWHOIiE1n33XbKVQ/gx0OT2PB9MGu+30Gvj9sjFxTGOrf/+HfmS4lTOy7y3oy+HAu+QPNO9dCqdXw7YnGJ9//zwgM+HdiaRrXKcvKhYSW3Ra3yZMlTeGdAMz4evd6kfZfXazGw26vEJ2bg6GRIIX2lrDcAn33+JkqlgtPnQ5kwfRu2yYZg7OC+6/Ts/eqLOF0jpi0dSmpSJmMGLGHJrs8MgdtTol+n+nR9vTZ2tgrslHJj8PY8aNSmKrFRyaQnZ1GvRSWunw1l+qrhxcrOW4KXpzOrfx2Gv58b/fsvpkuXelSu5Et8Qjo9utTHQSui0+nRaHSsWPknqjwmyPZhQYAnphmCThtbBar/gDmxWqUxmkxvmhnM4Cm9iI9KJCEqiRMHThBAUCFCTY7CpC7r7yYRVZkqHF0dcPd1Y+vc3UW2vXvuAVUaVeL83su07NmY9354hw3fbeHcHtOA71FIDJcOXWfP0kMm2xdfmo2zhyHI8vAzVXHNh1whL2S8nY8k4vDAG0/BoPLrgBNlc6qiqZqKZ7oHSY9NVSJb9GjEya3nijwnS1Cr1CYWOVYUxv6VR2ne/VV2/LLfZLterychKonSFX15HBqLf5AvLXo05tLBq8br6envwe2z9ylTuTSCILwUbPl/Dv/xS2YN3qyw4iVCcmwqS76YZvySLlcjkKA6ZQmsGoCnnztJMSlP3WfPzzuze8khHj2I44F4gzJCkCFtUQcbp+wkXZtKoJAnw56/uizxNpOyY9IaOkvqa1kZKkRR5PzB63QY2objW88Z2lqqLzEHC/YHJkydS8FqsZBrRt5OhFAAACAASURBVCpeqownZcok4zA5N2k9j/Q40py0/HOWsgcmtXeS1Xup357MTG0bmNTiiXmMoSARaCCrIA1Sn1XA8ph6bOl4cDmMqLvRdBjUki4j2vDn1vMm7JyRhcyU1CamFywGZMVlsHXVSWZ+1gU7eyWxj1PxLe2GKIro9SI9Otdl6blwY/u6Pp78tf8my+bsQ+dqYItkuVomzexFi7bVCA+NZ/boTdirCu6BnZ0Nvv7uCHnBkSC9J1rzz1L+9RYtLCCUD/JmQJXPAbh88j6D32/N2p8OmLChOkl9oii5PXK1Hn9/d/p2qs+AD5dTvowXP0/tw7DP1wJQ2tWJNevex8ZGztCOPxITZZjcKnNzccirF8zQaKhY3Z9Spd05c9zgtdj1nWYIwIYFh/HydubbpUO5ezWCtLhUFn+5AZJTEaQ1mJJzl6nN19AN6vELDo5KZszpS+btWDbe3ku7IU2pV9mfbZvOobooqZe1tSX/jMXEAsEhwdcQXN95kMSbH3ZgzY+mk0bAWMcmpqcX/uxJPKsSsORemnjCSd9LmUDEnWjKVg8g7FoEAKHXI3BycyQhKgl70YlMeRquoinDo0OHTPoOCoXr2iwyhhbqhos6z1tn7jHw657YOdqRU0ytWfS9x3QY2przey/Ta2xXZg6aT4VaZQms6m/i0wmGCbwgCHgHerHsxlxSE9J4eD2SD+t/ib2THRrV0wdFaSRRAVN7ApkgIzUmHVetqfWEIAhUa1r5qVm3fIRcCiOobnnu/HX/mfZ/GijtlNRtW4OLB64VMiV/maHX6c2yp2AI7AZM6sHKrzaBAGeCz3Pl6E3AwMzVaFaZnKxczu68yKBveqFQKlg5ceM/OXwr/mVYgzcrrHiJcOXwDVr0aMTRjacACL8ZSfjNSOwc7fhg7mB2LtxP2PUIY3tbeyW1W1fHs7Q7WWnZ3D3/gPjIgtV2L38PdDo9j0JiSCcNF8EDe0mKjJuuFEliHDq0L0wBslqjIJxcHZi9dzzRoXEc3HD6hfRrRdHIzVazc8lhdi45TM3mlXnv2x5sW3KUxMeGgH/l6a8RRchMzybyfiw/jjJNv92z8S96DG3BqiXHcPdwYuWSYwz9sA3bt15g7caPUCVkULdJEKX83MjJURMbXdjPafr4P9h6dByLZ+/l5zUj+KDPIuNnHXs24NqF+0QnXUalTsNF4Ukph4rInlEpdcS0XkSHxDB8em+qNQpCYW9LUDV/dq49TWpG8b5vVSr78c3X3ZkwbTsqlZY7IbHsOnSdT99tg1arp1+3hjx4EMfiRUf4bEp3xr27AoD+n3YgOT4dN08nyr7iyyu1A7l5LoyuI1pz9tBtWnWqxeg+BtYzMTaNr4YuZ/3pSUwZuIiIuzHPdK4A2Vlq0tNy6PVuC/5YcZJ9ewtqkZRF7PckBnzcjiXTdj7zOP4p3L8YRoP2tYzBWyl/T2M9l5dQmhDxKvaiI0rBDlEUidVH4CIYGCknN0dmHZxE4qNkOo1oy55lR/6WMeryBIByS+AzmJGSiYunM/Xa1sTOwZZT285xats53vthEKFXw018PbPSsnHxdGbg5B6kJabzQd0vyU43BFiWgkRnd6ciBUVKlymNOkqFLfYm2x3c7FBFmPbZb0J3Dq/7s9hzsoR7F0J5rX/zvy14c3R14NWO9Ti26RTtB7dCrdLw2a/vEXYjwigE9jJCEARGL/uAO2fvsW/FUYvtcnPUuHg6E1SnHHKFnPI1A43BW/VmlXF0c+T45jPk5qgJuRxG91EdcfVyIS2xBIsuVhhYt/+4z9uz64tbYYUVLxx3zoVg62BLny+7IpcoDaqyVHQa0Y73fxjEu98PoPuojgz7rh89Pu9MVlo2Fw9cI/LOI5q/3Yj+X72NjVKBZ2kPBkzuyd68iUs2GThTWLnMTnBAzbObHD+JE1vOMbz+BKYNWEB8VDItutZ/YX1bUTLcOHWPbUuO0mvk68jkMn49MoGrp+7zfpvvGNXpR9r2aEi5KqZKdco8VcbfN5xl4U8HyMlWs3DeQR5Hp/DuO0vpOrAp4SFxvPPabJITMqhU1byEuZOzHdXrBqLX65FJGMJXW5ahz1sDcdd5EKSsg1Jnw92k4+jFp18td/F0ovOw1qz4+g/a9mlK8KJDjOo+n06Vx5GaVHx6caMmFfl+Rm8+HbWO+2FxBdft7mPeaF2dzLyJ9PsjVnL1SgSPIhKZu+59flg1nCbta+Lkao+rpxOiKDL9vZXM/nQtmxcdxdHZlolDlpkcy97JFq1Gx8Wjt576PJ/E1+P/oF2XetRuVOGZ9ndytef6uTAeRyQW3/hfRlxEAqWDCp5Rd1830vLsUmSCjApiDZzqy3igu8ED3XVssMVXZpDZ7/JhezS5Gr7p/gPxUUl0HdnhbxtnWkI6zu6OxpTGolChdjnGr/+UjxqMM247/ttpmnRtaNLu6IaT/HRyKvcvhTGowkhj4GYOChsFXT7qQPdRHdk2b4/FdlN/mkyS6yOTFLsUIR5XG9MUzIGTe3L12E0eXHlY7PlYQlpiOi5ezs+8f3HoOvINGrxeG2d3J87uukT5moFcPnzd5PvGEl5pUJEWPRoX2+7vsJgQRZHI21GosotOWw6s4o9veW/mnZ3B2591olaranyycDguns7c+SsER1cH3hj2mrH93b9CqNq40gsf7/9niOKL+fdvwcq8WWHFS4Z9y4/gX8mPD38aQvCCfUTfNwgLaDVaxnWYjo2tDU5ujoWkmBOikwi/FYVP2VJM2jyakMsPWTJmLaosw6qqs+hCKok4CqaqfDliJrZCWZNt0tQiUSeRQLdk3m2ys2HfjORMNs/ZRdPO9ejzaQd+n3+gyNx8qTCCyZ9N6R9k6c8ZBSmFgp1EXj8vLcvEUFgq9iFNo7KQEipYmgTkj196HiYpfZKvVEl6pGhfkLonlfmXiqQIeUIqoiQ9UiodLz2mXpKSacncOP5eFOHXHrL6/FRS4tOZ9/FKY1pnyLUIFh+ZyBsuQw19qDVEnDc8T+0bV+TglguGTvLG9PhhPD3qTzH2/d0Hq/l1/xcIWbko8lO48mptHt6LIStNxe7N53nvs/YsHbeJbfd+oFPLnlR2eBV5nrqdu9IPhWBDTOpN/O0qF5yn9BnLN423lVgz6PU0erMO18+EEHI3lk/af8/cPWOxmXuAozsM9UM6J8nzkHcrtx4ci4OjLQnx6Wg0OsYOX0lqWAKOqQXP0ZQfB/E4NJ4AQcGKhUeQ5xjObcEYSUqSRoNvoCe5Kg0pKTnGcV4+G8rlfJ+5/OdNoyHmVhb3Lj80eR5MREKk4jOSCaNcIm4hyyh4fke3mcL0rZ/zZkwavy04SMT9OJMUYWnKqCCxIUCr461+jTm247Jp2q/0Wc4fi6RuSpQ8aybfCyWwCpDZmDG+Fi0JFhU29L55+h61WlYl4s6jQjW/NoKSt1p2xOnxGZJjC74L679eCxtbG7z8PZHJZJzfe4WuI9sTVLssIZdNxUFeBK4dv4VXgCcN36jDlSM3LUrkC4JAemIGseEJJrVg9y+F0aZ/C078fsZYr5aakM6M/vNoO6BFkYxKky4NqNWqOvuWHyHyTrTZNgDVmryCs6MrFZyrEK4IISdVBTIRpcYet5BSxseh99guXNh/1Wj6/Vz4mya2Ds72XDtxi5un7pKRkgkpsOSLtcXuJ1fI8avgQ+mKPviW9y6yrZu3K58uHE7UvcesmrQJn7KlqN26Oo5uDs/N7G0ppjYSwCvAk73Lj7B07Do+X/4BZ3deZPu8vXQf1ZEN07ey7ts/aNbtVZq81YCzuy7y4MpDen/Z1WoZ8D8EK/NmhRUvIR6FxLB49Gqadm1Au4Et8avoY5Qc1+RqivTQiYtIoGnXV1k/bYsxcANwFFxQkUmq3qBGJop60l3icZK7IBNKEJQ9I84fvE7fLzrT+7M3kD1N3ZsVz429K49zaP1JPn1tusn2bYsPA7A/fRVbIhcC8OY7zQ3/929SbL/pyVkkxJh/Bp1dHWjerhp3rkXjU9rA9OaqNETejzUGbsa2Nl5ka58u1adDvyaM+nEAq2caUv+SYtP4oNV03hndgZqvWmaknF3sGdpvMYN6/sKwfouJCCusyrd12XE8SjnzWrd6BP9uWawhNjKJlPjix/3BtJ6suTiNWk1fKcGZlQzZ6So+f30GO1ef5J2xnZgb/BmfLxiMb1mvIvdzL+WMXq8nNqpwuuvLinN7LtOoUz2avFWfs2YmpgfXnKDvuK7G75UylUtTvkYga6b8zpUj17HJY5N3LjzA6++0+lvGGHY9AplcxpCpffl04XCL7QJe8cPR1YG48MLqgEfW/8nbozqabHtw5SHrp22h+6iOtBvYEgBXLxeqN61MhyGt+fCnIeRmq1kyZo3FwE1pp6R0RV9S4tK4eeoOLnI36rg0okfbPlSzq4e/UN7IMFV5NYjU+PQXE7gBGrXGeP2fBb7lvXmlfuH3+aOfh9JhSBuu/3nbZHvZagFFsp8NOtRmyrax/PnHX/w202B54ujqgI2tDR6+bia1kkOn98XNx5VK9SrQqndTvtn6BUo7G4JqF1wvG6WCcjUCzR7reXH58HUQRZp2a8ilg9fxLuPFD0e+oePwdsZzPB18HndfNzoMaY1GrcXO0a6YXq0wgfiC/v1LsDJvVljxkkKn1fH7Dzup/3oths8c+FTKXTlZKt79fgArJmww2V6B6sQRzQO9oV5m4dqfOLfqJmd2XLDcWUlqkqTy4BJmSWEj5+ejk4i6H8PFA9cY/m1PVnyzxVhYbiJEIZUnl7Jg0uOYkzUHU/YrX/hDup+EeTNhPaSwIIphYhsgM+OJJGXHpCIlUnZMKh4iZfX05hkOIyxIqZtcb510s6mRsAh4B3gg5KrQ60Xjdc2KMQhZ9PQdzuboJeizs/H2Nciuzxy2BDLyWA4pcyO5rw62MspX9jMwNfmMYV7fZ3ZfxreCD+qEFCMjlByXht6M8INWr0b2ZO2B9PrkPxO5Bdev45CWfNr5Rx7ejTVen5y0bD4ZuIRJc/ry5bsrGTikGb8tP8n3S95BDji72nPuyC3iD10p0J/Jn6xJGLFD605yaN1JJq8cwYqNH7B53n72rj1l2QJCKpQjecbyn5muw9vw6eszSE0oMP82OTaAm6vxR0V8mqRvyTMjeTaFPAGWOxcfMm24oQ6vdrNX+HB2f5zdHNBotHj7uTO+30JiUw3jk8kE+n3+BqvmH0J0tkfItjDuvPMRpXYaUiERzDNlloRHzJ2zqJYy3EWLniRoHzN99nQUgoLkxBS88MVD5mM8ZnpSJruXHGbErIGEXA7jlfoVWTFxI6JeJDkm1VhHJiJw/1IYVZtU5s5fIWbP4XkQeTuKw2tP0HZgC4ttou/HcGjtcSrVr1joswdXHuLq5cyAST3Y+N02Y4ZCVlo2qyf/RvWmlRk8tQ+p8Wk8uh/DtRO3ObjmRLEqg2qVmsd51i6bZ+9g8NQ+rJy4kVgzAWTzHo1ZMX5Doe0lgV7Uk4yhdtoDH2wEJZG3owmsFmDR964ouHg68/aoTsjkMu4/YaUQeTeadDOp0UOm9uXRgxijxP6TOL/3CmHXItDr9Xj4uuHu40b/r3oQfiuSOm1qsGfpIWOt+Z6lh3l0PwbXUi4kRCXi4umEu68bi0avMl7zVr2b0vXjN/my3be4ebsSI0m/fhHIFzNxcLany8gOpCaks2/5Yd4e1YnU+DR2LNzP3mWHad7dwMClJaThHehlUvNuRRH4j9e8WYM3K6x4yXHp0HXu/BXC+3PeIeCV0sY0Skv49coP2DvasXfpYeLFx6SRhAwZIiIBVMBXKAOUAaBOtTrsePj3FPMD/BG1iMh7jxnVeioAWek5DJrYldVTt/1tx7TCFOmJGQRWDTCauSrtlHy5ZiQ/f7SM9KRM5Ao5W6IX4ezuxI3T94iLKlAozFan8ijjBogiIuDnXBVn21IMnfAW88f/ZmyXq8vmcc59tKKasNh6lK8ViEatNdag2Dko8bAtzePs+5R2MLBQoigSlnmFAGXJazUUChk+AR48vF34HchMU1GlZgBr94/B28+Ndz5qy+WzD5j63gqyM4pWAnwS04YtQ2EjJ/jhz4bg7RmRmZrN/EMT+WPBAVbOslyP9Ly4diaEa2cMQUmH/k1o07UecY9SwNGgBFq1TiBXz4WRnfnialv/bqTpE8kSMwhMNTwvXvJyROjuoBCVRmESgIjb0az55nd8y3tzZEPBvdI9IZl/ZMMphs/sbxq8vSAc3XiKfhPfJqhuBUqV8SIhqvAEWhRFTm0/T6X6FenyUQd2Ljpg8vmlQ9dJiknlvR8GsXTsOpPA7NaZe4UUKZ8WOq2uSEXCyg2DWHx5Nl93mWX0GC0JMsV0ognFlzLYYk8kITiLbkTeeUTFOuWeOniTK+S8M6U3KydupOeYtwp9/vsP5sV2fhi2CL0FtclWvZvi6efOwxsRJEQn0XFEOyrVq0BaYjquXi7MHb6YmDy/tP5fvU2NZlWZ2utHHofGYudoR4VaZXkcGkfAK6WNzOTxzWcIux5BzzFvYaNUcGH/VW6cvGM8pkwmQ29BNflpkJ2Rw28zgylXvQw9x3Rh+fgNdH6/HW6lXEhNSOfexTDqv16L3UsO039id4NCpRX/72EN3qyw4j+A7Iwc5n20jF5fdMHGVkFWal5dlGD4IyEIhjotQTCwXa/LehEnGlJpKgk1AdCJOkK4TpBYE0WesuScdxcx58g3aDVacjJV2DrYsnfZYRPTUClKYmorXc3e/st+BkzojqjWIMjlxDyIJSs1i1fb1eDc3iumDIS0awm7IeoKWACTtTIpU+bkWPBz/qRNyhhYYk6k2y1YH5hYDqgLs58WLQakbISUOZHUJElr8fL7Ma03NKHVzI/PAvLv1erJm2g/uDWtezXi/sVQJv02mvkjl7Nv5TEA9q86yhtDX0On1bHhu21GNlSlzyIy+w6VHBsiFxToRR2haVfA6RVqNgpi8fjNoNeTpkskNieUco61UAp2bFqwl+PHTxDo2QB7R1vIVaPN1VJK5k+cEM699DPIBDk6vRZfZXns5c6Wjdj1esYuHELzznV5r/lUxi95l6h7MQZDcjBha+euGMqe9afZvvwECbEFDJaYKklvlDK6+RYCFphQTbaKTT/uYcv9Ofz06RpO7b5i+CDLgniE5FnKv5c9gz4DYF/CEsrXKsvkocsNDaTPvYS90ycWpDTKpKyn9DnNv1YKaW2gob8OvV+lZZe6jH93FeQZ08vkMlq9WYslU4MR8p+t9AIm0KSmLf/6WPAKM4Hk/bfIoJn5jpA+/5bYbkEmkKB7REVZTZPtZWSVCdPdwEXubsLw5WSqeHgj0rQPQTC2EfUioiiiftLfTjq+52DhcnPUqLJUzBm2kPaDW7Fh+laLbUMuG2T0P/p5KL9+vsZkgh9+M5Lo+4/x8HMv5Lv2d6L32C5sn7eHhzcjmb57POM7TDepIywKjwijMnWM6YTOuBEq3kLhLCvWPsEcBkzqwbaf9yBXyC2zuGZgTtRFYaOgdEUf3LxdqPxqEDWaV+HK0ZtsmL6VfhO7k5GUSbmagaQmpBvrDbVqLfcuhBhLDmxsFSiUCiJuRdFvQneuHrtJ8IJ9aDVawq5HoFZpeGNYGyJuF6Su+gf58vGCd0mOTWXT99uJvv+YMpVLE3Wv6IXXohB+K4r1U/9g2Ix+/PHDTlr3bUbwgn2oc9Q4ezihylKRkZKFi6cz6UkZxXf4Pw7B6vNmhRVW/BPQ6/RsnhWMnYMtCkktgV5vkD3K/9+1lEGQJJ1kKgm1jO3kgpwyYhBxROFPeQBu/HmH/oEfIFPI8fJ3R9SLjJg9kD5fdmXz7B3PNd6t8StQq9RM7v6Dyfbf5+ymaZf69B37Fn/M3/+f8ub5LyInU8WOhQZPr6ZdG3LnXAhHN5w0fj73vaXMfX85B9UbTSYXj9UPqOhUz2ghIRPkVHSoR7jmGoKAcdIZkxNCZacmxsmbBwHE34vGvUYi1fNq0BRKQ6DhY1cBHzvDNkvebfmwc1Cy6eYsTu26zK6VJ1h1YRqq7Fy6B3xcqK1CqaB8FT8+7/azYYOFRYGnxfrZu9iz5gTrr82i1yujyU5/+skowMjXprPg0EQm/jKIGR+vK36HZ0CzN2rSrkdDxg42Vbts360ee38/b/h++E9BQHgi+HsaW4m0pAzcfVxJiSsI5G1szaQ9vyDsW36UD+YO5urRG0W2O7XtHOd2X+LLTSPR10xHnSf4kxSdgndmWewcbHEr5fyPBG9KOyVDpvXh2KbTRjGXLXN30+2TN0vE4OSKOThQ2KLAG39kpbVc3HvtqcbTYUhrbp66y+PQWAZP7cO2n5+PrW7Trxk9Rnfms2aTTMywRVFk43fmsz8Mwia+xt8zkjNZMHIFXUZ24M65EOydTGvLou8/LpSqGfMwnrTEDNS5GmwdlJQK8GTGvq8Y32E6Chu5SaD3NEhNSOfc7kuUr1UWrwCDx2FaYjq2Dkq8A724ey6EoLrlDTVzVljGv1yv9iJgDd6ssOI/BlV2LhThK/TwRiRlKpcm9G5haXIHnIjFdIValaMBNETejQFRz9ddZ7Pwwiw6Dm9HTt7qo3cZL75sP40HUrU2k5oX6Sp83gqsuyMDyo0lXpKGByDYKDiz+zJKByXzj3/N+f1XjZ/pdXoEmUBGajaH1p8kMzXbVBlPmoYiMToWJEyCkUmRmmFL+1Cbl2kWJQyItG7P3Iq8JaU9wRJ7l2NBvc+c6fkLqsMxhzM7LvDwRiQfzR/Gjl/2E3ot3zNQx81Td9kQuoBfPlvNriVH0Om12MhsTfaXCTJ0Kg2qHDUIAnpRj0JQFpq8OeSWQumZiUIhZ+CYN0hPymDgmDfARomNUsHJ3Zd5cLXAr/BJ03OFQsa8fV9yYMNpfv3qd9CLnNh+gbioZNM6wbxrti1mEXM+XFnAyEkhCeQEiWE39nl+VzkFip9ibmFl1dT4TEKuRbD01BSG1B6PVsKCmbCoUlbPPm+Cl7eaH3Y1gs5lPmXHw58NxzNhfKU1lVImSNJGajTuYmCZBQmr0WN4Sxq0qsqEPgtAIVGbzM6htI8TBy4+QFQ9ReBZAsZDWv9WEreH/OtZXNAOhvdLjgK1qEIpFEyWVWI2NoKtgTEzqUUtPN7ze6/QtGtDdv96CM/S7rz1QXvO77vy5IGKH3gJkZaYzqx3FgBQq1U12g1qxY5f9plNG9SotXz+6Rhcon1R5vluemBHvGs4g6f2xW/NcUKvhbN/xdFi69qeB73HdmHbz3tIfFQQKN6/FEaP0YZ6s99mBpOZWvidatOvOWWrBZCZlcGiXxbBEyVoOrTIkKPVFH+v81G9aWVsHWy5fPg6lRsGERsWV0hh9GlxZMNJrh2/ZeKhVxy2/rSnUICmVqnZ8uMuAJq/3YhGnepxbs9ls/s7uzvRrPurzPtwqQnzOL79NGzslEzbOY5PGk0gNeHZPNkuHbrOhz8N4cTvZ2g3sCWH1//J5lk7GD5zAGun/M6bw9tag7f/AViDNyuseAkweGofQq+Gc2qbZYW7EkMEnVaPHh2iKJpMrFNJxAX3InY2YGTDcSa/L748m3gzRe5FIfxWFKXKeBUK3vJRtWFFghfu5/D6UyiUChNBluU35hgsEVwdOLf/KrfOvvg6lf9VxITFsWDkct76sANlqvhzfPMZAEa3+oageuVZfGEmu5YcQS7KyNXnYCsrMPXVi4ZnytPXjUFjO7F29m50YuEJWo4+A5najR6BI8lMzmTt9O1AQSrgsEndSIhKIs2CJ9u4pcPJSMkyBG55CLkaabYtGGK/P4MvPv3FKAEUChmftZvByB/6s+rqTAbV+PKZ+4qNSqJ557qc2mV+4vcs6P3ha1Sq7s+EfgbVUOlf9Sr1yhF5P/aFHeufRIAsiHj3MFxSfHAW3MkkhSh9KJVktUu0/6OQGKo2rsSw6X1JS8wg+Jf9pEoFYf5G1G1bkwUjl9P5/ddp0aMxoVfDiX0YT0JUIqkJ6dTvU43DO+KwEQpSuG2xx8XJleM7T/LLJyto0KEObQe04PD6ZzfLLgpObo6IomgSuIEhdfPrrrOp81oNfjw+hTGtpxgDOLlCTo/RnUiJS2P1ZEPNa5YiC42oNp6LKIpo/bNp164dDukXuXrsZrFj8fL3oEnXhiwftx6ZTEbbgS1Y/Nnq5z5HvU7/1AIeapUatcqyD5unnzstezWhfvva/Dp6TaG6thY9G9O4c33unn9A+M2C76xHDwzv4ZjW35gEboFVAygV4MGlQ0UHXE5ujszYO5GVX21Cr9OjsFGgzvubqcnVcHTjKfp/1YOIvNpmK4qCYBUsscIKK54fmSlZJMekvJC+bOxsUKvU+FCGUG5RVnwFG0FJuphCPI94hScmP8WsPtdsXhW1SkN6CVdB81mpT5tOYmP4ImwdlCwbv4HgvLQVUaOlYu2ydPuoAyNqf4Feq0UtWY2fuHEU2+fvZfevhxAEgY4j2lGhWgC7lx42XbWXMmXS+pt8Rk7q4SZVz9MVZm6e/PlZYYmRE/UlX4F+UXU4RUGTq2Hbz7tp0685bwxtzf6VRwF4cCkUURTRq9X4iWV4kHaOSq6NUMrs0erVROmuUT+wHjFhcXQd3po1X2/GVqckVR2Lm9KQaqQTtTxS3cHhiBNyQWHCJuV72G2Zs4u33m/H+ll54gPSWkKdDhc3BzZ8H2ysMTSpKzRzjW2UNlSuHcDdCwZmWJCyrnmVkg4udnyxcBgJj1IMQWF+jWF2jqQjw7Pz1cr3uHshjKFf90BhY3iO9Do9MrmMhm2qcP6AYaJlohAqZX/yF0yeYE1++nglc/aM41zXenzbd56hqZRtk9ZM2ktW/6UecSoNLh4OePo54+7pTMOWlRnb9ceCtnnPuq29Da3fqsPiCXnCMmbqNeEJL8Hiat0sMHKWvAZN0lfzVWCl76W0E5P7qsNGUNKpfncutNywswAAIABJREFUh1wgNOw69RvXo3x8BRIikgq9F5beu8Pr/p7Apzic2naO5m83Yvv8vchkMsrXCsS7jBfNur/KtWM3ObbjT2Q5iieKeCHjUQ5TB/yAk+DKzVN3+Xzp+5zZcYHsjBzzBwK8A71IfJRsrNkqCZR2Sj755V1sHcwr78ZFJHBg1THsHGwZs+JDvu0xh8CqAXT5qAM7ftlnkl4doAkinLsoscPR0QHHAFv6d+jHr5+tZcSsgdy/FFakyThAl486sC6vzrrjiLYcWHnsb2Ucnwc7Fx2gfK2y2NorzY5x77LDHNt0ymK935PBZOf3X8fB2b7Y4C0zNYt9K47wSsOK7F95lDfebcuSMWuMn9+78OCF2Tz8T+DlfLxKDGvw9pQYu3IkkfcesXlW8L89FCv+H2HrT8Ubd5YUeq0OmVyGi+COUrQlmjD0og5HnKlErUIpbsVCBo8fPP3q/Xs/DMLGVsHBtSd4e1RHY/AG8OORr9nw3Tazuf8BlfyY0d8wsRVFkT3LjlCpbnmmbf+CGYN+KXIiY8XT4dimUzRoX5sP5g5m96+HiL7/mNSEdGxsbWjSoQGD6vQkeF8wSQmRyJDR1KMZQZWDiI9MJOL2I9oPbEGFWv1Zs2Etd26dR6vWI+p1lLWvaayVM4e0pAxkcgE7R1tUWaYpTf4VffAO9OTy0VumwUwREEWRzsPbGoM3c/hx95es/2EPAZV8WHLqa8b1WkBqomFBQmmnZN2tH8jJzkUQBFLi0/Hyc2ftjGB+/3kf3qVd6TCoJVeO32L6tjFM7TcfNy8X9Ho9vUZ35NMWU9AWUbtZqownw6b2RpDJuH0hlFIB7nj5u5OelIVGXfLAvnbjigwb+ybpqdlkpmShkMv47oOVZtu+/UFbfp+33+xn/xVkJuXQvHZLZBEOVPGuQZI+1RC8veQIvRpOix6NAUNtaOjVcEKvhnN210UGTu5JfHwCJ28cxxVPk/0ySMEbfwBUWSpO/HGWtz7qYHG+0fCNOlRrUpmstKwSmT8DjF01kupNK+Nfya/Idk5ujrz1YXu+6f4D734/gLiIBBaPXl2oRtlGUFKJWmhENdXqBFG2QjkOzjcEzb/NDKbz++0sKkQCeJb2ID0pg9wcNW7errj7uhF6LbxE5/JvQBRFFn6yAgTBYoD5NEItKyZsQGlnwb7mCexbcZQRswfRdkALNs/cXuJjWPH/D9bg7Smxe+kh0hPTjeaRT3qQWGHF341GneqhzlFz5aj5dBQ7RzvjKqyd4EB5qjzX8eIjkwms4g+CrFBtycaIRZzff5VVX21C1Iv0+uItOo1oh5ObI6rsXCZ0+h5HVwc6jWhH5YYVuZc3cY24E82aKb+bVaOzc7QttD3q3iPcvF0ZNq0PCz5dBTxR5yJZideb8UszqUeyxBJIYNK3ZCwvgp172XDx4DWu/3mHDkNb0+m9dgAMndab08EX2PzteUDAKW+SGfXwMVGXHnN882kAKtQuy9ENJ9HkipSTVzV0KICArVkWR8qg7fxlH8Mmdefy4Rt4+LnjWdodmUzA08+diNuPQKsruN4WFDjzWZxBlUez7t5PLByzDlW22rQ2Uq9n1LzB3Dh1j9M7DKmVWcmZTF42HDcvJ3Kz1Ti7O5CenMnkHj8S8zDPvFvC8saGZLPm680AjG03lWk7xpGRkonS1obY8ARm7BzLlx2+KzhmftApCPiU9WL+iW+YMWQx6UmZ6HR6ur73Gh/PHUz5GgEc+f0v1s0wiAMJed6Fbl7OvD+jLz5lPJjx8Vre7NWQmo0r4eBky6g2U9E+GfBJJ5FyOdUbB5Eak0JieJz5NpYUTfOvq/QdkX4uZbgtBNYWFQLz74lghqE0HKjQLunJmVSsXRYEGVnpKhxdHMz3XdyxJbD0zr9oWBJi2jhjGx2Ht0XrncWlY9fw05dFj55oQnHDy0SUxa+iDwdXHzfbT9VGlajZshorJ25k8NQ+2DvZFRs0NHmrAae2n6Nu2wIVz99jl2PnYIuNUkFmahbxUUmE34pk1VebcPZwplWvJqyf+ge5Typ1PgEbQUmjtq+aCIH4lPUiJqzodPsOQ1uzc6HBNqHn553ZOOPlD0qkCy5BdcsTcTv6qXxYpcjNURd7baWICYsj/GbkM9fMWZEHK/P2v4U7f90H4KN5Q3F2c2LW4AX/8ois+F+DXwUfciywTzK5DN8KPoXqGJ4HcREJOLo5UqayH9Ehpgzc7bP3adG9ER2HvWbctmTsOk78cdZkDFlp2bw+qCX3LoSSlZaDvZMdCqUCrabwBP/0jotMCx7L5G4FKpVzj09h3bQtePp54OHnRnJMyWSsrSgZ1Co1uxYfBGDVV5uMtRTFIexaRMlM3M0gJS6NZeM3UK56GUKuhpP0OBVRFClbLYCBX3Vj3skpzHhnIbHhCcX2lRCVxF97r9CmdxP2rT6Bi6cTzbs0oH7bGjy8FU3tllVZPK7A42r3yhPsWWPwBBNzc6neJIhZu8ex+sYchjcYT9TdGIvHunvuAb18RwAFwcKco9+wO301qqxcMlOyUOWoyUrLxt7JDkEQcPNyodEbdVj2lSEAXPDZWmN/Q6f2ZsGxSTy89YjzR28TGfKYr1d9wC9fb8fOXsncLZ9Qys+NNwMNtgNiMUxdQCVfGrSryZrpL/8kuDjYOdoaxSZUWbl4lnb7l0f0/NDr9OxecggHZ3veG/Mu10KvkJ2WDX9WwEZrKhDkWdqDtCcm6RVqlaXtwJY8uPKQVXmKkHuXHeHN4W2LVGd0dneiRouqLPtyHbfP3KdFj0ZcOnyd2Qe/prfvcGM7D183uo/qRJ8vuzHhjemEXY+w2OeTEATBJH2zTBV/7BxtESywVA7O9sjkMjJTs6jVshqhV8OLTbF8maCwUTBm+Yf8NiuYE7+f+UeOufvXg0V+LpPLqNzAsFD6Inzm/t/CGrz9b2LN15tLTHVbYcWLRPCCfRY/e31QS468iAJ3wVQ98uapO3T+oD2/jllr0mx6359ZdGEmb3sNw8nNAWdPZ2IfFl5ptXO05ZdRa0CQkZ2ezYHVx9meuJLunsPQak3/wJzbe5nm3RoY2bSOI9qSEpvK2Z0XaTeoFXYOhgmOJT80c4yApRV5SwybJTyN79ALWeH/B+rfnkRJAjdTtkZXaJuglfRRxDnkZum4dz7EpM1rfRqz6ftgmnVrSI9P3mTh6DUm+5jcA8nPU/rMY97xr/Et60XN5lUIvx3Nkd/OUK9tDQSZgE8FbzhYUFeiz2MBBZkMeyd7tGodQ6p9TkKewI7ekuKolInNu8djWk8hOHkVN07dY/bQxXy79XPGvj7dOFYHFwcWnp3O0rHrDV1IlFJXT9vOvtV/0rBDLToPaU65qgF80vY7EuPSkclkpH/2Oh83nYw+OW8Sn8cImngh5o3Vy9+dNwc2Y/nE3xB1+hL5BEqfU6Mvms785/n3+kk8zXvxZB2gERKvPyEvAMhIzcbF0zlvNxGZBdbc7PFL8u78C+9XPrIzctg75zg9P+9MZmo2fq/5sGqSqTx/anwaQfXKE3nnEaosFQMm9SAhKonVkzaZsD8JUYm4erlg52hn9Cdr0L42STEplK8ZSLkagaiyVMb0y7TEdHYvOUT7wa0LiXMkx6ayc+F+mnRp8FSBmzkc23SaeWe+M4ifvPurcWz56DC0DQdWHUeukNO8RyMWjVr1XMf7p6HVaPm622ySXuBi6fNi6PR+JD5Kws3blbO7/h4RJyv+fbwYM5z/QWSlZZMSZ139t+LlgZu3K/6V/Hhw5eEL7Tewij/tB7dm/bQtZj+XyWW06tWYzNRsYkLjCn3uV9GnkIHu1p/38PBGJB/MHWzoQyZDoZDhX8mXnqM7E3a9oH2/cd34ttdcAG6duUfTLg1e1KlZ8RJix6KDVGtciT/m7iYrzYz0fxEY13EmfcZ0xt7Jjvmj1nBm1yUWjvvNYD2x0fLK+M3T97C1V1KpXvlnHvfjsDj2Lj9Cdno2Y1+fbvJZdno26cmZNGxfy+y+cVFJ7F5+jPHdf6JvlTEk5E0GJ60cwaaf9pcoRcrDz40+X7zF6m/+eCrxipcZqfHpuPu4AobvkRgzC0MvK2ztlbh5uxbbbsvc3eTmqGnQobZJe42oZse+YJKdYug1uTPVm1Um8s4jDq45brZOcueiA/Qe28X4+4BJPek3vjth1yJYOXEjG7/bZmLe7ODiwNhVI/l1zJpCfSVEJxl9xJ4GcolxvEKpYMFfM/ht1nZ++XglQ6f3NZab5MPWQUmjTvXoN6E7uxcXzSi9rEiISnypGC5NroY9Sw5ToXbZf3soLy9EDGqTL+LfvwQr8/aC8OqbdUmKSTHr6WKFFf8E3v6sk0Xj0edB5N1HrP56M79emsXAip8Yt+evfO9afJCxKz9i5Lyh9C79fqH9E6KSDMXxeSvb+fuNbvUN60IWsHfZIX69NJu4iAR8ypYCDHUABzUGlbxrJ24bV4djQmPJzVZRvWklbp68azyGxRodM5+XKM3vKVbhX3gNzT/MALwwWPD9s8TWSCHICvZNjk1l97IjADTr1gC/8qUIuRJOhZqBtHj7VUBAp9Vh72THsc1niLgdTZ3XarBi4ibuXwoj5mE88ZEJRjaq+8j23D3/gNy0LBP/PuNzIhOo1KQiu5Yd5pvNo5k74lf2rTxWovOUIjMli+k7vuRN235GNlnKYE3rM5cfDk1mSJXPTPswYZwKrps+MwsHBxtObjhuevi8cUuVLn3Le9Pzs44s/XI9uVkW6p5K8FwZ75sFptFSf+Z8Hg0fiOZ/Lmrbk8cXRRD1+Jb14sK+AosFSzV3LwvWT9vKsBn92D5vLzFhhRe1pDi26RRh1yKYdXAyy8at5+D+gyQTR+lr5Ughi/m35uPv488rDd602IfS1obWvZviH+TLqkm/cfuv+yz70rIh/FebRhEfmcgFic+mFKnxafiW9zabSWEOdV+rwe0z9wDDYtzcP6eyYsIGLh8xmJYvHr2aodP7megE/DYzGO9ALzz93Im8+6hEx7GiaIiiiFajRaG0Tu+LgvAfT5u0Mm8vCE26NqT94NbYOdoV39iK/xkM+qYXVRtV+tuPE1S3PDFhcYXSUp4FHr5ubH60hFGLRzBq8QgOan7jrffb4x1YqpB5KUDVxpWwdbDl/sUw1tyfV+hzrVqLvZMdzd9+FQCZTGDC+k/Ym70er4D/Y++so6O42jj8zGY37kZIiJCQ4PLhLi3WQosXt0KBFmtpKd5SWkppsVLF3d2Lu6c4BBISYsTdZXfn+yOwZNndGAkJNM85OYeduXPn7jK7M++97/v7WbPk/PdkZWQz2HOi6pgTG88xrul0JrWbzeT2c9T6O/D3cWo09aJO6+qv/F7LKTt0H9eZ90a8qJ00MjXkysEbBPmEkZ6Sgb2LLfcu+tLNZiTjWnwDQNWG7iTGJGNobMCiU9+wwW8pFSvb83uumrK6rauzfbHuWiCAufumUK2hB0fXneF8Eb0W5/ZfQsijMJ0PTdEhscSEFY8dyHOMTA3pN6UbrXo0ZtnkjWRlFE00oSzzfHJEmzJpWSYjNYNlX66n+/j3cHCzz7d90IMQxjaeSo+J7yE6ZlJFqI2xYIaRYIJVtCN+T/y4dVa7nLyNoxXf7fsaEwtjzKzN6DS8HRvn7FDtl0r1NH67G3X+HwPdPtU5nv1/HKXnxC641qik2paXUnHjLg1UgeDi83PYNn+vKnB7jjYRl6jgGHyulvt4FheiUkSiV/5o/7ZT4qG5IAidgV8BPWClKIo/lfQ5S4PL+72p3ao6iuxC+DmV89YjkUio3swL20o2nN91pUTO0eaj5jT7sCGGFaQoK6Sz74eTGApG+R+oDUFC2/6tSIpN5vIBb0SlktPbLnL3nA+HUjcw8scB/DYhR5q8Yce6zN79FQZG+nxoMZT0lAyW3fwZC1szEqLU07yGVBnPmodLiHkaRwVXO9KS0/mq/Rzunn/0opGo5H2TQXQf25lRPw+iyfv16e+i/eFi+y/7Gf59X6JD4wgPiMx39Uu9PqYA9S+FoCDqlf8Jivj5Qc4EQHpKBo4eDqrPsMsn73Lw72MqwRIBMLEwQpGdTYhPjsWEo4cDE/8YweqZWxjfdDoAbfo0JSY4CuWz1DITUwMSQ6NRpGlXdBMVIDOQYmZtyoLhfxRp/DWbV+XL1Z8xp/dCnRMos7Z+iZ6eBFGhULtOcn9q4svKpiIa7Z9TpV4lOgxpw/af9xFbTB6Rr2PVV81XLncwkNt/Mff7lQjPri1B5zWmqtXLfZyoo7/XiEKuYMXXGxm1YDAbZu8gOV7dK1MpKkkkFgEBC2yQZ8mZ+N5URJnmo5lJqiXeN72xERzUtouiSMWO5nRu3BVRKdJp8DtIA8xUypPWDpZsDv5b9UCfEJ2EubWpSjFWF/JsOX9PWkePie/T5qPmmFqakByfgkQiASEnPS86JBZjMyMc3Ctw4/htRFGkz5cf4H30Nhf3XNPoU6InQWYgUykzWlWwpMr/3LSu/hmbGZV5WxgbR2vqt6/N8fVnS3soKoIehFK5lovWidZycvGafhIEQVgNdAWiRFGspWV/W2Af8LzeZbcoinNebvcyJRq8CYKgB/wBdABCgeuCIOwXRfFBSZ63NLh2+AbXDt/Iv2E5/zl2LzlEm4+a039aDw4tP6FWd/CqdBrWloToREYOHIUhRphiibSiSLphGoZPrAvv6UaOR5FLNSemrh9HL/sXKmTf91vC4Jm9+OPqj3jWz6ldOPD3MZaOXaVqc3rrRQbO7MUfE9QLz8OfRPGe0UA2+v9Of9dPiX2+AvHSw5g8S87OxQfZsfAA254uo3br6tw956N1nOu/28mni4aweuZW0pLK9k2+HN1I9CS069scvxtPuLDnxapXs6716fZpRxAgIjAaSztztfqcrzvNxczKmH+P32X5zZ/ZMi9HjOHsDvVJEqmBLMc+IA86SvtxXLGtSON3qe7Ekgs/8HnLmQTeC9bapmbzqrTq1ZQhHuMK17mOr2+DDnWo3qQKf32xtnD9vYHEJcQSp4wiJa34fjdfJ/JsObuXHKL94NbsWXpYtT1RjCOCYGxwAER8uY2TWBkpMtKzUzX+77PETAzQnJSLNgvh6qYkXLKrAnB7ZQDmbSQcU+xVrcLM/PAnrh78Fwtbc7aFLWfDnB0Ymxlh42j14rdYC0qlkl2LD2Jha05ijPqEnMxAhq2TNWlJ6STFJqvUJFv2bMLEFjO19ndy4zk+WzKMiMBoooJjqNWiKqG+4SRGJ6mlU340+UNsnWzwPna7TD9X1W1bg/aDWnNm68VC+TWWJBFPoqjobs+lvdfp/PE7/LP6VGkP6b/OWuB3YH0ebc6Loti1MJ2W9MpbY+CxKIoBAIIgbAW6AW9d8FZOOdp4fkM7u/0Shg4yEqwiSJOkExsVjz2OWAiFLwp/jsxAhpOXIyvXrMIBF0wF85wdEZBiEE/LifUY+dnH3Dnnw+JRfxe43+kbx5MUm0zIozC1GevL+7y5euAG7wxogcxARuijMO5eeKh2rCiKZL3kWWNkakjXMR0BSI5PJSY0tzKXjnooUcmCj/9gwLTuTMsdvOWaTVdkK1n3zTaGzu5TqIdY9XqsFwg6SmiKZda+IDVEBd3/cn9lhcKO6dn7fG/EO/yz5gzvDmjJsXUvZrADbgfxRetvdR5+88QdEJWsf/w7G3/YqfP8VvbmBav3EkWq1Hfn8Y3CeXf2+epDLu69xv1Lj3S2WXLhB77/aCHhT57VPuVeFcpVoykqX3oAVIo511+u9i26N8bCzlyngFBJUihVyZdQvU9BR01c7vPo6aEUlfjL77B2RTAS9Fm2fDkZigxcJF7PB5P3+XSswpUGEU+iMLEwxtLegoSoRJSikgiC8aKuaoLNRnTAl9t4UZd0UskWs5AJOYqiClFBHFFUpZ5avwpRjqinxCT7hdCJgWBIyNmnNJG8q3F/SYxJYsOcHfSY8D4SPQmOHg5813tBvuN/OXCDnJU3bbV8eQUxIY/C+PXTFRgY6ZNiHsfKrctACR713MgWpBjrmzL8h36c3nIRvxsBjPxpID5XfEmOS9HZZ2lyavMFzu24grwMZVz53wqk47C2/D1pHTWae2HrZF2s1kHlFA5RFM8JguBW3P2WdGKsExCS63Xos23llPOfIDo0FrtKNmSKGdwJ90bmZ4l1dCWqUIsEYkkUY4vcd8ehbTi+7gyZpL8I3J5hkmHJicOnOLrmNJVrOXNcuYOll3/EwtZcR28viA2Pp0/FUUxqO1tjn1Kp5MTG8xxZdUojcAP4Z/VpGnasq7Ztf+I6mnWtT3pKOoXJVbj+zy3c67hhaa97zMnxKZzafIHRC4bkGImX88ZhaWdOmz7N+GfNC5GQJl3qc/XIzXyPlepLMbU04cjKk1r312tXiweXfQs0jhE1Puf7fVMKNuhcdBzalp+H6U63NDQ2IDwgknM7Xz1tunar6ti72HJ4xYlX7qusE6LwxUFwwS7dBRuJA/bJbhhiQqwyIv+DyyB7lh6m58T3AUgiDhsqqGVGCEJO6mQqSVSmOoE8xE+8i794nwDuU5lqGpkU2WQhydCSYokZ6WhXat30wy56248gPjLHhiA/ssUsEsQYMsX8sxveG/kucQVI4Q1Me0x8RBIeYk08hJoobhmQUCGcYT/2ZdfiQ/g9m0DZMm8PA6b3zEnVLKO8jsBNZiArcFulUsne347w6eJhWNiakxJfOMXe/wqCWDx/xUQzQRBuC4JwRBCEmgU5oKS/Edqm6dTeriAIowRB8BYEwTubN6cYuZxyCsKTO0F41HMjnCDcqIbes+UdQRBwwZMoworUr55Uj4oeDoQ8CkNXQPTUL5yt8/cyofkMAOLC49kU+Ge+/oQKhbLIKWSGJoa41XLG1NKYlXcXMnV9TprYpLaz2fnXPhLlcZjbmBS4vy3zdjPv8PQ82zy6/ph132yjejMvPp7bn4Eze1GndY3you03hF1LDnF45UmCfXLU5gRBoF67mlw7nH/wJs+Sc/vsff64Pl/r/lotqxFwO7BA43hnQCtsnayZ8MfI/BvnQhCEPI2FBYlAmP+rBxwObvY07FRPLfXubSZLmY6poC61byepRLyYv2l7WSQ1MY2gB6HUalkNAQFRy++2iBIQiCMSCRKssMMQE5QokaCZGqCPIRl6mtdePNGYY611HKIoIooi09+bi14+ip0hoj8h+CNHTiSh+Iv3tZptA1Rr4kmXUe35cYCmaNXLpJCAveCoeq0nSDEKt2Lu5HnEhr1YJUpNzPEEHbNoKNUaV8m33/zQN9Sn88fv0K5fi1fu63XRffx7TN80UW2bIAg0fr++zqD2qV84v49fRXJcCoNnf0T38e9hYmH8Oob75lB8VgG2z2OYZ3+jCjmSG4CrKIp1gd+AvQU5qKTTJkMB51yvK4H606ooisuB5QDmgvV/uNq/nLcRvxtPGPXLYDYfVKCPgdo+QRCQiEULMNr1b8HpzRcAMMaMRDFWLUUmmjCssFW9jgqO4e55H2SGMnZGreLjGp8TE6pl1U9UMr7JNI4rd+RZb6YLt5qVEASBL1d+yo2Td2nbtzkXD1/FV3kLI8GUT1p+jpmnAe41PQk5m/9D2N7fjjDixwFMWTeW+UN+09kuIy2To89WbvQN9fnj2k94H7tFdmY23kdvc+dcrkxtXSmMBUit0iqMUBAKaxj8H0BAxLaSDdEhMUSkvJjVVz0bFjDV7bteC/jrxs8a2/tP60GzDxvxRatZBepn3bfbyMrIomnXBphampCSULAZa0EQmPjXJzTt2pCnfuGa+6UCrtUq4VbLRWtNnDZri+ckRCfS5L163Dp9jz5ffcDfkzQ9uUoEHdeiTmNsXbYBudOinwcKuR/+80xtzPs7pjWFsyymE+fi5KbzfLZkOA+vPeZe5nVsxYqq1TSlqCSJeCyxJZ1UPIQX2gZyMZtAHlKF2mr9SQQJVjJb4szDsEysgICEWCLIIgtjwVStbbqYSij+SNBDRElgoJStC/bw29V5jG8yTWOscWIU+ujjLHg82+JAiphEKP44ox5I1WxelXG/j2BS628K9DloC0SNMSNK8RT7l/5bA+8F8+fna3hv5Ls0/aAhSbHJ3D5znyd3ggvkr+bo4UCjzvWwrmiFQq7g9JYLWFWwZMCMnlqtdaQyKQ071eXueR9SE3VPyrwuzu24jN8Nde9WG0crRi8YQvCDUCICdVs5BPuEopAr8b8dSLdxnZEZyIgIiOT01ksa5uzlFJkYURSLbD4rimJSrn8fFgThT0EQbEVRjMnruJIO3q4DnoIgVAaeAv2AASV8znLKKTMo5ApObjxHg9b1CDsbj5HwYtVJKSqfzbQWHqcqFTmx4RwAFXEliEfEidGYYEYKiehjQKVnN10jU0MSY5LYtfgguxYfpNcXXVn7aCldTQbq7P/cziso5YUf29XDNxlVbzLLb/3C0TWn+ajiKB4r71BZqJFTv5EF3Acf1/uMnjOGLbP35nsDHlP/a9Y+Wsovw/4o0M06KyMLcxtTln2VUx/ctm9zBn3Tm+0/7y+/YZUhGnSsy+gFQ/is4RSV+txzAm4HUq1xFR5ee1ygvqwrWKq9btmzCV4NPBjbqHBpkFvm7SEiMJqNT/6ku9XQfNt3H5/ju9VlVAeSYpKZ2XUeGWmaGSS2lWyYvGYsqQmp/Dz0d61tniMXs4kmHBD5ftBCll9fxL/HbrH5xz1lRhThdWAgGJMiJmAqvPi/jVKGYCXkL7tfltm1+CC9Pu9Cwvw4fLmNhWiNEpFk4nGmCtGE4Yib2jFSQQaigCiKGqmTponWfLKiH1NHzgJELLChMtXU2ihFJUH44kVdJM8C8zQxhUMX9/Fuv1ZaxxlPFO6oZ3CZCuZEiJoTEF+u+pTPGkzJ87rOjQK5xnuJJwoLdNeAP0+NNrcxo167mjT7sCEGRgYEPQjh1KYLWu8NH03+kLSkdC7svqqmyBqEFHGlAAAgAElEQVTyKAxRFOn7dTeOrDpFUmwyJhbGdBreDqsKllw7fINB3/QhPTmdqOCYUhX9iItIIC4iQW1bzNM4RtX5Uqv1Qm4u7LnGkNkf8eReMJvn7sazvjuLzs0hMz2LM9suleSwyzYir01tMj8EQXAAIkVRFAVBaExORmS+9TQlGryJoigXBGEccJQcq4DVoijeL8lzllNOWcP33wDkYjbBgi+uYlUMBWOyxSwCeYgj+dccaCN36oogCLhRjWwxiwzSsKYCUuHFV/vHw9NZPvmFWeuuxQdp2rUB28JW0NfxE4zNjXFwsyPscQQZ6TkP0Zb25nkKMORF4L0QOusPwNDUEKWoQCLoqQrvnyMJNCE0/Qk7Ildydvslzu28wq3T97T2V7dtTQ4uP655c9axmuXkWVHNa+vMtkvYOdvyyc+DOLX5Aj5XtNdA6VpVKLIVQFFXAcr46kFxcevUPRaN/EsjcAM4vv4sYxYN1Rm8HUzdxOmtF9j6017qtKpOXOSLhxtLewuGzemrsg7IDyNTQ1beX4xCriA2LB73Oq4FChrjxWhO+xxjkbAQe5yYteJrmn7QQOtDUUxoLFM6zKFhp3osPDuHq4f+Zf3s7Rrt4sQoYomgIq4ICARkPWDxnN+o7V5XLZ2sLPBKq3D5IUhwlnrhL79LrDICE8GCJDEOKTIq6LlonP/5Od8Eq47IoGhSElJp1b4FN06YkUoSAgIVcckJZkTtKZV5UcXNi/W71/Dg0iN2LDygsT+GCBxwUQVuAMaCKcnxMfw8omj2GLlJSUgtcOAGYE8lAniAq+iFVJCRKMYRQwRe1M332KTYZLX6Ua+GHoxeOIQ7Zx9w7fAN1QTHgOk9uXveh7vntWeP3D3vQ2ZaJu8MaIlVBUsyUjM4seEc0c8yUp4fV6OZF6MXDmXjnB1lYiXuOfkFbs/Z8uNuBszsRURAJJ4NPBhR4/NyARN4nVYBW4C25KRXhgLfAjIAURT/BnoDnwqCIAfSgX6irtzk3P0WoM1rw1ywFpsI775yP9WbeBL0ILTMe4SU899CLmYTRmBOkTkSKuKKoVC0PPTB3/Zhw3c78mzjXNWRyWvHcmTFSY68NHMolepxKH0zmRlZGBjqgyCoROA2/7iHOq2raxUsKSwKUUEQD3F/qQY3XUwlTgxn8MChTNs4gWFVJ2hNOYOch/FZ2yfxZduXlAd1BG9rHv7KpX3XWTFlo3pzQaB1n2Z41HNDIVcQ8vApN07cw97FFkNTQ57cDVapmulS1SvR4O35+/mPBG/50bRrA6T6Ui7kMs3e+ORPUhNTSYxJpsr/KpOdKcfawZJve/zMpX3XARj6XV9iw+M5+PexPPvvMeF9+k3tQVxEPBVc7ehpMxzXGs48fRyOPJ8VriDRFzMDc6wyK6BESaptLMNnDcR3d2iBUo0HzuxFi+6NWTNzi8rjSikqecxdvAT1B9gIywDs4l1V9bKvhUKm8OpUoczVT6GUKnMdlyFPIV1MwUSw0JgEUpFf8FYGv1N9p3Tn9ul7GhMFmWI6T3mCu1BDtS1LzCQEP7VUytxIJBJsnayZuf0LJjSbobH/qfgEa+zVMj8AEmwjMIm21vq5xomRZJNNBeGFSXeqmEQskbgInmptB87shYWtGX9+vjbf9/2cdDGVCIJRosQEc+xxUgsuC0utltVo3q0RzlWdCH8SyfF1Z1XCJ6+KmZUpfad2JzE6ifM7r+SZqlhW8ajnxvAf+nNs3Rk86rqxfvZ2nQHgCXHnv6+SCljWMXB2Fit98UWx9BXw5Zel8lmVuEl3aTBwZm9Sk1I5uelCmfYIKee/hVSQ4YJn/g1fkU8XD6NaE08SY5JY9MkyrXU2crmCTrK+Gttdqjvx25V5PL75RGOfBgV4INIT9FAo5SiQo5drNTBcDMSJyiREJ3Jq83mdgRtAQlQippbGVPSoQLh/LmlqHeev5OXIqmmbNYcripzdfomz23NWRrwaetDmo6bEhsWTmpDKh592JDtTzu5fDyHPfnFTexV59EJRBh8wS5MrB/9lwIye1GtXS7Uqa2xuxOT236ldB4bGBmqz/kqFMt9ZaVNLE9oPbsOqaZswtTJh95JDAAQ9CMnzOEClumed5QAC6KGHeaw9W5ftoJph/isHkKP4t2vRQSavHUufrz5kZtefiEmPxDJXnepzZPEmJBKLNa8xXbAg12JuQ3EdQZMgyW2JUPD+EF+sxuor9dHHOmemXKJ9XKrzv0HfoW3z9zLs+36kJKQR6vtCCsBAMMJMtMRPvIMFtmSRThopuFNDZ19KpZKokBjkWdo/ZGvsiSZM7f4jiiJR0ZF4vWT6rTpGqECw6McT8SEWWJFKMplkaB3Hph92MWLeQOb9M4NpnecW6P0bCSZUpnqB2haEexcekhyXysCZvVg7c2uxTt4nx6ewcspGTCyM+WhyN+6e98GlmhMWduac3X6JgDtBxXauksL/ViDnd14mNiyOlt0bY2CkX77A8QbzxgdvXUa1596FhwQ9CFVtO7buNM27NaZ2y2rcO+9TfoGW80ZiZGrIwJm9yEzXrNNq8n596rapiZm1CV+0/laldrfqwRKSYpOZ2Fxz9rUgDJrVm3XfbqNm82r5Ny4gLoIXfuIdrEQ79DEglgjMscJAMGTQzN581ytvn6FOw9vhXseNqevH6zR/fc6WkGWkp2QUqDbO19sfX29/1eubpx/g6FGBsb8O59i6s/hc9SvYGyRHyllb+l85r8bmubv54NOOSGV63DnnQ7DPU8b+OpyZXX9StXk5XcvexYYnOsyyn1OnTQ3unnvAsXVnCj2mROKwwk5je0aYnFS3ghtJZ6Rl8v1Hi1h8/nuafdCQI9uPkY3md11OFkaYaumhZKjexJPYiASigt5MVcc3ifWzt/PZkuGs+HqD2u+8neCIjehACgmYYUElIX+FXvc6rsSGa0+HMxJMEESBENEfB5zJJotwaSAOcmet7Z/jgDOpJKNEiR2OeWaKrJq2iQWnZuc7zpJGFMUSe+ZLTUxjzcwtNPuwIR2GtGFs46mM+mXw6xMSekWOrj0DwP2LRSuJeKsoO0mHReKNlzezrWSD1UvF6gq5kofX/PBq6EGj9/5XSiODTsPa8uFnnUrt/OW82dRsUY3LB/5lw3c7NP7GNZnGV+/Mxt7VjqZdGwA5KovWDpZM6/RDkc/ZskcT2g9qzd0LOlK/ROWLv4IgKjHAgKrUxQgjlMipTDXsBScqVq5AVkaWVgPY3Ayc0Yv5Q39nbr8lebazd7bF1smaD80HF2xsWsYa9jicPyaspm7bmng1cC/QYVb25mx68ju2jpb5Ny6n0Bz46xh12tTgj+s/oZAr8rQQqN+hDlUbe6qlWmpDVIpFXlE1woQ0NIM0PQsIuVV4S4AVX29g4Kxe1GtYhxQSkedadVKICuKJwYzXd2198vNgNj35k1a9mubdsLC/BfkgKhQv/p6Zk78JNWyvglKhzFm1+mkgUpn6XLpEkGAuWGukOuqiebdG3D7zQOd+Z6EK1tgTRiAtRtfDVV4Vc0G7nYBSVOIv3iecYLLJIp5oEshT/A7ImXD8L3B5vzepiWkoFUqC7odQ0b1CaQ+pnMIiFtNfKfFGB2+uNSrxz6pTakIHg77pTWpiGm61XFg5bZMqRao0kGcr/lPqYOUUL5W8KhLy8GmebUwtTOg/rQeNOtej29hOeB+7VajCcci56e+JX8tx5Q5kBjI867uTEJX4KkPXQBAEzAVrbAQHVfrkp78OY+VLdWkv07JnE0L9wjmx4SxRIXk/PCw6N4cbJ+++8ljl2XK2/byPZh80oP3g1vm2j49MYPr7P5YXgZcg62fvIPBeMItHLWP/n0eZvGYsA2b2pF3/lmwK/IsN/n/Q9+tujJw3kFkf/JRnX2N//ZhpGycUWMnyZcwESxKJI1PMUG3LkKYQERSluyYrDx5c9mVS62/4dPEwZsyYQZDkEdEWwQSKD/HnnlZz5pLE/3Ygy7/eQMNOBUsBLefViI9MYOfCAwyZ3eeV+rm07zqdP27H6AVDdLYxEcxwFbwYNHBwjoKlDkJ4TEVccRW8sBecqCLUIptsUsS87wtntl9i1C9FnDx7Q7l25BYffd0NPWlOTapL9UoYm6uvUH7263DVJGs55RQHb3TaZJfRHVDIlSz78sWStaGxAYIAUcHRKvGB0uLkpvOlev5y3myMTA1Jzyf9Y2KLGRgaGzBn/1T0DWWM+d/kAvcvkUg4kLIBfUN97l3w4Y/P1/L4RgA2jlZM3TCBM1svvupb0ImxuTHm1mY8vhWYZ7smXepz6pmfnS7MrU354fB0rh+9xa9jlhfL+ESlgnXfbqNR53qMXTKMpNgUFHIFd8/7cOdlQQpBwuNbQTk1O29Qzc2bhDxbzrxBSxn32wiqNvTAq6GHat+F3Vdp3q0Rzbs35rOG+VsDmNuasXjMck5vyfu6ygsPahGMH/auNsiz5SQ9TX2l+p2UhFS+aP0Nn8wfxP2km5zafp5Fn/yFRFnyIiW9J3VFqRRVdX9ZGdnEhcdjV0m3bLsGOq57NbXJ5yudOkQpJLLCqbqq+eO94d+7qOAYbp66R/vBrVUWMLpIFOOI5ikCEvTQwwl3ZII+AXeCGNd4Gj0mvM+is3NUisQp8ak8vvmE+5cf8eDiIzwbupMcm0KbPs25uOcqci31odlafOIccSOIR5hiodH+OTsW7Oe3q/OK8AnoxqGyPVUbVUEq0+P8rqulbvci0ZOgVLy43mLD4rh26Abt+rXgxMZzDP++HyGPnrJ6xhYADE0MqdHMi6TYFK4c/Le0hl1OLgQx5+9N5o0O3pZ9uV5j28qpmwC4eUq77PjrwN7FFkMTQ4J9QvNvXE45OkiMScbS3kIlXayNB5dzZO8XjvwLmb60UIXTu2JWkxSbTH/nMWrbY8PiESQC1g6WGv4yxcVnS4axbf7efNu51XJh7aytOveb25ixK3o1CVGJXM8jna6oXP/nFteP3kGiJ0EiEeg6ugPZWXJ8rhS8Hq6c4kGpUHJ83RlcqjkB8J5+P0wsTUiMScLe2bbAK593z/tQr12tVwrepIIUd6rTvnUbqtRz4+8vi6fmZcWUjWyZt4fuE95DKshQFtEHsjDYONlQsbI9e349jCiKrJ25hWpNPEt98vHLFaNRZMtZ8tmqUh3H6+Lmybv0ntQV9zquqt/xCq52ZKRmqlLL48RIUkjCgxzVyTiieMgNaogNVRkNe5YeZs/Sw6p+K7jaUf/d2rTs3pi+k7thYWdOamIaVRt78MFnHdHTk5CSmMbv41YRmUedo0DBLAwyUwuX+ZEXhsYG9Jvag71LDyNIBAbN6kVsWDyHV54slRrjlj0aU6tVddJTMrCwM6d5t0Zc2nedOm1rsvOZRcPG73eqecrValGV5ZM30KpX01KvjXbyrEhKfGq+pQr/CcTXl81QErzRwVtBfS5eNz0mdsHS1pz5Q38r7aGU8wbz5E4Qleu46gzepHYvlOkK+yCqb6iPqaUJPayHad2/4bsdjFk0lB8H/FqofguCRCLBrZYzCz7+M9+2G7/fycxtk/ii1Syt+5Nik9n2816ad2tMy55N2LXkkEq8pdgQlSjlOY/Rh5Ydo//0nrqDN11S12/4ykBZweeqHw5u9gTcCWT0wqGYWpnQuk8zooNjuH32ARIJKJVgYCTjp8Haf3+dqjiQmphaLOM5s+UC3cd3Lpa+npOSkIpUJlWb3S8pGnWuR1Z6FqaWJtRoXpX7Fx/SZXQHTCyMdfpjFQqtnm/alVxzr9IBnNh4HrealZDJJKryA23ebm8TuxYfYuCsXrTq1TRHDTI4Bs8G7qyYvIGMtExiicRTqEOCGEMkoVhhhzUVeIA3XmJdDAQjjT4jg6I5svqUhl1MbgbN6k2TLvXZ/+dRIEdBNVNMV+svkhBs0a5MWVJUruPK5f3XCbyfowL75G4wTp4VGfxNb9XK1uvAq6EH7Qe15vaZ+ypxknXfbGP0giFcOfAvF3ZfxbN+ZWLD4vC/Hah2rP/tIAZ/26dMPK8OmN6TlIRU/p60jlotqxXPd7ycUuGNDt7KKiunbEQqe42ePOW8lcRFJOBS3alE+m4/uHWehqO3z9xn5PxBSCSSAik3FoYBM3pydvvlArW9evBfPl04NM9x1G5dg3M7L2NXyQavBu46zb6Lg+wsOTID3bUi5ZQ8Tx+HI0gEAu4GkRSbzP2LDzGxMObBJV+USiUSqYQp68YzYt5AVk3bpHZs/Q51aNe/JYNcPy2WsZham5KaWPzKdq+rxq1Vr6aYWJhwZNVJ7l98CORIilva606Nex3M2Dyei3u92ffnsbcySNOFKIpsnLNTbdu1wzcYOqcvy75ajwQ9FKKcKJ7iRV3VdeIguuDPvQKZXGvDyNRQTaHVBU/8uY+paIExpiQQgx4yLIRCpNIWgSZd6nP7zAMyUnPqSROjk3DyVA8Yn/qF8+ReCA061OHf43fU9rnWcHrla7fxe/8jNTENX29/KrpXoF3/lqQkpPLn52s02p7fdYVu4zuz77d/+HrdOKzsLbh+9JZaFkBaUhqVvBw5seFsqSsS/7P6FCN+HIhzNScmrxnL5y1nllh2TZmnPG2ynJdRyBVlYpalnDebuPB4bPOoO1HExevclxf2zrZM/OsTHt8MzLPdqU3nGfRNb9bP3l6k8+jinQEt+bj65wVuv/+vo6z1W8oQj3Ea+yb8MRI9qYSN3+/k5+PfvJZU5fjIBKzszYmPfHbTewVj2ZIgUgwliXgkSFCioCKumAql+zBenGyYswNbJ2vObNMtRrV21hbaD26jsT3lWR109eZePLrm/8r1MwlRiejpSeg6ugMHlx1/pb5yIzOQom+oX+L1PbfP3CclPpWrufxQn8/GW9qZI89WkJJQPKuU2oIwde+3Fy+ig2O4cewWorzcfiMuIoHTWy/S8/MuzF98h1giqYCzWoCvJ+hhIBqRJWaiLxgU+hwWdubEhL4IOPQEKV7UJVVMIp00HKlc8H6LOO9Qu1V1nKs60qBDXU5sOIvvvwE4VnEg4ommIfatU/d4Z0BLjeCt39QeSPSK9nvs6OFAt3GduXvuAa41q1LvnVrERyay/Zd9pKdkaD3mwWVfajSvintdV8xtTLGsYEHrPs2wsDXn6JrThPlHkJmexd7fDnP7zP0ijas48b8VyJFVJwj2CWVkrUmlXj9YmrzpNW9l66mjnHLKUZGdJcfQpPill6dsGE9qQho7FuzLs92epYdp/H7xW21kZ8qpmktwIj96THifrT9p1sdZ2lvg8b/KjGs8DYDqTT2LNItYpZ4bg2b1pt+U7kgkL34SpVI9xv8+Qi2Artm8KoH3gqnRzKvQ53kdRIk56qSeQm08hJpUoTbhBKmMpd8G7l14SLUmeZvdn9h4nhrNq2JorP7A+cOh6WSkZLDg1GyW317AlPXjGfqdpll9Yfjqndm07dcC9zqur9RPbgLuBGHjaFVs/WlDEATaD2qNlYN2G4KP5w3ko6+7legYdLHsq/UkxiTRtGsDqjWuUipjKEv4evvz1C+cAcP6EyvRbkchIFDU5QRzWzOitaj5mgjm2AoORQoIC4OhiSEtezZh56KD/PXFWqo18WTQN71p2bOJ1hT1tn2baxXU+mPC6jwzSvKi0Xv12DZ/Lxf2XGPHgv1s+mEXh1ec0Bm4PWf3kkNMWvkpVw/dYMu8PexecojNc3fRsFNdRv40kJY9m3Bp3/Uij6s4SUtO5+jaMwyY3pMR8wYAOYIw5UqYbx7lK29vGXbOtnQZ1Z6z2y/x5G7eRrXllH38bgRQo5mXSpgkN0X1QKrW2JO9vx+hZvNqdBv3nmqVODMtk7CAKB5ceqSqofO/FUSLHo25uOda0d/ESyiVSr5YMaZAyphdx3TkztkHHF5xQmNft7Gd8b2eY7Itz5KTlpjOT0dnsmjk3/naCjwnU0xHrJHGut2rcXVzYUPw71hYWZCSkIqZlSkIOcGmgZE+7vXcqFSlItnZcvUUGl1pXblX5HL/O3f7/FbtCpkylkgcnkLtF90LAq5iVcIIxI2qheqrLHPr1D3qtK7BnXO6fa32/f4PSy/PZVTdr9A31EdPKsHK3oIBlUYj1ZdSqZoTKJXM2T+Vdd9ue6XxrJ6+mcHf9snXcL6gyPSlyLNLNntDFEVWz9hC0APtq9UH/zpKVIhusaTXQVpSOlkZ5atvAFcP3cDQ2IAeXXqx/+heLLJfeLQpRQXppKIvFGyyT6ov5fer80iJTwUBLGzNSzV9rveXXdn+y34g57rc/+dRjM2Nyc7I0prFZONopSYK8pzwgMg861ll+lKd9k1B90OpVNWx0J+DUqHk2uEbaqvXmelZqvrBhh3rMvyH/qr3kZqYxq7FBwt1juLm8gFvrCpYIggCA6b3xMzK5L+nhPmGr7y9VcGbkakh+ob6/2klnU7D22JiYYypZcGMPcsp21zYdZVh3/fVGrwVlRldfmTqxgn0cxqltt3c2pSqjavQundT3hnQklkf/MSyr9Yz99C0PIM3fUN9Vj1YTGRQNBVc7Piu1y95WgDIDGQkx6XgWsOZoAcheY5VKtNT1T+8zN7fjjBr+yTV6172H3NUvo1NQX/RxXhgvikhaWIyoQQgbJKgL5jjey+U5lXa0OfdAdhVtOXeeR9W3l9M1zEduX/pEetmbeXGybu07NlEaypPWUCiJZlCXzBAIb5dfpPXjtxk5E8D8wzeVk3bRN+vu3FcuQOAoAchhPlHIJcrkMsVPL4RAPBKwYFUqodXIw8867tTq0U1DIz0yUx/9VSkih4OxD4988r95Iffs89AG77/6t73usjr//e/SEZaJg8PBmIrOvJYchcr0R5kSpS2mXzx0RfY2dijpyfB56of3v/c0hmofLpoKDsXHeTEhrPFPkaPOq7M3DYJAyN9RERObjjP2R3aU5wlEgm9v+yKr3cAsWHqarG6hKfc67jin4/FzMsYGOkz8a9RpKekk5meRVZGNme2XlQJoUDO9T5i3gD8bwWSlZFNn68+QKafU9985eC/PLqu2xdyw3c7+PjHAZzadF5DsMT72G28j91WvW72QUPqtKnBnbM513azDxuSEp/6WsVDntwN5sndYCp5OZIQlUhseDwtezTmQjFO0pZpyq0CyhZ9p3THwc2enwYvfa3nbdKlPo9vBmr8+JQG9y88YtSCwQiCwP/erc22+XuL5WGinNJBqVSSkpCKrZO1phS6rhWcfFZrbp2+R2J0IgNn9mLTD7tU25PiUnKk8f+5RcehbVly8QcGthnO52M/56lROEKaFEcqa4gpTNs0gVDfMKZ1nkv9d2vzh/d8Tm+5yCPvx6QlpSMRBBQKJcpnK4X2zrZc3u/Nh+M68eWnk5GTxfNCCRc81VJ0jq07y/LbCziy8qRGQDh980Q2fq9e3J+Vno2hiUGBcvkj9IKpoqiN5NlnZyyYUiHDFZ/IO/heNiErI5teth/jUt2JB5d9qd2yOu0Ht6aSlyOX9l3Pt/8CrZrl16YQ/68AIkqUogKJ8EIwKVGMwwTz/MfyBqFUKAs0QdVR76N820gkRSvSadmzCZ/MH0R0aCxXD91g69Ld9Pm1M5E+MRxbdLHIoiMSPQmKbEWxCwWV8/ZgLdhjqbQlkVj0svQxC3Pg8K+nVftrNPOi75TuSPVzVEtFUSQ7M5uEqCQeXvXjzjkfuoxq/8rBW7qYSjzRGGCINRX4++YvrJy+hcjAKEIehWFmaczEv0fjVssZyLmfCYKg+m7IDKSc3HS+UFlC+oYyarWsRqWqjgiCgFRfSnZmNrsWH9J5jKmVKZHB0az7JmeFXSqTMmbRUFbP2KIKEtOS0lg7cyvDf+iPIMDWn/YSHRqLRCKhXf8WtO7TjOAHIdw9/5Awf/XUVYVcwcopG+k1qSsNOtbhwF/HdKZbXj7gzbjfRvDw6mOyMrKo3tSLjJSMUlF+DPUN4/GtQOxdbLFysKRqoyp5BqnllB2E50aOZQFzwVp81+pD0pLSi3TjMrM2pWqjKvSc2IWH1/w4tvYMEYElO0Mu0ZMwdcMEokNjWfH1hhI9V0GwtDNn7uEZeNRzI/BeMFvm7eHa4Rv55m2XU3YxNDZg5PxB/PXFWt1COIV8yJdIJOxLXM8HZoN0tklxjIZYGaaZOTUxyWICUYTiIdRCqi+l29jO9JvSnazMbAZXHqv6ztZrV4vJa8aSEJ2oCnIkEgmCREBUikQGRXN+1xWMWygJORaHsZhjBisXs3nMPapST+3Bt0p9d75a9alamuXY30Yg1ZPw62cr1MZ8XLmDv79cpzUtxbmqI2nJ6cSGxdOiR2PO+51Ecs9Mo51ZW4HtR7cglekRGx7Pma0X6TS8HaJSJCsji1un77++CaJC/r+mi6kE44cLnhhiTCKxRBKKJ3VUQerbgFdDD1yqOXFiY96GxgVhxd1FfFJ7Uv4Nc5+/gTtT1o/ny3azSYhKJER8jAIF1tijMMrCqrYJY4Z8ysovNms1Qc6PoXP6qh40C4KxuTEdhrTm32N3CPUN09pGIpHQb1p3pDJpsYsQlVP2kRnIsHawpHpTT1xrOtO0SwOu/3MLpVJJclwK/rcCc5QRqzri5FmRf1af1loH95wg0RcJEmxwQC7LRM9dzu4z2xlfbyY2Fa14Z2Ar0lPS2bnwoM7sieLC0t6Cnp93IS0pjWpNPJnd4xcGzezFlcM3iQ+P56vVn3Hg72Nqk272LrZ0HdOR1dM3q/WlJ9VDIhG0rlpWdK9A3bY1caziQOijMI6tO6PRxtbJmk7D22Fkasidsw+4dkTTf9SmohV9Jn/I8q82lIlJmupNPPlf+9pY2lmwatomDqVt/lcUxYalPa6SwtDJWXT5rHC/+brwmzmpVD6rMrfyNnbpx6yatqnAhqu5SY5LweeKL/5ta1CtcRXC/COKNXgzsTDWKDpVKpRsmbebhKiip2pWa1yFgDvBxaL8kxCdxNpZW+j1eV1/yR8AACAASURBVFe2/LSHjkPa4l7HlTUzX58nSjnFS0ZaJjsXHWD0wiFs+G4Hyc8U814FfUMZ6akZHJVvZ9lX69i95BDdxnZm3G8j6CDpg1zMJvppDJWF6qpjzARLss3T+dv7JzyqeJCdJeerdt+qpXSmiknsOrWNW92uYmNnQ8TxJGSCvsb55WI2D/55rNa/VJBR0aAS/eZ2oVGDxijkShKjE6nfvg4A7wxoRXRoDNHBsdRrW4NPan+p0e/H1Sey/M4ijeDN1NKEn098g0xfhsxQxu2z99k5aAsOoqlaoJglZnL3dCD+t5/gfzOQq4dvMOLHAez97Qjrvt3G6IVDC2QuXloYCSZ4iDWJIJhMMjDD8q0L3AACbgfRvFujYunL2Myo0JYY5rbmGJoakhCVSJIYhx5SnIVnwhoZkHUtkz/S/uBU1tFnaVpZjG86nad+4QXqX6ZfuFtzjWZeJMUk02FIG8L8I3Cq4qDhg6VvpA9izn3SzNq0WH5HynlzyM7MJjIoWmXEnXtywMzKFI96btg4WePrHcDl/d4Mn9ufv79Yp/V7kSTGI8MAR+GZSI/cFMUjOeOGTmTE3FHcu/CQDbO3k5FWfIbdeZEQlcjq6Ztp3acZLtUr8de/P2Nd0YqPpnQjOTYVvxsBDJjRk4SoRNX9Kio4hqTYZN4d2IrTWy+qfBUVcgW6plvCAyKp2sgDhVyBiaWx1jYxT+NUGS0NOtRhzKKh3D59n8sHvFVtYsPj+WfVKfp89QHbftYuHKYn1UMhV2BqacJHkz9kx8IDJfad9bnqh8xAxuBv+2Bd0Qr8S+Q0ZYuys25VJMrcylsT4d1i6SuvwtSi4NXAnVk7vmRCsxkvJMKLgGd9d75YPprxTaejkCuQSCT8dnUeW37cVaz5xiYWxjTpUh+Jnh4tujdiyejl/+lawLcBU0sTen3RlUfXHxdbcbGFrTkbA//kyd0gTMyN0ZNJGeY1nmQxgXRSsRfUfeYSxVgmbRhD957diH0ax7CqE1T7ksR4YgjHlaroCXpkihk8wYcq1EYqqD+MpoupxBGFk1BZbXuKmEgKSTgIzugb6lPRvQLVm3piZGqIhZ05lvYWNOpUlxsn77JwxF9a39Pqh7/i8+wG/cvwP1Tbd8euoafNcNXreDGaFBKphAeCIKAUlTzmLq5UpXbDGsze/TUDXMeo2g+Y3pOrh28Uut6iRCjkitzbyCc/D2btrK2v7J3U8/MutO3bnAnNZhTquLWPljKs6gQCxAe4UU0jQH4s3uOn+fOo1sSTdbO2MXrREJUyan5U+V9l3h3YipVTNxXYdqb3pK5EBsVQwc0OawdLdi85pDEJOmLeQALuBOF/8wnBD5/m22erXk25csC7WO+l5RQdh8r2pCdnvJZ7uUs1J7p+2pF9v/+jMemg65qPsQvBLsaF0nyudPKsSOP3/0dGSiZ3z/sQExqLQqHEq4E7FT0qcGKD+mp9w0718Kjryo6FB1QBnC4EQeCzX4fzx4TVhRpT3bY1adq1AcE+oRxbd1b1ne4/rQcH/z5OcrxmUDZ98+dEBEYRERBJvXa1uH/5Eft+/6dQ5y0szwPGE+LOt3/l7dNiWnmbVb7yVqwU5mZTo5kXT+4G55laGB0aR3pyBjWbe71SkBX0IJS132xTfXmVSiW/DPu9QDfSwpCamMbZ7ZcZvWAI2ZlyGnWuVywpRuWUHikJqaz7dhsj5g3k1un7BU5FyRTTiSUSKfrY4qB2w02MSeID0xepkwtOzQbACBOiCcMe9eAtRZrIH4PX8e8aH/pP66G2L5IQqlBbtZJlIBjiInoSQTCVcFdra4gxaSRrjPW5hxFAVkYWQQ9CNERNVtxdxK+jl+t8v5/V/5odkavQk+lx77wPplY5Qe/OhQfU2lkJdiDCY+4hESWIKKmEBwaCIR2GtcPS/kWdWPvBrXn6OKJsBG7lAHB0zWm6ju7AnqWHX6mf3UsOFckuQGaYI2YgIKBEqUUsRmTl1E38k7WVuxd8sHMquMGxiYUxrXo1JTYsjp2LCqZM97ydzEBG+0GttKrmXf/nJi17NOHS3vzvYSYWxnQZ1QETC2P+WX2qwGMvp2QQBIHRvwwhLCDytZRoBD98yvKvNtBtXGcs7Mx5fPMJ53ZcBkAPKXKy0UfdQiAhKgFbwblI55PKpNi72BIfmfBKZR5P/cIxNmvOkZWnVPfIT34ejKGxPr+NW6XR3vvoLbIzs/n4xxzpfCMTA0J9wzm65jRpyeoWK6IokhiThJNnRbWA1s7ZlvdHvsvORQe0WgLcPnOf22fuU7m2CyN/GojPFV/O7bzCiQ3naD+4tdbfsJBHT9nw3Q6GfteX3yesLvGV8sbv1yf0UZhGPd/bSrlgSRml79fdECSCVn+o3EgkEsYsGsa+349wctN5ne3iIxP4tsfPRAZGv9K4sjKyuHb4hlraSm7Fo+JEIVewfvZ2araoytVDN/I/oJw3gn2/H6Hr6PYFeqh7Kj4hmyzscSKLTHy5jYtYBWNBs9YLoEbzqhxO34wgkTBn5vcc//M8JilWCIJAghhDpjwDQ8GYgDtBOFS2VztWgp6GSIOxYEqlTlZUM6ynVm8gCALWoj0Bog/OeKCHHhGEICBgKBjl+Z7k2fI864jaD27NvYs+LBz5N3MPTiUpNoVve/yitRDbSrDDCjuN7d3Hdub60VsADPu+H/cuPMT72esywX90tS03wT6hvDfy1TM1LGzNMTYzYu6haZjZmFG9cY6H3Hv6/XCr5axVOdW1hjNRwTn1QBWoxFP8cc1lxZAmpiBFn0/mD+Lh1ZzrLiMtEyNTQ60PpobGBmSkZdJtbGda92mGRE/CxBYz6Dqmo1o7iUSCe90ctT1dqxvZmdkcWaU92Lpz9oFK5S4/UhPT+HPialWaXTmliyiKHFl1kvCAyNd2Tnm2XJWCXrdtTT5bMpyszGziEuP4bclSnNJeePCliknIKJgfnFcDd977pD1RQdE56bzkvL+Ah09w9WrBpu92aRxTvakXQ2d/xMKRfxEdmreFhUKuQKYvJeOZa0C1RlWIj0xQrSy9zPPg6jlOVRzoM/lDEOHEhrM8ffwioNnxy37aD25Nl1Ht8T56mxsn7vDBmA7YVLTGyNRQa/CmJ9Wj/eDWXNp7nWVfradO6xqMXjCE+MgErBy0+zla2JpjaGLI0bWnade/RYmvujXoUAczK5P/TPD2pvPWBm/3LjyEAqh9KZVKfui3mFHzB/Hw2uM8axKK60ezZvOqTF47jk9qT3rllJ/8SElILQ/c3jJinsZhaW+Rb7sMMQ052bgJOQ+VxphiLlrxmLt4UVfrMe8b9kcq1aNxl/q069ueaza3eJRyH0QwxZzK5NSoybMVmNuoB4BKFIiiqBbApYupmBm7MOaXoRoKjTaCAyaiOU95ghIFtlTEXMjbmLhaE898VV0HzuyF340AYkJjGV0vfy85bWyZt5v+03qy1ncpK77eWLYCt3JUFIcQQmJMEn43Atg6fy8xIXH8fnUe5rZmrPX7jQqudnSzHEpaUhqmliZ0GNKGPUsP065/C9U1YSyYYSSa4ifewQxLMkhDgYLKVKduu5pcOZCT4nz96E3W+i5FKpUi1ddj1fTNuNaohFRfxjsDWpISn8Ll/d4s+3KdSqb/ZS/HDz7rSEZqJh2HtWX3kkMFsqyQ6EnyTQfTRXFnhPxXcX7mH/aqRs3axC9eFy8HOG4uVfCPfoiQJiWbLAQEtQmMvGjTtwV/TliNqZUp8ZEJRIlPSSQWI8EEU0cj/terHmneIo4eFWjY+X+kJ6ejkCvYPG83lao65hu8xYUn4FjFgUfXH6Mn1SPIJ5THN59QwdWuQMHJ08cRrPtmG/qG+nT+uB3vDmqNzEBGVkYWD6/6cXz9ObIzsxm9cAjRITGsnbUNiZ4Eebb2jC9LewvsXWzpM/lD7l14yLXDN7hz7gFGpoY6/Vo3fr+TT+YPZPPc3dg52yIIgtqETcNO9fCo8yzdsxhET/76Yu0r91HO6+OtDd7uX3pU4LYxIbE8vOZHYvTrqQnzueLHD30XlXjgVs7bS3Jcimq2XhcxhFOBSmrbJIIEmZjj+6UnaP/6y+UKLu27Tvfx71HJ1B2FINNok5aUhlKh5Ls9X/Ntj58BsMORIHxxET2RCBKyxSyC8OXbj77G1slaow8AQ8G4UObRGamZ2Dhq7+s5Z7ZdoseE9wvcpzZWz9jC4RUn6f3VB1wsQIpZOaWDVFY8t7DPGk5R/fvyQW/CAyLZ9MMu3v+kPZsC/yQlIRVzazMMTQxIjEkm9NFThn3fnx0LDlC5tgtcB1uxIumkYIODSqTn1ql7pCWnI5FI6PbZe8wf+huPbwbiVrMSPb/oyu3T98lITaCH5dACqVI6V3Vi2ZfrOLXpPD2/6EpsWJxGDc9zDI0N6DWpK7ZO1pzfdZUbJ+4Uy2dVTuHp+XkXkuNSNERk3mSyg8FZrEo2meghQy+XPUl+iKJIdpac+MgEksR4MsnAU8gRpiIMvHfepeEHtXFzd+HAn0exc7bh7nkfKrja8b93a+fbf5X6lVVKkAq5gqWfraDb2M46gytdZGW8MNuGnN+bqo2rMGBGTyBnkSDUNxxRFPMMoGLD4pDKpKyevpnRC4Zw/chNRFHMMz00ISqRZV9tYPC3fTAxN8qxEJErsKlohaGpIenJ6bTs1RS5XFHqpt9vJOVpk28+SqWywHUFxXW+xzefvLbzlfP28eReCJVru+Bz1U9nGyky5GhOEChRIGgxc36ZhSP+4vsDUxlVR1PVEaCv0yh+vzqP3bFriHgSxclN51mzaAMBPEAQBQQkuFOdpSNW8k7v1gV/c3kQExqLW00XVt5bxMha2guOgx+EFos1Rp02NTi5UXcqdTmlhyAI9JvanUfXdF//RaXTsHZkpmex6YddHF5xgsMrTgA5KYvmtmasur8YA2MDNs7Zwc6oVRiZGtKv0ihiw+I1/PRcazrTqmdTlW3NpX3epCWlEXgvmDPbtBsX58W2+Xv55JfBrPtmG9vm72XppR8xszLVWjPTuEt9rv9zC19vf0YvGMLDa491Gh+XU7IkRCeREp9a2sModgRBQB/DQh+XnZmNVCZFni0nmjBVRsdzbIQKHD9wkidCzsryc9XwuPB4HD0q5Nm3Wy0XfK9ryiVWcLNTpToXFXm2nPsXH3L/4sNCH5uVkYWppQlpyekFFnTJyshi1bRNatu6jeuMua05S0YvY27/xSTFFq4WThAEJq0cQ6hveJlWTi5Ryk26Xy8SiYQ2fZtzYdeVPAVJRFEkiqckk1OwbYQJjrgVyjTV3MYM9zqu3Dp975XHXU45xY3v9ce0698yz+DNDkcecw8vsa7q2s8QcwqwCyId321cZxw9HHTul2fJGfO/yVSpnyNG8sOBqQz+pg/bf9nH5h93q9p9t28KW3K9fhXkWXKkMj1cazjz/ift+WLZaHy9/ZnYfAYVKtvzv3dqMXbpCKSygs8Ca8OzvjtWDpZafXzKKX0+HNuJ22fuq9lUFJXaravTefg7OTPnzx6qDi0/rtFOqVSSEJVIL7uPadGjMbO2TaKr8UBm7dA+uSGV6lG9iScja00iJSGVmKdxhQ6eXr5nRYfGsnnubnpMeB+Xak6EPHqKIBGwdrBUEyiR6Uup3ao6/x67DeQEfV1GtWfHgv2FOn85xUNhPPv+C9hUtMpV1SJqvR8JaD6vdRjalrPbL+fZd0V3e4IehGpsl+iVrl3Kgb+O0X96TzILYZ9g7WBJ9wnvk5qQSuD9ELyP3mbdt9uRSHI+m4KkTb+MKIr4evsXyY6rnLLDGxW8Va7tQvMPG5GenK4hlW5mbcqgWb2R6EmY98M8pJEyqgi1gBwJ8wAe4EHNAp+ry6j21GpZHV9vfw3FoXLKKW0SopOwc85bvU5PkOIoVsaPOxiKxsjJRkSJ20uznLqo374OPlfyfzh+fCOnPqef0yjaD27D53+PovOId3jqF05CZCL1363NlA5zCnTO/DCxNMbvRgCzPvyJ7/dPZUqn75l/dBZHsrYCkJ6SwbLJ6xm7ZHg+Pemmde+m2Dnbsl2H/045pYuDmz0m5sZ4X7pBJumYYYlUS2rv/9k7z/AoqjYM37O72fRCOkkIKSRA6E2kd+lNQJQuiBQVRFDkQ4oookhXqYJUkd577713SEJICAlppNfdne/HwpKwKZsCBJj7unLpzpw5cyZkZ+Y9532fx1A+HN6Wo+tP8/jp7P6ORQcIuhac6zEnNp1l05ydbEtahcJIzsxBC/Ta1GpdjXsXA4mNiAO06o3VW1Tm4j7D0heVJkoy0vVXzmMj4lj503pkchlNe9THwc1OT1lywK89Wfv7Vl2NVWxkPBY25gadV0IiN2ydbajdtnqOojgNu9WhZsuqnNt1CVW6iksHrmVJ769QrxyXDl7XTcCbY02sGIWNYK9rkyamIM/m9TToejCuPs4EXAnKdYzPRFAyExsRh42DFbGvqDzmReKjEzi67hSVG/kZfIxX5dKc3naewCsPqNigPC36NGL3koM5+tAZyrZ5ewvZw1vAG77yVuydWzMr2gVefcDB1cd0s4mZUWeokSvkHN54nOSUFOyE50vrVkIJzIzMSRENT1tYP30b879Z+kYFbp2HtcG3pvfrHobEK+L09gs0+bherm2szZwpZ/Y+Lqa+lMYXb6GiwbUJrj4l2ThrR77GtH/FEdqZ92TfsiOEBTymcfd6JMYVXbrQZ7/2YuPsnUQ/esLQmqO5uO8qLWTdaCHrRluznnSw6s3uvw+QllJww3uPiu5snLXjtXoVSeSMX/0yTJ39KzFEIKIhmHuEigVPQ9eoNZw+dprtJzez7eRGjp8+RooBQigLRi2nvXlPuth/qgvQMtN5eBv86pTF3FobNE3oOJUBk3tQrraPQeNSpaswMctZvU+j1rB/xVFWT9mUZburT0kCLgcRGZI1RSwtJQ0j44IHuRISAN2+7UiZap6YmBmjNFHS8YtWyBXaZ0rF+uWo2qQiswYtIC4qnvTUdAZN74OjuzYw86joTuv+TTmy9nnKsBNuRBFGmPiAVDGZKDGM+9zGjTJ657556i6elUvnOr4y1TyznXTcNm8vvcZ3o2mP+oW5/EJx55x/vle/RVGrVHt+z2XcfEu+pJG9g4hF9POaKNbBm7OnI8P+Gkj7IVq5ZFEUObU1e8PQ5IQUlk1YQ5qQgjJVPwfbLMOKVCPDXyIz0lWE3HlU8MEXEWaWpgyZ2Y/Ry7+i68j2fL1gEB4V3fXayRVyfKp70fGLVq9hlBKvg6tHbuJQyo4KdfMW/FAKJjkKlOREQkwiTh76MvqGsGLSOuZ88TdDao7G1DznmohEMZ5A8SYB4nUixNA8AyafGl45Kj+mp2oDttTkNIyMFfjW8MLEzJi1YYvYo1rDn2enGDR2QSZIgdtT0sQUEsU4NMXImuDozUPYJ5bCTfDCVnDCS/ADROLFJwXq78CR/dx9dIvS+OJFBUDkPrcMOlalUhP/gv9S7XY1+G3vOEzMjYl9HMvCK9P4cdN3VKhfjlFNJ/LNwkG5BmXP0Gg0yI0U+Ur3B3ivdTUuH9RP9793IVCa3JMoNObWZggyGanJadg4WlG7bQ0sbMyp/2FtvKt4MGfoIjRqDVeP3OTCvqvMHf4P3b/ryFd/fUaXr9uy8+/9WdRPBUGgjFAJC6yJ5jFyFJSlKopMzyuZTEbj7nV1qou58eRxnC5YzExSXDJ/frWYxCdJfPnHgDdmJTrz5UaHPTFIaVri7adYp02G349g27w93D2vX3yaHQkxiTw5+IRYYrAVstbqJMpjsc1wIps06mLFl38MwMbBikeBjwm9F0bQtWCMzY0Jvx+Bd2UPTCyM+aBvIxZ+m9WkU61SM63/XL287qGzPiU1OY0l//v3VV6GxCti7e9bGTrrU+6cC8hWSUuTkmnlOHNdQR4v41UaV0Aul/HvnLUkEIcFVpgLVrkekx1B14OJfpT9S3WUGEYSCZSmLDJkxBJFANcpQ/ZqYr41vFCaGGW7yvEisRHxNP64Hl2/ac+2eXs4u/MSP28fwz7NOu5fe8Dwej/kKGqS35fltxG1qCKQWxhjgjEmhPGAEqID9sLrn/mNeBSFrZDVPL4kpQniDlbkbjXxIqIosnvbHkppfHTPBntKkiamkiTGG/w3X662D/0mdcfYzJiHdx8xpdcc3d/pmJXDiAiJpvf4ruxfcYRFo1cyfsMo/td6cp79Jscn61TmDKFhtzpaEYhspNRvnrpL68+aFUhsQULiGX988bcu+IoIjuJ/bSZToW5ZqjSuwF/Dlui1T0/L4PsvfiBDmYqbjwsPb4RRijJ6k4mWgg2W2GR7znqd36N0hVK4eDvrjMJzIjUxFSNlzq+2Z3dd4s65AFoNaMr66dvyutzXyovWO3fPBVCmqgfns8k+kzAcAUmwpMgxMjZCnaHWya6e2nZer03djrW4duxWto7zCsEIhWhElBiOHU4IgkCcGEOqOgVTofjPtNy/9oDabWrw4EYIgiDQakBTnoTF4uBmR2RoNOd2X+a91tX0jnum3PSiXO2+5UekVYS3nIP/HqNux5ocXX+6yPpUmhrRv+8AIgnDFkeiieC+eBs/ahokdvKMcrV9sC2p/0ItiiJPiHwuDw2UwIE0MYV48Yme35t7eVd+3j6G3z/9y6Dz2jrb0G1kBz5xH0zU0xfZFrJuALQb/AGLb84iPTWDX3vP4fYLoi/S9wWt5QNlMH5qmO5EKQLFm1iKNrptrwP3cq48DtdPkSzov1g6aaizmQsogQOxROmpR+bEiAWDWDByGRcPXNPbN6XXHAC6f9cR0K4i5OVV+AwzKzODAjdbZxtaftqEsMDHOdazJMUlY25tZtB5JSRy4pnFkYmZMQOn9sLGURtw/fTR9GzbB3MPG+ywzvCEm+CMUk9IKy9ObDqLd1UPjEyM8lwxkytkCLLcn1HOno48eaFONDNKE6Uuk+N1EhkSjXc1T531Vah/OA261H5p5zMyNkKVrnrj68EM4g2/xmIXvPUY+yFKEyWh98J0Es2ZUZoo6TayA8ZmxhxafRxreyuGzOzHkrH/6mRg3QUfIsRHBKA1FzbDEi8MLxItSgZN68PDu4/YsVD/WrJj95JDVGlcERNzE9zKuhB+P4I1U7ewT7OOK4dv8Mn3ndm79LCuvUwuY9C0PljbW/Fr7zkMnzeQiOAoNGoNa6Zu4d5TMQmJt5fbZ/1p/HE9jm88WyRmnQA3o6/w6EwcpQVfACyxIUGM5R5XKUtVw8d25h63z+jXH6hRZSsxXQJHIgjNsoJS0tOJn7eNYc3ULZzbbZhZ9rNALTu2z9/L9vl7MbUwYeX9uRibGWNsquSrOv/TBnLi88mQdxUNGr0gzRUvHhOCO4bVbL0MWvZvyulxx0gVkzERngciYQThgEu++zPCCEw18EIAl0wCJuQe6FjZWvD9quFY2Vlg52JLi36NiY2IIzAbsZOhs/qRGKtN27e0NcfaIe+gsFKD8rlaITxLVXMp48yTx7HsW34kVwU5pYnS4BU8CYm8kMllVKhXju+aTyI9hxpjtahCRQbWwnNxLRPBFBvRjgSeYEXunp3P0Gg0LB2nFaX6Yk5/gm6EEB+dkG1blzIl2ZPpHSmnsasycv4uTNgwinsXA1g+YV2RPVMLQsidR7Qd1IJDq48DWtEVW+f8ZRcYgpmlKa0GNKXBh+8zY+A8kBbniz3FruZNla5i8ferePI4lmpNK+rtT09NZ0zrybo/ZqWJEWqVGvULX0RHwYUyQkXKCBVxEUq/llQoI6UCMytT/C8F6bZZ2lrw3fKv+H3/BCrUK6eX5qhWqQm4EsSlA9dYMHIZoigycGpvpg2Yi1/dsvz322Zqt6vOoOl9+X7FMMau/pr01HRdge7ts/4kxCTi6lPytUvjSrw69i49TMcvi67e8cyZM1gLWR+sloIN6WQvc5wsJhIuhhAvxuitXCUnpjL98I9M2T2WCRtGMXX/eOQoyMimryTiMeP5zKqtsw2Lrk3nn3H/FbkRaUpiKl0c+tPOvCeiKDJ5+xgAFMp3O3DLCRkyRF5v7ZtGrcE5tTQPCSBE9CdKDCNAvIEMBZZC9ilXuSET5FStX5lE+fNZ+HQxjSjCKUHu9Z7fLvuSY+tO8eV7Yxhc/Vtcyzgz4+hPbI1fQa9xXTG1eD45cWHvVYxNjanUsDyjlw8j5FZo7uOSyWjQ5f0cV9M/+7UnbQY249qxWyz5379smr0zT+nvnj904fB/+feWexFBEJiyaywNurxf6L4k3lzKvleGnYv2Ex+dkEVNMjPppGGK/kqZOdYkUzAhq1U/b+Dj0Z1o0OV9zCxNGb9uJAOn9sbBTRsgKnJJmXxGwOUg3m9bA9nTFbpqTSvS/5ceuj62zduNX52y9PjhwwKNsagQRZGj607Rqn9T3TZ5IW1wnmFqYULbz5vT/5cedPiiJSc2nWXzn7vejbKBpz5vRfHzuih2K2/POLvzEp/92pNL2RRep2ZSAot8GG1wKtWrxqWMM84ejnzQrzFVGvux9vetWNiYY2VrwcN7YdRqVZVGH9Vh7vB/8K3pjSAI3Dnnn8U4ce3vW/Gu6kH15pWZ8dk8Ri//il2LD3BqyzmcPR3xruqJg5s9lraWlCrrwp5/DmkPXKDvUyTx9hJ49QF+dctSvrZPzt5vmevcMqU+ypTPZZU16doZVI2o1su3BxBfyDUQRa24gxJjbHAgiTjCCKaMWFFX0zC2zS+A9mFhZWfJnFO/IAgCJqIZMWIEtoJWUTZDTCeC0Cwre/MvT+PBzYe6yZqXgZWtBYIg8OV72uBNfI0zrcUHEZWYkUWC/xFBOOCayzEvH0HQBlxlqESGURrJ6Yl4UDbfYjyZsY50otM4Lxb9tAy1SoMMGWWomONLjJWtBSMXD8XKzpJ9y48AWvP4YXXGAtoU355ju7Dw6nQu7r/GzM/nc2bnRQQBZhyeCJ4OBAAAIABJREFUxJ9fLWbLX7tzHI9MLqPfpO5sz+EebmZpSlxUQr5V62RyGQ/vFl6Ey6NiKQ6vPUmZap4c21B0qdoSbwY2Dlb0nvgRCTGJutWwnDDGlGT0V8hiicSW3M22cyI2Io6Q26F0GdGO6i0q888Pq4kJj6XLiHZcO3qTexfy1khIT01n/cxtdBvVnjVTt+BTwwszS1Os7C2pUK8s6akZOLo74FfbV3dMqbIuPH4Q9crTKW+cvEOFemVx9SlJ6L0wQu8+ws3XpVDf5Xqd3sOnhhd7lx7mUUC4bruhSrhvBVLa5MtBrVJz7dgtarSozAUDfXGKGw9uPuTHLtOo3qKyTqI5LPAxP7SbQrOeDajU0I9rR29hbm1G/196kBSbxE8fzdDrJ+ByEHYutrQf8gEbZ+0g6GYIrT9rhrOHA/ExiSTEJJIUn0zn4W1Y9N1KPSGGT8Z0JtQ/PM9CX4k3m+3z99J3UndEUeT2Wf9C9dXmo5ZcWHsTJ9x026LEMD29nyjCsMZOZ81hgRXWoj0h+ONBuSxtUxJTSUlMxdbZBhMzY1yTvLCqoSBFnoRGpSE+JAbvyAoIgsC2xJUsG/8f1naWfOT8WaGuJS9G/TOUiweuEXb/MfD6zVyLA+744s91Soj2GGNKDBGYYI6ZYPFax1WlcQX+vj4DE3MT0lLSMTZV8vf3K0mMTdZLb5JlqnuJCXtC4NUH2fZ59chN/NJ9uRx5lm+bTsT/clCuY/j94AT+HLaEa0ezV6QMvhXKlF5zKFPdi2F/DdBtb2Pag32adbn+fTl7ONJ1ZHs2zdlJ6L2wbNuU9nPL1oQ4Nxzc7IiLKhp/q4jgKBp9VJdt8/YUSX8SxRNnD0f6T/6ESwevZfF0E0V48jiWlZPW59mHTJBhIVrzUAzEBQ8EBGKIIJ20Qt1L5EoFJzaf1U1gfDKmMxf3X2Xs6hF8UmqQQX3cvxZMu0EtAO0kudJESZlqHtRoUYX4mETuXQjk0Orj9Jn4EV6VS3Ni81la9G2crfibmaUpvcZ3Zdn4NYWyqcmJjbN2MnBqL+aNWMrNU3fxrelV4ODNxtEaz8rueQbeEsWbYhu8AZzcco7BM/rmGLzJ5DI8K7kTkMfDtiBY2Jjj6G6f4wPfUJITUji+8Yze9gOrjnFg1THGrfmGas0qcm7XJWIj4mjaoz4xYbFY2JhxfNNZXXsTc2MyUjPY9MdOnEo7cOvUXb5fOQxrBysiHkRyeO1JmnxUj4+/74SJuQnzRizVHStXyNFItQ7vBMvGr6HPxI9IS0nnfm5Gw5lW4Z6ttj3bXtqvFHOWTMZ3qx9xqdFYYkMS8SQSjy9VsnQTRwzeVMiyzUQwRSXmnHa4eMwqtias4NKh61RuUJ6F367A0taC3uO7oVapdZ5BnYe15forUMa7efIunYe10X0OD4rE2dOR8PsRL/3cxRWlYExZsSoJxJJOKu74FMoIu6i4deouS8auxsTChNiIOCxszPl6weckxaWQnqZvaI1GO71arVlF7pwLIOhGCI8CwokMjuLuBW09sNJEib2rLf6X7jPv4u9cO3YLIxMjylT1JC4qnj++/Jvrx24TFxXP7wcmsG769hwDt8z4XwzE8wVbl+xqMas3r0yND6qQmpRKbEQcC79dkevsvpOHA4FX8vdcajWgKRtmFE3acVJcsvTi9w7w4Yi22LvZkZqc9W8xI12VY41bdjgL7sSJMdznFn51fdGcVONJ+SId69H1p2naoz6jW0wy+BgHNzvsStryfrsanN5+ga7ftCMlMZUzOy7o3r26jGiHhY05p7aew7OSOye3nMu2L4VSgXs5N2xLliAs8HG+xv5e62oolIoc+wZQZag4s+Mi9Tu/R6h/eKEmGBNiElEYFetX/1eDtPL2cokIjsLCxlxX7J2ZivXK0WfiR/z7y0Yu7i/a1bn2Qz6g/Pu+jO/4W5H2+yKb/9xFWOBj/OqWxbeGF7fP+uPk4aCnCnZ03Sndytmzl8q5w/+hySf1cHCzo0bzyuxZegjH0va810qrRimTyzAxM2blT9oZMhNzEwb+1pNLB65z/cRtgyTXJd48Vvy4jk/+1xmPCqU49N+JPNvL5DKdL46tkxWtBzTlyOoTBMbdoYZxA2KJwhRLKlAWI0GZ5VgBARERQW9NLuc743+/bcbSzoK2n7egl+dQnZXA8olrdW32adbh6G6PqYXJS1f++u+3zbzfvibD5w0kJSkVI6UR4YGP3+ngDbS1TfmV3n/ZpKakk56aoauxSYxN4ufuM4kUHxFLFDLkaFBTAn1bgw5DW1KpYXkqN/LDsoQFzh6OGBkrUCgViBqR6LAnqNJVWNlZUNqvFKnJaZhamDD8r4HEP0nE2ETJ5j93sX/FEYPGau9mh0kuHocATXvUR9SILPpuRa7tMmNkbER6ajaBai4oTZQkJ6Tk3VBC4imbZu/EyNiI4FtZV3ndfJzJryCvtWCLNbZMnjyRUU0nFt0gnxJ6L4wVP67L1zGPH0QyvtNvfDGnP/6X7nNyyzk+/LotJzY/nzTf888hytbSlrQcXH1Cp7T5IvHRCfzQ3jAf0RepUK8cyfHJ2LvaakVURJHYSP1V8ov7rzJgSk+uHr3FB30bc3LLeZLjk/N9PrVKTVxUPO7l3fT+bd8lJKuAl4yJuXGOy9CVG/shk8to/HG9HIM3QRDwq+PLrdP38qUatGnOLo6szT7NsHxtH5w8HDi8pvDF39eOaWdwMwdnhnL16E2uHr1Js54NaNm/KYFXH+BVxYOkp1/oD4e3YdC0vvzaew4HVh1DrVIjitDpq9bUaV+T3/sXz1pBicIhiiL/Tt5I5YZ+DJ31KXv+OUTAlaBs28rkMj6b8gkht0PJSFfRbtAHHF57is1zdlC9RRXcTcvgnKpvCv8MB0ryiCDc8NJtixOjMSX3lBh1hpql4/7L0QPu2QrF7vT/WBk0l5GNxhNyp/D1Ojnxdf0f+OrPAYT5P2bb/Oyl1iVePxf3XaVpz/rsX3FUty1WjCaN1Cy2Ew/FAOLE6Cwqd1vn7mHr3OepftMOTuT22XtcPnSD83sv8UBzD2tXC6zsLAm5qvWi8q3mxYhFQ/ii1uh8j7VSg/K5iohUbuSHlZ0lm//Yla9+ox5G41vTi/AgwyYXytYqY1AdkIREZrJbQbJ3taV+l/dZkWmi7U1n+/y9NOvZgDVTtzBj4Pws+xJjk3SZXyWcbHAp45wvn0QLG3PKVPPk3sVAkuKyD7QEmcCRtadoM7A5tiVLoM5Q8ceXi7Ntu3rKJnqP78rqKZvo91N3Fo5aUSBxrS1/7mborE+ZO/yf16qmKVFwin1xh5HSKNvZjhotKtN3YnciQ6IIzSX39/12NWjaoz41Wxkubw5aUZTMhZyZqdW6GrVaVqPzsDZUaVwh2zavkgOrjnFh7xUq1i+Hg5utzkDz+vE73LsYSOWGWpuEjLQM/vxqMT93n8GqyRte55AlXgFXj95k/shllKtdhl7jumJpmzWgMjZVMnh6X3Ys3M+uxQfZv+IoVw7f0IkpnNtzmWFzc683sxJsUWCEv3iNR2IQAeINYonGBQ9dGxMzY6o3q0StVlWxttdKpLcZ2JxDq/NeFexg1YeVk9ax5NZsFt+cRYW6ZfP5WzCc/SuPGfxCLPF6uHzoOlZ2lvjWeD5ZEMUjXPHM0s4VLyLJvmbsGelpGaz6eQPn91zGX3MdB1yweeSC7JolDrgQyE38LwfxOCiClp82yfdYP/q2A3uWHtLbLpPL6DysDTU/qJLvwA3g0sHr+Nb0xrKEYTVDnb5qzamt+n6pEhL5pVnPBuxefJCM9NevxiuTFY0qYu/x3bRKSJkws9K3Cek8rDU1WlTG3tUwewMjYyMGTu0FQPfRnXJsl/gkkYQnidy7GMj147fY8mfOYkbJ8cmc33OZ8u/7sGHGdgbP6IuZZf59NzVqDXuXHqJ574b5PvatQSyin9dEsV95U6vVyGQyvdmBWq2r4X/pPknxKexcdCDH468cvoGjuz2Xs1GtLCjLJ65FrpDz8fedKO3nhou3E8kJqRxZW/iVuNwwtTChXuf3OLDymJ4c+5rfNlOnQ01cvJ2o06EWtdtWx9nTEUEQ9G5EsZHx2S7LS7x9aNQadizcj42jNW0/b46ZlRlb/9qNibkJnb5qzb+TNxCTyaw0I12FiZkxKQnJzB68kA0RS3AsZU9ESFSO53AWSqERXUkjBSfckAsKeo3rSrVmlQAQNSLBtx6iVmno82N3Qm6H4n/pvkECCump6boVk3qd3+PXveNob9Gr8L+YbDAyVuQ7HUji1bNp9k4GTe+jq1kTkOkpQwqCgCAa9nKXIiZhjFkWAQUzwQKlaEyqmMK0AfNYemc253ZdyvJdyYtbp+7i5ps1dVMQBD79+RP2Lj1UqJXki/uv4VGxlC5zIycURgrCgyIMftmOESMI4wEyZCgxwR0fjAQltVpVJS0lnatHbubZR5lqnnzQt3GuK/4SxZ8mH9ejw9BW/DV8CaX93PCsXJrrx27lOKn9SinCG/XPH8+kRovKjFw8hId3wwi59ZCy7/mQkZahKzkBrQCdZ+XSxEVl7y/3IpUblufgv8cJC3yMT6bJphc5s+MiTT6ux86/DzB01qccWXMSe1dbuo3qwIGVR3X3uWdc2HeVQdP7cmbHRZZPXMvH33diydjV+b7uuxcCKV2hFH0mfqRTlm7eqyHjOvya777eOF5z4FUUFLvg7eapuzTr1UAnwWykNEJuJEeTljV4U5oo+bHrNNoMbJ7rsnFyQkqusswFRa1Ss+rnDbQe0JTmvRrh6uOMi7cTq6dsKvJzPaNsrTK0H9ySC3uvEhcVj7OHY5Yb6bPZVVWGmod3HnFmx8VcZ3Ek3h1iI+L479fNGBkbMW7tN0SFxvDXsCVa095MtgH3rwVTuoIbt0/fRSaTYWVvmWvg9gyZINP5+dg4WlPjgyqMaDAu27ZrwhZhYmac72s4sekswXn4YxUUUwsTmvZowJyhi15K/xJFhyiK3Dhxh4r1y3H9+G0UGOmZdqeKyRihzKWX56SSjHk2ab7mWJJKEsnxpvz+6V/8fnAi/pfuM6XnbIP6PbLuFBM3fZtl25CZ/dj594FCpwArlAqD6kC9q3oQcOm+QX2GiAGoSMePmgiCQLqYym0u0r1+Hxzd7SlVztWg4E0UReRG8hzrgySKDy36NOLJ4zge+Yfj7OlI6L0w4iLjadS9LhXqlWPGwHmUf9+XO+f8ObDq2Ose7kvjwr6r3Dhxh2Y9GzBy8VAuHbjGzafeuc84sOoY5ON38CjgMR2GtkRpYsT8b5bl2O7BzYfUbFkVn+perJ++jc9+68XlQ9eRyWX0Gt+NTXN2cunAtazj3XuF+h/WJi05jSuHb+TvYjPx7D37GSc2nSUiOO/nvcTrp9ilTV46cA0jYyOqNauEU2kH4qLi9R4CbQY2p6SnI3U71kKukOtJ479Kdi0+yLKJa5g9ZOFLFwC5fOg63zb7kSePY6n/YW0G/taLvpO667Xb8uduQv3DsXG0plX/JrQb1AJjU8NeZCTebjLSMvjlk1n8/f0qbeD2AuY25iTEJAKg0WhQq9RY2OibrObG2NVfM3tIzkHQ8olrdbL8+WHW8Z85ueVs3g0LQK3W1dg+f68u5ViieHNi01kadq0DaFMkg7hDrBiFRtQQK0YRxB29VMqcsMCaOPRr0+KIwQJrAM7tvswAv69xKePMnNO/GNTvuDXfMPnjWVm2WdtbFVokwMzSlPfb1eDGiTt5tn2/fQ3O7LyUZztRFHlCBJ5Ced0qplIwoTTliLOK5N6FQINFUgIuB/HHF38TfPvlTLRIFB2+Nb1RZ6jwq6v1M2v9WTN6T/yIW6fuMuOzeYTcecTeZYfzbU3xJpKanMa+FUdRq9TYlrTh/rXgLCv6ZlZmellMNo7WOfYXFviYy4eus33BvjxXvjfM3E6zng2IjYxn05ydVKhXDv9L97l+/BYKIzmf/96bOh1q6tqf33MZZw8H/OqULVIrrYArQa/1ffpV8qabdBe74A1g56L91G5bnZafNmHv0sN6+5MTtLLQnhVLsfSH/C8XF5ZqTSvSvNfzXOGrR25yatuFLF4oL4tns63HN57h+onbmFrkrGbmUaEUTT9pQJNP6lOmmvZFxsbBinqd3sNIWewWXSVeEanJaVlVqkSN7kf+ggTx/WvBNO1RP1/9y+Qygq7nbFOwY8E+vCqVzlefDbu+T0piCqt+NqxW84kYSYB4g0DxJkli7mkucoWcivW1D0uJNwONRkPInVDcyrqikCnxFaqRThpB3CadNHypYrBxt5GgxAglYWIwGlGDRtQQJj5AiYmePcJXtcfg4pW3uXBJbyfiouI5v+dylu07Fu6jdpvqhl9oNnw4oi3LJ6zRS53PDplMZtAKWApJGKNfO2Ml2HBwz0EcSzvw7+SNBRqvRPFAFEUeiHfxF68RIF4nwvYB549e5NLB6+xfcZSL+6+ydNx/LPpuxTsbeKenptOj1GBGNf0RZw8H+kz8CNDWjQ745RPaD25B5+FaWxk3Xxcmbf4O3xpelK/twxdz+mfxlgR4r011PhzeFu8qHnlOgv47eQOf/dqT6EdP+Hv0SixszPGo4E7F+uXZ+tceEKHrN+107dfP2M6yCWuK+DfwDvGG17wVy+ANYNvcPdw555+tRcCDGyE88g8nJSnttRTOelRyx7eWt+6zsakyW98MQRCo3NBPrx6jKNCoNWyYuV1vOb5srTL0nqBV6rt86Dr/jPuPP79czI2T2lnaOh1qUqVJBQZN78P/Vg3ng76Ni3xsEm8uwXdCKe333Jh7SI3vaNbL8KJmQ9MhIx9G52tcg6b3ZUoPw9LVgsQ7pJGKF36UxpdwQrgpXkAjZr+q9uHXbdk+T1KYfNPY9fdBOn3ZCtCm7ToKrngJfjgKrsiE/D3a3ARvTDHjPre4zy1MscBNyL5O5crhG0zZPZbqT2s6s2PIzH48eayfifHw7iNaD2iWp4VATti52JKekm5QzbJnJfdcJ1EyI0dBOml62+PEGBQqJcc3niE16d2YkX9bCeIOdjhRRqiEt1ARu2hXlq1bYtAkwLuESqVGo9awa/FBLG0tsCtZAt8aXuxZepi1v2/F0d0Bvzq+1O1Uiy1/7SbwajD2bnZY21libJY1w2nznF341PBiyMx+DJnRL9fzxkbGs376NobO6oe5tRkbZm5n34ojGCkVfPBpY4zNlESHxVK1ScV8XU+FeuXwruKRz9+CRHGn2AZvof7hnNlxUW+7U2kH7l8LZsufu9m3zDC/naJm0+ydzB3+j+7zsLkD6feTfvpiqbIujFg4CCcPh1c2Nkd3exzd7VEYKSjhZEOnL1vTa1xX3f5diw8SfPMh7uXcCLnziNtn7r2ysUkUf9zKu/MwKFpbB/f0x8RMqZs1tHezY0fyKgb+1ovPf+9Nk0/q41vDi4VXp/Pz9u+ZdnhirmajoF0NcHCzo1J9rVFrlxHtcm3fbVQHLh24RvzTdM7ciBEfE0cMxmiDSLmgwFvwQ46cB2SfZmZtb/nOzjS/yagyVJzcco4mH9dFrckgUYwnUYwnQLxBgHgDf/E6iaJ+ANVuUAt+3v49ZWt6I2qev7jaCPZ4CxXwFipgk8li4EWm9JjNgVXH6TS8DUvvzqFWq6oonhrLAwyY0pMn4XHZ+lnFhMdy/3pwrhkTudG4e12DMzzqtK9pUMokgLFggoB24uPZJEeqmEwgN/Dg5Sm8Srwa1KIaDWoshOdpfnJBgSOuxJD/FPZ3hQ0ztzPm3+GYWJjy0agOuu1dRrSnpJcTB1Ydw7OSO16VS7Nswhq9lMOQO6HMGDgfx9IO6FmhZkN4UARLxq6m++hOfNC3MZcOXGPzn7uwsDFHla7Ct4ZXtoIxcoUcG0frbBcRarasQvUWlfW2v+u86WmTb1TunFNpB6YdmsjYNr8QfDu02Mh6b527J1uzxODboQyp/p3OUNbW2YYWfRqxbto2anxQmfaDW/JLz9lFOqN5bMNpjm04DcCTx7GkJKUieyEV7tTW8xibGbNh5vYiO6/E24GTuz0H12T1G/S/FMSAX3tybMNpRi0eyqSu0/Gr60v7IS1p1lOFtb0li0avZN+yw8w5PYVOX7XJ9W9Lo9Gwf8VRpuwei9JUSUaais+n9uaLWqPxvxyUpe2kLd8hk8v4oV3uClhqUc1NzmOKOQ64EM5DQgigrFgNY8EEC6yJ54lOVQu038fuoztxdP3pgv2yJF47F/dfxbGJJSGKuyhUxsQSRTmqoxAUaEQNQdxGFEUsBRvdMU17NODHLtNQq9S6e3N+UKnUPLz7CEEQKOFkwwf9mjDwt16kJKVhW9KGwCsPmNBpao7H3z57D3tXW548Nly58hkyuQx1hn6tql47mQwTC5N8mfiWpRp3ucJ1ziAX5WSQThkqGZx+KlF8UZOBEv2sCFMseELkaxhR4XkZGU0vEhb4mHsXAvn3l03YuZSg/y89MFIqWPHjWp1vm1dld4xMjHD1dSHUXz+wuns+gO8/+MngWrKEmESW/O9fqjevTPfRnVjz22b2Lz9C8z6NWPz9Khzd7en308csHfef7phW/ZvQ9JMGyI3kbP5jZxYP4mXjpdTKbHnDF5zfqLvy4weRfNvsR8LvF4+g7Rl3zvnnuC/zy4G9mx3eVTwwMlYQeDWYm6fvkPZ0vyAIKE2McjQkLyizBy/UC96iw56wYeZ2BEGg57guJMQksm3uXsms8V3laYqZibkx9iVtUGeoEOTPVxJObTvPd/98QaNudfjUdxhx0Qmc232ZTl+25n8df2P2ycm6YK2f71fs06xj8PS+zB+Zs8LW1H5/MrXfn7rPQ2b2o+WnTfEfvgTQqsnOOTWZnYv2ZzFWzokAblCGipgK2hVCF0oTKN4kkJuUpzpppKBEiQYNcrTX1n5IS1ZOWk/Ck7xX9CSKJ7FiNBHHH+KhLk8Qd/ClCoqnwYZMkOEplieA61jyPHjTqDUG2VTkRN2OtRj0ex8WfLucce2zTipUb1aJJnnUiGakqTC31veRMoQbJ25TpXEFTm3L3betpLdTvoVRjAQlFaiFSsxARMRIkESu3haMMCYF/RKUKMKww/k1jKgQCDI0Ilpvtszp0TmkxReWvcsO07BrbZLjUzi4+jhJsUlZFBn3LD2Ms6cjqUk5TwQVxF7h4v6raNQaevzvQzbM3E5acjqO7vaYWphgYp41EN/zz2HO7LhICSebQivZSrwZFNu0yerNK2drgF3cArf8cPd8AL/0nE1aSjrRj2L479fNunzznj90Yfz6UVkCraKYWRJFMVtVQQCLEuaU9HSi1adN6T66Y6HPJfFmU6aaJ0c36ac8Ht94hg7WfXhwI4SkuGS++GMAxmZKLuy7yszjP+kFaTdO3aHd4A9YdG2GwedeN30bDbvV0X3+37/D2b3koEGBG4CKDF3g9gw3vEkjlUQxDgEBW/cSfPrjJ/SZ+BF9Jn7EjRO3pcDtDSeacOzTXQBQo0IpZH2pEQQBoQgfcyvvz2Xs6q8ZXv+HbNODz+6/iMo0DbWY8+pYSS9HwgILlqp289RdKjUsn2e7yo38uH78doHOoRCMpMDtLUMQBOwpyUOTe6SLaWhEDeFiMBrUWfwNJfR5cOMh/X/pSUpiKuVr+2Qrpd9v0sd8+HXbIj/35UPXOb/nMh9/35k9/2i9Ie9eCNTTOlBlqIgKjeHexUCpNtUQikqs5E1NmxQE4XegPZAOBACfiqIYKwiCB3ALdEUmp0VRHGxov06lHWjRuxHxMQmF8rB4kzi0+jh3LwTqpMq/WTSY5ISUXP1BCktCTCKzBi/Eu0ppIh/qS2UDOHs4MvC3Xiz4drnk//GWE/3oCXVreXNh1wW9faYWJnhWLk2bz5vTcWhLwgMfM6nbdHqN60rvCVovmmeo0lS0M+/JmFXD2RC5hG3z97Jn6SHCAnJ+YY16GI2VnSWzT07G1MKEpLhkNv+xy+Cxy7IpKJAjR4OKx4QiKEUUweYsn7jW4D4lij8iIsLTf3sFRqSJKRgLz1UTNaIGkaKZkR/212cIgkBbs5764xBFHnAXDWomDfyVWGUMRukmuAgeem1V6SqMC+Bz+AxD0q9Kejqy6w2e6JQoemwFRz4f1Y/pP81EjRo7nHEW3F/3sAqFIHt+3xc1L2cVTqPRMLrFJOZdmMq66Vtp8kl9Dq0+nqXNrEELXpp43t0LgXpG3RKFQ8CgEsRiTWHTJvcBY0RRVAmC8BswBhj9dF+AKIpVC9JpYmwS4Q8i3imD6VD/8Cz50qe3XyAqNPuAqijJSMvg9tmc0z7jouIJuBqk8/4CKF/bh17jujJryCIiszFw9q7iQfCth69FCVSi4KSnpuul7VrbW/L98i8xNjNm9tBF9PqhK+umbaXfpO6YmBuTGJukS/0FUCjkyI20aYlTes7GzqUEH33bkW+XfEFUaDS/5KIY+dNH01EYyek9oRsrflyXr7HLkKMSM7JIu4dyHwsrc8xTLLBPL6kn+y7x5mODHTGyCOxEJ1zwIIAbeIl+KAVj1KKK+9yiJPmzpcgOx1L2tB/Ski5O/XksPiSROIxQ4ow7SsGYMB5gg71W6CQJLEQ7QvAnWgzHTsialuZY2oFjG84UeCwymQxBEHJUCbRxtM5WpVni3UaukGNhbEVpQRKgyS/+l+4z+ZOZjF7+FZcOXCf45kMCrgTp9hekdlZCojAUKngTRTGzvvZpoGtObfNDUlzyO19kmZdi36siJTGVfydvpH7n9/C/FER4UAQP74Vx8/RdEqL1/bPkCjmjV3zF8glrOL7p5RgqSxQxT2cpza1MSY5P0W5Sa9O+Zh//iRU/rmX/ymMAHPpXO+O4e8lBeo3vRoW6ZZnQeZqu9mDyzrGsm7ZV13X0oyfMG7EUgEHT+rDy/lys7C2Z0mO2Xt3Os7/5LiPacfng9XxdggfluM0l7ERnzLEgQgiBUgZuAAAgAElEQVQlhWT84msiF+Rv/jSbRLbY4UyYMpBoYzVGcaaYYcFNzmMhWiNDhgueemlh9q62dBja0uCU3Kn7x2PjaM2crxZxLuIYTpTCEVfSSOU+tyglepNMgm6V7aEYQBqpmGJBBI9IEZNwE7yxtreiRd9GPLgRUqjg6vyeyzTsVocja09mu79ux1qFCg4l3k46fNEyW9/cNw5RA6IIophFLTYLL6EW7vCak6jSVciNFLQb8gFzhy2RJqjfZCTBEh39gcwRl6cgCJeAeOAHURSPZXeQIAifA58DmFCwIm6Jl0/dju9hbmNO+D8RJMQk5miWrFapGdFgnE6JSeLNISEmETffklm22ZUsQd9JH9NqQDNAa/I+uOq3hNx5xJRef2RpO3zuZ1w/cTvHiYcFo5azYNRyVgXNY9KW0YxuMYmLB65laaNQyDEyNsLCxswga4BnWJpZMmXkb5y7eJ6DOw7hIuq/tEu8fQiCgEuaNx2Hf8Ci35ZiiyPugk+ux8SEx/L57314cPNhlrT83uO70X7IB6ybvi3LBISFjTmfVx7JY/EhzpTC+qmNgAmm+IiVCeCGLnUzUnyEMaa4CVofUBdKEyMPp2I3L8q5+HFkzcl8exy+yI2Tdxg4tTf3LgRmEUIwtzajYbc62Ja0KXBNncTbi72rXYGEMySe82xC2s23JEbGRlLw9gbzOmX+i4I8gzdBEPZDtnJEY0VR3PK0zVhABax6ui8McBdFMVoQhBrAZkEQKoiiqCfxJYriQmAhgJVg+4b/Ot9eMisD5oUUuL2ZNO1Rnx0L92fZ9uRxHMPrjSUmPJaSnk7MvfAbMoUcMongLLwyjfTUdB7cfMiKSevzPE9PjyH8F7qQ0hVK6QVvlRr54VPdiw1R/9DWrCfpqYapr3Yb1YE9Sw8TGRKVo7myxNuLjUUJnI3cdTXDuc22a9Qa5o9cxqjFQ+nt/YVue/fRnehfbjizTvxM/8k9iAyJwtzajL3LDgOQSByOuGbpSybIkIkyFBiRLCYSRwzeZBXaKqFyYvPqrZQRiq5uZfmENbTo04g2nzcnPSUdI2MjEp8kcnzTWULvhRXZeSTeHnISLnuVyBUybJ1t8myXuZYtO6xsLZ4aaFsjyLIKEsWGx6JSv9xXSSs7S5ITUl7qOSQkciPP4E0Uxea57RcEoS/QDmgmPk3CF0UxDUh7+v8XBEEIAHyB3PWNJSQk8kXn4W0o6enEf79uIiY8/75RmdFoNFr55UwEXn2AT01vzmy/QGxkHMamStaFL0KhVCCKoFGrOb7pLFP7zTX4PH0mfkTik8QsIifP8KhQioOrj1PS24kfN3/LmFaT8+zPys4SVYYq2/pLiXeD4FuhuJdzJehGiEHtD/57nB5jPtR9nnv+Ny7svUJESBQ93LXaWvZudkRlWiVTYEQ6aRiT1WBbg4ZSlCHMMgBVcjqCJut3SBCEIk/RSUtJZ/uCfUXbqcRbjTpDjY2DFbGRBbfJKCxW9lZ8veBzg9rmlu2oNDFCkAkM+2ugtu3T+k8rO0siQ6KY0tvwyeaCkJGW8VL7l3gFvOFLRYVVm2yFVqCkkSiKyZm2OwAxoiiqBUHwAnwASS5H4qVjZmWGYyk7g1/i3nQCLgdh42BdJP6AVraWxEfE6urdABRKuc4UOCUxlS9rj+Gnbd9zcvM5Vv2ykan7xrF32ZF81RV0+rI1o5pO1NuuFTfpwNi2U/h52xis7SwN6s+9vCsBL5h7S7xbXDt6k9afNTfsey9A28+bk5KcQq8JXWjRqzHHN55h0eiVWZpFvZDeWBJ3griDj1hZZ+PyWHyIFSWQCTJWblzOiCEjifePxSqTKXiCGIspWW0sJCReNZvm7KTX+K7M/2bZ8xXqV8yT8FjGd8zZwL6wlPYrRY+xH2Z9Hr2E+jcrO0us7a0K5Rcp8Zp5w4O3whrg/AlYAvsEQbgsCML8p9sbAlcFQbgCrAcGi6L48qUT3xEcStkjkxVbi77XSo+xH9J7fDecSju87qG8Eq4euck/P6wuklRVEwuTLDn8TT6pT61W1aja5HkaWODVB8RGxJOSmEJ6qgpTCxMuHzLczqPH/z7E0taCX/f+QK2W1TAxM6aktxP9JnVn8Y1Z/DlsCTZO1jiUsjO433sXAvGrIymovctEhcZgbGqYN1lKSjLTZ/9O+zqdmTZjGi1qtOKP7xbmeZxSMMEVT/y5RoB4g3uiNuXXSXAD4J9x/7H+8H+YVYR4m0gSxTieWIbzmBBc8CjwtUlIFAXx0Qlsn7eXTl+1ft1DeeNZPnEtTXvWf93DkHiHKazaZJkctm8Asle0kCgU5tZmjF72JWd2XkQQYMPMHcUil724cGDlUaztrXj8IPJ1D6V4I2QN/h3c7IgJi0VQaOX0RVUGX8//nJun7vD396uyHHdu92XK1vJm2oFxnNySv0zozX/uxtjSFFdve37Z9T/d9l1LDvDzxzMZs2IYJpYm2YqZ5ERaSrqkJilB0I1gytXyytX6BGDyz5NxSi+NcYY2/VEtqrnHVcqKVXUrajlhLljhQ+Vs990+c4+BFUfS9sPOnDpyhkijxzSo34RLm24V7IIkJIqY4NuhfNCvca5WE8UdQS7X/X/mLJEcKULPt2eo0lV53iskijHiOyBYIlG8SIpLZuVP69GoNbQf0hJTCxPJ0ycT968Fv+4hvJHUbluNo+tP6z73m9Sd2Ig4ZAo5O5L/ZefiA9w5cxcTCzOWjl/D1tilCDKB71r8lK/zJMcn888YbTD4M88939oNasGE9aNYP2MbyybkzybExduZx0FSsP6uc3DVcb74oz/Bt0JRGMlZHvAXKyatY8PM7bo26WIasXdjsBGeq6rKBTkOYkmeEIktjgU+vyiKJMYmsuefQwC07dWWO+dyDyQlJF4l9Tu/R2m/UtiWLEH0IykZKr90/64jSfEptOrflMBMPm8SbyBS8Cbxqrl8SOuBdfXozdc8Eom3AUEux6uKJzv+PqRT+eoxtgv3LtxneP0JOHvY8+XsTzG1MKFKIz+SE1LoYNOPTVFLkCvkefSeN2VrlWHwjH6M6/ArlwxcbXuGkVJBu8EtWP3LpkKPQ+LNRqPRsGz8Ggb82pOK9cqya/EBPv35ExJiEjm85iRW9pZ0/7Etv4ycpnesEhOSMcyaQqGQ8/fNWZRwsmZAhRE8DongAXfQoEFAQCOqcRd88K3lzf6VR4v6MiUkCoxbWRe2zN3DD2tGMKLBuNc9nAJh0GpbIWjdvynf/D0EURRJTUrjwt4r/Nh1GnvVa4mLSuDGidus+30rZap7vtRxvIjCSIG1vSXRYU9e6XnfVqSVt5dM28+bozBSsOWv3a97KBISbyXlanlz7fjz1K5PRnck8GowlnbmNOhSm6NrTzK23a8AeFQsxbj/vqaEkzW/D5jHqCVDGdfhtwKf29WnJBPWj6KX51BiI+LydayDmx0ff9+JTXN2kfDEcE84ibeX+OgEFn27ghF/D2bRdyu5duwWDbu+T+vPtD6FIbcf8vhJGFY4ZEl7iiQMN/K2mLB1tmHNo0Wc23OZ3YsPsCLgL6rav4dLvAcmgtanVC2qiXUNZfv8vS/nIiUkCsiJTWdp/VkzEmISqdK4Qhafw7cBQQbVm1Vi2sGJBe7Do0IpfujwK2e2X8DezY6+E7uxV70WjVpDN6cBAFjaWlCqnEsRjdowuoxoS7n3fPi195wiESiTeDUIgrAErSJ/hCiKFbPZLwCzgTZAMtBPFMWLefVb7IM3U0tT5HJJnENC4mWhVmmyrKB1/7YDg2uN4fc9Y2n7WVOiH0Zz4+QdAIKuhzCg4kgWXJrKb33/pFTZgj/ArGwt+GnraMa0npzvwE1hpODj7zsxf+RySbZZIgupyWnsXLif99pU4+SWc3qm8Y644s81XERPFCgIIxhTzFAKxrn2W9LTiaV35zB76CJdYNb8s4YIGpkucANtGiYPlVzmErZCwdMwJSSKmpA7j1jz2xZUGWr6/vjRWxe8iRq4eOAaU3rOzruxAUQ9jGb6Z/NZ8eN6IjJZ0VRrVomzuy4VyTkM5eDqE0SFxkiBW1Hx6lbelqIVd1yew/7WaBX5fYDawLyn/82VYh0VNez6PqH3wlgzdcvrHoqExFtLQmwi1ZtVRJAJiBqRjHQV7T9vxtcNx7Ni0nombhxFq/5NEeRyBJmAIBO4dzGQHzd+y59fLUElppMqJuerAN67qgf/Bs9HEGSkFMDs9IN+jdny1x4pcJPIliuHb+BXN3sFUhvBHg/KEUc0ETzCmVKUFErn2WdkSBQpiakcXXuSqk0q8uOm71Bp0lEn6gsiGGNKutbqVEKiWBEXFU9yfHK+g4AEMZa74hWd0upDMeCNFT1BkD3/MYCIFzxESzhZk5b8ar/fkSFRHFh17JWe821GEIvmJy9EUTwK5FZg2hFYLmo5DdgIQqai7BwotsGbTCajRZ/GVGta6XUPRULiraZJ97r8MWwpAAqlAgtrM26cvENUaAw3Tt7h41JD6fhFSyo1KK87RpDJmDFkHv/tXslD0Z8owrnHVeLE6BzOkpVfd/1AYmwSlw5cZWXQXMrV9jF4vIIg4OpTkuBbD/N1nRLvFsnxydg4WGW7z0hQ4iJ4UErwzrJqlhsqlZrjm86w9O4fTN0/nrW/b2GQ72iSSNB7iY0kDFveDbsSiTcPURQRNYarMKaLaYQTjA+V8RYq4CNUwgJrHnH/JY6yeCIIAh4VSvHgpvT8kQDAXhCE85l+DHOhf44rkNmg9OHTbblSbNMmlaZKzuy4yInNZ1/3UCQk3mpEjYgmIwNRrWbo7H7sXXaY4xuff+80goaRTSex6MpUenl9CYB7OVfW71lLKbwwFky1/Ygi/lzDXLRCIRjles7U5DR6e38BwKE1J/jj1C8AtJB1y3O8HYa25OC/0gykRO7sWLCfriPb888Pq4usz2n951KqrAsuPiW5cfIOgiDgKLoSIFzHVfRCjoJwglFijFIwKbLzSkgUNYFXH1C5kR9Xj+QtfBZOMO74ZKkTtRHsiRTDXuYQiyWCTCAxtvC+qhKvEZGiTJuMEkWxZiGOz85zIs/RFdvg7cPhbfCrU5btC6SibwmJl0lkSBROHg6E3H5EzZZVGFJjtF6blMRUnoTHYedSguhHT0hOTAE0usANtDOSLqInEYTmakpcqqwLSfHPH37Xjt5iQIWv6flDV/Zp1nF6xwXGtf8122PrdqxFemo6AZeDCnq5Eu8ICU8SCbkTStUmFXUKvUVByJ1HhNx5pPtc0tKNr8YMZvaUP0hISMABF8wEiyI7n4TEy+DwmpN8vWCQQcFbBuko0Z+MkGXz3jnv4lSScghuSpXLuqAgCAIyuazIvGoFAfzq+DJm1XBW/bye4FuhRdJvZtzLuxFy2/B+u45sT0lPR/4a/g8addF7zkkUkOKT8fsQKJXpsxvwKIe2Oopt8LZm6hZMzHMvIJeQkCg8wbcf4VrGiUvXL/DXnLm0GFqfhxfCaditDovH/Eunr9qQlpyGo7s9i6/NICUxBZVajZmlGS+qqyswQo0q1/M169WQq0eyFsoH3wplSs/ZTOk5mx/WjGBr/Ap6e31BXFQ8AMamSrp8046IB1HsWnywSK//lZC5tuIlmMZKZM/+FUcZOuvTIg3eMuPgZsfHYzqz6qf12CaWxDbvUgUJiWKDKIpY21vp7rM5UQIHogjDgecCVRpRgwb9e1lSbDKjmk7U2+7m68L4dSPpM/Ej3TZBJmBmacrxjWe4dqzwZvYajYbE2CRcfZz5btmXfPnemOwbZr4H5/Pe3OazZiwd95/BYwq/H0GVRhXwrOhOgOQNJ6HPVuBLQRD+QytUEieKeS9pF9vgTa1SkxQnLU1LSLxsHoc/5sj9XSix5ODMM6gcDlHvw1rI5XJmHJ1EQkwivjW8CPUP57MKI1Ao5KhUajzqlyLlmBqZ8FypMpxgHPNI13Zws8tV5ezn7jPpPb4bf56ZwudVRpKWnM7gGX1Z9fMGokIlY1kJwzEyNsLE3Bi5Ql5ks/vPsHe1pfvoTiwYuZz0VEkBTuLNIyPVMMGnEjgQwA00ogZ7SpJKEiEE4I5+rbIgE2jeuyHGpkpEUXu/l8llhAU+ZljdsaQmpWZtLwj0HNeF99vXxNzajFmDFhT4emQygVJlXYmPSmDLX7sK3E/O/ctITU4jOR8iW8c3nuHklnPSqlsxQuDV+bwJgrAaaIy2Nu4hMAEwAhBFcT6wE61NgD9aq4BPDem32AZvEhISr4Yg1W0cIj1QCE9vB1EW7F90nOMLLmIt2Ona/fdQ+1BVPX0Jbli5CSvvrUARboYxJkTyCGNMMRXMczyXRtQgM4HKTSqwd9nhHNutmLSOFn0b8cOaEayZuoVTW8+/GYFbTrO40mrba6F8Cy++/eZbkjNSEdHgiBtWQolC91ulcQVqt63Owm9XSIGbxBtLREgUqozcMyVAG2B5ixWII5pg7mKMKT5UQi48f4U0szKjQRetwnnQ9RCd/cuTx3G5TpyIosjKSesBaDf4A/r99DHh9yPYvST/GRYajcjJLWf5pUfRWAVkxqtyadoPbcn66dvyPy4pcCt+vKLgTRTFT/LYLwJf5LffYqs2KSEhkTueldxRGGkfnkoTJZ2+ak0JJ5t896NRaZ4Hbk9x0LgSzeMs21KTskojt+/TBo9krQJlIvG44oWL4JGlTbnaPqwL/5t2Q1sQKN7iPjcZ+814Rgz7mkQxDo2oIUaM4IkYieaFACchJolfesymUbc6nN9zJd/XJfFukyoms3nXRpziPfEWKuBNRaIJJ0GMLXCfJmbG9Bj7Ic6ejlLgJvFG0+TjelRu4JfF4zM3BEHARrDHUyiPi+CRJXArX9uHAb98gv/F+4xsPAH/S/eJCo0hKjQmXyve2+fvZem4/0hNSqXzsDY5tksWEwkUbxIg3uD/7d13fFX1/cfx1/fe7EUSEhJGQsLeoCIiKqKiRami1kGts+5q16/LUW1ttY7aamvdVnGCE6U4EJAhirJXGCEhg5BAQhLInvf7+yMhJiZhZdx7k/fz8bgPcr7n3HM+N19y7/2c78qwydTYjl0yJiQ8mHOvm8Kzv5rFnp3db5IW8TxK3kS8UJ+Bsdz692s599rJAIw+YxjDJgxm4oUnHdN5jDH4+Df/8K6lBgdNy7ev2slf5n03mUlVeRXlxRX0NDHEmrhmixz/4umb+OUzN/PR05+xbMsiejviGWhGEZYTQ5+Dg0hjBzvZhMXiopadbGryxTq6XyR3PnUjs+57G9cxTGvdKVpbJ8i6vnuIW2WTQXzNEBz1dWSMoT9D2ddkVuaj07NPJJf93w+5+v7L+HzWUha8sqS9wxXpVP1HxvHAZY9TlF981M/xD/Rrsby4sJSctNx2G9NVU1UDBs6/6Zzm17IHyCGD/gxhoBlJLHGksJkaW4NfoB+Jo/vjF9BynC1q/J7dyvv69FvP5e1H5nr8zZoTzh5F30Gx7g7DKxhr2+XhLuo2KeKFslP38vpf3yN1fd06O2sXbmLrymTKSyqO8Mym+o/oh60tocQeJMT0aCjPD9tDbFFck2Mfu+5p5pe9+V1Bo/cth8PBH17/OQ6nwRhDz94RpKxP5/aTfo/LutjFVgaZUY2eavHByRDGNkw/HWljSGYjQ2xd2ScvLeb9f86nuPB7s6KIHAWLq9mSFQ7jwNimX8yCQgP5wU/PIjQipGG9trKicsqKyojq1xMfXx8K9hay+M0VFO47/lY7EU/i6++LdR35y+fYKSO56GfTyM3MIyImnD0pObz10AdMv2UqPaLDWP3pembccT7P/t+sdostJy2XsWeNov/wfqz44FuKC777DNhLJoMY3fC54W8CibdD2EsmUxJOI2FkHPNL38AYw1N3vsS8Zxa0KZZe8VEEhQZyIO/wk7q4W2hECFffdznVVdXsXJfG+sWbWbdok7vD8kztu1SAWyh5E/FSSV9tb7J9TIlbfcvQ0PGJ1HxTzZd8Qa7dQ6BvED2H9MA3KajZ4sUul4vqRnceffx8GHTiABwGQiND6TuoN49e+++6WEor2Z9Vt2C3xTZrxSuikCh6N1k3yBhDuI0iYlgwl91wCRu+2OK5iZta1jyeDz5U2gr8G623Vmtr8QvwZerlk+k/Mo7qymoqy6tY/MbyJmMqg3sEERQWRH52gcarSJf0+awl/PThqyjaX8Q7f5/XbP8JZ49i4oXjKcov5pGr/0V1Vd3YuDFnjuCGB2ey6PXlFO47yNlXnc4Ttz7frq1SqRvS2bl2F+lbMunZO6JJ8ubA2eRzAyDIhJBjM6iuquGDf33Ms7+exVX3XMrP/3MTP//PTdxzwUOs/mzDkS/cwvv6pBknM//5hW1+TR2tuLCEP174MFf8fgZrFmzkjB+dwrizRvLl+9+yc90ud4cn7UzJm0g31SMqjIEnJLJg1lISzTBqbS2vJD/Jm/fOZcnWFS0+J2vnXibNOJmvP1pNYEgAP/rVdKzL4vR1UFNd3WT9q0OcxkmNrcJa2/Ch68DgovlYCJ8AJ4PGDeD1B96lstyzu6iIZ+tDIqkkkWiH4W8CqbKV7PFP4apLryZtSyaL3lje6nNLD5ZptmPp0jK2ZvHSH97gpHPHcNs/r6O8pAKn08G+jDyi46IoLijh2V/Pava8Tcu2Nlkbbu6/P+mQ+FZ9so5Tpp/IlJmnkbJuFyvmrgLARW2TzxKoG9/qiz80akl8628f8NbfPuCBub/nr/Pu4uoBdzTcUDxafgF+JI6O53/Pesd6w+UlFbzxl/f42ZPX89Sd/8XX35ept59G2JAAVs/e0tCFXDpvtsmOouRNpJuacec03n70o4Ztp3HiU+NHTHxUk+MCgvwxjroPyqCwAHasSSUwJICy4nIevfap+vIg/vj2r1u9Vl8S2cEG+tgE/AmgkP0cpIBetl/Dh7DLusivymX1nM3N7qyKHCtf48dAO5Js0qmx1Thx0qdyACtnr3d3aCIeY+3CTaxd+F33upj+0RQXlBzTdPgd4UBeEQtmLQVg+i1TmXD+Caz6dD3R9CGTZOLtEIwx1Nhq0tnBQEaCo/nnxp8ueYzf/Pd2Zmc+x/tPzKf3gBgieofz5C3PU11ZzZ6de7n8txfy6UuLKSpo2tPj4p9P4/0nPm73ZUY6Um1NLSkb0hk5ZTBzl7xH+hPJBPoGUx1Zil9xCBE10e4O0TMoeRMRb+NwOPAP8ic/+7uuYr0TY+g7KJaefb+bSj0kPJi3Mp9jS30XzfLiCn7z0u0AHNh38KivF2zCGGzHkEc2Bykgmj5E0ZtkNtLTPxprLQWV+4lzDVLiJu3G1/jRnyHuDkM6WKWtoJwSgghtNnGSHJt9GXnuDqGZj19YxJW/n0GfQbGs/nQ9I344gHnz57F/dwGJo/ozZtd4qgpbT7D+ceOzzHnkQ2bt+Df79+STsyuX5zc83uSYmx65mnMdlzcpi46LInNbVoe8po706UuLqRicTyLD8TV+UAMUQl5EJtUHqvC1xzChi3gkJW8i3UxAcAA/e/L6Zt1dXt35FC/8/jVm3HE+859bRMbW3ZQcKCV5bSr3nP/QYc9pj2I2SKdxEkvTSVCG2LGcd/MZfPTUp0Sbfsf+YuSo9R3cm/1Z+eqOKl2GtZY0tuHEhxDCyCYNYx3EM1g3gbqYtx/7iAnnn8Blv7mQj55eQGBKFHFEkeBKZNgVg5j//EJcNbU4HC13DdyzM6dZctbYvKLXufjn5/PhU3WLe0f1jfSOtUVbYK1lT/JeBjZapxUgtCCKyImBFH/rPS2JHUXdJkXEq0z76Vm88/d5ZCV/Nz7Nx8+H6uoa3n38f2z+cjuPLPgju3fs4fEbnunQ7gV9B/UmPCCSIBPacRcRrr7/MhwOB04fJ6/8cba7wxFpF9mk05NYephIAHoSy367l/3kEE0fN0cn7W3Vp+tZ9WnTbs8JI+NI25wJQG2tC4fz+JL2+y56hAtvP4+zf3IGqz5ex9rPN5Kza9+Rn+iB7GE+tKP69SSqLACHw0FZcTl+Ab5kbPW+1sU28/LkTaMXRbqZqL6RTRI3qFtXp6q+RWb7tzv5cdytvPX0u5QNyOMXt/+KFLuZEnv03SSPRlBYEJf+ajofPPlxu563QWtrsXVD1RXVxPSP5uMXPH/WNJGjVU5pQ+J2SE9iOIh3tpjIsfvirRVMmXkaMf3bNpZr49IkHrzyCX4x8R7KSyp4cP7dVJZ5Zy8Fh3FgcTVbvDybDA6uLefB/93Nc+v/zoTzT2DwiQP46UM/dlOkcrzU8ibSjdTNntWfk84d02SQOtRNj35ImS3mvQ/eZQAj8DPhDCCMNLZirIPgFlrJTCtdVQ7nmvsv49X73/aqweDe6u3HPjryQSIiXsblcvHCb1/jtieup6qs8tgW6K4X0z+aC26eSm1NLcZhcDgc/PnSv7P5y20dEHHniGcIqSQRHRiDLXNQSC5hRFKQXsxPEm4nfng/Mrdl0bNPJBGx4e4Ot3NZdZsUES8SGBJA6cEycnblNil3OBwU7j3AieeOYd3CTeSQSSLDG6YWdhgHCXY46WxjIKOanfdoxrwBhPUM5aTzxjL4xETWLNjQseu4aS02kS4tkGCKbCFh5rtJlgrYRxgRh3mWdDXVVTU8/YuXGTZhMGfNPI2fPvRjXC5L2uZMvpq7iprqGk6ZfiInTzuBtZ9vZOPSpIbZNMdMHsGJ547hjb+827CWXVfgZ/w5IWwSU24+mTcff49ERuA03623emgilpGnDWXH6hR3hek+St5ExFsc3F/EP29+jpl3Xcys++Y0lD+z9lG2fLW9yfo9jd/oG7Zty+MJrMvSeH4Ah8PB+GnjCIsMoVf/KHz9fAEoOVDK2oWbWDK75XXkRESOVh8SSGMbxT4FBNaEUkwhFjTDaDfkqnWxdeUOtq7c0VA2fOIQbnjoxzgchq0rk3nx96+TODqeWx6/Fleti4N5RVx932X8YtK9XSpxg55GYj0AACAASURBVLrFxQeOS2DJ7K+IMM27lPbsE0lJYQmnzZjAw1f/yw0RSlsoeRPpZmqqavAP+m467cCQAAaOTeBPFz9GTf0HmAMH1baqbprhQ8+z1RhaHwzeeHa3a/58OVu+3Ma2b5JZ9s7XXe6DUUTczxjDAEZQWV1OGSX0JkFLBUiDbd8ks+2bZG58+CeMO2skqRvS2b4qhe2rUvjZkzeQMCqO+S8sZOyUEZz149P4fNZSUtanuTvsdhE/vC8r561pNr79kF8/fyv7MvNY9MayTo7M/Qze321SI/lFuplr/nQ5859d0LB9zk/OwFrbZH2fPiSyi61U2UoAqmwlqSTRl8QWz+ly2SbJm4+vD2sXbmJPyl4lbh4qPDqMGXdOIyQ82N2hiLSJvwkkwkQrcZMWWZeLuf/+lMt/eyF9B8UC8MyvXuGdxz4iOCyIksJSXr5nNjP/cHGrSw14m3cf/x8Jo+L42ZM3MP68sU32hUaEsGNNCk/d8RKrP9vgpgjdzNr2ebiJWt5Euhmnj4M9KXsbti//zUUcyCtqcoy/CSDRDiebdGptDU58SGQYfiagxXO6XC7Ce4U1bGuJJc83/NQhDJswmMJ9B1n+7kp3hyMi0iF2rE7l/JvOIWNrFqddMoEDuUUsfG0Z277dSelf3uX+937Lirmr2LBkC0MnDGLbN8nuDvm4jDtrFKdeNJ6K0kqKC0sI6xnK568uZfqt57Lm841A3ZCGn9z3I+Y88qGbo5W2UPIm0o1ExISTn1PYpCx2QC9euuvNZsf6GX8SGHpU562pqsHhdPLQx/ewZPYKdq7rGl1PurKV89awfvEWKkor3B2KiEiH+erDVTh9HLhqXayYu4oxk0dwy9+vobamluzUfTw48wmK8osZcvIgPn5hkbvDPS6+/r6cMv1Env31LIwx/PrF23jqZy/yy2dvYV6jnjZX/mEGX7z5JQdy23fpH2/j7d0mlbyJdCPW2mZTKVdX1hAY0nKL2rG4ZcxveHbdY1hrWfbO120+n3Q8JW4i0h2sXbiJS381nRVzV7Fp+VY2Ld/a7JjSA6VuiKztAkMCuPRX0xsmHLPW8s+bngUgdWM6ZUXlGGM45ydnUJRfTPLaXe4M1/0smm1SRLzH1Gsms+CVJU3Kdu/I5tJfTidhZNxRnWPthjXMn/8xNZU19OobzfVXX09EZCRV5VX8/fqnSd2Y3gGRi4iIHJ/Sg2VUVVQz5KQBrSYv1VU1WDeOYzpe5990Dis++JaMrVlNyh0OB6NOG4ZxGHpEncmqT9eT9NV2N0Up7UnJm0g3EhoR0qS7hMPhYNC4BB6c+U+SVx/5btzukjRy9++jtysBX2PYt62EO5b/gimDzuMH15ylxE1ERDzSgpe/4Lzrp3SplqeAIH+i46KaJW4Al/zyAt57Yr7XjuHrSMbLl4FV8ibSTfj6+VBdWd2kzFW/uPayd45uwoo0m8JgM4ZDKwb4GX9iquLYR1bDoqciIiKe5kBeEb3iozHGNGthM144y9bpl0xg9OQRvP1o88lHplw5ibysfCVurfG+BtYmvGZO1HN+cgZX3XMpA8cleOUfmYi7XXXvj/j243XNyivLq/jbp/ce1TkcOJuVhZpw9uZl8/ajH7U5RhERkY7y8QsLufHhq5qU+fj6MP2WqV7TpTB+eD9uffxaaqprefbXsyjYe6DJ/qvvv4w+A2M1i3AX5jXJ27izR/HJi4tIHBXPXa//Av9AvyM/SUQACAoNpKy4nJ3rmncXefH3rzdbB6Y1LmqblR20BdTkfdeKJyIi4onSNmey6pP13PTo1Vzxu4s44exR/Oal20lek9rizU1PMvmyidz48E8YdfowZt03h2/mr212THivHix4eQmhkSFuiNB7GNs+D3fxmm6T6xZt5kf/dyGz7pvDlCtPU+ubyDGoKKtsdUbJ6bdMxVXr4oe3ncf85z5v/lxbxn5ycOBDBNGk2x3EMxiHcVBuS8khgyEcXfInIiLiTpuWbyV5TSqxA2IYOn4AT9z6PFUVVe4O67Cu+N1F7N6ezX/vbr6sD8CYySOYeOFJ5O3OJzAkgI/+81knR+hFLG5dYLs9eE3ytmT2CtI2ZXD9X2ey+M3lVJRVujskEa9x3QNXsmT2ihb39YqPxuF08NOHftwsecu2GVRRQSxx1FBDNmmEEkEa2xgwuj9FOwsYXD4ah/GaRnwREenmKsoqSd+SSfqWTHeHclgh4cFc++crWDJ7Bdu+3dnqcaf88CRe+N3rnRiZuJNXfeNKT9rNf+9+kyVzvnJ3KCJepbamlqzknBb3vXT3mxhjePSap5qUV9kKKikjwQwlwAQRYsIYzBiKKWQAI7j+shuJqYjHabzmHpCIiIhX8PH14YYHZ/Lan985bOI2+ozhZG7d3YmReT9v7zbpVcmbiByf4sISInuHt7ivV1xPANYu3NikvIA8ounTpMwYQxAhmGAX5SWaXVLEGxyakGHyZRPdHYqIHKWZd13Mu4//j5IjLB5++qWnsPC15Z0UVRdh2+nhJrplLtLFRcSEExIeTG7m/pb3x0bgqnXx8Gd/bFK+/OvlvPHAe1DT9PiQXkE8PvcBlryumaxE2srhcBAR04P8nMIOu8Zpl0zA5bKc9eMzSNucybnXTQFrWbNgI5uWb+2w64rI8aupriGsZwi5mftbnBAsJDyYH99zKelJuzVhWDej5E2ki5t+61Te+8f/Wt2/Y3UKCaPi+N05DzQpd1kXOWQSSkTDmLZKW87+3Hw+/s9iUjekd2TYIl2ew+Hgjqd+SllROVtX7mDlvDUdcp1l73zNrf+4jqSvtxMYEoCvvy9V5VUkjolX8ibiod55bB6PLbqfuGF9+ezlL3jlj7NxOB3ED+9H/LA+jDt7NLPum0NRfrG7Q/UqBvd2eWwPSt5EurgD+w4Sm9iLtM0tD8z+5TM3c/f5DzUrdxgH/e0QUtiMj/XDUndnL5Fh+Af543Cq17VIWwSFBeIf4IfTx0nK+vQOvdbzv3m14efktc2XDBERz+Jyubh3+t/4zX9/xqcvLSa8Vw9+/tSNrJj7Lbt3ZPPl+982W2xcjoK1mm1SRDxbduo+wqPDWtz37t6XWPP5BtYs2NDi/kATzBDGUmtrMZiGFrgDuQe1joxIG5UcKOXxG59xdxgi4oGMMcQk9GLFB99wzf2X4+Pnw9O/fLnZotxtdd0DV1JUUMzcf33SrueVjqNb5yJd3MnTxrFp+bYW94X2DOXVP71zxHM4jbMhcXP6OBl2ymDysvLbNU4R6V4cTgcjJw3Fx1f3kUW+78o/zOCGv87E6ePkqTtf4olbnmv3xA3qJjSrKKlo9/N6Mm+fbVLvmCJdXFjPUKZeM5mzrzqDvN37ydqRzTt/n4fL5aK2ppZ+g2PZfphpiBubNONkxv9gHAtfXcretNwOjlxEurJr/3wFuzZlcMuVk6goqeCdv8874sx6It3FvKcXkDBqK9tXpeCq7bgJST548uMOO7fH8u5ek21L3owxfwZuBvLqi+6x1n5Sv+9u4EagFviFtXZBW64lIsenz4AY9u/J58nbnqff4N4MGT+QFzf/g17xUXz53koWvfHlEc/hcDi4+bGr2bg0iX//7MVOiFpEurqAYH/WfLaB5e+upGfvCM6/6Ryi43ryzC9fcXdoIm5XVlzO1pXJ7g5DPFB7dJt8wlo7rv5xKHEbAcwERgLTgGeMMc52uJaIHKMNy5J45Y9zyEndx+rPNvDmg+/jH+yPq9byyPcW5m5N74ExpG3O5Jv5azs4WhHpDnpEhdF3cG/KiuvWi8zPKeT9f87HL8DPzZGJdB8Tzj+Bmx+7hoFjE9wdSqdSt8mWzQDmWGsrgTRjTAowAdDCUCKdzda1nDVeByYmPpqr+t/e8uHWUkYxtdQSSjhOHyfn/OQMPnv5i86KWES6uOsfnMmA0f059aLxXP3Hy0hauYOK0krmP/e5u0MT6TastViXi4rSbjTmzQIu7+432R7J253GmGuBNcBvrLWFQF/gm0bHZNWXNWOMuQW4BSCAoHYIR0Qaq66qxjgMNOoyX1tTy8V3TsPX34c3/vIeRQUlAFTYMjJIJpQInDip6HWAGRdeysp5a1pd5FtE5FjN+uMc5j45n/jh/fjX7S+Ql1VA4b72n4xBxBv98NZzierXk6CwwA7tRrz6sw2s/qzl2abFcx0xeTPGLAJiW9h1L/As8Ffq8ti/Av8AfkrdGnjf12Kaa619AXgBIMxEencqLOKB9mcVED+8b5N13m4Y+kueXfdYw/Yzv5oFQAbJDGY0jvpezjbXMuu/rzQpOxpX33cZDqcDY+reCsqKyzmYV4TT10luRh5ZyTnsy8g7wllEpKs6uL+Ig/uLyNye7e5QRDzOwHEJLH/vG067eIK7Q+mavDzbOGLyZq2dejQnMsa8CMyv38wC4hrt7gfoHVrEDdYs2MCZV0xqkrzlpO3j4ojreCPtGXz9fAGosOUEEdokSTPG0Mv2pYA8olq8h9Myay0fPvUpRfnFAASFBhIWFUptdS294qM46byxBIYE8P4T849wJhERke7l5Xtmkzgmnud/+5q7Q+mS3DlerT20acISY0zvRpuXAFvqf54HzDTG+BtjEoHBwKq2XEtEjk9FWRVBYYEt7pt132xOuHAktaOLONhzH2WOItLsNly2tuEYg+FYb1PVVNUQGBLQsF1WXM7etFzysvJJ+noH+/cUEBDsf1yvR0REpCsrLixh07KtVFdWuzsU8UBtHfP2mDFmHHXf7NKBWwGstUnGmHeArUANcIe1jb4NikinKSsqIzAkgIDggGaDkhe+vpxn5/yHAdUjiTFhxJBAGSWksZ2BjMRaC/GVRGQefasb1K0bc9W9P6KqooqMrVlkJedQkFOI08fJzLsuZv3izbz54Pvt+TJFRES6tBGnDuHUi06m5EApbz/6obvD8V7Wu5ve2pS8WWuvOcy+h4CH2nJ+EWkf859byM+evJ5/3vxck/JC8oisjsFpvnsrCDIhWGvZZ3cTmOjDzJkz+fLZtZQeLDvq61VX1fDqn94mIMiffkP7MHziYGb8bBpFBcU8NPNJigtL2u21iYiIdAd+AX5Yl4va6hp3h+LVvL3bZEctFSAiHmRvei5rPt/ImVdMYtk7XzeUV1FJCGHNjvcnADA8/cJ/+NetL/LD2847rrt8FWWVpKxPI3VDOsYY0jZnekTiNuPOadTWuPh81lKqKqrcHY6IiMgRbViyhQ1Lthz5QOnSlLyJdBPL313J1Gsmc951U/j81aUARBBFDpmE0KPhOGst5ZTSj4H4+viyNy2X7JS93PzYNXzw5MfkZxcc87WttR6zTpwxhj4DY4mICWfXxnS2rkxusr/fkD6cc/UZYOvXvCsqY9OyrSSv3eWmiEVERKRdWLr+bJMi0nUsen05p18yoaEFzt8E4mN92W1T6E1/aqghi1Si6N0wzT/Al+9/w6ZlW5l6zWQiYnqwfvFmrIVdmzKITYhmb3oeB3IPuvGVHT1rLc/+elar+wOC/fH19yU/u4DPZy3FGMNJ543lnKsn88Zf36O4wP0thyIi4tmCwoK47oErqCyv4uV73nJ3OFLPAKY7j3kTEe+zYu4qrr7vMi75xQXkZxeQuiGW5J3JZLELJ07iGYSfqZspMrJPBLf94zrKisvZv6eAha8upaqimkEnJuJwOPjBDWeRkbSbC2//AZ+8tJikr7a7+dW1Xcr6NFLWpxEdF8V1f7mSA/sOsvD15WxatpUrfjeD3dv3UFle1aT7qYiISGM/uGEKC19bxjlXT3Z3KPJ9LncH0DZK3kS6oTf++h5hPUOJ7B3B2LNGMf3Wc0n6age7NmWQs2tfw3GPXfcfImPD6dEzlOj4KB748PcEhQXx1YerCAwNpLqiiqEnD2Lj0iS2f7vTja+o/eXt3s8zv3yF0MgQLrztPHz9ffl2/louumMaFWWVSt5ERKRVvQfEMPLUoSx9+yt3hyJdjLEe1HQYZiLtKeYcd4ch0u0YYxh80gAGju1P3LC+rFu0mfQtmTh9nJQeLKPkQCkAQ8YPZOC4BD59abGbIxYREfFcxhiCwgKPaaZmT7DIvrfWWjve3XF0lLCwfvbk8Xe0y7m+WHKPW35XankTEay1JK9JJXlNKgCnXjSe6becS0z/aPak7MXHz4l1WRxOB3MenuvmaL3T5MsmMuiERNZ/sYX1ize7OxwREelA1lqvS9y6BU1YIiJd0cp5axhx6lCMMWxevpVNy7e6OySvdsXvLuK0iycQGhlKwsh4nE4naz7f4O6wRERExMsoeRORFv337jfx8fWhRouBttniN1ewNy2X2loXNz96DZf/9kL2ZxeQviXT3aGJiIh0IxY8aMjY8VDyJiKtUuLWPvKzC1j+3jcAOBwOJl00nuGnDFLyJiIi0smMd+duONwdgIhId5KfU4h/kD/DJgx2dygiIiLiZZS8iYh0Il8/H6zLEhYVSmRsuLvDERER6V6sbZ+Hmyh5ExHpJP2G9OGqe39EcWEJWCjYe8DdIYmIiHQfFoyrfR5HwxgzzRizwxiTYoy5q4X9U4wxB40xG+of9x/pnBrzJiLSCRJHx3PHv35K7u79BIcH8/xvX3N3SCIiItJBjDFO4GngXCALWG2MmWet/f4U3l9aa394tOdV8iYiXU7P3hEMOXkgA8b0x+Gs62Dg4+vD249+SFlxuVtiStucyRO3Pk9Yz1BS1qdRXVntljhERES6tc7r8jgBSLHW7gIwxswBZgBtWn9JyZuIdAljzhzB6ZecQmlRGfnZhSSvTmHNZxuorqqbMXPq1ZPpER3mtuQNYM/OHPbszHHb9UVERLq99svdoowxaxptv2CtfaHRdl9gd6PtLOCUFs5zqjFmI5AN/NZam3S4iyp5E5EuIXl1KiecPZr0pN0se+frhnKnj5OEUXGMmDRUi42LiIhIe9lvrR1/mP2mhbLvp47rgP7W2hJjzAXAh8Bhp6NW8iYinc7X35eefSKora4lLyu/Xc5ZUVbJq396mxOnjuG2f15HeUkFALXVtaRvyWT2w3PJ272/Xa4lIiIi3sl0XrfJLCCu0XY/6lrXGlhrixr9/Ikx5hljTJS1ttUvLEreRKTDGGPoPzKOUacNJTouihPOHsWazzdSXVlNfnYhwT2C6Nk3kkWvL2+3BavXLdrEpmVbqa2pxbpxKl8RERHxQJ333WA1MNgYkwjsAWYCVzU+wBgTC+yz1lpjzATqVgI47F1tJW8i0m78A/0YOmEQw08ZTGBoINZlSU/azTfz17J/TwFDPruXvKx8Pn1pccNzfHx9OOfqMzj32jNZ/u5KdqxOaXMcNdU1bT6HiIiIyPGy1tYYY+4EFgBO4GVrbZIx5rb6/c8BlwG3G2NqgHJgpj3CnWfjSXemw0ykPcWc4+4wROQ4/OCGs4jqG8nWlcls/3ZnQ7fF77vnrV9SU1XLY9f/p0m5w+Fg8uUTGXryIJbMXkHy2l2dEbaIiIjUW2TfW3uEcVxeLSy4r5048tZ2OdfC1X9yy+9Ki3SLSJsNHJdAUFggbz74PusXb24xcesRFcboycPZsCSJxNHxvLDpH032u1wulr79NW899AEnnTe2s0IXERGRbsJgMbZ9Hu6ibpMi0iY9+0Twh1d/zsr/reEPr/2cyNhwnL7OpgdZqCitIG9PAfvScpn98Aekbsxo8XynXTKB5e+u7ITIRaSxoLAgInuHk7Uj+8gHy1FxOB1E9OpBSHgwJQfLKC+poKK0Alety92hiYiXUvImIsfF19+XC24+h0kzJlBZUUVG0m6+fP8bMrftoaqi6rjP22dQLJ+9/EU7Rioih0TGhtN7YCx9B8XSKz4Kp893N1pKD5Yy/ZZz+WL2CowxGGPYvyefXRszSNucSUVZpRsj9yyhkSFExIQTGRtORGzdv8E9gpodV1tTi6vWxemXnMLHLy4iINifwJCAJr93oMXJlYypm2W8pqqmIemr+7eSitLKZmXlJRVUV1Z3zAsW6Uo8aMjY8VDyJiLH5Zr7L2P5uyvxC/Bj0oyT+WL2ijafs2efSPKzC9ohOpHuyenjJKZ/NH0GxtB7YAwRMeFN9hfsPUBO6l42f7mN3Mz91NbUNtk/8Yfjef2Bdxu2o/pGMmBsAjPunEZAcAAAVRVVpG/ZTerGdHIzu87yG/4BfkT2iWiSkIVFheJwNB1hYq2luKCEgr0HKNx7gOQ1qRTuPUBZcXmL5+0VF8WQ8QP55MVFxxWXj69PQ9L33b8B9IgOIyYhuqE8IDgAvwDfhucdSv5aSwxdLlddItgo+WspQSzKL272/0SOncPhwOVSi6tHUPImIt1JUFgQP/r1dM68YhLDThnM6s82cO/0h9vl3JMvn8jSOV8f+UCRbiwgyJ/eA2PoMzCW3gNjCAoNBOq+kNfW1LIvI4/slL2s+GAVhfsOtOla+/cUsH9PAas+WddQ5h/oR/+RcZw4dQy94qMakoTczDxSN2aQkbSbyvLjb33vTA6ngxOnjmHMmSM48/JTef2BdynYe4Cs5Bw2L99GUX6x25ccqamuoeRADSUHStv1vA6HA/8gPwKCA5okhoEhAUTGhhMQHEBweBAnTzuhXWYB7s6i43oy+IQBfPvJOg7kHmTDF1tIT9rt7rDESyl5E5GjMmbyCMZPG0dFSQWL3/iyyd359hIZG9HmL5siXUGPqLCG1rPYxF74+tW1qFhrqSitIGdXLtmpe1m/eHOrLT4dpbK8iuQ1qSSvSW1SHh0XxcCx/Tlx6mj8A/0BqCirJG1zJmmbMsjLOuzSRZ1q0AmJnHrReHx8fVi7cCOv3DubEROHsOiN5e4OrdO4XC7KS+pa2gr3tXyMr78vky46uUPe77uTUacPw+njZNZ9c4iICWfslBGceeUkAPZn5bPhiy3sSdnr5ii7CQt4eQOokjcRL3flHy5u0lWmIwwZP5CSwlL+fsPTHTbQPn5YX3Zv39Mh5xbxNA6Hg+i4nvUJWiw9+0Q0tGABHNxfRE7qPpLX7OLL976husrz1y7M272fvN37+Wb+2oaygCB/EkbFcfL5JxDdr2dD+d70XHZtzCA9aXenjdOK7teTM684lR7RPUhZn8bbj37UpvG5IsejcN8Blr79XQ+T6Lgoxp01knOvmwLAnp05bFiSRN7urtMl2dO4c6bI9qDkTcTL+QX4dvhd0Ut/NZ3wXj06dIa00y6ZwEf/+azDzi/S2fwC/IhN7NXQghYWGdrQBc/lcpG3O5+c1H2s+mQd+dmFbu+e1xEqyirZviqF7au+63ZnjCGmfzQDxvbn5Gnj8PX3BQPlxeWkbc5k18YM8nMK2+X6QaGBnPGjU+g3tC/7s/JZ9PpyDuQVtcu5RdpD3u79LHxtWcN2n4GxnDxtHL3iowBI35LJhiVJHMg96K4QxcMoeRMRjxAUFtTp3b9E2iokPLih9az3gF74Bfg17KuqqGJvWi7ZqftI+moHxYUlbozUc1hr2Zuey970XL7+aHVDeWBIAImj4zl1xsn07BNRfzBkp+5l18YMMrdlHVULpNPHyUnnjWX0GcMpKypjxQffsmDW0g56NSLtKzt1L9mp33WhTBgZx5lXnEpETDjWZUlZn8amZVv1ftIWXn6jTMmbiLjd8FMGs+2bZHeHIdKMMYaefSIaJgeJjuvZZPbBkgOl5KTuI31LJivnrVE3vDYoL6lg68pktq787r3AGEPvATEMGNufiReehI9f3deW0oNlpG3KYNemzIZxskNOGsDEC8fjcDpY+/lGXr7nrS7ZmindS3rS7obJTRwOBwPHJfCDG6YQHB5MbU0tyatT2fzlNspLKtwcqbewSt5ERNrq5PNPYPbfPnB3GHKMjDHEJEQzaFwC/UfGER0XRf6eAjBHfq6ni4yNIGFUHOsWbSI/u5Cc1L1s+GILebvzNd13J7LWNrRErPjg24byoLAgBoyJZ/JlEwmP6cGYM0fw0VOfMvvhuVrrTLosl8vFznW72LluF1DXyjz05IFcdMc0AkMCqKqoYtvKZJK+TtaNpC5MyZuIuJXD6cDhdHjFhAzdma+/Lwkj4xg4LoGYhOi6Qgs5u/aRuiGdbz9ex7QbzyY2oRcv/uEN9wbbDiZccCLGYTTLnocqKypjy4rtbFmxHYDHv/gzy9/7xs1RiXSu2praJq3Vvv6+jDh1CJf95of4BfhRXlLBli+3sWN1KjXV+owF6mabVMubiMjxO/Gc0axbtMndYUgjPXtHkDimP4mj4xk+cTAZSVlUV1aTtiWTdYs2damFmUVEuorqymo2Lk1i49IkAAKCAxh1+jBm3n0xPr4+lBwoZdPSJFLWp3fvHgRe/tKVvImIW42ePIJZ981xdxjdUkCQP/1HxpE4Op6Y/tEN5fnZBezalMHHzy/klAtO5NU/ve3GKEVE5HhUlFawZsEG1izYANRNsDTmzBENY0MP5B5kw5IkMpJ2a3yoF1HyJiJu4x/oR3VltT40OpgxhtjEXgwY05/+I/rVTc1O3TTuGUm71Zom0oGqKqsZcepQHv/iz+4OpYFxGHasSaX8CDP8On2c6tLehZQcKOXrj1Y3zPIa3qsHY6eM5MzLT8XX34c5j3wI7bNKh0fTOm8iIsdp4oXjWfm/Ne4Oo0sJCQ8mcXQ8A8b0JzymR/3EWpacXftI25zJqk/Xa0IHkU50IPcgV/a52d1hNDF2ykjOmnnaEZO32ppaAoL8PSrx9EbBPYI8cszZgdyDLHvna5YBZ14xicjY8G6RvGnMm4jIcRp0QiLL3vna3WF4rYiYcE44ZxRDThpIeWndNNElhaXs2pTBsndXalFXEWmVj5/PUU3I8zqatKetRp0+jAtuOsfdYUgXoeRNRNwiNDKE4oJid4fhNRwOB/1H9mPkacMaFjAu3HuAgJAAamtdvHq/xqWJiIgclgVcankTETlmZ/xooqb2PozAkACGnTKYYRMG4Rfoh3XZhoWg87MLGo477ZIJ8mlZcQAACxFJREFU9OgZ6sZIRUREvIUW6RYROS69B8SwNy3X3WF4jJj+0YyYNJT4YX3BQEVJBdu+2ckHT35MZbkWWxURERElbyLiBtFxUeTt7r6zG/r4+jBwXAIjJg2hR1QY1lpyM/JI+moHS+d8pdk3RUREOoqXf8YqeRORTjf5soksfmO5u8PoNKGRIYw4dQiDTxyAj58PNdU1pK5PZ/EbX1KUr3F/IiIinaY7J2/GmLeBofWb4cABa+04Y0wCsA3YUb/vG2vtbW25loh0HX0GxmBt3TpvXbFLYNzQPoyYNJTeA2IAKMovZtvKZOY88qFHThctIiIi3qFNyZu19spDPxtj/gE0npc61Vo7ri3nF5GuaevKZM684lQCQwLwC/Q7pudWlFZSUVpBeUlF3c8lFZTXl1XUlx3a53K5OugVfMc/0I8h4wcyfOJggsKCsNaStSObjUuTWPDKkg6/voiIiBwlzTZZxxhjgCuAs9t6LofDgY9f9+vNWV1ZrXEu0m0sfvPL43qeMQb/QD8CQgIICPYnMCSAgOAAAkMC6BEVWr/t31BmHOaoz1tdWd2Q9NX92yhBbFTmH+jH8IlDSBwdj8PpoKq8ih2rU5j//CLKisqO63WJiIhIZ7BgO/7GbkdqryzpDGCftXZno7JEY8x6oAj4o7W2xW9rxphbgFsAAgji8t9dhDFgvTwrPhZhUWEkr0nVYsUiR2CtpaKskoqyynY/t6+fDwHBAQSEBBAY7N+QIEbE9Kgrr08WL7jpHB699j989eEqXLXe/QEgIiIi3uWIyZsxZhEQ28Kue621H9X//GNgdqN9OUC8tTbfGHMS8KExZqS1tuj7J7HWvgC8ABBmIq2Pr5PZf5vbKd2dPEWfgbEMnzjY3WGIdGvVVTVUV5VQXFhy2ONOv+QUUjemd05QIiIi0r68vKfbEZM3a+3Uw+03xvgAlwInNXpOJVBZ//NaY0wqMARY06ZoRUREREREjkcXGPPmaIdzTAW2W2uzDhUYY6KNMc76nwcAg4Fd7XAtERERERGRbqk9xrzNpGmXSYDJwF+MMTVALXCbtbagHa4lIiIiIiJyfLp6t8kjsdZe30LZ+8D7bT23iIiIiIhIu/Hy5K09uk2KiIiIiIhIB+t+C6qJiIiIiEg3ZL2+5U3Jm4iIiIiIdH0W8PLlyNRtUkRERERExAuo5U1ERERERLoHdZsUERERERHxAkreREREREREPJ0Fl3cnbxrzJiIiIiIi4gXU8iYiIiIiIl2fBWu9e7ZJJW8iIiIiItI9qNukiIiIiIiIdDS1vImIiIiISPeg2SZFREREREQ8nLXg8u4xb+o2KSIiIiIi4gXU8iYiIiIiIt2Duk2KiIiIiIh4PqtukyIiIiIiItLRjPWgpsMwE2n/fd8zzP7bXFxenhUfi6CwIGbedTHVldXuDkW80NgzR3b4NYJ7BBHcI4jczP0dfi1P1yM6jIN5Re4Oo4HTz0lQSADFBaUdcv7QyBCKC0qO6liHjwPrslgvX0MHwDfAl6i+keSk7nN3KJ2qR1QYB/d7zv/vo9UjKpSD+4vbdI6wqFCK2ngOb+FwOnD6OPW9o5M4nA62fr2Dl+5+092htOrMKyaRtimDl7f9a621dry74+koPZw97cSA6e1yrs/LXnfL70rdJj1AWVEZL9/zlrvDEC/1Ou+6OwQRERERz2fRIt0iIiIiIiLS8dTyJiIiIiIi3YP17qFZSt5ERERERKTLs+D147LVbVJERERERMQLqOVNRERERES6Pmu9vtukWt5ERERERKRbOLSkTVsfR8MYM80Ys8MYk2KMuauF/cYY8+/6/ZuMMSce6Zwe2fJ2zZ8vx1Xr3VmxiIiIiIg3SBgRR/KaVHeH0aUYY5zA08C5QBaw2hgzz1q7tdFh5wOD6x+nAM/W/9sqj0ve3nzwfXeHICIiIiIiXVHndZucAKRYa3cBGGPmADOAxsnbDOA1a60FvjHGhBtjeltrc1o7qUclb8UU7l9k38twdxxuEgXsd3cQ0ozqxXOpbjyT6sUzqV48k+rFc3XXuunv7gA6UjGFCxbZ96La6XQBxpg1jbZfsNa+0Gi7L7C70XYWzVvVWjqmL+AdyZu1NtrdMbiLMWaNtXa8u+OQplQvnkt145lUL55J9eKZVC+eS3XTNVlrp3Xi5UxLIRzHMU1owhIREREREZH2lQXENdruB2QfxzFNKHkTERERERFpX6uBwcaYRGOMHzATmPe9Y+YB19bPOjkROHi48W7gYd0mu7kXjnyIuIHqxXOpbjyT6sUzqV48k+rFc6lupE2stTXGmDuBBYATeNlam2SMua1+/3PAJ8AFQApQBtxwpPOauslNRERERERExJOp26SIiIiIiIgXUPImIiIiIiLiBZS8dTJjzOXGmCRjjMsYM75ReYIxptwYs6H+8VyjfScZYzYbY1KMMf82xrQ0rai0UWt1U7/v7vrf/w5jzA8alatuOpEx5s/GmD2N/k4uaLSvxTqSzmGMmVb/u08xxtzl7ni6O2NMev1704ZD6xAZYyKNMQuNMTvr/41wd5xdnTHmZWNMrjFmS6OyVutB72Odo5V60eeLeAUlb51vC3ApsLyFfanW2nH1j9salT8L3AIMrn905hoV3UmLdWOMGUHdDEEjqfvdP2OMcdbvVt10vica/Z18AkesI+lg9b/rp4HzgRHAj+vrRNzrrPq/k0M3o+4CFltrBwOL67elY82i+edCi/Wg97FONYuWP6/1+SIeT8lbJ7PWbrPW7jja440xvYEwa+1KWze7zGvAxR0WYDd2mLqZAcyx1lZaa9OomxFogurGo7RYR26OqTuZAKRYa3dZa6uAOdTViXiWGcCr9T+/it6vOpy1djlQ8L3i1upB72OdpJV6aY3qRTyKkjfPkmiMWW+MWWaMOaO+rC91C/gdklVfJp2nL7C70fahOlDduMedxphN9d1eDnU3aq2OpHPo9+95LPC5MWatMeaW+rKYQ+sH1f/by23RdW+t1YP+jtxPny/i8bTOWwcwxiwCYlvYda+19qNWnpYDxFtr840xJwEfGmNGAi2NodL6DsfpOOumtTpQ3XSAw9URdd1U/0rd7/mvwD+An6K6cDf9/j3PadbabGNML2ChMWa7uwOSI9LfkXvp80W8gpK3DmCtnXocz6kEKut/XmuMSQWGUHeHp1+jQ/sB2e0RZ3d0PHVDXR3ENdo+VAeqmw5wtHVkjHkRmF+/2VodSefQ79/DWGuz6//NNcbMpa6b1z5jTG9rbU59t+9ctwbZfbVWD/o7ciNr7b5DP+vzRTyZuk16CGNM9KEBsMaYAdRNfrGrvktFsTFmYv1MhtcCrbUQSceYB8w0xvgbYxKpq5tVqpvOV/9F55BLqJtkBlqpo86OrxtbDQw2xiQaY/yoG9w/z80xdVvGmGBjTOihn4HzqPtbmQdcV3/Ydej9yl1aqwe9j7mRPl/EW6jlrZMZYy4BngKigY+NMRustT8AJgN/McbUALXAbdbaQ4Npb6duZqRA4NP6h7Sz1urGWptkjHkH2ArUAHdYa2vrn6a66VyPGWPGUddlJR24FeAIdSQdzFpbY4y5E1gAOIGXrbVJbg6rO4sB5tavXOIDvGWt/cwYsxp4xxhzI5AJXO7GGLsFY8xsYAoQZYzJAv4EPEIL9aD3sc7TSr1M0eeLeANTN0meiIiIiIiIeDJ1mxQREREREfECSt5ERERERES8gJI3ERERERERL6DkTURERERExAsoeRMREREREfECSt5ERERERES8gJI3ERERERERL/D/RPIhDhg/7dcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1440x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig_3, ax_3 = plt.subplots(1, figsize=(20, 7))\n",
+    "\n",
+    "gdf_model.plot(ax=ax_3, column='od550du', cmap='viridis', legend=True, vmin=0, vmax=3)\n",
+    "\n",
+    "gdf_earth.plot(ax=ax_3, facecolor=\"none\", edgecolor='white', lw=0.5)\n",
+    "\n",
+    "gdf_nes = data_nes.shapefile\n",
+    "gdf_nes.plot(ax=ax_3, column='od550du', cmap='viridis', edgecolor='black', lw=0.5, vmin=0, vmax=3)\n",
+    "\n",
+    "ax_3.margins(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Difference between Providentia Interpolation and NES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.1. Visual comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf_diff = gdf_prv_after.copy().rename(columns={'od550aero': 'providentia'})[['geometry', 'providentia']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf_diff['nes'] = gdf_nes['od550du']\n",
+    "gdf_diff['obs'] = data_obs.shapefile['od550aero']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.1.1. Absolute difference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "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>geometry</th>\n",
+       "      <th>providentia</th>\n",
+       "      <th>nes</th>\n",
+       "      <th>obs</th>\n",
+       "      <th>absolute_difference</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>0</th>\n",
+       "      <td>POINT (109.62880 40.85170)</td>\n",
+       "      <td>0.504757</td>\n",
+       "      <td>0.504896</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.391183e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POINT (-28.02917 39.09109)</td>\n",
+       "      <td>0.007563</td>\n",
+       "      <td>0.007563</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.650604e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POINT (-149.88000 70.49950)</td>\n",
+       "      <td>0.013467</td>\n",
+       "      <td>0.013468</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.439117e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POINT (-97.48562 36.60518)</td>\n",
+       "      <td>0.011034</td>\n",
+       "      <td>0.011034</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.619126e-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POINT (40.19450 -2.99600)</td>\n",
+       "      <td>0.002979</td>\n",
+       "      <td>0.002980</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.042769e-07</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>361</th>\n",
+       "      <td>POINT (-114.37625 62.45130)</td>\n",
+       "      <td>0.014462</td>\n",
+       "      <td>0.014462</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-7.643789e-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>POINT (126.93479 37.56443)</td>\n",
+       "      <td>0.184404</td>\n",
+       "      <td>0.184427</td>\n",
+       "      <td>0.152831</td>\n",
+       "      <td>2.291842e-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>POINT (-0.88235 41.63340)</td>\n",
+       "      <td>0.018486</td>\n",
+       "      <td>0.018484</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-1.265558e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>POINT (8.99023 13.77668)</td>\n",
+       "      <td>0.900909</td>\n",
+       "      <td>0.901096</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.867402e-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>POINT (36.77500 55.69500)</td>\n",
+       "      <td>0.013143</td>\n",
+       "      <td>0.013144</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.505649e-07</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>366 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        geometry  providentia       nes       obs  \\\n",
+       "FID                                                                 \n",
+       "0     POINT (109.62880 40.85170)     0.504757  0.504896       NaN   \n",
+       "1     POINT (-28.02917 39.09109)     0.007563  0.007563       NaN   \n",
+       "2    POINT (-149.88000 70.49950)     0.013467  0.013468       NaN   \n",
+       "3     POINT (-97.48562 36.60518)     0.011034  0.011034       NaN   \n",
+       "4      POINT (40.19450 -2.99600)     0.002979  0.002980       NaN   \n",
+       "..                           ...          ...       ...       ...   \n",
+       "361  POINT (-114.37625 62.45130)     0.014462  0.014462       NaN   \n",
+       "362   POINT (126.93479 37.56443)     0.184404  0.184427  0.152831   \n",
+       "363    POINT (-0.88235 41.63340)     0.018486  0.018484       NaN   \n",
+       "364     POINT (8.99023 13.77668)     0.900909  0.901096       NaN   \n",
+       "365    POINT (36.77500 55.69500)     0.013143  0.013144       NaN   \n",
+       "\n",
+       "     absolute_difference  \n",
+       "FID                       \n",
+       "0           1.391183e-04  \n",
+       "1           3.650604e-07  \n",
+       "2           3.439117e-08  \n",
+       "3           4.619126e-07  \n",
+       "4           2.042769e-07  \n",
+       "..                   ...  \n",
+       "361        -7.643789e-08  \n",
+       "362         2.291842e-05  \n",
+       "363        -1.265558e-06  \n",
+       "364         1.867402e-04  \n",
+       "365         5.505649e-07  \n",
+       "\n",
+       "[366 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_diff['absolute_difference'] = gdf_diff['nes'] - gdf_diff['providentia']\n",
+    "gdf_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig_4, ax_4 = plt.subplots(1, figsize=(20, 7))\n",
+    "\n",
+    "gdf_model.plot(ax=ax_4, column='od550du', cmap='viridis', vmin=0, vmax=3)\n",
+    "\n",
+    "gdf_earth.plot(ax=ax_4, facecolor=\"none\", edgecolor='white', lw=0.5)\n",
+    "\n",
+    "gdf_diff.plot(ax=ax_4, column='absolute_difference', cmap='bwr', edgecolor='black', legend=True, lw=0.5)\n",
+    "\n",
+    "ax_4.margins(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.0004256221585662301"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_diff['absolute_difference'].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0012512493387983464"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_diff['absolute_difference'].max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.1.2. Relative difference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "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>geometry</th>\n",
+       "      <th>providentia</th>\n",
+       "      <th>nes</th>\n",
+       "      <th>absolute_difference</th>\n",
+       "      <th>relative_difference</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>0</th>\n",
+       "      <td>POINT (109.62880 40.85170)</td>\n",
+       "      <td>0.504757</td>\n",
+       "      <td>0.504896</td>\n",
+       "      <td>1.391183e-04</td>\n",
+       "      <td>0.027554</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POINT (-28.02917 39.09109)</td>\n",
+       "      <td>0.007563</td>\n",
+       "      <td>0.007563</td>\n",
+       "      <td>3.650604e-07</td>\n",
+       "      <td>0.004827</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POINT (-149.88000 70.49950)</td>\n",
+       "      <td>0.013467</td>\n",
+       "      <td>0.013468</td>\n",
+       "      <td>3.439117e-08</td>\n",
+       "      <td>0.000255</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POINT (-97.48562 36.60518)</td>\n",
+       "      <td>0.011034</td>\n",
+       "      <td>0.011034</td>\n",
+       "      <td>4.619126e-07</td>\n",
+       "      <td>0.004186</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POINT (40.19450 -2.99600)</td>\n",
+       "      <td>0.002979</td>\n",
+       "      <td>0.002980</td>\n",
+       "      <td>2.042769e-07</td>\n",
+       "      <td>0.006856</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>361</th>\n",
+       "      <td>POINT (-114.37625 62.45130)</td>\n",
+       "      <td>0.014462</td>\n",
+       "      <td>0.014462</td>\n",
+       "      <td>-7.643789e-08</td>\n",
+       "      <td>-0.000529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>POINT (126.93479 37.56443)</td>\n",
+       "      <td>0.184404</td>\n",
+       "      <td>0.184427</td>\n",
+       "      <td>2.291842e-05</td>\n",
+       "      <td>0.012427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>POINT (-0.88235 41.63340)</td>\n",
+       "      <td>0.018486</td>\n",
+       "      <td>0.018484</td>\n",
+       "      <td>-1.265558e-06</td>\n",
+       "      <td>-0.006847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>POINT (8.99023 13.77668)</td>\n",
+       "      <td>0.900909</td>\n",
+       "      <td>0.901096</td>\n",
+       "      <td>1.867402e-04</td>\n",
+       "      <td>0.020724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365</th>\n",
+       "      <td>POINT (36.77500 55.69500)</td>\n",
+       "      <td>0.013143</td>\n",
+       "      <td>0.013144</td>\n",
+       "      <td>5.505649e-07</td>\n",
+       "      <td>0.004189</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>366 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        geometry  providentia       nes  absolute_difference  \\\n",
+       "FID                                                                            \n",
+       "0     POINT (109.62880 40.85170)     0.504757  0.504896         1.391183e-04   \n",
+       "1     POINT (-28.02917 39.09109)     0.007563  0.007563         3.650604e-07   \n",
+       "2    POINT (-149.88000 70.49950)     0.013467  0.013468         3.439117e-08   \n",
+       "3     POINT (-97.48562 36.60518)     0.011034  0.011034         4.619126e-07   \n",
+       "4      POINT (40.19450 -2.99600)     0.002979  0.002980         2.042769e-07   \n",
+       "..                           ...          ...       ...                  ...   \n",
+       "361  POINT (-114.37625 62.45130)     0.014462  0.014462        -7.643789e-08   \n",
+       "362   POINT (126.93479 37.56443)     0.184404  0.184427         2.291842e-05   \n",
+       "363    POINT (-0.88235 41.63340)     0.018486  0.018484        -1.265558e-06   \n",
+       "364     POINT (8.99023 13.77668)     0.900909  0.901096         1.867402e-04   \n",
+       "365    POINT (36.77500 55.69500)     0.013143  0.013144         5.505649e-07   \n",
+       "\n",
+       "     relative_difference  \n",
+       "FID                       \n",
+       "0               0.027554  \n",
+       "1               0.004827  \n",
+       "2               0.000255  \n",
+       "3               0.004186  \n",
+       "4               0.006856  \n",
+       "..                   ...  \n",
+       "361            -0.000529  \n",
+       "362             0.012427  \n",
+       "363            -0.006847  \n",
+       "364             0.020724  \n",
+       "365             0.004189  \n",
+       "\n",
+       "[366 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_diff['relative_difference'] = (gdf_diff['nes'] - gdf_diff['providentia'])*100/gdf_diff['nes']\n",
+    "gdf_diff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig_4, ax_4 = plt.subplots(1, figsize=(20, 7))\n",
+    "\n",
+    "gdf_model.plot(ax=ax_4, column='od550du', cmap='viridis', vmin=0, vmax=3)\n",
+    "\n",
+    "gdf_earth.plot(ax=ax_4, facecolor=\"none\", edgecolor='white', lw=0.5)\n",
+    "\n",
+    "gdf_diff.plot(ax=ax_4, column='relative_difference', cmap='bwr', edgecolor='black', legend=True, lw=0.5, vmax = 7)\n",
+    "\n",
+    "ax_4.axhline(y=0, color='black')\n",
+    "\n",
+    "ax_4.margins(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.19982211142673692"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_diff['relative_difference'].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.944350654874811"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_diff['relative_difference'].max()"
+   ]
+  },
+  {
+   "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>geometry</th>\n",
+       "      <th>providentia</th>\n",
+       "      <th>nes</th>\n",
+       "      <th>absolute_difference</th>\n",
+       "      <th>relative_difference</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>306</th>\n",
+       "      <td>POINT (103.78038 1.29767)</td>\n",
+       "      <td>0.001023</td>\n",
+       "      <td>0.001099</td>\n",
+       "      <td>0.000076</td>\n",
+       "      <td>6.944351</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      geometry  providentia       nes  absolute_difference  \\\n",
+       "FID                                                                          \n",
+       "306  POINT (103.78038 1.29767)     0.001023  0.001099             0.000076   \n",
+       "\n",
+       "     relative_difference  \n",
+       "FID                       \n",
+       "306             6.944351  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_diff.loc[gdf_diff['relative_difference'] == gdf_diff['relative_difference'].max()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.1.3. Scatter plots between interpolated values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f50feb530d0>"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(gdf_diff['providentia'], gdf_diff['nes'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.1.4. Scatter plots between interpolated values and observations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f50fe478510>"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(gdf_diff['nes'], gdf_diff['obs'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f50fe478790>"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(gdf_diff['providentia'], gdf_diff['obs'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 6.2. Numeric comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.2.1. Add model data"
+   ]
+  },
+  {
+   "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>geometry</th>\n",
+       "      <th>model</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>FID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POLYGON ((-180.70312 -90.50000, -179.29688 -90...</td>\n",
+       "      <td>0.000144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>POLYGON ((-179.29688 -90.50000, -177.89062 -90...</td>\n",
+       "      <td>0.000144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>POLYGON ((-177.89062 -90.50000, -176.48438 -90...</td>\n",
+       "      <td>0.000144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>POLYGON ((-176.48438 -90.50000, -175.07812 -90...</td>\n",
+       "      <td>0.000144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>POLYGON ((-175.07812 -90.50000, -173.67188 -90...</td>\n",
+       "      <td>0.000144</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46512</th>\n",
+       "      <td>POLYGON ((173.67188 89.50000, 175.07812 89.500...</td>\n",
+       "      <td>0.023041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46513</th>\n",
+       "      <td>POLYGON ((175.07812 89.50000, 176.48438 89.500...</td>\n",
+       "      <td>0.023041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46514</th>\n",
+       "      <td>POLYGON ((176.48438 89.50000, 177.89062 89.500...</td>\n",
+       "      <td>0.023041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46515</th>\n",
+       "      <td>POLYGON ((177.89062 89.50000, 179.29688 89.500...</td>\n",
+       "      <td>0.023041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46516</th>\n",
+       "      <td>POLYGON ((179.29688 89.50000, 180.70312 89.500...</td>\n",
+       "      <td>0.023041</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>46517 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                geometry     model\n",
+       "FID                                                               \n",
+       "0      POLYGON ((-180.70312 -90.50000, -179.29688 -90...  0.000144\n",
+       "1      POLYGON ((-179.29688 -90.50000, -177.89062 -90...  0.000144\n",
+       "2      POLYGON ((-177.89062 -90.50000, -176.48438 -90...  0.000144\n",
+       "3      POLYGON ((-176.48438 -90.50000, -175.07812 -90...  0.000144\n",
+       "4      POLYGON ((-175.07812 -90.50000, -173.67188 -90...  0.000144\n",
+       "...                                                  ...       ...\n",
+       "46512  POLYGON ((173.67188 89.50000, 175.07812 89.500...  0.023041\n",
+       "46513  POLYGON ((175.07812 89.50000, 176.48438 89.500...  0.023041\n",
+       "46514  POLYGON ((176.48438 89.50000, 177.89062 89.500...  0.023041\n",
+       "46515  POLYGON ((177.89062 89.50000, 179.29688 89.500...  0.023041\n",
+       "46516  POLYGON ((179.29688 89.50000, 180.70312 89.500...  0.023041\n",
+       "\n",
+       "[46517 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_eval = copy.deepcopy(gdf_model)\n",
+    "gdf_eval.rename(columns={'od550du': 'model'}, inplace=True)\n",
+    "gdf_eval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.2.2. Add observations data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "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>geometry</th>\n",
+       "      <th>model</th>\n",
+       "      <th>obs</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>9587</th>\n",
+       "      <td>POLYGON ((-71.01562 -53.50000, -69.60938 -53.5...</td>\n",
+       "      <td>0.000458</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9845</th>\n",
+       "      <td>POLYGON ((-69.60938 -52.50000, -68.20312 -52.5...</td>\n",
+       "      <td>0.000429</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11388</th>\n",
+       "      <td>POLYGON ((-68.20312 -46.50000, -66.79688 -46.5...</td>\n",
+       "      <td>0.000508</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12161</th>\n",
+       "      <td>POLYGON ((-65.39062 -43.50000, -63.98438 -43.5...</td>\n",
+       "      <td>0.001136</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13187</th>\n",
+       "      <td>POLYGON ((-68.20312 -39.50000, -66.79688 -39.5...</td>\n",
+       "      <td>0.002681</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41394</th>\n",
+       "      <td>POLYGON ((-156.79688 70.50000, -155.39062 70.5...</td>\n",
+       "      <td>0.013695</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42998</th>\n",
+       "      <td>POLYGON ((-69.60938 76.50000, -68.20312 76.500...</td>\n",
+       "      <td>0.013201</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43058</th>\n",
+       "      <td>POLYGON ((14.76562 76.50000, 16.17188 76.50000...</td>\n",
+       "      <td>0.017579</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43569</th>\n",
+       "      <td>POLYGON ((10.54688 78.50000, 11.95312 78.50000...</td>\n",
+       "      <td>0.013934</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43757</th>\n",
+       "      <td>POLYGON ((-86.48438 79.50000, -85.07812 79.500...</td>\n",
+       "      <td>0.011777</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>366 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                geometry     model  obs\n",
+       "FID                                                                    \n",
+       "9587   POLYGON ((-71.01562 -53.50000, -69.60938 -53.5...  0.000458  NaN\n",
+       "9845   POLYGON ((-69.60938 -52.50000, -68.20312 -52.5...  0.000429  NaN\n",
+       "11388  POLYGON ((-68.20312 -46.50000, -66.79688 -46.5...  0.000508  NaN\n",
+       "12161  POLYGON ((-65.39062 -43.50000, -63.98438 -43.5...  0.001136  NaN\n",
+       "13187  POLYGON ((-68.20312 -39.50000, -66.79688 -39.5...  0.002681  NaN\n",
+       "...                                                  ...       ...  ...\n",
+       "41394  POLYGON ((-156.79688 70.50000, -155.39062 70.5...  0.013695  NaN\n",
+       "42998  POLYGON ((-69.60938 76.50000, -68.20312 76.500...  0.013201  NaN\n",
+       "43058  POLYGON ((14.76562 76.50000, 16.17188 76.50000...  0.017579  NaN\n",
+       "43569  POLYGON ((10.54688 78.50000, 11.95312 78.50000...  0.013934  NaN\n",
+       "43757  POLYGON ((-86.48438 79.50000, -85.07812 79.500...  0.011777  NaN\n",
+       "\n",
+       "[366 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_eval = gdf_eval.sjoin(data_obs.shapefile.rename(columns={'od550aero': 'obs'}))\n",
+    "gdf_eval = gdf_eval.drop(columns=['index_right'])\n",
+    "gdf_eval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.2.3. Add NES data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>model</th>\n",
+       "      <th>obs</th>\n",
+       "      <th>nes</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",
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+       "      <td>POLYGON ((-71.01562 -53.50000, -69.60938 -53.5...</td>\n",
+       "      <td>0.000458</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000420</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9845</th>\n",
+       "      <td>POLYGON ((-69.60938 -52.50000, -68.20312 -52.5...</td>\n",
+       "      <td>0.000429</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11388</th>\n",
+       "      <td>POLYGON ((-68.20312 -46.50000, -66.79688 -46.5...</td>\n",
+       "      <td>0.000508</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12161</th>\n",
+       "      <td>POLYGON ((-65.39062 -43.50000, -63.98438 -43.5...</td>\n",
+       "      <td>0.001136</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.001565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13187</th>\n",
+       "      <td>POLYGON ((-68.20312 -39.50000, -66.79688 -39.5...</td>\n",
+       "      <td>0.002681</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.001954</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>41394</th>\n",
+       "      <td>POLYGON ((-156.79688 70.50000, -155.39062 70.5...</td>\n",
+       "      <td>0.013695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.013110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42998</th>\n",
+       "      <td>POLYGON ((-69.60938 76.50000, -68.20312 76.500...</td>\n",
+       "      <td>0.013201</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.013245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43058</th>\n",
+       "      <td>POLYGON ((14.76562 76.50000, 16.17188 76.50000...</td>\n",
+       "      <td>0.017579</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.017365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43569</th>\n",
+       "      <td>POLYGON ((10.54688 78.50000, 11.95312 78.50000...</td>\n",
+       "      <td>0.013934</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.014070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43757</th>\n",
+       "      <td>POLYGON ((-86.48438 79.50000, -85.07812 79.500...</td>\n",
+       "      <td>0.011777</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.011703</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>512 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                geometry     model  obs  \\\n",
+       "FID                                                                       \n",
+       "9587   POLYGON ((-71.01562 -53.50000, -69.60938 -53.5...  0.000458  NaN   \n",
+       "9845   POLYGON ((-69.60938 -52.50000, -68.20312 -52.5...  0.000429  NaN   \n",
+       "11388  POLYGON ((-68.20312 -46.50000, -66.79688 -46.5...  0.000508  NaN   \n",
+       "12161  POLYGON ((-65.39062 -43.50000, -63.98438 -43.5...  0.001136  NaN   \n",
+       "13187  POLYGON ((-68.20312 -39.50000, -66.79688 -39.5...  0.002681  NaN   \n",
+       "...                                                  ...       ...  ...   \n",
+       "41394  POLYGON ((-156.79688 70.50000, -155.39062 70.5...  0.013695  NaN   \n",
+       "42998  POLYGON ((-69.60938 76.50000, -68.20312 76.500...  0.013201  NaN   \n",
+       "43058  POLYGON ((14.76562 76.50000, 16.17188 76.50000...  0.017579  NaN   \n",
+       "43569  POLYGON ((10.54688 78.50000, 11.95312 78.50000...  0.013934  NaN   \n",
+       "43757  POLYGON ((-86.48438 79.50000, -85.07812 79.500...  0.011777  NaN   \n",
+       "\n",
+       "            nes  \n",
+       "FID              \n",
+       "9587   0.000420  \n",
+       "9845   0.000441  \n",
+       "11388  0.000532  \n",
+       "12161  0.001565  \n",
+       "13187  0.001954  \n",
+       "...         ...  \n",
+       "41394  0.013110  \n",
+       "42998  0.013245  \n",
+       "43058  0.017365  \n",
+       "43569  0.014070  \n",
+       "43757  0.011703  \n",
+       "\n",
+       "[512 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_eval = gdf_eval.sjoin(gdf_nes.rename(columns={'od550du': 'nes'}))\n",
+    "gdf_eval = gdf_eval.drop(columns=['index_right'])\n",
+    "gdf_eval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.2.4. Add Providentia Interpolation data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\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>geometry</th>\n",
+       "      <th>model</th>\n",
+       "      <th>obs</th>\n",
+       "      <th>nes</th>\n",
+       "      <th>providentia</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>9587</th>\n",
+       "      <td>POLYGON ((-71.01562 -53.50000, -69.60938 -53.5...</td>\n",
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+       "    <tr>\n",
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+       "      <td>POLYGON ((-69.60938 -52.50000, -68.20312 -52.5...</td>\n",
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+       "    <tr>\n",
+       "      <th>11388</th>\n",
+       "      <td>POLYGON ((-68.20312 -46.50000, -66.79688 -46.5...</td>\n",
+       "      <td>0.000508</td>\n",
+       "      <td>NaN</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12161</th>\n",
+       "      <td>POLYGON ((-65.39062 -43.50000, -63.98438 -43.5...</td>\n",
+       "      <td>0.001136</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.001565</td>\n",
+       "      <td>0.001565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13187</th>\n",
+       "      <td>POLYGON ((-68.20312 -39.50000, -66.79688 -39.5...</td>\n",
+       "      <td>0.002681</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.001954</td>\n",
+       "      <td>0.001954</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>41394</th>\n",
+       "      <td>POLYGON ((-156.79688 70.50000, -155.39062 70.5...</td>\n",
+       "      <td>0.013695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.013110</td>\n",
+       "      <td>0.013110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42998</th>\n",
+       "      <td>POLYGON ((-69.60938 76.50000, -68.20312 76.500...</td>\n",
+       "      <td>0.013201</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.013245</td>\n",
+       "      <td>0.013245</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43058</th>\n",
+       "      <td>POLYGON ((14.76562 76.50000, 16.17188 76.50000...</td>\n",
+       "      <td>0.017579</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.017365</td>\n",
+       "      <td>0.017365</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43569</th>\n",
+       "      <td>POLYGON ((10.54688 78.50000, 11.95312 78.50000...</td>\n",
+       "      <td>0.013934</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.014070</td>\n",
+       "      <td>0.014070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43757</th>\n",
+       "      <td>POLYGON ((-86.48438 79.50000, -85.07812 79.500...</td>\n",
+       "      <td>0.011777</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.011703</td>\n",
+       "      <td>0.011703</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>954 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                geometry     model  obs  \\\n",
+       "FID                                                                       \n",
+       "9587   POLYGON ((-71.01562 -53.50000, -69.60938 -53.5...  0.000458  NaN   \n",
+       "9845   POLYGON ((-69.60938 -52.50000, -68.20312 -52.5...  0.000429  NaN   \n",
+       "11388  POLYGON ((-68.20312 -46.50000, -66.79688 -46.5...  0.000508  NaN   \n",
+       "12161  POLYGON ((-65.39062 -43.50000, -63.98438 -43.5...  0.001136  NaN   \n",
+       "13187  POLYGON ((-68.20312 -39.50000, -66.79688 -39.5...  0.002681  NaN   \n",
+       "...                                                  ...       ...  ...   \n",
+       "41394  POLYGON ((-156.79688 70.50000, -155.39062 70.5...  0.013695  NaN   \n",
+       "42998  POLYGON ((-69.60938 76.50000, -68.20312 76.500...  0.013201  NaN   \n",
+       "43058  POLYGON ((14.76562 76.50000, 16.17188 76.50000...  0.017579  NaN   \n",
+       "43569  POLYGON ((10.54688 78.50000, 11.95312 78.50000...  0.013934  NaN   \n",
+       "43757  POLYGON ((-86.48438 79.50000, -85.07812 79.500...  0.011777  NaN   \n",
+       "\n",
+       "            nes  providentia  \n",
+       "FID                           \n",
+       "9587   0.000420     0.000420  \n",
+       "9845   0.000441     0.000441  \n",
+       "11388  0.000532     0.000532  \n",
+       "12161  0.001565     0.001565  \n",
+       "13187  0.001954     0.001954  \n",
+       "...         ...          ...  \n",
+       "41394  0.013110     0.013110  \n",
+       "42998  0.013245     0.013245  \n",
+       "43058  0.017365     0.017365  \n",
+       "43569  0.014070     0.014070  \n",
+       "43757  0.011703     0.011703  \n",
+       "\n",
+       "[954 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_eval = gdf_eval.sjoin(gdf_prv_after.rename(columns={'od550aero': 'providentia'}))\n",
+    "gdf_eval = gdf_eval.drop(columns=['index_right'])\n",
+    "gdf_eval"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.2.5. Calculate relative difference between NES and Providentia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>model</th>\n",
+       "      <th>obs</th>\n",
+       "      <th>nes</th>\n",
+       "      <th>providentia</th>\n",
+       "      <th>nes-prov</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",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9587</th>\n",
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+       "      <th>9845</th>\n",
+       "      <td>POLYGON ((-69.60938 -52.50000, -68.20312 -52.5...</td>\n",
+       "      <td>0.000429</td>\n",
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+       "      <td>0.001709</td>\n",
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+       "    <tr>\n",
+       "      <th>11388</th>\n",
+       "      <td>POLYGON ((-68.20312 -46.50000, -66.79688 -46.5...</td>\n",
+       "      <td>0.000508</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>0.014902</td>\n",
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+       "    <tr>\n",
+       "      <th>12161</th>\n",
+       "      <td>POLYGON ((-65.39062 -43.50000, -63.98438 -43.5...</td>\n",
+       "      <td>0.001136</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.001565</td>\n",
+       "      <td>0.001565</td>\n",
+       "      <td>-0.011830</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13187</th>\n",
+       "      <td>POLYGON ((-68.20312 -39.50000, -66.79688 -39.5...</td>\n",
+       "      <td>0.002681</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.001954</td>\n",
+       "      <td>0.001954</td>\n",
+       "      <td>0.001149</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>41394</th>\n",
+       "      <td>POLYGON ((-156.79688 70.50000, -155.39062 70.5...</td>\n",
+       "      <td>0.013695</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.013110</td>\n",
+       "      <td>0.013110</td>\n",
+       "      <td>0.000853</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42998</th>\n",
+       "      <td>POLYGON ((-69.60938 76.50000, -68.20312 76.500...</td>\n",
+       "      <td>0.013201</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.013245</td>\n",
+       "      <td>0.013245</td>\n",
+       "      <td>0.000210</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43058</th>\n",
+       "      <td>POLYGON ((14.76562 76.50000, 16.17188 76.50000...</td>\n",
+       "      <td>0.017579</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.017365</td>\n",
+       "      <td>0.017365</td>\n",
+       "      <td>0.000002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43569</th>\n",
+       "      <td>POLYGON ((10.54688 78.50000, 11.95312 78.50000...</td>\n",
+       "      <td>0.013934</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.014070</td>\n",
+       "      <td>0.014070</td>\n",
+       "      <td>-0.000013</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43757</th>\n",
+       "      <td>POLYGON ((-86.48438 79.50000, -85.07812 79.500...</td>\n",
+       "      <td>0.011777</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.011703</td>\n",
+       "      <td>0.011703</td>\n",
+       "      <td>-0.000004</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>954 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                geometry     model  obs  \\\n",
+       "FID                                                                       \n",
+       "9587   POLYGON ((-71.01562 -53.50000, -69.60938 -53.5...  0.000458  NaN   \n",
+       "9845   POLYGON ((-69.60938 -52.50000, -68.20312 -52.5...  0.000429  NaN   \n",
+       "11388  POLYGON ((-68.20312 -46.50000, -66.79688 -46.5...  0.000508  NaN   \n",
+       "12161  POLYGON ((-65.39062 -43.50000, -63.98438 -43.5...  0.001136  NaN   \n",
+       "13187  POLYGON ((-68.20312 -39.50000, -66.79688 -39.5...  0.002681  NaN   \n",
+       "...                                                  ...       ...  ...   \n",
+       "41394  POLYGON ((-156.79688 70.50000, -155.39062 70.5...  0.013695  NaN   \n",
+       "42998  POLYGON ((-69.60938 76.50000, -68.20312 76.500...  0.013201  NaN   \n",
+       "43058  POLYGON ((14.76562 76.50000, 16.17188 76.50000...  0.017579  NaN   \n",
+       "43569  POLYGON ((10.54688 78.50000, 11.95312 78.50000...  0.013934  NaN   \n",
+       "43757  POLYGON ((-86.48438 79.50000, -85.07812 79.500...  0.011777  NaN   \n",
+       "\n",
+       "            nes  providentia  nes-prov  \n",
+       "FID                                     \n",
+       "9587   0.000420     0.000420  0.000568  \n",
+       "9845   0.000441     0.000441  0.001709  \n",
+       "11388  0.000532     0.000532  0.014902  \n",
+       "12161  0.001565     0.001565 -0.011830  \n",
+       "13187  0.001954     0.001954  0.001149  \n",
+       "...         ...          ...       ...  \n",
+       "41394  0.013110     0.013110  0.000853  \n",
+       "42998  0.013245     0.013245  0.000210  \n",
+       "43058  0.017365     0.017365  0.000002  \n",
+       "43569  0.014070     0.014070 -0.000013  \n",
+       "43757  0.011703     0.011703 -0.000004  \n",
+       "\n",
+       "[954 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gdf_eval['nes-prov'] = (gdf_eval['nes'] - gdf_eval['providentia'])*100 / gdf_eval['nes']\n",
+    "gdf_eval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31.73012028502732"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Difference is higher now because we have used spatial joins (nearest 1 neighbour vs nearest 4)\n",
+    "gdf_eval['nes-prov'].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-46.491689814524314"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Difference is higher now because we have used spatial joins (nearest 1 neighbour vs nearest 4)\n",
+    "gdf_eval['nes-prov'].min()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 6.2.6. Scatter plot between model and interpolated values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f50fefa5cd0>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(gdf_eval['providentia'], gdf_eval['model'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f50fea7c9d0>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(gdf_eval['nes'], gdf_eval['model'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/tutorials/data/timezones_2021c/timezones_2021c.dbf b/tutorials/data/timezones_2021c/timezones_2021c.dbf
deleted file mode 100644
index aed52eac47a3f420d8ecd782863d6145e1a7f300..0000000000000000000000000000000000000000
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diff --git a/tutorials/data/timezones_2021c/timezones_2021c.prj b/tutorials/data/timezones_2021c/timezones_2021c.prj
deleted file mode 100644
index f45cbadf0074d8b7b2669559a93bc50bb95f82d4..0000000000000000000000000000000000000000
--- a/tutorials/data/timezones_2021c/timezones_2021c.prj
+++ /dev/null
@@ -1 +0,0 @@
-GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
\ No newline at end of file
diff --git a/tutorials/data/timezones_2021c/timezones_2021c.shp b/tutorials/data/timezones_2021c/timezones_2021c.shp
deleted file mode 100644
index 5dbd64b964295a7689fb70646caf4db8f0745ddb..0000000000000000000000000000000000000000
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diff --git a/tutorials/data/timezones_2021c/timezones_2021c.shx b/tutorials/data/timezones_2021c/timezones_2021c.shx
deleted file mode 100644
index dda8f5adb453c5460d2c39d7741a2c62d71c2153..0000000000000000000000000000000000000000
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