From eb9f24fc3c9068a67cc33c5faf1dd2602de5c1ee Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 4 Apr 2023 14:46:58 +0200 Subject: [PATCH 01/21] Preparing release v1.1.1 --- nes/nc_projections/default_nes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index ab85ea1..71e9e7f 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -1939,7 +1939,7 @@ class Nes(object): # Missing to nan if np.ma.is_masked(data): # This operation is done because sometimes the missing value is lost during the calculation - data[data.mask] = np.nan + data = data.filled(np.nan) return data -- GitLab From c7a515633cc4f926fa08d77f814f71b50c5ae17e Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 4 Apr 2023 14:48:08 +0200 Subject: [PATCH 02/21] Preparing release v1.1.1 Bugfix while parsing masked data --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 0753213..63c2076 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,7 +1,7 @@ # NES CHANGELOG ### 1.1.1 -* Release date: 2023/03/30 +* Release date: 2023/04/04 * Changes and new features: * Sum of Nes objects ([#48](https://earth.bsc.es/gitlab/es/NES/-/issues/48)) * Write 2D string data to save variables from shapefiles after doing a spatial join ([#49](https://earth.bsc.es/gitlab/es/NES/-/issues/49)) -- GitLab From 5d8d94227f863cfa7792d448066d9c540dee4bf0 Mon Sep 17 00:00:00 2001 From: ctena Date: Wed, 5 Apr 2023 09:05:45 +0200 Subject: [PATCH 03/21] Hotfix about LCC grid_mapping name --- nes/load_nes.py | 2 +- nes/nc_projections/lcc_nes.py | 16 ++++++++++------ 2 files changed, 11 insertions(+), 7 deletions(-) diff --git a/nes/load_nes.py b/nes/load_nes.py index 2113b48..eb7e413 100644 --- a/nes/load_nes.py +++ b/nes/load_nes.py @@ -212,7 +212,7 @@ def __is_lcc(dataset): Indicated if the netCDF is a LCC one. """ - if 'Lambert_conformal' in dataset.variables.keys(): + if 'Lambert_Conformal' in dataset.variables.keys() or 'Lambert_conformal' in dataset.variables.keys(): return True else: return False diff --git a/nes/nc_projections/lcc_nes.py b/nes/nc_projections/lcc_nes.py index 34cb9cb..7738487 100644 --- a/nes/nc_projections/lcc_nes.py +++ b/nes/nc_projections/lcc_nes.py @@ -160,11 +160,11 @@ class LCCNes(Nes): } else: - if 'Lambert_conformal' in self.variables.keys(): + if 'Lambert_Conformal' in self.variables.keys(): + projection_data = self.variables['Lambert_Conformal'] + self.free_vars('Lambert_Conformal') + elif 'Lambert_conformal' in self.variables.keys(): projection_data = self.variables['Lambert_conformal'] - if not isinstance(projection_data['standard_parallel'], list): - projection_data['standard_parallel'] = [projection_data['standard_parallel'].split(', ')[0], - projection_data['standard_parallel'].split(', ')[1]] self.free_vars('Lambert_conformal') else: msg = 'There is no variable called Lambert_conformal, projection has not been defined.' @@ -172,6 +172,10 @@ class LCCNes(Nes): sys.stderr.flush() projection_data = None + if not isinstance(projection_data['standard_parallel'], list): + projection_data['standard_parallel'] = [projection_data['standard_parallel'].split(', ')[0], + projection_data['standard_parallel'].split(', ')[1]] + return projection_data def _create_dimensions(self, netcdf): @@ -407,7 +411,7 @@ class LCCNes(Nes): @staticmethod def _set_var_crs(var): """ - Set the grid_mapping to 'Lambert_conformal'. + Set the grid_mapping to 'Lambert_Conformal'. Parameters ---------- @@ -422,7 +426,7 @@ class LCCNes(Nes): def _create_metadata(self, netcdf): """ - Create the 'crs' variable for the lamber conformal grid_mapping. + Create the 'crs' variable for the lambert conformal grid_mapping. Parameters ---------- -- GitLab From 9287dd62fe8b5999a0dd85afcb73e5db121cba3b Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Wed, 5 Apr 2023 13:41:10 +0200 Subject: [PATCH 04/21] Implement projection across all types --- nes/methods/horizontal_interpolation.py | 1 + nes/methods/spatial_join.py | 18 ++- nes/nc_projections/default_nes.py | 34 ++++- nes/nc_projections/latlon_nes.py | 57 ++++++- nes/nc_projections/lcc_nes.py | 143 +++++++++--------- nes/nc_projections/mercator_nes.py | 99 ++++++------ nes/nc_projections/points_nes.py | 20 +++ nes/nc_projections/rotated_nes.py | 128 +++++++++------- .../1.1.Read_Write_Regular.ipynb | 79 +++++++--- .../1.2.Read_Write_Rotated.ipynb | 74 +++++++-- 10 files changed, 435 insertions(+), 218 deletions(-) diff --git a/nes/methods/horizontal_interpolation.py b/nes/methods/horizontal_interpolation.py index ec3840f..2fe40d9 100644 --- a/nes/methods/horizontal_interpolation.py +++ b/nes/methods/horizontal_interpolation.py @@ -742,6 +742,7 @@ def lon_lat_to_cartesian_ecef(lon, lat): lat : np.array Latitude values. """ + lla = pyproj.Proj(proj='latlong', ellps='WGS84', datum='WGS84') ecef = pyproj.Proj(proj='geocent', ellps='WGS84', datum='WGS84') diff --git a/nes/methods/spatial_join.py b/nes/methods/spatial_join.py index 33df091..20dcd06 100644 --- a/nes/methods/spatial_join.py +++ b/nes/methods/spatial_join.py @@ -26,6 +26,7 @@ def spatial_join(self, ext_shp, method=None, var_list=None, info=False): info : bool Indicates if you want to print the process info or no """ + if self.master and info: print("Starting spatial join") if isinstance(var_list, str): @@ -83,12 +84,14 @@ def prepare_external_shapefile(self, ext_shp, var_list, info=False): GeoDataFrame External shapefile """ + if isinstance(ext_shp, str): # Reading external shapefile if self.master and info: print("\tReading external shapefile") # ext_shp = gpd.read_file(ext_shp, include_fields=var_list, mask=self.shapefile.geometry) ext_shp = gpd.read_file(ext_shp, include_fields=var_list, bbox=get_bbox(self)) + else: msg = "WARNING!!! " msg += "External shapefile already read. If you pass the path to the shapefile instead of the opened shapefile " @@ -98,9 +101,11 @@ def prepare_external_shapefile(self, ext_shp, var_list, info=False): ext_shp.reset_index(inplace=True) if var_list is not None: ext_shp = ext_shp.loc[:, var_list + ['geometry']] + self.comm.Barrier() if self.master and info: print("\t\tReading external shapefile done!") + # Standardizing projection ext_shp = ext_shp.to_crs(self.shapefile.crs) @@ -122,8 +127,10 @@ def get_bbox(self): tuple Bounding box """ + bbox = (self.lon_bnds['data'].min(), self.lat_bnds['data'].min(), self.lon_bnds['data'].max(), self.lat_bnds['data'].max(), ) + return bbox @@ -139,10 +146,12 @@ def spatial_join_nearest(self, ext_shp, info=False): info : bool Indicates if you want to print the information """ + if self.master and info: print("\tNearest spatial joint") sys.stdout.flush() grid_shp = self.get_centroids_from_coordinates() + # From geodetic coordinates (e.g. 4326) to meters (e.g. 4328) to use sjoin_nearest # TODO: Check if the projection 4328 does not distort the coordinates too much # https://gis.stackexchange.com/questions/372564/ @@ -177,12 +186,14 @@ def spatial_join_centroid(self, ext_shp, info=False): info : bool Indicates if you want to print the information """ + if self.master and info: print("\tCentroid spatial join") sys.stdout.flush() if info and self.master: print("\t\tCalculating centroids") sys.stdout.flush() + # Get centroids grid_shp = self.get_centroids_from_coordinates() @@ -216,13 +227,14 @@ def spatial_join_intersection(self, ext_shp, info=False): info : bool Indicates if you want to print the information """ + var_list = list(ext_shp.columns) var_list.remove('geometry') grid_shp = self.shapefile - grid_shp['FID_grid'] = grid_shp.index grid_shp = grid_shp.reset_index() + # Get intersected areas # inp, res = ext_shp.sindex.query_bulk(grid_shp.geometry, predicate='intersects') inp, res = grid_shp.sindex.query_bulk(ext_shp.geometry, predicate='intersects') @@ -230,6 +242,7 @@ def spatial_join_intersection(self, ext_shp, info=False): if info: print('\t\tRank {0:03d}: {1} intersected areas found'.format(self.rank, len(inp))) sys.stdout.flush() + # Calculate intersected areas and fractions intersection = pd.DataFrame(columns=['FID', 'ext_shp_id', 'weight']) intersection['FID'] = np.array(grid_shp.loc[res, 'FID_grid'], dtype=np.uint32) @@ -270,9 +283,12 @@ def spatial_join_intersection(self, ext_shp, info=False): for var_name in var_list: self.shapefile.loc[intersection.index, var_name] = np.array( ext_shp.loc[intersection['ext_shp_id'], var_name]) + else: for var_name in var_list: self.shapefile.loc[:, var_name] = np.nan + for var_name in var_list: self.shapefile.loc[:, var_name] = np.array(self.shapefile.loc[:, var_name], dtype=ext_shp[var_name].dtype) + return None diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 71e9e7f..16b7e41 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -194,6 +194,9 @@ class Nes(object): # 75 is the standard value used in GHOST data self.strlen = 75 + # Set projection + self._create_projection(**kwargs) + else: if dataset is not None: @@ -249,6 +252,9 @@ class Nes(object): # Get string length self.strlen = self._get_strlen() + # Get projection + self._get_projection() + # Writing options self.zip_lvl = 0 @@ -800,7 +806,10 @@ 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): - + """ + Select a slice of time, lev, lat or lon given a minimum and maximum limits. + """ + loaded_vars = False for var_info in self.variables.values(): if var_info['data'] is not None: @@ -820,7 +829,7 @@ class Nes(object): else: self.hours_start = int((time_min - self._time[0]).total_seconds() // 3600) - # Last time Filter + # Last time filter if hours_end is not None: if time_max is not None: raise ValueError("Choose to select by hours_end or time_max but not both") @@ -841,7 +850,7 @@ class Nes(object): self.lon_min = lon_min self.lon_max = lon_max - # New Axis limits + # New axis limits self.read_axis_limits = self.get_read_axis_limits() # Dimensions screening @@ -854,6 +863,7 @@ class Nes(object): self.lat_bnds = self._get_coordinate_values(self._lat_bnds, 'Y', bounds=True) self.lon_bnds = self._get_coordinate_values(self._lon_bnds, 'X', bounds=True) + # Filter dimensions self.filter_coordinates_selection() # Removing complete coordinates @@ -862,6 +872,10 @@ class Nes(object): return None def filter_coordinates_selection(self): + """ + Use the selection limits to filter time, lev, lat, lon, lon_bnds and lat_bnds. + """ + idx = self.get_idx_intervals() self._time = self._time[idx['idx_t_min']:idx['idx_t_max']] @@ -896,6 +910,20 @@ class Nes(object): self.lon_min = None return None + + def _get_projection(self): + """ + Must be implemented on inner class. + """ + + return None + + def _create_projection(self, **kwargs): + """ + Must be implemented on inner class. + """ + + return None def get_idx_intervals(self): """ diff --git a/nes/nc_projections/latlon_nes.py b/nes/nc_projections/latlon_nes.py index 91b7dce..d2bd35b 100644 --- a/nes/nc_projections/latlon_nes.py +++ b/nes/nc_projections/latlon_nes.py @@ -1,6 +1,7 @@ #!/usr/bin/env python import numpy as np +from pyproj import Proj from .default_nes import Nes @@ -99,6 +100,53 @@ class LatLonNes(Nes): return new + def _get_pyproj_projection(self): + """ + Get projection data as in Pyproj library. + + Returns + ---------- + projection : pyproj.Proj + Grid projection. + """ + + projection = Proj(proj='latlong', + ellps='WGS84', + ) + + return projection + + def _get_projection(self): + """ + Get 'projection' and 'projection_data' from grid details. + """ + + if 'crs' in self.variables.keys(): + projection_data = self.variables['crs'] + self.free_vars('crs') + + self.projection_data = projection_data + self.projection = self._get_pyproj_projection() + + return None + + def _create_projection(self, **kwargs): + """ + Create 'projection' and 'projection_data' from projection arguments. + """ + + projection_data = {'data': None, + 'dimensions': (), + 'grid_mapping_name': 'latitude_longitude', + 'semi_major_axis': str(6371000.0), + 'inverse_flattening': str(0), + } + + self.projection_data = projection_data + self.projection = self._get_pyproj_projection() + + return None + def _create_dimensions(self, netcdf): """ Create 'spatial_nv' dimensions and the super dimensions 'lev', 'time', 'time_nv', 'lon' and 'lat'. @@ -245,10 +293,11 @@ class LatLonNes(Nes): netcdf4-python Dataset. """ - mapping = netcdf.createVariable('crs', 'i') - mapping.grid_mapping_name = "latitude_longitude" - mapping.semi_major_axis = 6371000.0 - mapping.inverse_flattening = 0 + if self.projection_data is not None: + mapping = netcdf.createVariable('crs', 'i') + mapping.grid_mapping_name = self.projection_data['grid_mapping_name'] + mapping.semi_major_axis = self.projection_data['semi_major_axis'] + mapping.inverse_flattening = self.projection_data['inverse_flattening'] return None diff --git a/nes/nc_projections/lcc_nes.py b/nes/nc_projections/lcc_nes.py index 34cb9cb..2f5ed32 100644 --- a/nes/nc_projections/lcc_nes.py +++ b/nes/nc_projections/lcc_nes.py @@ -76,14 +76,11 @@ class LCCNes(Nes): # Complete dimensions self._y = self._get_coordinate_dimension('y') self._x = self._get_coordinate_dimension('x') - + # Dimensions screening self.y = self._get_coordinate_values(self._y, 'Y') self.x = self._get_coordinate_values(self._x, 'X') - - # Get projection details - self.projection_data = self.get_projection_data(create_nes, **kwargs) - + # Set axis limits for parallel writing self.write_axis_limits = self.get_write_axis_limits() @@ -127,6 +124,9 @@ class LCCNes(Nes): return new def filter_coordinates_selection(self): + """ + Use the selection limits to filter y, x, time, lev, lat, lon, lon_bnds and lat_bnds. + """ idx = self.get_idx_intervals() @@ -140,39 +140,77 @@ class LCCNes(Nes): return None - def get_projection_data(self, create_nes, **kwargs): + def _get_pyproj_projection(self): """ - Read the projection data. + Get projection data as in Pyproj library. Returns - ------- - projection_data : dict - Dictionary with the projection data. + ---------- + projection : pyproj.Proj + Grid projection. """ - if create_nes: - projection_data = {'data': None, - 'dimensions': (), - 'grid_mapping_name': 'lambert_conformal_conic', - 'standard_parallel': [str(kwargs['lat_1']), str(kwargs['lat_2'])], - 'longitude_of_central_meridian': str(kwargs['lon_0']), - 'latitude_of_projection_origin': str(kwargs['lat_0']), - } + projection = Proj(proj='lcc', + ellps='WGS84', + R=self.earth_radius[0], + lat_1=np.float64(self.projection_data['standard_parallel'][0]), + lat_2=np.float64(self.projection_data['standard_parallel'][1]), + lon_0=np.float64(self.projection_data['longitude_of_central_meridian']), + lat_0=np.float64(self.projection_data['latitude_of_projection_origin']), + to_meter=1, + x_0=0, + y_0=0, + a=self.earth_radius[1], + k_0=1.0, + ) + + return projection + + def _get_projection(self): + """ + Get 'projection' and 'projection_data' from grid details. + """ + if 'Lambert_Conformal' in self.variables.keys(): + projection_data = self.variables['Lambert_Conformal'] + self.free_vars('Lambert_Conformal') + + elif 'Lambert_conformal' in self.variables.keys(): + projection_data = self.variables['Lambert_conformal'] + self.free_vars('Lambert_conformal') + else: - if 'Lambert_conformal' in self.variables.keys(): - projection_data = self.variables['Lambert_conformal'] - if not isinstance(projection_data['standard_parallel'], list): - projection_data['standard_parallel'] = [projection_data['standard_parallel'].split(', ')[0], - projection_data['standard_parallel'].split(', ')[1]] - self.free_vars('Lambert_conformal') - else: - msg = 'There is no variable called Lambert_conformal, projection has not been defined.' - warnings.warn(msg) - sys.stderr.flush() - projection_data = None - - return projection_data + # We will never have this condition since the LCC grid will never be correctly detected + # since the function __is_lcc in load_nes only detects LCC grids when there is Lambert_conformal + msg = 'There is no variable called Lambert_conformal, projection has not been defined.' + raise RuntimeError(msg) + + if not isinstance(self.projection_data['standard_parallel'], list): + projection_data['standard_parallel'] = [projection_data['standard_parallel'].split(', ')[0], + projection_data['standard_parallel'].split(', ')[1]] + + self.projection_data = projection_data + self.projection = self._get_pyproj_projection() + + return None + + def _create_projection(self, **kwargs): + """ + Create 'projection' and 'projection_data' from projection arguments. + """ + + projection_data = {'data': None, + 'dimensions': (), + 'grid_mapping_name': 'lambert_conformal_conic', + 'standard_parallel': [str(kwargs['lat_1']), str(kwargs['lat_2'])], + 'longitude_of_central_meridian': str(kwargs['lon_0']), + 'latitude_of_projection_origin': str(kwargs['lat_0']), + } + + self.projection_data = projection_data + self.projection = self._get_pyproj_projection() + + return None def _create_dimensions(self, netcdf): """ @@ -258,21 +296,6 @@ class LCCNes(Nes): x = np.array([self._x['data']] * len(self._y['data'])) y = np.array([self._y['data']] * len(self._x['data'])).T - self.projection = Proj( - proj='lcc', - ellps='WGS84', - R=self.earth_radius[0], - lat_1=kwargs['lat_1'], - lat_2=kwargs['lat_2'], - lon_0=kwargs['lon_0'], - lat_0=kwargs['lat_0'], - to_meter=1, - x_0=0, - y_0=0, - a=self.earth_radius[1], - k_0=1.0 - ) - # Calculate centre latitudes and longitudes (UTM to LCC) centre_lon, centre_lat = self.projection(x, y, inverse=True) @@ -333,20 +356,6 @@ class LCCNes(Nes): x_grid_edge = np.concatenate((left_edge_x, top_edge_x, right_edge_x, bottom_edge_x)) # Get edges for regular coordinates - self.projection = Proj( - proj='lcc', - ellps='WGS84', - R=self.earth_radius[0], - lat_1=float(self.projection_data['standard_parallel'][0]), - lat_2=float(self.projection_data['standard_parallel'][1]), - lon_0=float(self.projection_data['longitude_of_central_meridian']), - lat_0=float(self.projection_data['latitude_of_projection_origin']), - to_meter=1, - x_0=0, - y_0=0, - a=self.earth_radius[1], - k_0=1.0 - ) grid_edge_lon_data, grid_edge_lat_data = self.projection(x_grid_edge, y_grid_edge, inverse=True) # Create grid outline by stacking the edges in both coordinates @@ -371,20 +380,6 @@ class LCCNes(Nes): inc_y, spatial_nv=4, inverse=True) # Transform LCC bounds to regular bounds - self.projection = Proj( - proj='lcc', - ellps='WGS84', - R=self.earth_radius[0], - lat_1=float(self.projection_data['standard_parallel'][0]), - lat_2=float(self.projection_data['standard_parallel'][1]), - lon_0=float(self.projection_data['longitude_of_central_meridian']), - lat_0=float(self.projection_data['latitude_of_projection_origin']), - to_meter=1, - x_0=0, - y_0=0, - a=self.earth_radius[1], - k_0=1.0 - ) lon_bnds, lat_bnds = self.projection(x_bnds, y_bnds, inverse=True) # Obtain regular coordinates bounds diff --git a/nes/nc_projections/mercator_nes.py b/nes/nc_projections/mercator_nes.py index 249c0bc..3846da1 100644 --- a/nes/nc_projections/mercator_nes.py +++ b/nes/nc_projections/mercator_nes.py @@ -81,9 +81,6 @@ class MercatorNes(Nes): self.y = self._get_coordinate_values(self._y, 'Y') self.x = self._get_coordinate_values(self._x, 'X') - # Get projection details - self.projection_data = self.get_projection_data(create_nes, **kwargs) - # Set axis limits for parallel writing self.write_axis_limits = self.get_write_axis_limits() @@ -127,6 +124,9 @@ class MercatorNes(Nes): return new def filter_coordinates_selection(self): + """ + Use the selection limits to filter y, x, time, lev, lat, lon, lon_bnds and lat_bnds. + """ idx = self.get_idx_intervals() @@ -140,35 +140,59 @@ class MercatorNes(Nes): return None - def get_projection_data(self, create_nes, **kwargs): + def _get_pyproj_projection(self): """ - Read the projection data. + Get projection data as in Pyproj library. Returns - ------- - projection _data: dict - Dictionary with the projection data. + ---------- + projection : pyproj.Proj + Grid projection. """ - if create_nes: - projection_data = {'data': None, - 'dimensions': (), - 'grid_mapping_name': 'mercator', - 'standard_parallel': [kwargs['lat_ts']], # TODO: Check if True - 'longitude_of_projection_origin': kwargs['lon_0'], - } + projection = Proj(proj='merc', + a=self.earth_radius[1], + b=self.earth_radius[0], + lat_ts=np.float64(self.projection_data['standard_parallel'][0]), + lon_0=np.float64(self.projection_data['longitude_of_projection_origin']), + ) + + return projection + + def _get_projection(self): + """ + Get 'projection' and 'projection_data' from grid details. + """ + + if 'mercator' in self.variables.keys(): + projection_data = self.variables['mercator'] + self.free_vars('mercator') else: - if 'mercator' in self.variables.keys(): - projection_data = self.variables['mercator'] - self.free_vars('mercator') - else: - msg = 'There is no variable called mercator, projection has not been defined.' - warnings.warn(msg) - sys.stderr.flush() - projection_data = None + msg = 'There is no variable called mercator, projection has not been defined.' + raise RuntimeError(msg) - return projection_data + self.projection_data = projection_data + self.projection = self._get_pyproj_projection() + + return None + + def _create_projection(self, **kwargs): + """ + Create 'projection' and 'projection_data' from projection arguments. + """ + + projection_data = {'data': None, + 'dimensions': (), + 'grid_mapping_name': 'mercator', + 'standard_parallel': [kwargs['lat_ts']], # TODO: Check if True + 'longitude_of_projection_origin': kwargs['lon_0'], + } + + self.projection_data = projection_data + self.projection = self._get_pyproj_projection() + + return None def _create_dimensions(self, netcdf): """ @@ -233,11 +257,6 @@ class MercatorNes(Nes): def _create_centre_coordinates(self, **kwargs): """ Calculate centre latitudes and longitudes from grid details. - - Parameters - ---------- - netcdf : Dataset - NetCDF object. """ # Create a regular grid in metres (1D) @@ -256,14 +275,6 @@ class MercatorNes(Nes): x = np.array([self._x['data']] * len(self._y['data'])) y = np.array([self._y['data']] * len(self._x['data'])).T - self.projection = Proj( - proj='merc', - a=self.earth_radius[1], - b=self.earth_radius[0], - lat_ts=kwargs['lat_ts'], - lon_0=kwargs['lon_0'], - ) - # Calculate centre latitudes and longitudes (UTM to Mercator) centre_lon, centre_lat = self.projection(x, y, inverse=True) @@ -324,13 +335,6 @@ class MercatorNes(Nes): x_grid_edge = np.concatenate((left_edge_x, top_edge_x, right_edge_x, bottom_edge_x)) # Get edges for regular coordinates - self.projection = Proj( - proj='merc', - a=self.earth_radius[1], - b=self.earth_radius[0], - lat_ts=float(self.projection_data['standard_parallel'][0]), - lon_0=float(self.projection_data['longitude_of_projection_origin']), - ) grid_edge_lon_data, grid_edge_lat_data = self.projection(x_grid_edge, y_grid_edge, inverse=True) # Create grid outline by stacking the edges in both coordinates @@ -355,13 +359,6 @@ class MercatorNes(Nes): inc_y, spatial_nv=4, inverse=True) # Transform Mercator bounds to regular bounds - self.projection = Proj( - proj='merc', - a=self.earth_radius[1], - b=self.earth_radius[0], - lat_ts=float(self.projection_data['standard_parallel'][0]), - lon_0=float(self.projection_data['longitude_of_projection_origin']), - ) lon_bnds, lat_bnds = self.projection(x_bnds, y_bnds, inverse=True) # Obtain regular coordinates bounds diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index 756fe3c..41a8ce8 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -114,6 +114,26 @@ class PointsNes(Nes): return new + def _get_projection(self): + """ + Get 'projection' and 'projection_data' from grid details. + """ + + self.projection_data = None + self.projection = None + + return None + + def _create_projection(self, **kwargs): + """ + Create 'projection' and 'projection_data' from projection arguments. + """ + + self.projection_data = None + self.projection = None + + return None + def _create_dimensions(self, netcdf): """ Create 'time', 'time_nv', 'station' and 'strlen' dimensions. diff --git a/nes/nc_projections/rotated_nes.py b/nes/nc_projections/rotated_nes.py index ec62611..8470541 100644 --- a/nes/nc_projections/rotated_nes.py +++ b/nes/nc_projections/rotated_nes.py @@ -6,6 +6,7 @@ import numpy as np import pandas as pd import math from cfunits import Units +from pyproj import Proj from copy import deepcopy import geopandas as gpd from shapely.geometry import Polygon, Point @@ -85,9 +86,6 @@ class RotatedNes(Nes): self.rlat = self._get_coordinate_values(self._rlat, 'Y') self.rlon = self._get_coordinate_values(self._rlon, 'X') - # Get projection details - self.projection_data = self.get_projection_data(create_nes, **kwargs) - # Set axis limits for parallel writing self.write_axis_limits = self.get_write_axis_limits() @@ -128,6 +126,9 @@ class RotatedNes(Nes): return new def filter_coordinates_selection(self): + """ + Use the selection limits to filter rlat, rlon, time, lev, lat, lon, lon_bnds and lat_bnds. + """ idx = self.get_idx_intervals() @@ -141,34 +142,59 @@ class RotatedNes(Nes): return None - def get_projection_data(self, create_nes, **kwargs): + def _get_pyproj_projection(self): """ - Read the projection data. + Get projection data as in Pyproj library. Returns - ------- - projection_data : dict - Dictionary with the projection data. + ---------- + projection : pyproj.Proj + Grid projection. """ - if create_nes: - projection_data = {'data': None, - 'dimensions': (), - 'grid_mapping_name': 'rotated_latitude_longitude', - 'grid_north_pole_latitude': 90 - kwargs['centre_lat'], - 'grid_north_pole_longitude': -180 + kwargs['centre_lon'], - } + projection = Proj(proj='ob_tran', + o_proj="longlat", + ellps='WGS84', + R=self.earth_radius[0], + o_lat_p=np.float64(self.projection_data['grid_north_pole_latitude']), + o_lon_p=np.float64(self.projection_data['grid_north_pole_longitude']), + ) + + return projection + + def _get_projection(self): + """ + Get 'projection' and 'projection_data' from grid details. + """ + + if 'rotated_pole' in self.variables.keys(): + projection_data = self.variables['rotated_pole'] + self.free_vars('rotated_pole') else: - if 'rotated_pole' in self.variables.keys(): - projection_data = self.variables['rotated_pole'] - self.free_vars('rotated_pole') - else: - msg = 'There is no variable called rotated_pole, projection has not been defined.' - warnings.warn(msg) - sys.stderr.flush() - projection_data = None - - return projection_data + msg = 'There is no variable called rotated_pole, projection has not been defined.' + raise RuntimeError(msg) + + self.projection_data = projection_data + self.projection = self._get_pyproj_projection() + + return None + + def _create_projection(self, **kwargs): + """ + Create 'projection' and 'projection_data' from projection arguments. + """ + + projection_data = {'data': None, + 'dimensions': (), + 'grid_mapping_name': 'rotated_latitude_longitude', + 'grid_north_pole_latitude': 90 - kwargs['centre_lat'], + 'grid_north_pole_longitude': -180 + kwargs['centre_lon'], + } + + self.projection_data = projection_data + self.projection = self._get_pyproj_projection() + + return None def _create_dimensions(self, netcdf): """ @@ -235,10 +261,12 @@ class RotatedNes(Nes): """ Calculate rotated latitudes and longitudes from grid details. - Parameters + Returns ---------- - netcdf : Dataset - NetCDF object. + _rlat : dict + Rotated latitudes dictionary with the complete 'data' key for all the values and the rest of the attributes. + _rlon : dict + Rotated longitudes dictionary with the complete 'data' key for all the values and the rest of the attributes. """ # Calculate rotated latitudes @@ -257,51 +285,49 @@ class RotatedNes(Nes): """ Calculate the unrotated coordinates using the rotated ones. - :param lon_deg: Rotated longitude coordinate. - :type lon_deg: numpy.array - - :param lat_deg: Rotated latitude coordinate. - :type lat_deg: numpy.array - - :param lon_min: Minimum value for the longitudes: -180 (-180 to 180) or 0 (0 to 360). - :type lon_min: float + Parameters + ---------- + lon_deg : numpy.array + Rotated longitude coordinate. + lat_deg : numpy.array + Rotated latitude coordinate. + lon_min : float + Minimum value for the longitudes: -180 (-180 to 180) or 0 (0 to 360). - :return: Unrotated coordinates. Longitudes, Latitudes. - :rtype: tuple(numpy.array, numpy.array) + Returns + ---------- + almd : numpy.array + Unrotated longitudes. + aphd : numpy.array + Unrotated latitudes. """ - if 'centre_lat' in kwargs: - centre_lat = kwargs['centre_lat'] - else: - centre_lat = 90 - float(self.projection_data['grid_north_pole_latitude']) - - if 'centre_lon' in kwargs: - centre_lon = kwargs['centre_lon'] - else: - centre_lon = float(self.projection_data['grid_north_pole_longitude']) + 180 + # Get centre coordinates + centre_lat = 90 - np.float64(self.projection_data['grid_north_pole_latitude']) + centre_lon = np.float64(self.projection_data['grid_north_pole_longitude']) + 180 + # Convert to radians degrees_to_radians = math.pi / 180. - tph0 = centre_lat * degrees_to_radians tlm = lon_deg * degrees_to_radians tph = lat_deg * degrees_to_radians + tlm0d = -180 + centre_lon ctph0 = np.cos(tph0) stph0 = np.sin(tph0) - stlm = np.sin(tlm) ctlm = np.cos(tlm) stph = np.sin(tph) ctph = np.cos(tph) - # LATITUDES + # Calculate unrotated latitudes sph = (ctph0 * stph) + (stph0 * ctph * ctlm) sph[sph > 1.] = 1. sph[sph < -1.] = -1. aph = np.arcsin(sph) aphd = aph / degrees_to_radians - # LONGITUDES + # Calculate rotated longitudes anum = ctph * stlm denom = (ctlm * ctph - stph0 * sph) / ctph0 relm = np.arctan2(anum, denom) - math.pi diff --git a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb index 06542ff..c2b54e6 100644 --- a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb +++ b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb @@ -36,16 +36,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -55,10 +55,53 @@ "nessy_1" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'dimensions': (),\n", + " 'grid_mapping_name': 'latitude_longitude',\n", + " 'semi_major_axis': 6371000.0,\n", + " 'inverse_flattening': 0}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_1.projection_data" + ] + }, { "cell_type": "code", "execution_count": 5, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Proj('+proj=longlat +ellps=WGS84 +no_defs', preserve_units=True)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_1.projection" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { @@ -90,7 +133,7 @@ " datetime.datetime(2019, 1, 2, 0, 0)]" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -101,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -115,7 +158,7 @@ " 'positive': 'up'}" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -126,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -154,7 +197,7 @@ " 'standard_name': 'latitude'}" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -165,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -235,7 +278,7 @@ " 'standard_name': 'longitude'}" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -246,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -255,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -273,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -380,7 +423,7 @@ " 'coordinates': 'lat lon'}}" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -398,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -431,16 +474,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb index ccb839d..4004378 100644 --- a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb +++ b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -63,7 +63,10 @@ { "data": { "text/plain": [ - "[datetime.datetime(2021, 8, 3, 0, 0)]" + "{'dimensions': (),\n", + " 'grid_mapping_name': 'rotated_latitude_longitude',\n", + " 'grid_north_pole_latitude': 39.0,\n", + " 'grid_north_pole_longitude': -170.0}" ] }, "execution_count": 4, @@ -72,13 +75,53 @@ } ], "source": [ - "nessy_1.time" + "nessy_1.projection_data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Proj('+proj=ob_tran +o_proj=longlat +ellps=WGS84 +R=6356752.3142 +o_lat_p=39.0 +o_lon_p=-170.0', preserve_units=True)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_1.projection" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[datetime.datetime(2021, 8, 3, 0, 0)]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_1.time" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, "outputs": [ { "data": { @@ -93,7 +136,7 @@ " 'positive': 'up'}" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -104,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -133,7 +176,7 @@ " 'standard_name': 'latitude'}" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -144,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -173,7 +216,7 @@ " 'standard_name': 'longitude'}" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -184,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -202,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -305,7 +348,7 @@ " 'grid_mapping': 'rotated_pole'}}" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -323,7 +366,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -333,7 +376,6 @@ "Rank 000: Creating rotated_file_1.nc\n", "Rank 000: NetCDF ready to write\n", "Rank 000: Dimensions done\n", - "Rank 000: Cell measures done\n", "Rank 000: Writing O3_all var (1/1)\n", "Rank 000: Var O3_all created (1/1)\n", "Rank 000: Filling O3_all)\n", @@ -357,16 +399,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } -- GitLab From ec9c263873e10c4b04c02ba64edc64e2ce486506 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Wed, 5 Apr 2023 14:10:08 +0200 Subject: [PATCH 05/21] Fix bugson projections --- nes/create_nes.py | 2 +- nes/nc_projections/default_nes.py | 12 ++++++------ nes/nc_projections/latlon_nes.py | 2 +- nes/nc_projections/lcc_nes.py | 2 +- nes/nc_projections/mercator_nes.py | 4 ++-- 5 files changed, 11 insertions(+), 11 deletions(-) diff --git a/nes/create_nes.py b/nes/create_nes.py index cfb0205..c10dd67 100644 --- a/nes/create_nes.py +++ b/nes/create_nes.py @@ -95,7 +95,7 @@ def create_nes(comm=None, info=False, projection=None, parallel_method='Y', bala nessy = PointsNes(comm=comm, dataset=None, xarray=False, info=info, parallel_method=parallel_method, avoid_first_hours=avoid_first_hours, avoid_last_hours=avoid_last_hours, first_level=first_level, last_level=last_level, balanced=balanced, - create_nes=True, strlen=strlen, times=times, **kwargs) + create_nes=True, times=times, **kwargs) elif projection in ['regular', 'global']: nessy = LatLonNes(comm=comm, dataset=None, xarray=False, info=info, parallel_method=parallel_method, avoid_first_hours=avoid_first_hours, avoid_last_hours=avoid_last_hours, diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 16b7e41..887503b 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -166,6 +166,9 @@ class Nes(object): # Initialize variables self.variables = {} + # Set projection + self._create_projection(**kwargs) + # Complete dimensions self._time = times self._time_bnds = self.__get_time_bnds(create_nes) @@ -194,9 +197,6 @@ class Nes(object): # 75 is the standard value used in GHOST data self.strlen = 75 - # Set projection - self._create_projection(**kwargs) - else: if dataset is not None: @@ -217,6 +217,9 @@ class Nes(object): # Lazy variables self.variables = self._get_lazy_variables() + # Get projection + self._get_projection() + # Complete dimensions self._time = self.__get_time() self._time_bnds = self.__get_time_bnds() @@ -252,9 +255,6 @@ class Nes(object): # Get string length self.strlen = self._get_strlen() - # Get projection - self._get_projection() - # Writing options self.zip_lvl = 0 diff --git a/nes/nc_projections/latlon_nes.py b/nes/nc_projections/latlon_nes.py index d2bd35b..8f77a6b 100644 --- a/nes/nc_projections/latlon_nes.py +++ b/nes/nc_projections/latlon_nes.py @@ -138,7 +138,7 @@ class LatLonNes(Nes): projection_data = {'data': None, 'dimensions': (), 'grid_mapping_name': 'latitude_longitude', - 'semi_major_axis': str(6371000.0), + 'semi_major_axis': str(self.earth_radius[1]), 'inverse_flattening': str(0), } diff --git a/nes/nc_projections/lcc_nes.py b/nes/nc_projections/lcc_nes.py index 757e683..40c9844 100644 --- a/nes/nc_projections/lcc_nes.py +++ b/nes/nc_projections/lcc_nes.py @@ -185,7 +185,7 @@ class LCCNes(Nes): msg = 'There is no variable called Lambert_Conformal, projection has not been defined.' raise RuntimeError(msg) - if not isinstance(self.projection_data['standard_parallel'], list): + if not isinstance(projection_data['standard_parallel'], list): projection_data['standard_parallel'] = [projection_data['standard_parallel'].split(', ')[0], projection_data['standard_parallel'].split(', ')[1]] diff --git a/nes/nc_projections/mercator_nes.py b/nes/nc_projections/mercator_nes.py index 3846da1..bc20d97 100644 --- a/nes/nc_projections/mercator_nes.py +++ b/nes/nc_projections/mercator_nes.py @@ -153,7 +153,7 @@ class MercatorNes(Nes): projection = Proj(proj='merc', a=self.earth_radius[1], b=self.earth_radius[0], - lat_ts=np.float64(self.projection_data['standard_parallel'][0]), + lat_ts=np.float64(self.projection_data['standard_parallel']), lon_0=np.float64(self.projection_data['longitude_of_projection_origin']), ) @@ -185,7 +185,7 @@ class MercatorNes(Nes): projection_data = {'data': None, 'dimensions': (), 'grid_mapping_name': 'mercator', - 'standard_parallel': [kwargs['lat_ts']], # TODO: Check if True + 'standard_parallel': str(kwargs['lat_ts']), # TODO: Check if True 'longitude_of_projection_origin': kwargs['lon_0'], } -- GitLab From 9d2fa9975ccbc9afa8827cbbfcda6f00b3539b5d Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Wed, 5 Apr 2023 14:12:20 +0200 Subject: [PATCH 06/21] Add projections to tutorials --- .../1.1.Read_Write_Regular.ipynb | 18 ++-- .../1.2.Read_Write_Rotated.ipynb | 18 ++-- .../1.Introduction/1.4.Read_Write_LCC.ipynb | 86 ++++++++++++++----- .../1.5.Read_Write_Mercator.ipynb | 78 +++++++++++++---- tutorials/2.Creation/2.1.Create_Regular.ipynb | 53 ++++++++++-- tutorials/2.Creation/2.2.Create_Rotated.ipynb | 53 ++++++++++-- tutorials/2.Creation/2.6.Create-LCC.ipynb | 54 ++++++++++-- .../2.Creation/2.7.Create_Mercator.ipynb | 53 ++++++++++-- tutorials/2.Creation/2.8.Create_Global.ipynb | 53 ++++++++++-- 9 files changed, 384 insertions(+), 82 deletions(-) diff --git a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb index c2b54e6..2b3f582 100644 --- a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb +++ b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -63,10 +63,7 @@ { "data": { "text/plain": [ - "{'dimensions': (),\n", - " 'grid_mapping_name': 'latitude_longitude',\n", - " 'semi_major_axis': 6371000.0,\n", - " 'inverse_flattening': 0}" + "Proj('+proj=longlat +ellps=WGS84 +no_defs', preserve_units=True)" ] }, "execution_count": 4, @@ -75,7 +72,7 @@ } ], "source": [ - "nessy_1.projection_data" + "nessy_1.projection" ] }, { @@ -86,7 +83,10 @@ { "data": { "text/plain": [ - "Proj('+proj=longlat +ellps=WGS84 +no_defs', preserve_units=True)" + "{'dimensions': (),\n", + " 'grid_mapping_name': 'latitude_longitude',\n", + " 'semi_major_axis': 6371000.0,\n", + " 'inverse_flattening': 0}" ] }, "execution_count": 5, @@ -95,7 +95,7 @@ } ], "source": [ - "nessy_1.projection" + "nessy_1.projection_data" ] }, { @@ -480,7 +480,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, diff --git a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb index 4004378..7ebecba 100644 --- a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb +++ b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -63,10 +63,7 @@ { "data": { "text/plain": [ - "{'dimensions': (),\n", - " 'grid_mapping_name': 'rotated_latitude_longitude',\n", - " 'grid_north_pole_latitude': 39.0,\n", - " 'grid_north_pole_longitude': -170.0}" + "Proj('+proj=ob_tran +o_proj=longlat +ellps=WGS84 +R=6356752.3142 +o_lat_p=39.0 +o_lon_p=-170.0', preserve_units=True)" ] }, "execution_count": 4, @@ -75,7 +72,7 @@ } ], "source": [ - "nessy_1.projection_data" + "nessy_1.projection" ] }, { @@ -86,7 +83,10 @@ { "data": { "text/plain": [ - "Proj('+proj=ob_tran +o_proj=longlat +ellps=WGS84 +R=6356752.3142 +o_lat_p=39.0 +o_lon_p=-170.0', preserve_units=True)" + "{'dimensions': (),\n", + " 'grid_mapping_name': 'rotated_latitude_longitude',\n", + " 'grid_north_pole_latitude': 39.0,\n", + " 'grid_north_pole_longitude': -170.0}" ] }, "execution_count": 5, @@ -95,7 +95,7 @@ } ], "source": [ - "nessy_1.projection" + "nessy_1.projection_data" ] }, { @@ -405,7 +405,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, diff --git a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb index 8baf84b..4f29a7f 100644 --- a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb +++ b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb @@ -36,16 +36,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -55,6 +55,26 @@ "nessy_1" ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Proj('+proj=lcc +lat_0=40 +lon_0=-3 +lat_1=43 +lat_2=37 +x_0=0 +y_0=0 +R=6356752.3142 +units=m +no_defs', preserve_units=True)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_1.projection" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -63,7 +83,11 @@ { "data": { "text/plain": [ - "[datetime.datetime(2022, 11, 15, 0, 0), datetime.datetime(2022, 11, 16, 0, 0)]" + "{'dimensions': (),\n", + " 'grid_mapping_name': 'lambert_conformal_conic',\n", + " 'standard_parallel': ['43.', '37.'],\n", + " 'longitude_of_central_meridian': '-3',\n", + " 'latitude_of_projection_origin': '40'}" ] }, "execution_count": 5, @@ -72,13 +96,33 @@ } ], "source": [ - "nessy_1.time" + "nessy_1.projection_data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[datetime.datetime(2022, 11, 15, 0, 0), datetime.datetime(2022, 11, 16, 0, 0)]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_1.time" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, "outputs": [ { "data": { @@ -91,7 +135,7 @@ " 'positive': 'up'}" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -102,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -249,7 +293,7 @@ " 'standard_name': 'projection_x_coordinate'}" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -260,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -407,7 +451,7 @@ " 'standard_name': 'projection_y_coordinate'}" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -418,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -447,7 +491,7 @@ " 'standard_name': 'latitude'}" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -458,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -487,7 +531,7 @@ " 'standard_name': 'longitude'}" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -498,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -516,7 +560,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -561,7 +605,7 @@ " 'coordinates': 'lat lon'}}" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -579,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -610,16 +654,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb index 47357c7..8455cda 100644 --- a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb +++ b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -63,7 +63,7 @@ { "data": { "text/plain": [ - "[datetime.datetime(1996, 12, 31, 0, 0)]" + "Proj('+proj=merc +lat_ts=-1.5 +lon_0=-18 +x_0=0 +y_0=0 +a=6378137 +b=6356752.3142 +units=m +no_defs', preserve_units=True)" ] }, "execution_count": 4, @@ -72,13 +72,56 @@ } ], "source": [ - "nessy_1.time" + "nessy_1.projection" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'dimensions': (),\n", + " 'grid_mapping_name': 'mercator',\n", + " 'standard_parallel': -1.5,\n", + " 'longitude_of_projection_origin': -18.0}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_1.projection_data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[datetime.datetime(1996, 12, 31, 0, 0)]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_1.time" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, "outputs": [ { "data": { @@ -91,7 +134,7 @@ " 'positive': 'up'}" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -102,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -186,7 +229,7 @@ " 'standard_name': 'projection_x_coordinate'}" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -197,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -259,7 +302,7 @@ " 'standard_name': 'projection_y_coordinate'}" ] }, - "execution_count": 7, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -270,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -299,7 +342,7 @@ " 'standard_name': 'latitude'}" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -310,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -339,7 +382,7 @@ " 'standard_name': 'longitude'}" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -357,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -366,8 +409,7 @@ "text": [ "Rank 000: Creating mercator_file_1.nc\n", "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n", - "Rank 000: Cell measures done\n" + "Rank 000: Dimensions done\n" ] } ], @@ -384,16 +426,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/2.Creation/2.1.Create_Regular.ipynb b/tutorials/2.Creation/2.1.Create_Regular.ipynb index 8c0c6e0..07581e2 100644 --- a/tutorials/2.Creation/2.1.Create_Regular.ipynb +++ b/tutorials/2.Creation/2.1.Create_Regular.ipynb @@ -44,8 +44,7 @@ "text": [ "Rank 000: Creating regular_grid.nc\n", "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n", - "Rank 000: Cell measures done\n" + "Rank 000: Dimensions done\n" ] } ], @@ -57,6 +56,50 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Proj('+proj=longlat +ellps=WGS84 +no_defs', preserve_units=True)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regular_grid.projection" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': None,\n", + " 'dimensions': (),\n", + " 'grid_mapping_name': 'latitude_longitude',\n", + " 'semi_major_axis': '6378137.0',\n", + " 'inverse_flattening': '0'}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regular_grid.projection_data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { @@ -65,7 +108,7 @@ " 42.05])}" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -76,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -85,7 +128,7 @@ "{'data': array([1.85, 1.95, 2.05, 2.15, 2.25, 2.35, 2.45, 2.55, 2.65, 2.75])}" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/2.Creation/2.2.Create_Rotated.ipynb b/tutorials/2.Creation/2.2.Create_Rotated.ipynb index 63a85b6..bbb2c07 100644 --- a/tutorials/2.Creation/2.2.Create_Rotated.ipynb +++ b/tutorials/2.Creation/2.2.Create_Rotated.ipynb @@ -45,8 +45,7 @@ "text": [ "Rank 000: Creating rotated_grid.nc\n", "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n", - "Rank 000: Cell measures done\n" + "Rank 000: Dimensions done\n" ] } ], @@ -58,6 +57,50 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Proj('+proj=ob_tran +o_proj=longlat +ellps=WGS84 +R=6356752.3142 +o_lat_p=39.0 +o_lon_p=-170.0', preserve_units=True)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rotated_grid.projection" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': None,\n", + " 'dimensions': (),\n", + " 'grid_mapping_name': 'rotated_latitude_longitude',\n", + " 'grid_north_pole_latitude': 39,\n", + " 'grid_north_pole_longitude': -170}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rotated_grid.projection_data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { @@ -95,7 +138,7 @@ " 27. ])}" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -106,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -153,7 +196,7 @@ " 33.4, 33.6, 33.8, 34. , 34.2, 34.4, 34.6, 34.8, 35. ])}" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/2.Creation/2.6.Create-LCC.ipynb b/tutorials/2.Creation/2.6.Create-LCC.ipynb index 3c1e675..ea8bfa1 100644 --- a/tutorials/2.Creation/2.6.Create-LCC.ipynb +++ b/tutorials/2.Creation/2.6.Create-LCC.ipynb @@ -48,8 +48,7 @@ "text": [ "Rank 000: Creating lcc_grid.nc\n", "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n", - "Rank 000: Cell measures done\n" + "Rank 000: Dimensions done\n" ] } ], @@ -61,6 +60,51 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Proj('+proj=lcc +lat_0=40 +lon_0=-3 +lat_1=37 +lat_2=43 +x_0=0 +y_0=0 +R=6356752.3142 +units=m +no_defs', preserve_units=True)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lcc_grid.projection" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': None,\n", + " 'dimensions': (),\n", + " 'grid_mapping_name': 'lambert_conformal_conic',\n", + " 'standard_parallel': ['37', '43'],\n", + " 'longitude_of_central_meridian': '-3',\n", + " 'latitude_of_projection_origin': '40'}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lcc_grid.projection_data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { @@ -147,7 +191,7 @@ " 784862.875, 788862.875])}" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -158,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -246,7 +290,7 @@ " 774152.312, 778152.312])}" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/2.Creation/2.7.Create_Mercator.ipynb b/tutorials/2.Creation/2.7.Create_Mercator.ipynb index a69ee6a..453bada 100644 --- a/tutorials/2.Creation/2.7.Create_Mercator.ipynb +++ b/tutorials/2.Creation/2.7.Create_Mercator.ipynb @@ -46,8 +46,7 @@ "text": [ "Rank 000: Creating mercator_grid.nc\n", "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n", - "Rank 000: Cell measures done\n" + "Rank 000: Dimensions done\n" ] } ], @@ -59,6 +58,50 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Proj('+proj=merc +lat_ts=-1.5 +lon_0=-18 +x_0=0 +y_0=0 +a=6378137 +b=6356752.3142 +units=m +no_defs', preserve_units=True)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mercator_grid.projection" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': None,\n", + " 'dimensions': (),\n", + " 'grid_mapping_name': 'mercator',\n", + " 'standard_parallel': '-1.5',\n", + " 'longitude_of_projection_origin': -18.0}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mercator_grid.projection_data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { @@ -105,7 +148,7 @@ " 6317540., 6367540.])}" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -116,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -177,7 +220,7 @@ " 1.02989825e+07, 1.03489825e+07])}" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/2.Creation/2.8.Create_Global.ipynb b/tutorials/2.Creation/2.8.Create_Global.ipynb index 107f0dd..6ee8f61 100644 --- a/tutorials/2.Creation/2.8.Create_Global.ipynb +++ b/tutorials/2.Creation/2.8.Create_Global.ipynb @@ -39,8 +39,7 @@ "text": [ "Rank 000: Creating global_grid.nc\n", "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n", - "Rank 000: Cell measures done\n" + "Rank 000: Dimensions done\n" ] } ], @@ -56,7 +55,7 @@ { "data": { "text/plain": [ - "{'data': array([-89.95, -89.85, -89.75, ..., 89.65, 89.75, 89.85])}" + "Proj('+proj=longlat +ellps=WGS84 +no_defs', preserve_units=True)" ] }, "execution_count": 4, @@ -65,7 +64,7 @@ } ], "source": [ - "global_grid.lat" + "global_grid.projection" ] }, { @@ -76,7 +75,11 @@ { "data": { "text/plain": [ - "{'data': array([-179.95, -179.85, -179.75, ..., 179.65, 179.75, 179.85])}" + "{'data': None,\n", + " 'dimensions': (),\n", + " 'grid_mapping_name': 'latitude_longitude',\n", + " 'semi_major_axis': '6378137.0',\n", + " 'inverse_flattening': '0'}" ] }, "execution_count": 5, @@ -84,6 +87,46 @@ "output_type": "execute_result" } ], + "source": [ + "global_grid.projection_data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([-89.95, -89.85, -89.75, ..., 89.65, 89.75, 89.85])}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_grid.lat" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([-179.95, -179.85, -179.75, ..., 179.65, 179.75, 179.85])}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "global_grid.lon" ] -- GitLab From 58bc05294fb02a71997cabd84f552bc6f4df7b8b Mon Sep 17 00:00:00 2001 From: ctena Date: Wed, 5 Apr 2023 16:04:21 +0200 Subject: [PATCH 07/21] Fixed dtype for reading variables --- nes/nc_projections/default_nes.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 71e9e7f..63f8253 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -1849,6 +1849,7 @@ class Nes(object): variables[var_name] = {} variables[var_name]['data'] = None variables[var_name]['dimensions'] = var_info.dimensions + variables[var_name]['dtype'] = var_info.dtype # Avoid some attributes for attrname in var_info.ncattrs(): @@ -1991,6 +1992,7 @@ class Nes(object): for var_name, var_info in self.variables.items(): if var_info['data'] is not None: self.variables[var_name]['data'] = self.variables[var_name]['data'].astype(data_type) + self.variables[var_name]['dtype'] = data_type return None -- GitLab From ec86f7271c0d938a6c38f3a7598594acce253d44 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Wed, 5 Apr 2023 16:27:04 +0200 Subject: [PATCH 08/21] Add warning on fill nan and fix bugs in function to create variables for points --- nes/nc_projections/default_nes.py | 21 +++- nes/nc_projections/points_nes.py | 24 +++-- nes/nc_projections/points_nes_ghost.py | 19 +++- nes/nc_projections/points_nes_providentia.py | 19 +++- .../1.3.Read_Write_Points.ipynb | 100 ++++++------------ .../1.6.Read_Write_Providentia.ipynb | 79 +++++--------- tutorials/2.Creation/2.3.Create-Points.ipynb | 46 ++++---- .../2.4.Create_Points_Port_Barcelona.ipynb | 27 ++--- .../2.Creation/2.5.Create_Points_CSIC.ipynb | 19 ++-- 9 files changed, 165 insertions(+), 189 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 887503b..0732166 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -1966,9 +1966,15 @@ class Nes(object): # Missing to nan if np.ma.is_masked(data): - # This operation is done because sometimes the missing value is lost during the calculation - data = data.filled(np.nan) - + try: + # This operation is done because sometimes the missing value is lost during the calculation + data = data.filled(np.nan) + except: + msg = 'Variable {0} data missing values cannot be converted to np.nan.'.format(var_name) + warnings.warn(msg) + sys.stderr.flush() + pass + return data def load(self, var_list=None): @@ -2384,6 +2390,13 @@ class Nes(object): var_dict['data'] = var_dict['data'].astype(str) var_dtype = var_dict['data'].dtype + # Ensure data is of type numpy array (to create NES) + if not isinstance(var_dict['data'], (np.ndarray, np.generic)): + try: + var_dict['data'] = np.array(var_dict['data']) + except AttributeError: + raise AttributeError("Data for variable {0} must be a numpy array.".format(var_name)) + # Convert list of strings to chars for parallelization if not np.issubdtype(var_dtype, np.number): try: @@ -2391,7 +2404,7 @@ class Nes(object): unicode_type = len(max(var_dict['data'].flatten(), key=len)) if ((var_dict['data'].dtype == np.dtype('" + "" ] }, "execution_count": 3, @@ -421,29 +421,13 @@ " 'nan', 'nan', 'nan', 'nan', 'nan'], dtype='" + "" ] }, "execution_count": 13, @@ -796,7 +763,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -959,6 +926,18 @@ "execution_count": 21, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:314: UserWarning: Variable day_night_code data missing values cannot be converted to np.nan.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:314: UserWarning: Variable season_code data missing values cannot be converted to np.nan.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:314: UserWarning: Variable weekday_weekend_code data missing values cannot be converted to np.nan.\n", + " warnings.warn(msg)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -1333,23 +1312,8 @@ "output_type": "stream", "text": [ "Rank 000: Creating points_file_2.nc\n", - "Rank 000: NetCDF ready to write\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:592: UserWarning: WARNING!!! GHOST datasets cannot be written in parallel yet. Changing to serial mode.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Rank 000: NetCDF ready to write\n", "Rank 000: Dimensions done\n", - "Rank 000: Cell measures done\n", "Rank 000: Writing ASTER_v3_altitude var (1/173)\n", "Rank 000: Var ASTER_v3_altitude created (1/173)\n", "Rank 000: Var ASTER_v3_altitude data (1/173)\n", @@ -2418,7 +2382,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -2447,7 +2411,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 24, diff --git a/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb b/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb index 7916cfd..f1496d0 100644 --- a/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb +++ b/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb @@ -43,16 +43,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -792,7 +792,7 @@ " datetime.datetime(2018, 4, 30, 23, 0)]" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -803,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -812,7 +812,7 @@ "{'data': array([0]), 'units': ''}" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -823,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -881,7 +881,7 @@ " 'axis': 'Y'}" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -892,7 +892,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -964,7 +964,7 @@ " 'axis': 'X'}" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -975,7 +975,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1348,7 +1348,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1682,22 +1682,7 @@ "Rank 000: Var country created (81/175)\n", "Rank 000: Var country data (81/175)\n", "Rank 000: Var country completed (81/175)\n", - "Rank 000: Writing daily_native_max_gap_percent var (82/175)" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:593: UserWarning: WARNING!!! GHOST datasets cannot be written in parallel yet. Changing to serial mode.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", + "Rank 000: Writing daily_native_max_gap_percent var (82/175)\n", "Rank 000: Var daily_native_max_gap_percent created (82/175)\n", "Rank 000: Var daily_native_max_gap_percent data (82/175)\n", "Rank 000: Var daily_native_max_gap_percent completed (82/175)\n", @@ -2089,7 +2074,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -2109,16 +2094,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -2130,7 +2115,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -2858,7 +2843,7 @@ " datetime.datetime(2018, 4, 30, 23, 0)]" ] }, - "execution_count": 15, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -2869,7 +2854,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -2878,7 +2863,7 @@ "{'data': array([0]), 'units': ''}" ] }, - "execution_count": 16, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -2889,7 +2874,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -2949,7 +2934,7 @@ " 'axis': 'Y'}" ] }, - "execution_count": 17, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -2960,7 +2945,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -3035,7 +3020,7 @@ " 'axis': 'X'}" ] }, - "execution_count": 18, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -3046,7 +3031,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -3073,7 +3058,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -3092,14 +3077,6 @@ "Rank 000: Var station_reference data (2/2)\n", "Rank 000: Var station_reference completed (2/2)\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_providentia.py:595: UserWarning: WARNING!!! Providentia datasets cannot be written in parallel yet. Changing to serial mode.\n", - " warnings.warn(msg)\n" - ] } ], "source": [ diff --git a/tutorials/2.Creation/2.3.Create-Points.ipynb b/tutorials/2.Creation/2.3.Create-Points.ipynb index b06aed5..ba45c3b 100644 --- a/tutorials/2.Creation/2.3.Create-Points.ipynb +++ b/tutorials/2.Creation/2.3.Create-Points.ipynb @@ -229,6 +229,16 @@ "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -236,7 +246,6 @@ "Rank 000: Creating points_grid_1.nc\n", "Rank 000: NetCDF ready to write\n", "Rank 000: Dimensions done\n", - "Rank 000: Cell measures done\n", "Rank 000: Writing station_code var (1/2)\n", "Rank 000: Var station_code created (1/2)\n", "Rank 000: Var station_code data (1/2)\n", @@ -246,16 +255,6 @@ "Rank 000: Var area_classification data (2/2)\n", "Rank 000: Var area_classification completed (2/2)\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n" - ] } ], "source": [ @@ -798,6 +797,18 @@ "execution_count": 13, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable pm10. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -805,7 +816,6 @@ "Rank 000: Creating points_grid_2.nc\n", "Rank 000: NetCDF ready to write\n", "Rank 000: Dimensions done\n", - "Rank 000: Cell measures done\n", "Rank 000: Writing station_name var (1/3)\n", "Rank 000: Var station_name created (1/3)\n", "Rank 000: Var station_name data (1/3)\n", @@ -819,18 +829,6 @@ "Rank 000: Var pm10 data (3/3)\n", "Rank 000: Var pm10 completed (3/3)\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable pm10. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n" - ] } ], "source": [ diff --git a/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb b/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb index b27f816..073d11e 100644 --- a/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb +++ b/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb @@ -397,6 +397,14 @@ "execution_count": 11, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -404,27 +412,12 @@ "Rank 000: Creating points_grid_no2.nc\n", "Rank 000: NetCDF ready to write\n", "Rank 000: Dimensions done\n", - "Rank 000: Cell measures done\n", "Rank 000: Writing station_name var (1/2)\n", "Rank 000: Var station_name created (1/2)\n", "Rank 000: Var station_name data (1/2)\n", "Rank 000: Var station_name completed (1/2)\n", "Rank 000: Writing sconcno2 var (2/2)\n", - "Rank 000: Var sconcno2 created (2/2)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Rank 000: Var sconcno2 created (2/2)\n", "Rank 000: Var sconcno2 data (2/2)\n", "Rank 000: Var sconcno2 completed (2/2)\n" ] @@ -700,7 +693,7 @@ " \n", " # Read metadata\n", " metadata = {'station_name': {'data': current.columns[2:4].to_numpy(),\n", - " 'dimensions': ('station'),\n", + " 'dimensions': ('station',),\n", " 'standard_name': ''},\n", " 'altitude': {'data': altitude,\n", " 'dimensions': ('station',),\n", diff --git a/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb b/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb index 4484f43..1366fc6 100644 --- a/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb +++ b/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb @@ -363,6 +363,14 @@ "execution_count": 11, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -370,7 +378,6 @@ "Rank 000: Creating points_grid_nh3.nc\n", "Rank 000: NetCDF ready to write\n", "Rank 000: Dimensions done\n", - "Rank 000: Cell measures done\n", "Rank 000: Writing station_name var (1/2)\n", "Rank 000: Var station_name created (1/2)\n", "Rank 000: Var station_name data (1/2)\n", @@ -380,14 +387,6 @@ "Rank 000: Var sconcnh3 data (2/2)\n", "Rank 000: Var sconcnh3 completed (2/2)\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n" - ] } ], "source": [ @@ -601,7 +600,7 @@ " \n", " # Read metadata\n", " metadata = {'station_name': {'data': current.columns[0:2].to_numpy(),\n", - " 'dimensions': ('station'),\n", + " 'dimensions': ('station',),\n", " 'standard_name': ''},\n", " 'altitude': {'data': altitude,\n", " 'dimensions': ('station',),\n", -- GitLab From b21c66cd141a433f5f36199577af17c7b0de0139 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Wed, 5 Apr 2023 16:47:07 +0200 Subject: [PATCH 09/21] Remove data, dtype and dimensions in projection data --- nes/nc_projections/latlon_nes.py | 16 +++++++++++++--- nes/nc_projections/lcc_nes.py | 17 +++++++++++------ nes/nc_projections/mercator_nes.py | 13 ++++++++++--- nes/nc_projections/rotated_nes.py | 13 ++++++++++--- .../1.Introduction/1.1.Read_Write_Regular.ipynb | 11 +++++++---- .../1.Introduction/1.2.Read_Write_Rotated.ipynb | 11 +++++++---- .../1.Introduction/1.4.Read_Write_LCC.ipynb | 13 +++++++++---- .../1.5.Read_Write_Mercator.ipynb | 12 ++++++++---- 8 files changed, 75 insertions(+), 31 deletions(-) diff --git a/nes/nc_projections/latlon_nes.py b/nes/nc_projections/latlon_nes.py index 8f77a6b..2e22316 100644 --- a/nes/nc_projections/latlon_nes.py +++ b/nes/nc_projections/latlon_nes.py @@ -124,6 +124,18 @@ class LatLonNes(Nes): if 'crs' in self.variables.keys(): projection_data = self.variables['crs'] self.free_vars('crs') + else: + msg = 'There is no variable called crs, projection has not been defined.' + raise RuntimeError(msg) + + if 'dtype' in projection_data.keys(): + del projection_data['dtype'] + + if 'data' in projection_data.keys(): + del projection_data['data'] + + if 'dimensions' in projection_data.keys(): + del projection_data['dimensions'] self.projection_data = projection_data self.projection = self._get_pyproj_projection() @@ -135,9 +147,7 @@ class LatLonNes(Nes): Create 'projection' and 'projection_data' from projection arguments. """ - projection_data = {'data': None, - 'dimensions': (), - 'grid_mapping_name': 'latitude_longitude', + projection_data = {'grid_mapping_name': 'latitude_longitude', 'semi_major_axis': str(self.earth_radius[1]), 'inverse_flattening': str(0), } diff --git a/nes/nc_projections/lcc_nes.py b/nes/nc_projections/lcc_nes.py index 40c9844..1fda0b1 100644 --- a/nes/nc_projections/lcc_nes.py +++ b/nes/nc_projections/lcc_nes.py @@ -174,21 +174,28 @@ class LCCNes(Nes): if 'Lambert_Conformal' in self.variables.keys(): projection_data = self.variables['Lambert_Conformal'] self.free_vars('Lambert_Conformal') - elif 'Lambert_conformal' in self.variables.keys(): projection_data = self.variables['Lambert_conformal'] self.free_vars('Lambert_conformal') - else: # We will never have this condition since the LCC grid will never be correctly detected # since the function __is_lcc in load_nes only detects LCC grids when there is Lambert_conformal msg = 'There is no variable called Lambert_Conformal, projection has not been defined.' raise RuntimeError(msg) + + if 'dtype' in projection_data.keys(): + del projection_data['dtype'] + + if 'data' in projection_data.keys(): + del projection_data['data'] + + if 'dimensions' in projection_data.keys(): + del projection_data['dimensions'] if not isinstance(projection_data['standard_parallel'], list): projection_data['standard_parallel'] = [projection_data['standard_parallel'].split(', ')[0], projection_data['standard_parallel'].split(', ')[1]] - + self.projection_data = projection_data self.projection = self._get_pyproj_projection() @@ -199,9 +206,7 @@ class LCCNes(Nes): Create 'projection' and 'projection_data' from projection arguments. """ - projection_data = {'data': None, - 'dimensions': (), - 'grid_mapping_name': 'lambert_conformal_conic', + projection_data = {'grid_mapping_name': 'lambert_conformal_conic', 'standard_parallel': [str(kwargs['lat_1']), str(kwargs['lat_2'])], 'longitude_of_central_meridian': str(kwargs['lon_0']), 'latitude_of_projection_origin': str(kwargs['lat_0']), diff --git a/nes/nc_projections/mercator_nes.py b/nes/nc_projections/mercator_nes.py index bc20d97..10135f4 100644 --- a/nes/nc_projections/mercator_nes.py +++ b/nes/nc_projections/mercator_nes.py @@ -172,6 +172,15 @@ class MercatorNes(Nes): msg = 'There is no variable called mercator, projection has not been defined.' raise RuntimeError(msg) + if 'dtype' in projection_data.keys(): + del projection_data['dtype'] + + if 'data' in projection_data.keys(): + del projection_data['data'] + + if 'dimensions' in projection_data.keys(): + del projection_data['dimensions'] + self.projection_data = projection_data self.projection = self._get_pyproj_projection() @@ -182,9 +191,7 @@ class MercatorNes(Nes): Create 'projection' and 'projection_data' from projection arguments. """ - projection_data = {'data': None, - 'dimensions': (), - 'grid_mapping_name': 'mercator', + projection_data = {'grid_mapping_name': 'mercator', 'standard_parallel': str(kwargs['lat_ts']), # TODO: Check if True 'longitude_of_projection_origin': kwargs['lon_0'], } diff --git a/nes/nc_projections/rotated_nes.py b/nes/nc_projections/rotated_nes.py index 8470541..41afd48 100644 --- a/nes/nc_projections/rotated_nes.py +++ b/nes/nc_projections/rotated_nes.py @@ -173,6 +173,15 @@ class RotatedNes(Nes): else: msg = 'There is no variable called rotated_pole, projection has not been defined.' raise RuntimeError(msg) + + if 'dtype' in projection_data.keys(): + del projection_data['dtype'] + + if 'data' in projection_data.keys(): + del projection_data['data'] + + if 'dimensions' in projection_data.keys(): + del projection_data['dimensions'] self.projection_data = projection_data self.projection = self._get_pyproj_projection() @@ -184,9 +193,7 @@ class RotatedNes(Nes): Create 'projection' and 'projection_data' from projection arguments. """ - projection_data = {'data': None, - 'dimensions': (), - 'grid_mapping_name': 'rotated_latitude_longitude', + projection_data = {'grid_mapping_name': 'rotated_latitude_longitude', 'grid_north_pole_latitude': 90 - kwargs['centre_lat'], 'grid_north_pole_longitude': -180 + kwargs['centre_lon'], } diff --git a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb index 2b3f582..349ee47 100644 --- a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb +++ b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -83,8 +83,7 @@ { "data": { "text/plain": [ - "{'dimensions': (),\n", - " 'grid_mapping_name': 'latitude_longitude',\n", + "{'grid_mapping_name': 'latitude_longitude',\n", " 'semi_major_axis': 6371000.0,\n", " 'inverse_flattening': 0}" ] @@ -154,6 +153,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('lev',),\n", + " 'dtype': dtype('float64'),\n", " 'units': '1',\n", " 'positive': 'up'}" ] @@ -191,6 +191,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('lat',),\n", + " 'dtype': dtype('float64'),\n", " 'units': 'degrees_north',\n", " 'axis': 'Y',\n", " 'long_name': 'latitude coordinate',\n", @@ -272,6 +273,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('lon',),\n", + " 'dtype': dtype('float64'),\n", " 'units': 'degrees_east',\n", " 'axis': 'X',\n", " 'long_name': 'longitude coordinate',\n", @@ -418,6 +420,7 @@ " fill_value=1e+20,\n", " dtype=float32),\n", " 'dimensions': ('time', 'lev', 'lat', 'lon'),\n", + " 'dtype': dtype('float32'),\n", " 'units': 'ppmV',\n", " 'grid_mapping': 'crs',\n", " 'coordinates': 'lat lon'}}" @@ -480,7 +483,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, diff --git a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb index 7ebecba..07a94dc 100644 --- a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb +++ b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -83,8 +83,7 @@ { "data": { "text/plain": [ - "{'dimensions': (),\n", - " 'grid_mapping_name': 'rotated_latitude_longitude',\n", + "{'grid_mapping_name': 'rotated_latitude_longitude',\n", " 'grid_north_pole_latitude': 39.0,\n", " 'grid_north_pole_longitude': -170.0}" ] @@ -132,6 +131,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('lev',),\n", + " 'dtype': dtype('float64'),\n", " 'units': '',\n", " 'positive': 'up'}" ] @@ -170,6 +170,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('rlat', 'rlon'),\n", + " 'dtype': dtype('float64'),\n", " 'units': 'degrees_north',\n", " 'axis': 'Y',\n", " 'long_name': 'latitude coordinate',\n", @@ -210,6 +211,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('rlat', 'rlon'),\n", + " 'dtype': dtype('float64'),\n", " 'units': 'degrees_east',\n", " 'axis': 'X',\n", " 'long_name': 'longitude coordinate',\n", @@ -341,6 +343,7 @@ " fill_value=1e+20,\n", " dtype=float32),\n", " 'dimensions': ('time', 'lev', 'rlat', 'rlon'),\n", + " 'dtype': dtype('float32'),\n", " 'units': 'kg/m3',\n", " 'long_name': 'TRACERS_044',\n", " 'coordinates': 'lat lon',\n", @@ -405,7 +408,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, diff --git a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb index 4f29a7f..4db4033 100644 --- a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb +++ b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -83,8 +83,7 @@ { "data": { "text/plain": [ - "{'dimensions': (),\n", - " 'grid_mapping_name': 'lambert_conformal_conic',\n", + "{'grid_mapping_name': 'lambert_conformal_conic',\n", " 'standard_parallel': ['43.', '37.'],\n", " 'longitude_of_central_meridian': '-3',\n", " 'latitude_of_projection_origin': '40'}" @@ -131,6 +130,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('lev',),\n", + " 'dtype': dtype('float64'),\n", " 'units': '',\n", " 'positive': 'up'}" ] @@ -288,6 +288,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('x',),\n", + " 'dtype': dtype('float64'),\n", " 'long_name': 'x coordinate of projection',\n", " 'units': '1000 m',\n", " 'standard_name': 'projection_x_coordinate'}" @@ -446,6 +447,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('y',),\n", + " 'dtype': dtype('float64'),\n", " 'long_name': 'y coordinate of projection',\n", " 'units': '1000 m',\n", " 'standard_name': 'projection_y_coordinate'}" @@ -485,6 +487,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('y', 'x'),\n", + " 'dtype': dtype('float64'),\n", " 'units': 'degrees_north',\n", " 'axis': 'Y',\n", " 'long_name': 'latitude coordinate',\n", @@ -525,6 +528,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('y', 'x'),\n", + " 'dtype': dtype('float64'),\n", " 'units': 'degrees_east',\n", " 'axis': 'X',\n", " 'long_name': 'longitude coordinate',\n", @@ -599,6 +603,7 @@ " fill_value=1e+20,\n", " dtype=float32),\n", " 'dimensions': ('time', 'lev', 'y', 'x'),\n", + " 'dtype': dtype('float32'),\n", " 'units': 'ppm',\n", " 'cell_methods': 'time: max (interval: 1hr)',\n", " 'grid_mapping': 'Lambert_conformal',\n", @@ -660,7 +665,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, diff --git a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb index 8455cda..4913af1 100644 --- a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb +++ b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -83,8 +83,7 @@ { "data": { "text/plain": [ - "{'dimensions': (),\n", - " 'grid_mapping_name': 'mercator',\n", + "{'grid_mapping_name': 'mercator',\n", " 'standard_parallel': -1.5,\n", " 'longitude_of_projection_origin': -18.0}" ] @@ -130,6 +129,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('lev',),\n", + " 'dtype': dtype('float64'),\n", " 'units': '1',\n", " 'positive': 'up'}" ] @@ -224,6 +224,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('x',),\n", + " 'dtype': dtype('float64'),\n", " 'long_name': 'x coordinate of projection',\n", " 'units': 'm',\n", " 'standard_name': 'projection_x_coordinate'}" @@ -297,6 +298,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('y',),\n", + " 'dtype': dtype('float64'),\n", " 'long_name': 'y coordinate of projection',\n", " 'units': 'm',\n", " 'standard_name': 'projection_y_coordinate'}" @@ -336,6 +338,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('y', 'x'),\n", + " 'dtype': dtype('float64'),\n", " 'units': 'degrees_north',\n", " 'axis': 'Y',\n", " 'long_name': 'latitude coordinate',\n", @@ -376,6 +379,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('y', 'x'),\n", + " 'dtype': dtype('float64'),\n", " 'units': 'degrees_east',\n", " 'axis': 'X',\n", " 'long_name': 'longitude coordinate',\n", @@ -432,7 +436,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, -- GitLab From 4e143e5705251c5038a394829d20beb04dc01454 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Thu, 6 Apr 2023 15:35:32 +0200 Subject: [PATCH 10/21] Fix bugs in read variable and test all tutorials --- nes/nc_projections/default_nes.py | 83 +- nes/nc_projections/points_nes.py | 277 ++-- nes/nc_projections/points_nes_ghost.py | 261 ++-- nes/nc_projections/points_nes_providentia.py | 261 ++-- .../1.1.Read_Write_Regular.ipynb | 4 +- .../1.2.Read_Write_Rotated.ipynb | 4 +- .../1.3.Read_Write_Points.ipynb | 724 ++++++++-- .../1.Introduction/1.4.Read_Write_LCC.ipynb | 4 +- .../1.5.Read_Write_Mercator.ipynb | 4 +- .../1.6.Read_Write_Providentia.ipynb | 307 +++- tutorials/2.Creation/2.1.Create_Regular.ipynb | 4 +- tutorials/2.Creation/2.2.Create_Rotated.ipynb | 4 +- tutorials/2.Creation/2.3.Create-Points.ipynb | 15 +- .../2.4.Create_Points_Port_Barcelona.ipynb | 4 +- .../2.Creation/2.5.Create_Points_CSIC.ipynb | 4 +- tutorials/2.Creation/2.6.Create-LCC.ipynb | 4 +- .../2.Creation/2.7.Create_Mercator.ipynb | 4 +- tutorials/2.Creation/2.8.Create_Global.ipynb | 4 +- tutorials/3.Statistics/3.1.Statistics.ipynb | 26 +- tutorials/3.Statistics/3.2.Sum.ipynb | 94 +- .../4.1.Vertical_Interpolation.ipynb | 13 +- .../4.2.Horizontal_Interpolation.ipynb | 18 +- .../4.3.Conservative_Interpolation.ipynb | 72 +- .../4.4.Providentia_Interpolation.ipynb | 22 +- ...4.5.NES_vs_Providentia_Interpolation.ipynb | 1225 ++++++++++++++-- .../5.Geospatial/5.1.Create_Shapefiles.ipynb | 90 +- tutorials/5.Geospatial/5.2.Spatial_Join.ipynb | 505 +------ .../5.3.Add_Coordinates_Bounds.ipynb | 1260 +---------------- .../5.5.Calculate_Geometry_Cell_Area.ipynb | 40 +- tutorials/6.Others/6.1.Add_Time_Bounds.ipynb | 10 +- tutorials/6.Others/6.2.Selecting.ipynb | 52 +- tutorials/6.Others/6.3.Plot.ipynb | 30 +- 32 files changed, 2825 insertions(+), 2604 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 22ded72..00ca9e3 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -344,7 +344,10 @@ class Nes(object): Max length of the string """ - self.strlen = strlen + if strlen is not None: + self.strlen = strlen + else: + raise ValueError('String length cannot be set as None.') return None @@ -2009,6 +2012,9 @@ 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) + # Data type changes when joining characters in read_variable (S1 to S+strlen) + if 'strlen' in self.variables[var_name]['dimensions']: + self.variables[var_name]['dtype'] = self.variables[var_name]['data'].dtype else: if self.master: print("Data for {0} was previously loaded. Skipping variable.".format(var_name)) @@ -2359,13 +2365,12 @@ class Nes(object): """ for i, (var_name, var_dict) in enumerate(self.variables.items()): - if isinstance(var_dict['data'], int) and var_dict['data'] == 0: var_dims = ('time', 'lev',) + self._var_dim var_dtype = np.float32 else: # Get dimensions - if var_dict['data'] is None or len(var_dict['data'].shape) == 4: + if (var_dict['data'] is None) or (len(var_dict['data'].shape) == 4): var_dims = ('time', 'lev',) + self._var_dim else: var_dims = self._var_dim @@ -2373,7 +2378,7 @@ class Nes(object): # Get data type if 'dtype' in var_dict.keys(): var_dtype = var_dict['dtype'] - if var_dict['data'] is not None and var_dtype != var_dict['data'].dtype: + if (var_dict['data'] is not None) and (var_dtype != var_dict['data'].dtype): msg = "WARNING!!! " msg += "Different data types for variable {0}. ".format(var_name) msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, var_dict['data'].dtype) @@ -2386,28 +2391,24 @@ class Nes(object): raise TypeError("It was not possible to cast the data to the input dtype.") else: var_dtype = var_dict['data'].dtype - - # Transform objects into strings - if var_dtype == np.dtype(object): - var_dict['data'] = var_dict['data'].astype(str) - var_dtype = var_dict['data'].dtype - - # Ensure data is of type numpy array (to create NES) - if not isinstance(var_dict['data'], (np.ndarray, np.generic)): - try: - var_dict['data'] = np.array(var_dict['data']) - except AttributeError: - raise AttributeError("Data for variable {0} must be a numpy array.".format(var_name)) - - # Convert list of strings to chars for parallelization - if not np.issubdtype(var_dtype, np.number): - try: - # Get unicode - unicode_type = len(max(var_dict['data'].flatten(), key=len)) - - if ((var_dict['data'].dtype == np.dtype(' 0, complevel=self.zip_lvl) @@ -2478,7 +2480,6 @@ class Nes(object): for att_name, att_value in var_dict.items(): if att_name == 'data': - if att_value is not None: if self.info: print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) @@ -2515,6 +2516,7 @@ class Nes(object): if self.info: print("Rank {0:03d}: Var {1} data ({2}/{3})".format( self.rank, var_name, i + 1, len(self.variables))) + elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype']: var.setncattr(att_name, att_value) @@ -2522,6 +2524,7 @@ class Nes(object): if self.info: print("Rank {0:03d}: Var {1} completed ({2}/{3})".format( self.rank, var_name, i + 1, len(self.variables))) + return None def append_time_step_data(self, i_time): diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index 41a6571..ba0f234 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -157,8 +157,7 @@ class PointsNes(Nes): # Create string length dimension if hasattr(self, 'strlen'): - if self.strlen is not None: - netcdf.createDimension('strlen', self.strlen) + netcdf.createDimension('strlen', self.strlen) return None @@ -276,23 +275,6 @@ class PointsNes(Nes): return values - def _get_strlen(self): - """ - Read the string length dimension of some variables. - - Returns - ------- - int, None - String length. None means no string data. - """ - - if 'strlen' in self.netcdf.dimensions: - strlen = self.netcdf.dimensions['strlen'].size - else: - strlen = None - - return strlen - def _read_variable(self, var_name): """ Read the corresponding variable data according to the current rank. @@ -317,7 +299,8 @@ class PointsNes(Nes): elif len(var_dims) == 2: if 'strlen' in var_dims: data = nc_var[self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], :] - data = np.array([''.join(i.tostring().decode('ascii').replace('\x00', '')) for i in data]) + data = np.array([''.join(i.tostring().decode('ascii').replace('\x00', '')) for i in data], + dtype=np.object) else: data = nc_var[self.read_axis_limits['t_min']:self.read_axis_limits['t_max'], self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] @@ -352,41 +335,41 @@ class PointsNes(Nes): if self.variables is not None: for i, (var_name, var_dict) in enumerate(self.variables.items()): - if var_dict['data'] is not None: - - # Get data type - if 'dtype' in var_dict.keys(): - var_dtype = var_dict['dtype'] - if var_dtype != var_dict['data'].dtype: - msg = "WARNING!!! " - msg += "Different data types for variable {0}. ".format(var_name) - msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, - var_dict['data'].dtype) - warnings.warn(msg) - sys.stderr.flush() - try: - var_dict['data'] = var_dict['data'].astype(var_dtype) - except Exception as e: # TODO: Detect exception - raise e("It was not possible to cast the data to the input dtype.") - else: - var_dtype = var_dict['data'].dtype + # Get data type + if 'dtype' in var_dict.keys(): + var_dtype = var_dict['dtype'] + if (var_dict['data'] is not None) and (var_dtype != var_dict['data'].dtype): + msg = "WARNING!!! " + msg += "Different data types for variable {0}. ".format(var_name) + msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, + var_dict['data'].dtype) + warnings.warn(msg) + sys.stderr.flush() + try: + var_dict['data'] = var_dict['data'].astype(var_dtype) + except Exception as e: # TODO: Detect exception + raise e("It was not possible to cast the data to the input dtype.") + else: + var_dtype = var_dict['data'].dtype - # Transform objects into strings (e.g. for ESDAC iwahashi landform in GHOST) - if var_dtype == np.dtype(object): - var_dict['data'] = var_dict['data'].astype(str) - var_dtype = var_dict['data'].dtype + # Transform objects into strings (e.g. for ESDAC iwahashi landform in GHOST) + if var_dtype == np.dtype(object): + var_dict['data'] = var_dict['data'].astype(str) + var_dtype = var_dict['data'].dtype - # Get dimensions when reading datasets - if 'dimensions' in var_dict.keys(): - var_dims = var_dict['dimensions'] - # Get dimensions when creating new datasets + # Get dimensions when reading datasets + if 'dimensions' in var_dict.keys(): + var_dims = var_dict['dimensions'] + # Get dimensions when creating new datasets + else: + if len(var_dict['data'].shape) == 1: + # For data that depends only on station (e.g. station_code) + var_dims = self._var_dim else: - if len(var_dict['data'].shape) == 1: - # For data that depends only on station (e.g. station_code) - var_dims = self._var_dim - else: - # For data that is dependent on time and station (e.g. PM10) - var_dims = ('time',) + self._var_dim + # For data that is dependent on time and station (e.g. PM10) + var_dims = ('time',) + self._var_dim + + if var_dict['data'] is not None: # Ensure data is of type numpy array (to create NES) if not isinstance(var_dict['data'], (np.ndarray, np.generic)): @@ -396,119 +379,103 @@ class PointsNes(Nes): raise AttributeError("Data for variable {0} must be a numpy array.".format(var_name)) # Convert list of strings to chars for parallelization - if not np.issubdtype(var_dict['data'].dtype, np.number): + if np.issubdtype(var_dict['data'].dtype, np.character): try: - # Get unicode - unicode_type = len(max(var_dict['data'].flatten(), key=len)) - - if ((var_dict['data'].dtype == np.dtype(' 0, complevel=self.zip_lvl) + else: + if self.balanced: + raise NotImplementedError("A balanced data cannot be chunked.") + if self.master: + chunk_size = var_dict['data'].shape + else: + chunk_size = None + chunk_size = self.comm.bcast(chunk_size, root=0) + var = netcdf.createVariable(var_name, var_dtype, var_dims, + zlib=self.zip_lvl > 0, complevel=self.zip_lvl, + chunksizes=chunk_size) + + if self.info: + print('Rank {0:03d}: Var {1} created ({2}/{3})'.format( + self.rank, var_name, i + 1, len(self.variables))) + if self.size > 1: + var.set_collective(True) if self.info: - print('Rank {0:03d}: Writing {1} var ({2}/{3})'.format(self.rank, var_name, i + 1, - len(self.variables))) - try: - if not chunking: - var = netcdf.createVariable(var_name, var_dtype, var_dims, - zlib=self.zip_lvl > 0, complevel=self.zip_lvl) - else: - if self.master: - chunk_size = var_dict['data'].shape - # TODO: Change chunk size (add strlen) as tuple - else: - chunk_size = None - chunk_size = self.comm.bcast(chunk_size, root=0) - var = netcdf.createVariable(var_name, var_dtype, var_dims, - zlib=self.zip_lvl > 0, complevel=self.zip_lvl, - chunksizes=chunk_size) - + print('Rank {0:03d}: Var {1} collective ({2}/{3})'.format( + self.rank, var_name, i + 1, len(self.variables))) + + for att_name, att_value in var_dict.items(): + if att_name == 'data': if self.info: - print('Rank {0:03d}: Var {1} created ({2}/{3})'.format( - self.rank, var_name, i + 1, len(self.variables))) - if self.size > 1: - var.set_collective(True) - if self.info: - print('Rank {0:03d}: Var {1} collective ({2}/{3})'.format( - self.rank, var_name, i + 1, len(self.variables))) - - for att_name, att_value in var_dict.items(): - if att_name == 'data': - if len(att_value.shape) == 1: - try: - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, - att_value.shape)) - except ValueError: - raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, - att_value.shape)) - elif len(att_value.shape) == 2: - if 'strlen' in var_dims: - try: - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :] = att_value - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, - att_value.shape)) - except ValueError: - raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, - att_value.shape)) - else: - try: - var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, - att_value.shape)) - except ValueError: - raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, - att_value.shape)) - if self.info: - print('Rank {0:03d}: Var {1} data ({2}/{3})'.format(self.rank, var_name, i + 1, - len(self.variables))) - elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype']: - var.setncattr(att_name, att_value) - self._set_var_crs(var) + print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) + if len(att_value.shape) == 1: + try: + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value + except IndexError: + raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, + att_value.shape)) + except ValueError: + raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, + att_value.shape)) + elif len(att_value.shape) == 2: + if 'strlen' in var_dims: + try: + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :] = att_value + except IndexError: + raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, + att_value.shape)) + except ValueError: + raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, + att_value.shape)) + else: + try: + var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value + except IndexError: + raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, + att_value.shape)) + except ValueError: + raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, + att_value.shape)) if self.info: - print('Rank {0:03d}: Var {1} completed ({2}/{3})'.format(self.rank, var_name, i + 1, - len(self.variables))) - except Exception as e: - print("**ERROR** an error has occurred while writing the '{0}' variable".format(var_name)) - # print("**ERROR** an error has occurredred while writing the '{0}' variable".format(var_name), - # file=sys.stderr) - raise e - else: - msg = "WARNING!!! " - msg += "Variable {0} was not loaded. It will not be written.".format(var_name) - warnings.warn(msg) - sys.stderr.flush() - + print('Rank {0:03d}: Var {1} data ({2}/{3})'.format(self.rank, var_name, i + 1, + len(self.variables))) + elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype']: + var.setncattr(att_name, att_value) + + self._set_var_crs(var) + if self.info: + print('Rank {0:03d}: Var {1} completed ({2}/{3})'.format(self.rank, var_name, i + 1, + len(self.variables))) + return None def _gather_data(self, data_to_gather): diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py index eb7ec50..1d64337 100644 --- a/nes/nc_projections/points_nes_ghost.py +++ b/nes/nc_projections/points_nes_ghost.py @@ -330,28 +330,41 @@ class PointsNesGHOST(PointsNes): if self.variables is not None: for i, (var_name, var_dict) in enumerate(self.variables.items()): - if var_dict['data'] is not None: - - # Get data type - if 'dtype' in var_dict.keys(): - var_dtype = var_dict['dtype'] - else: - var_dtype = var_dict['data'].dtype + # Get data type + if 'dtype' in var_dict.keys(): + var_dtype = var_dict['dtype'] + if (var_dict['data'] is not None) and (var_dtype != var_dict['data'].dtype): + msg = "WARNING!!! " + msg += "Different data types for variable {0}. ".format(var_name) + msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, + var_dict['data'].dtype) + warnings.warn(msg) + sys.stderr.flush() + try: + var_dict['data'] = var_dict['data'].astype(var_dtype) + except Exception as e: # TODO: Detect exception + raise e("It was not possible to cast the data to the input dtype.") + else: + var_dtype = var_dict['data'].dtype - # Transform objects into strings (e.g. for ESDAC iwahashi landform in GHOST) - if var_dtype == np.dtype(object): - var_dict['data'] = var_dict['data'].astype(str) - var_dtype = var_dict['data'].dtype - else: - var_dtype = var_dict['data'].dtype + # Transform objects into strings (e.g. for ESDAC iwahashi landform in GHOST) + if var_dtype == np.dtype(object): + var_dict['data'] = var_dict['data'].astype(str) + var_dtype = var_dict['data'].dtype - # Get dimensions + # Get dimensions when reading datasets + if 'dimensions' in var_dict.keys(): + var_dims = var_dict['dimensions'] + # Get dimensions when creating new datasets + else: if len(var_dict['data'].shape) == 1: - # Metadata + # For data that depends only on station (e.g. station_code) var_dims = self._var_dim - elif len(var_dict['data'].shape) == 2: - # Different from metadata (e.g. concentrations of pm10) - var_dims = self._var_dim + ('time',) + else: + # For data that is dependent on time and station (e.g. PM10) + var_dims = self._var_dim + ('time',) + + if var_dict['data'] is not None: # Ensure data is of type numpy array (to create NES) if not isinstance(var_dict['data'], (np.ndarray, np.generic)): @@ -361,25 +374,16 @@ class PointsNesGHOST(PointsNes): raise AttributeError("Data for variable {0} must be a numpy array.".format(var_name)) # Convert list of strings to chars for parallelization - if not np.issubdtype(var_dict['data'].dtype, np.number): + if np.issubdtype(var_dict['data'].dtype, np.character): try: - # Get unicode - unicode_type = len(max(var_dict['data'].flatten(), key=len)) + # Add strlen as a dimension if needed + if 'strlen' not in var_dims: + var_dims += ('strlen',) - if ((var_dict['data'].dtype == np.dtype(' 0, complevel=self.zip_lvl) - else: - if self.master: - chunk_size = var_dict['data'].shape - else: - chunk_size = None - chunk_size = self.comm.bcast(chunk_size, root=0) - var = netcdf.createVariable(var_name, var_dtype, var_dims, zlib=self.zip_lvl > 0, - complevel=self.zip_lvl, chunksizes=chunk_size) + if not chunking: + var = netcdf.createVariable(var_name, var_dtype, var_dims, + zlib=self.zip_lvl > 0, complevel=self.zip_lvl) + else: + if self.master: + chunk_size = var_dict['data'].shape + else: + chunk_size = None + chunk_size = self.comm.bcast(chunk_size, root=0) + var = netcdf.createVariable(var_name, var_dtype, var_dims, zlib=self.zip_lvl > 0, + complevel=self.zip_lvl, chunksizes=chunk_size) + + if self.info: + print("Rank {0:03d}: Var {1} created ({2}/{3})".format( + self.rank, var_name, i + 1, len(self.variables))) + if self.size > 1: + var.set_collective(True) + if self.info: + print("Rank {0:03d}: Var {1} collective ({2}/{3})".format( + self.rank, var_name, i + 1, len(self.variables))) + for att_name, att_value in var_dict.items(): + if att_name == 'data': if self.info: - print("Rank {0:03d}: Var {1} created ({2}/{3})".format( - self.rank, var_name, i + 1, len(self.variables))) - if self.size > 1: - var.set_collective(True) - if self.info: - print("Rank {0:03d}: Var {1} collective ({2}/{3})".format( - self.rank, var_name, i + 1, len(self.variables))) - - for att_name, att_value in var_dict.items(): - if att_name == 'data': - if len(att_value.shape) == 1: - try: - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, - att_value.shape)) - except ValueError: - raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, - att_value.shape)) - elif len(att_value.shape) == 2: - if 'strlen' in var_dims: - try: - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :] = att_value - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, - att_value.shape)) - except ValueError: - raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, - att_value.shape)) - else: - try: - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max']] = att_value - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max']].shape, - att_value.shape)) - except ValueError: - raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max']].shape, - att_value.shape)) - elif len(att_value.shape) == 3: - try: + print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) + if len(att_value.shape) == 1: + try: + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value + except IndexError: + raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, + att_value.shape)) + except ValueError: + raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, + att_value.shape)) + elif len(att_value.shape) == 2: + if 'strlen' in var_dims: + try: + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :] = att_value + except IndexError: + raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, + att_value.shape)) + except ValueError: + raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, + att_value.shape)) + else: + try: + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], + self.write_axis_limits['t_min']:self.write_axis_limits['t_max']] = att_value + except IndexError: + raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - :] = att_value - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - :].shape, + self.write_axis_limits['t_min']:self.write_axis_limits['t_max']].shape, + att_value.shape)) + except ValueError: + raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], + self.write_axis_limits['t_min']:self.write_axis_limits['t_max']].shape, att_value.shape)) - except ValueError: - raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - :].shape, - att_value.shape)) - - if self.info: - print("Rank {0:03d}: Var {1} data ({2}/{3})".format(self.rank, var_name, i + 1, - len(self.variables))) - elif att_name not in ['chunk_size', 'var_dims', 'dimensions']: - var.setncattr(att_name, att_value) - self._set_var_crs(var) + elif len(att_value.shape) == 3: + try: + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], + self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + :] = att_value + except IndexError: + raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], + self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + :].shape, + att_value.shape)) + except ValueError: + raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], + self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + :].shape, + att_value.shape)) + if self.info: - print("Rank {0:03d}: Var {1} completed ({2}/{3})".format(self.rank, var_name, i + 1, - len(self.variables))) - except Exception as e: - print("**ERROR** an error has occurred while writing the '{0}' variable".format(var_name)) - # print("**ERROR** an error has occurredred while writing the '{0}' variable".format(var_name), - # file=sys.stderr) - raise e - else: - msg = 'WARNING!!! ' - msg += 'Variable {0} was not loaded. It will not be written.'.format(var_name) - warnings.warn(msg) - sys.stderr.flush() + print("Rank {0:03d}: Var {1} data ({2}/{3})".format(self.rank, var_name, i + 1, + len(self.variables))) + + elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype']: + var.setncattr(att_name, att_value) + + self._set_var_crs(var) + if self.info: + print("Rank {0:03d}: Var {1} completed ({2}/{3})".format(self.rank, var_name, i + 1, + len(self.variables))) return None diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py index 2132fe8..56c27e6 100644 --- a/nes/nc_projections/points_nes_providentia.py +++ b/nes/nc_projections/points_nes_providentia.py @@ -109,9 +109,6 @@ class PointsNesProvidentia(PointsNes): self.grid_edge_lon = self._get_coordinate_values(self._grid_edge_lon, '') self.grid_edge_lat = self._get_coordinate_values(self._grid_edge_lat, '') - # Set strlen to be None (avoid default strlen inherited from points) - self.set_strlen(None) - @staticmethod def new(comm=None, path=None, info=False, dataset=None, xarray=False, create_nes=False, balanced=False, parallel_method='X', avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, @@ -366,29 +363,42 @@ class PointsNesProvidentia(PointsNes): if self.variables is not None: for i, (var_name, var_dict) in enumerate(self.variables.items()): - if var_dict['data'] is not None: - - # Get data type - if 'dtype' in var_dict.keys(): - var_dtype = var_dict['dtype'] - else: - var_dtype = var_dict['data'].dtype + # Get data type + if 'dtype' in var_dict.keys(): + var_dtype = var_dict['dtype'] + if (var_dict['data'] is not None) and (var_dtype != var_dict['data'].dtype): + msg = "WARNING!!! " + msg += "Different data types for variable {0}. ".format(var_name) + msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, + var_dict['data'].dtype) + warnings.warn(msg) + sys.stderr.flush() + try: + var_dict['data'] = var_dict['data'].astype(var_dtype) + except Exception as e: # TODO: Detect exception + raise e("It was not possible to cast the data to the input dtype.") + else: + var_dtype = var_dict['data'].dtype - # Transform objects into strings (e.g. for ESDAC iwahashi landform in GHOST) - if var_dtype == np.dtype(object): - var_dict['data'] = var_dict['data'].astype(str) - var_dtype = var_dict['data'].dtype - else: - var_dtype = var_dict['data'].dtype + # Transform objects into strings (e.g. for ESDAC iwahashi landform in GHOST) + if var_dtype == np.dtype(object): + var_dict['data'] = var_dict['data'].astype(str) + var_dtype = var_dict['data'].dtype - # Get dimensions + # Get dimensions when reading datasets + if 'dimensions' in var_dict.keys(): + var_dims = var_dict['dimensions'] + # Get dimensions when creating new datasets + else: if len(var_dict['data'].shape) == 1: - # Metadata + # For data that depends only on station (e.g. station_code) var_dims = self._var_dim - elif len(var_dict['data'].shape) == 2: - # Different from metadata (e.g. concentrations of pm10) - var_dims = self._var_dim + ('time',) + else: + # For data that is dependent on time and station (e.g. PM10) + var_dims = self._var_dim + ('time',) + if var_dict['data'] is not None: + # Ensure data is of type numpy array (to create NES) if not isinstance(var_dict['data'], (np.ndarray, np.generic)): try: @@ -397,25 +407,16 @@ class PointsNesProvidentia(PointsNes): raise AttributeError("Data for variable {0} must be a numpy array.".format(var_name)) # Convert list of strings to chars for parallelization - if not np.issubdtype(var_dict['data'].dtype, np.number): + if np.issubdtype(var_dict['data'].dtype, np.character): try: - # Get unicode - unicode_type = len(max(var_dict['data'].flatten(), key=len)) + # Add strlen as a dimension if needed + if 'strlen' not in var_dims: + var_dims += ('strlen',) - if ((var_dict['data'].dtype == np.dtype(' 0, complevel=self.zip_lvl) - else: - if self.master: - chunk_size = var_dict['data'].shape - else: - chunk_size = None - chunk_size = self.comm.bcast(chunk_size, root=0) - var = netcdf.createVariable(var_name, var_dtype, var_dims, zlib=self.zip_lvl > 0, - complevel=self.zip_lvl, chunksizes=chunk_size) + if not chunking: + var = netcdf.createVariable(var_name, var_dtype, var_dims, + zlib=self.zip_lvl > 0, complevel=self.zip_lvl) + else: + if self.master: + chunk_size = var_dict['data'].shape + else: + chunk_size = None + chunk_size = self.comm.bcast(chunk_size, root=0) + var = netcdf.createVariable(var_name, var_dtype, var_dims, zlib=self.zip_lvl > 0, + complevel=self.zip_lvl, chunksizes=chunk_size) + + if self.info: + print("Rank {0:03d}: Var {1} created ({2}/{3})".format( + self.rank, var_name, i + 1, len(self.variables))) + if self.size > 1: + var.set_collective(True) + if self.info: + print("Rank {0:03d}: Var {1} collective ({2}/{3})".format( + self.rank, var_name, i + 1, len(self.variables))) + for att_name, att_value in var_dict.items(): + if att_name == 'data': if self.info: - print("Rank {0:03d}: Var {1} created ({2}/{3})".format( - self.rank, var_name, i + 1, len(self.variables))) - if self.size > 1: - var.set_collective(True) - if self.info: - print("Rank {0:03d}: Var {1} collective ({2}/{3})".format( - self.rank, var_name, i + 1, len(self.variables))) - - for att_name, att_value in var_dict.items(): - if att_name == 'data': - if len(att_value.shape) == 1: - try: - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, - att_value.shape)) - except ValueError: - raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, - att_value.shape)) - elif len(att_value.shape) == 2: - if 'strlen' in var_dims: - try: - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :] = att_value - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, - att_value.shape)) - except ValueError: - raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, - att_value.shape)) - else: - try: - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max']] = att_value - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max']].shape, - att_value.shape)) - except ValueError: - raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max']].shape, - att_value.shape)) - elif len(att_value.shape) == 3: - try: + print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) + if len(att_value.shape) == 1: + try: + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value + except IndexError: + raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, + att_value.shape)) + except ValueError: + raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, + att_value.shape)) + elif len(att_value.shape) == 2: + if 'strlen' in var_dims: + try: + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :] = att_value + except IndexError: + raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, + att_value.shape)) + except ValueError: + raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, + att_value.shape)) + else: + try: + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], + self.write_axis_limits['t_min']:self.write_axis_limits['t_max']] = att_value + except IndexError: + raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - :] = att_value - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - :].shape, + self.write_axis_limits['t_min']:self.write_axis_limits['t_max']].shape, + att_value.shape)) + except ValueError: + raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], + self.write_axis_limits['t_min']:self.write_axis_limits['t_max']].shape, att_value.shape)) - except ValueError: - raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - :].shape, - att_value.shape)) - - if self.info: - print("Rank {0:03d}: Var {1} data ({2}/{3})".format(self.rank, var_name, i + 1, - len(self.variables))) - elif att_name not in ['chunk_size', 'var_dims', 'dimensions']: - var.setncattr(att_name, att_value) - self._set_var_crs(var) + elif len(att_value.shape) == 3: + try: + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], + self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + :] = att_value + except IndexError: + raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], + self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + :].shape, + att_value.shape)) + except ValueError: + raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( + var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], + self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + :].shape, + att_value.shape)) + if self.info: - print("Rank {0:03d}: Var {1} completed ({2}/{3})".format(self.rank, var_name, i + 1, - len(self.variables))) - except Exception as e: - print("**ERROR** an error has occurred while writing the '{0}' variable".format(var_name)) - # print("**ERROR** an error has occurredred while writing the '{0}' variable".format(var_name), - # file=sys.stderr) - raise e - else: - msg = 'WARNING!!! ' - msg += 'Variable {0} was not loaded. It will not be written.'.format(var_name) - warnings.warn(msg) - sys.stderr.flush() + print("Rank {0:03d}: Var {1} data ({2}/{3})".format(self.rank, var_name, i + 1, + len(self.variables))) + elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype']: + var.setncattr(att_name, att_value) + + self._set_var_crs(var) + if self.info: + print("Rank {0:03d}: Var {1} completed ({2}/{3})".format(self.rank, var_name, i + 1, + len(self.variables))) return None diff --git a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb index 349ee47..afe6277 100644 --- a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb +++ b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -483,7 +483,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, diff --git a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb index 07a94dc..6a7a05d 100644 --- a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb +++ b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -408,7 +408,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, diff --git a/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb b/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb index 7cfb951..ec7fe84 100644 --- a/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb +++ b/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb @@ -49,7 +49,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -185,6 +185,7 @@ " fill_value=1e+20,\n", " dtype=float32),\n", " 'dimensions': ('station',),\n", + " 'dtype': dtype('float32'),\n", " 'units': 'degrees_north',\n", " 'long_name': 'latitude',\n", " 'standard_name': 'latitude',\n", @@ -233,6 +234,7 @@ " fill_value=1e+20,\n", " dtype=float32),\n", " 'dimensions': ('station',),\n", + " 'dtype': dtype('float32'),\n", " 'units': 'degrees_east',\n", " 'long_name': 'longitude',\n", " 'standard_name': 'longitude',\n", @@ -321,8 +323,9 @@ " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", - " 'nan', 'nan', 'nan', 'nan', 'nan'], dtype='" + "" ] }, "execution_count": 13, @@ -763,7 +800,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -877,6 +914,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('station',),\n", + " 'dtype': dtype('float64'),\n", " 'standard_name': 'latitude',\n", " 'long_name': 'latitude',\n", " 'units': 'decimal degrees North',\n", @@ -905,6 +943,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('station',),\n", + " 'dtype': dtype('float64'),\n", " 'standard_name': 'longitude',\n", " 'long_name': 'longitude',\n", " 'units': 'decimal degrees East',\n", @@ -930,11 +969,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:314: UserWarning: Variable day_night_code data missing values cannot be converted to np.nan.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:313: UserWarning: Variable day_night_code data missing values cannot be converted to np.nan.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:314: UserWarning: Variable season_code data missing values cannot be converted to np.nan.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:313: UserWarning: Variable season_code data missing values cannot be converted to np.nan.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:314: UserWarning: Variable weekday_weekend_code data missing values cannot be converted to np.nan.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:313: UserWarning: Variable weekday_weekend_code data missing values cannot be converted to np.nan.\n", " warnings.warn(msg)\n" ] }, @@ -1307,6 +1346,32 @@ "execution_count": 22, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ESDAC_Iwahashi_landform_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ESDAC_Meybeck_landform_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_5km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_5km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable GHOST_version. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_5km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable GHSL_settlement_model_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -1316,694 +1381,1097 @@ "Rank 000: Dimensions done\n", "Rank 000: Writing ASTER_v3_altitude var (1/173)\n", "Rank 000: Var ASTER_v3_altitude created (1/173)\n", + "Rank 000: Filling ASTER_v3_altitude)\n", "Rank 000: Var ASTER_v3_altitude data (1/173)\n", "Rank 000: Var ASTER_v3_altitude completed (1/173)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_BC_emissions var (2/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_BC_emissions created (2/173)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_BC_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_BC_emissions data (2/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_BC_emissions completed (2/173)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_CO_emissions var (3/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_CO_emissions created (3/173)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_CO_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_CO_emissions data (3/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_CO_emissions completed (3/173)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_NH3_emissions var (4/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NH3_emissions created (4/173)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_NH3_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NH3_emissions data (4/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NH3_emissions completed (4/173)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_NMVOC_emissions var (5/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NMVOC_emissions created (5/173)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_NMVOC_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NMVOC_emissions data (5/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NMVOC_emissions completed (5/173)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_NOx_emissions var (6/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NOx_emissions created (6/173)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_NOx_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NOx_emissions data (6/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NOx_emissions completed (6/173)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_OC_emissions var (7/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_OC_emissions created (7/173)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_OC_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_OC_emissions data (7/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_OC_emissions completed (7/173)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_PM10_emissions var (8/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_PM10_emissions created (8/173)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_PM10_emissions)\n", "Rank 000: Var 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EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions var (11/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions created (11/173)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions data (11/173)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions completed (11/173)\n", "Rank 000: Writing ESDAC_Iwahashi_landform_classification var (12/173)\n", "Rank 000: Var ESDAC_Iwahashi_landform_classification created (12/173)\n", + "Rank 000: Filling ESDAC_Iwahashi_landform_classification)\n", "Rank 000: Var ESDAC_Iwahashi_landform_classification data (12/173)\n", "Rank 000: Var ESDAC_Iwahashi_landform_classification completed (12/173)\n", "Rank 000: Writing ESDAC_Meybeck_landform_classification var (13/173)\n", "Rank 000: Var ESDAC_Meybeck_landform_classification created (13/173)\n", + "Rank 000: Filling ESDAC_Meybeck_landform_classification)\n", "Rank 000: Var ESDAC_Meybeck_landform_classification data (13/173)\n", "Rank 000: Var ESDAC_Meybeck_landform_classification completed (13/173)\n", "Rank 000: Writing ESDAC_modal_Iwahashi_landform_classification_25km var (14/173)\n", "Rank 000: Var ESDAC_modal_Iwahashi_landform_classification_25km created (14/173)\n", + "Rank 000: Filling ESDAC_modal_Iwahashi_landform_classification_25km)\n", "Rank 000: Var ESDAC_modal_Iwahashi_landform_classification_25km data (14/173)\n", "Rank 000: Var ESDAC_modal_Iwahashi_landform_classification_25km completed (14/173)\n", "Rank 000: Writing ESDAC_modal_Iwahashi_landform_classification_5km var (15/173)\n", "Rank 000: Var ESDAC_modal_Iwahashi_landform_classification_5km created (15/173)\n", + "Rank 000: Filling ESDAC_modal_Iwahashi_landform_classification_5km)\n", "Rank 000: Var ESDAC_modal_Iwahashi_landform_classification_5km data (15/173)\n", "Rank 000: Var ESDAC_modal_Iwahashi_landform_classification_5km completed (15/173)\n", "Rank 000: Writing 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"Rank 000: Var ETOPO1_altitude completed (18/173)\n", "Rank 000: Writing ETOPO1_max_altitude_difference_5km var (19/173)\n", "Rank 000: Var ETOPO1_max_altitude_difference_5km created (19/173)\n", + "Rank 000: Filling ETOPO1_max_altitude_difference_5km)\n", "Rank 000: Var ETOPO1_max_altitude_difference_5km data (19/173)\n", "Rank 000: Var ETOPO1_max_altitude_difference_5km completed (19/173)\n", "Rank 000: Writing GHOST_version var (20/173)\n", "Rank 000: Var GHOST_version created (20/173)\n", + "Rank 000: Filling GHOST_version)\n", "Rank 000: Var GHOST_version data (20/173)\n", "Rank 000: Var GHOST_version completed (20/173)\n", "Rank 000: Writing GHSL_average_built_up_area_density_25km var (21/173)\n", "Rank 000: Var GHSL_average_built_up_area_density_25km created (21/173)\n", + "Rank 000: Filling GHSL_average_built_up_area_density_25km)\n", "Rank 000: Var GHSL_average_built_up_area_density_25km data (21/173)\n", "Rank 000: Var GHSL_average_built_up_area_density_25km completed (21/173)\n", "Rank 000: Writing GHSL_average_built_up_area_density_5km var (22/173)\n", "Rank 000: Var GHSL_average_built_up_area_density_5km created (22/173)\n", + "Rank 000: Filling GHSL_average_built_up_area_density_5km)\n", "Rank 000: Var GHSL_average_built_up_area_density_5km data (22/173)\n", "Rank 000: Var GHSL_average_built_up_area_density_5km completed (22/173)\n", "Rank 000: Writing GHSL_average_population_density_25km var (23/173)\n", "Rank 000: Var GHSL_average_population_density_25km created (23/173)\n", + "Rank 000: Filling GHSL_average_population_density_25km)\n", "Rank 000: Var GHSL_average_population_density_25km data (23/173)\n", "Rank 000: Var GHSL_average_population_density_25km completed (23/173)\n", "Rank 000: Writing GHSL_average_population_density_5km var (24/173)\n", "Rank 000: Var GHSL_average_population_density_5km created (24/173)\n", + "Rank 000: Filling GHSL_average_population_density_5km)\n", "Rank 000: Var GHSL_average_population_density_5km data 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"/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! 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Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Rank 000: Writing GHSL_settlement_model_classification var (33/173)\n", "Rank 000: Var GHSL_settlement_model_classification created (33/173)\n", + "Rank 000: Filling GHSL_settlement_model_classification)\n", "Rank 000: Var GHSL_settlement_model_classification data (33/173)\n", "Rank 000: Var GHSL_settlement_model_classification completed (33/173)\n", "Rank 000: Writing GPW_average_population_density_25km var (34/173)\n", "Rank 000: Var GPW_average_population_density_25km created (34/173)\n", + "Rank 000: Filling GPW_average_population_density_25km)\n", "Rank 000: Var GPW_average_population_density_25km data (34/173)\n", "Rank 000: Var GPW_average_population_density_25km completed (34/173)\n", "Rank 000: Writing GPW_average_population_density_5km var (35/173)\n", "Rank 000: Var GPW_average_population_density_5km created (35/173)\n", + "Rank 000: Filling GPW_average_population_density_5km)\n", "Rank 000: Var GPW_average_population_density_5km data (35/173)\n", "Rank 000: Var GPW_average_population_density_5km completed (35/173)\n", "Rank 000: Writing GPW_max_population_density_25km var (36/173)\n", "Rank 000: Var GPW_max_population_density_25km created (36/173)\n", + "Rank 000: Filling GPW_max_population_density_25km)\n", "Rank 000: Var GPW_max_population_density_25km data (36/173)\n", "Rank 000: Var GPW_max_population_density_25km completed (36/173)\n", "Rank 000: Writing GPW_max_population_density_5km var (37/173)\n", "Rank 000: Var GPW_max_population_density_5km created (37/173)\n", + "Rank 000: Filling GPW_max_population_density_5km)\n", "Rank 000: Var GPW_max_population_density_5km data (37/173)\n", "Rank 000: Var GPW_max_population_density_5km completed (37/173)\n", "Rank 000: Writing GPW_population_density var (38/173)\n", "Rank 000: Var GPW_population_density created (38/173)\n", + "Rank 000: Filling GPW_population_density)\n", "Rank 000: Var GPW_population_density data (38/173)\n", "Rank 000: Var GPW_population_density completed (38/173)\n", "Rank 000: Writing GSFC_coastline_proximity var (39/173)\n", "Rank 000: Var GSFC_coastline_proximity created (39/173)\n", + "Rank 000: Filling GSFC_coastline_proximity)\n", "Rank 000: Var GSFC_coastline_proximity data (39/173)\n", "Rank 000: Var GSFC_coastline_proximity completed (39/173)\n", "Rank 000: Writing Joly-Peuch_classification_code var (40/173)\n", "Rank 000: Var Joly-Peuch_classification_code created (40/173)\n", + "Rank 000: Filling Joly-Peuch_classification_code)\n", "Rank 000: Var Joly-Peuch_classification_code data (40/173)\n", "Rank 000: Var Joly-Peuch_classification_code completed (40/173)\n", "Rank 000: Writing Koppen-Geiger_classification var (41/173)\n", "Rank 000: Var Koppen-Geiger_classification created (41/173)\n", + "Rank 000: Filling Koppen-Geiger_classification)\n", "Rank 000: Var Koppen-Geiger_classification data (41/173)\n", "Rank 000: Var Koppen-Geiger_classification completed (41/173)\n", "Rank 000: Writing Koppen-Geiger_modal_classification_25km var (42/173)\n", "Rank 000: Var Koppen-Geiger_modal_classification_25km created (42/173)\n", + "Rank 000: Filling Koppen-Geiger_modal_classification_25km)\n", "Rank 000: Var Koppen-Geiger_modal_classification_25km data (42/173)\n", "Rank 000: Var Koppen-Geiger_modal_classification_25km completed (42/173)\n", "Rank 000: Writing Koppen-Geiger_modal_classification_5km var (43/173)\n", "Rank 000: Var Koppen-Geiger_modal_classification_5km created (43/173)\n", + "Rank 000: Filling Koppen-Geiger_modal_classification_5km)\n", "Rank 000: Var Koppen-Geiger_modal_classification_5km data (43/173)\n", "Rank 000: Var Koppen-Geiger_modal_classification_5km completed (43/173)\n", "Rank 000: Writing MODIS_MCD12C1_v6_IGBP_land_use var (44/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_IGBP_land_use created (44/173)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_IGBP_land_use)\n", "Rank 000: Var MODIS_MCD12C1_v6_IGBP_land_use data (44/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_IGBP_land_use completed (44/173)\n", "Rank 000: Writing MODIS_MCD12C1_v6_LAI var (45/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_LAI created (45/173)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_LAI)\n", "Rank 000: Var MODIS_MCD12C1_v6_LAI data (45/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_LAI completed (45/173)\n", "Rank 000: Writing MODIS_MCD12C1_v6_UMD_land_use var (46/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_UMD_land_use created (46/173)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_UMD_land_use)\n", "Rank 000: Var MODIS_MCD12C1_v6_UMD_land_use data (46/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_UMD_land_use completed (46/173)\n", "Rank 000: Writing MODIS_MCD12C1_v6_modal_IGBP_land_use_25km var (47/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_IGBP_land_use_25km created (47/173)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_modal_IGBP_land_use_25km)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_IGBP_land_use_25km data (47/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_IGBP_land_use_25km completed (47/173)\n", "Rank 000: Writing MODIS_MCD12C1_v6_modal_IGBP_land_use_5km var (48/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_IGBP_land_use_5km created (48/173)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_modal_IGBP_land_use_5km)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_IGBP_land_use_5km data (48/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_IGBP_land_use_5km completed (48/173)\n", "Rank 000: Writing MODIS_MCD12C1_v6_modal_LAI_25km var (49/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_LAI_25km created (49/173)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_modal_LAI_25km)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_LAI_25km data (49/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_LAI_25km completed (49/173)\n", "Rank 000: Writing MODIS_MCD12C1_v6_modal_LAI_5km var (50/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_LAI_5km created (50/173)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_modal_LAI_5km)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_LAI_5km data (50/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_LAI_5km completed (50/173)\n", "Rank 000: Writing MODIS_MCD12C1_v6_modal_UMD_land_use_25km var (51/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_UMD_land_use_25km created (51/173)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_modal_UMD_land_use_25km)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_UMD_land_use_25km data (51/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_UMD_land_use_25km completed (51/173)\n", "Rank 000: Writing MODIS_MCD12C1_v6_modal_UMD_land_use_5km var (52/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_UMD_land_use_5km created (52/173)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_modal_UMD_land_use_5km)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_UMD_land_use_5km data (52/173)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_UMD_land_use_5km completed (52/173)\n", "Rank 000: Writing NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km var (53/173)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km created (53/173)\n", + "Rank 000: Filling NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km data (53/173)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km completed (53/173)\n", "Rank 000: Writing NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km var (54/173)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km created (54/173)\n", + "Rank 000: Filling NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km data (54/173)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km completed (54/173)\n", "Rank 000: Writing NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km var (55/173)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km created (55/173)\n", + "Rank 000: Filling NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km data (55/173)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km completed (55/173)\n", "Rank 000: Writing NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km var (56/173)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km created (56/173)\n", + "Rank 000: Filling NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km data (56/173)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km completed (56/173)\n", "Rank 000: Writing NOAA-DMSP-OLS_v4_nighttime_stable_lights var (57/173)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_nighttime_stable_lights created (57/173)\n", + "Rank 000: Filling NOAA-DMSP-OLS_v4_nighttime_stable_lights)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_nighttime_stable_lights data (57/173)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_nighttime_stable_lights completed (57/173)\n", "Rank 000: Writing OMI_level3_column_annual_average_NO2 var (58/173)\n", "Rank 000: Var OMI_level3_column_annual_average_NO2 created (58/173)\n", + "Rank 000: Filling OMI_level3_column_annual_average_NO2)\n", "Rank 000: Var OMI_level3_column_annual_average_NO2 data (58/173)\n", "Rank 000: Var OMI_level3_column_annual_average_NO2 completed (58/173)\n", "Rank 000: Writing OMI_level3_column_cloud_screened_annual_average_NO2 var (59/173)\n", "Rank 000: Var OMI_level3_column_cloud_screened_annual_average_NO2 created (59/173)\n", + "Rank 000: Filling OMI_level3_column_cloud_screened_annual_average_NO2)\n", "Rank 000: Var OMI_level3_column_cloud_screened_annual_average_NO2 data (59/173)\n", "Rank 000: Var OMI_level3_column_cloud_screened_annual_average_NO2 completed (59/173)\n", "Rank 000: Writing OMI_level3_tropospheric_column_annual_average_NO2 var (60/173)\n", "Rank 000: Var OMI_level3_tropospheric_column_annual_average_NO2 created (60/173)\n", + "Rank 000: Filling OMI_level3_tropospheric_column_annual_average_NO2)\n", "Rank 000: Var OMI_level3_tropospheric_column_annual_average_NO2 data (60/173)\n", "Rank 000: Var OMI_level3_tropospheric_column_annual_average_NO2 completed (60/173)\n", "Rank 000: Writing OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 var (61/173)\n", "Rank 000: Var OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 created (61/173)\n", + "Rank 000: Filling OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2)\n", "Rank 000: Var OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 data (61/173)\n", "Rank 000: Var OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 completed (61/173)\n", "Rank 000: Writing UMBC_anthrome_classification var (62/173)\n", "Rank 000: Var UMBC_anthrome_classification created (62/173)\n", + "Rank 000: Filling UMBC_anthrome_classification)\n", "Rank 000: Var UMBC_anthrome_classification data (62/173)\n", "Rank 000: Var UMBC_anthrome_classification completed (62/173)\n", "Rank 000: Writing UMBC_modal_anthrome_classification_25km var (63/173)\n", "Rank 000: Var UMBC_modal_anthrome_classification_25km created (63/173)\n", + "Rank 000: Filling UMBC_modal_anthrome_classification_25km)\n", "Rank 000: Var UMBC_modal_anthrome_classification_25km data (63/173)\n", "Rank 000: Var UMBC_modal_anthrome_classification_25km completed (63/173)\n", "Rank 000: Writing UMBC_modal_anthrome_classification_5km var (64/173)\n", "Rank 000: Var UMBC_modal_anthrome_classification_5km created (64/173)\n", + "Rank 000: Filling UMBC_modal_anthrome_classification_5km)\n", "Rank 000: Var UMBC_modal_anthrome_classification_5km data (64/173)\n", "Rank 000: Var UMBC_modal_anthrome_classification_5km completed (64/173)\n", "Rank 000: Writing WMO_region var (65/173)\n", "Rank 000: Var WMO_region created (65/173)\n", + "Rank 000: Filling WMO_region)\n", "Rank 000: Var WMO_region data (65/173)\n", "Rank 000: Var WMO_region completed (65/173)\n", "Rank 000: Writing WWF_TEOW_biogeographical_realm var (66/173)\n", "Rank 000: Var WWF_TEOW_biogeographical_realm created (66/173)\n", + "Rank 000: Filling WWF_TEOW_biogeographical_realm)\n", "Rank 000: Var WWF_TEOW_biogeographical_realm data (66/173)\n", "Rank 000: Var WWF_TEOW_biogeographical_realm completed (66/173)\n", "Rank 000: Writing WWF_TEOW_biome var (67/173)\n", "Rank 000: Var WWF_TEOW_biome created (67/173)\n", + "Rank 000: Filling WWF_TEOW_biome)\n", "Rank 000: Var WWF_TEOW_biome data (67/173)\n", "Rank 000: Var WWF_TEOW_biome completed (67/173)\n", "Rank 000: Writing WWF_TEOW_terrestrial_ecoregion var (68/173)\n", "Rank 000: Var WWF_TEOW_terrestrial_ecoregion created (68/173)\n", + "Rank 000: Filling WWF_TEOW_terrestrial_ecoregion)\n", "Rank 000: Var WWF_TEOW_terrestrial_ecoregion data (68/173)\n", "Rank 000: Var WWF_TEOW_terrestrial_ecoregion completed (68/173)\n", "Rank 000: Writing administrative_country_division_1 var (69/173)\n", "Rank 000: Var administrative_country_division_1 created (69/173)\n", + "Rank 000: Filling administrative_country_division_1)\n", "Rank 000: Var administrative_country_division_1 data (69/173)\n", "Rank 000: Var administrative_country_division_1 completed (69/173)\n", "Rank 000: Writing administrative_country_division_2 var (70/173)\n", "Rank 000: Var administrative_country_division_2 created (70/173)\n", + "Rank 000: Filling administrative_country_division_2)\n", "Rank 000: Var administrative_country_division_2 data (70/173)\n", "Rank 000: Var administrative_country_division_2 completed (70/173)\n", "Rank 000: Writing altitude var (71/173)\n", "Rank 000: Var altitude created (71/173)\n", + "Rank 000: Filling altitude)\n", "Rank 000: Var altitude data (71/173)\n", "Rank 000: Var altitude completed (71/173)\n", "Rank 000: Writing annual_native_max_gap_percent var (72/173)\n", "Rank 000: Var annual_native_max_gap_percent created (72/173)\n", + "Rank 000: Filling annual_native_max_gap_percent)\n", "Rank 000: Var annual_native_max_gap_percent data (72/173)\n", "Rank 000: Var annual_native_max_gap_percent completed (72/173)\n", "Rank 000: Writing annual_native_representativity_percent var (73/173)\n", "Rank 000: Var annual_native_representativity_percent created (73/173)\n", + "Rank 000: Filling annual_native_representativity_percent)\n", "Rank 000: Var annual_native_representativity_percent data (73/173)\n", "Rank 000: Var annual_native_representativity_percent completed (73/173)\n", "Rank 000: Writing area_classification var (74/173)\n", "Rank 000: Var area_classification created (74/173)\n", + "Rank 000: Filling area_classification)\n", "Rank 000: Var area_classification data (74/173)\n", "Rank 000: Var area_classification completed (74/173)\n", "Rank 000: Writing associated_networks var (75/173)\n", "Rank 000: Var associated_networks created (75/173)\n", + "Rank 000: Filling associated_networks)\n", "Rank 000: Var associated_networks data (75/173)\n", "Rank 000: Var associated_networks completed (75/173)\n", "Rank 000: Writing city var (76/173)\n", "Rank 000: Var city created (76/173)\n", + "Rank 000: Filling city)\n", "Rank 000: Var city data (76/173)\n", "Rank 000: Var city completed (76/173)\n", "Rank 000: Writing climatology var (77/173)\n", "Rank 000: Var climatology created (77/173)\n", + "Rank 000: Filling climatology)\n", "Rank 000: Var climatology data (77/173)\n", "Rank 000: Var climatology completed (77/173)\n", "Rank 000: Writing contact_email_address var (78/173)\n", "Rank 000: Var contact_email_address created (78/173)\n", + "Rank 000: Filling contact_email_address)\n", "Rank 000: Var contact_email_address data (78/173)\n", "Rank 000: Var contact_email_address completed (78/173)\n", "Rank 000: Writing contact_institution var (79/173)\n", "Rank 000: Var contact_institution created (79/173)\n", + "Rank 000: Filling contact_institution)\n", "Rank 000: Var contact_institution data (79/173)\n", "Rank 000: Var contact_institution completed (79/173)\n", "Rank 000: Writing contact_name var (80/173)\n", "Rank 000: Var contact_name created (80/173)\n", + "Rank 000: Filling contact_name)\n", "Rank 000: Var contact_name data (80/173)\n", "Rank 000: Var contact_name completed (80/173)\n", "Rank 000: Writing country var (81/173)\n", "Rank 000: Var country created (81/173)\n", + "Rank 000: Filling country)\n", "Rank 000: Var country data (81/173)\n", "Rank 000: Var country completed (81/173)\n", "Rank 000: Writing daily_native_max_gap_percent var (82/173)\n", "Rank 000: Var daily_native_max_gap_percent created (82/173)\n", + "Rank 000: Filling daily_native_max_gap_percent)\n", "Rank 000: Var daily_native_max_gap_percent data (82/173)\n", "Rank 000: Var daily_native_max_gap_percent completed (82/173)\n", "Rank 000: Writing daily_native_representativity_percent var (83/173)\n", "Rank 000: Var daily_native_representativity_percent created (83/173)\n", + "Rank 000: Filling daily_native_representativity_percent)\n", "Rank 000: Var daily_native_representativity_percent data (83/173)\n", "Rank 000: Var daily_native_representativity_percent completed (83/173)\n", "Rank 000: Writing daily_passing_vehicles var (84/173)\n", "Rank 000: Var daily_passing_vehicles created (84/173)\n", + "Rank 000: Filling daily_passing_vehicles)\n", "Rank 000: Var daily_passing_vehicles data (84/173)\n", "Rank 000: Var daily_passing_vehicles completed (84/173)\n", "Rank 000: Writing data_level var (85/173)\n", "Rank 000: Var data_level created (85/173)\n", + "Rank 000: Filling data_level)\n", "Rank 000: Var data_level data (85/173)\n", "Rank 000: Var data_level completed (85/173)\n", "Rank 000: Writing data_licence var (86/173)\n", "Rank 000: Var data_licence created (86/173)\n", + "Rank 000: Filling data_licence)\n", "Rank 000: Var data_licence data (86/173)\n", "Rank 000: Var data_licence completed (86/173)\n", "Rank 000: Writing day_night_code var (87/173)\n", "Rank 000: Var day_night_code created (87/173)\n", + "Rank 000: Filling day_night_code)\n", "Rank 000: Var day_night_code data (87/173)\n", "Rank 000: Var day_night_code completed (87/173)\n", "Rank 000: Writing daytime_traffic_speed var (88/173)\n", "Rank 000: Var daytime_traffic_speed created (88/173)\n", + "Rank 000: Filling daytime_traffic_speed)\n", "Rank 000: Var daytime_traffic_speed data (88/173)\n", "Rank 000: Var daytime_traffic_speed completed (88/173)\n", - "Rank 000: Writing derived_uncertainty_per_measurement var (89/173)\n", + "Rank 000: Writing derived_uncertainty_per_measurement var (89/173)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! 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network_provided_volume_standard_temperature)\n", "Rank 000: Var network_provided_volume_standard_temperature data (137/173)\n", "Rank 000: Var network_provided_volume_standard_temperature completed (137/173)\n", "Rank 000: Writing network_qa_details var (138/173)\n", "Rank 000: Var network_qa_details created (138/173)\n", + "Rank 000: Filling network_qa_details)\n", "Rank 000: Var network_qa_details data (138/173)\n", "Rank 000: Var network_qa_details completed (138/173)\n", "Rank 000: Writing network_sampling_details var (139/173)\n", "Rank 000: Var network_sampling_details created (139/173)\n", + "Rank 000: Filling network_sampling_details)\n", "Rank 000: Var network_sampling_details data (139/173)\n", "Rank 000: Var network_sampling_details completed (139/173)\n", "Rank 000: Writing network_uncertainty_details var (140/173)\n", "Rank 000: Var network_uncertainty_details created (140/173)\n", + "Rank 000: Filling network_uncertainty_details)\n", "Rank 000: Var 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Filling primary_sampling_instrument_reported_flow_rate)\n", "Rank 000: Var primary_sampling_instrument_reported_flow_rate data (146/173)\n", "Rank 000: Var primary_sampling_instrument_reported_flow_rate completed (146/173)\n", "Rank 000: Writing primary_sampling_process_details var (147/173)\n", "Rank 000: Var primary_sampling_process_details created (147/173)\n", + "Rank 000: Filling primary_sampling_process_details)\n", "Rank 000: Var primary_sampling_process_details data (147/173)\n", "Rank 000: Var primary_sampling_process_details completed (147/173)\n", "Rank 000: Writing primary_sampling_type var (148/173)\n", "Rank 000: Var primary_sampling_type created (148/173)\n", + "Rank 000: Filling primary_sampling_type)\n", "Rank 000: Var primary_sampling_type data (148/173)\n", "Rank 000: Var primary_sampling_type completed (148/173)\n", "Rank 000: Writing principal_investigator_email_address var (149/173)\n", "Rank 000: Var principal_investigator_email_address created (149/173)\n", + "Rank 000: Filling principal_investigator_email_address)\n", "Rank 000: Var principal_investigator_email_address data (149/173)\n", "Rank 000: Var principal_investigator_email_address completed (149/173)\n", "Rank 000: Writing principal_investigator_institution var (150/173)\n", "Rank 000: Var principal_investigator_institution created (150/173)\n", + "Rank 000: Filling principal_investigator_institution)\n", "Rank 000: Var principal_investigator_institution data (150/173)\n", "Rank 000: Var principal_investigator_institution completed (150/173)\n", "Rank 000: Writing principal_investigator_name var (151/173)\n", "Rank 000: Var principal_investigator_name created (151/173)\n", + "Rank 000: Filling principal_investigator_name)\n", "Rank 000: Var principal_investigator_name data (151/173)\n", "Rank 000: Var principal_investigator_name completed (151/173)\n", "Rank 000: Writing process_warnings var (152/173)\n", "Rank 000: Var process_warnings created (152/173)\n", + "Rank 000: Filling process_warnings)\n", "Rank 000: Var process_warnings data (152/173)\n", "Rank 000: Var process_warnings completed (152/173)\n", "Rank 000: Writing projection var (153/173)\n", "Rank 000: Var projection created (153/173)\n", + "Rank 000: Filling projection)\n", "Rank 000: Var projection data (153/173)\n", "Rank 000: Var projection completed (153/173)\n", "Rank 000: Writing reported_uncertainty_per_measurement var (154/173)\n", "Rank 000: Var reported_uncertainty_per_measurement created (154/173)\n", + "Rank 000: Filling reported_uncertainty_per_measurement)\n", "Rank 000: Var reported_uncertainty_per_measurement data (154/173)\n", "Rank 000: Var reported_uncertainty_per_measurement completed (154/173)\n", "Rank 000: Writing representative_radius var (155/173)\n", "Rank 000: Var representative_radius created (155/173)\n", + "Rank 000: Filling representative_radius)\n", "Rank 000: Var representative_radius data (155/173)\n", "Rank 000: Var representative_radius completed (155/173)\n", "Rank 000: Writing retrieval_algorithm var (156/173)\n", "Rank 000: Var retrieval_algorithm created (156/173)\n", + "Rank 000: Filling retrieval_algorithm)\n", "Rank 000: Var retrieval_algorithm data (156/173)\n", "Rank 000: Var retrieval_algorithm completed (156/173)\n", "Rank 000: Writing sample_preparation_further_details var (157/173)\n", "Rank 000: Var sample_preparation_further_details created (157/173)\n", + "Rank 000: Filling sample_preparation_further_details)\n", "Rank 000: Var sample_preparation_further_details data (157/173)\n", "Rank 000: Var sample_preparation_further_details completed (157/173)\n", "Rank 000: Writing sample_preparation_process_details var (158/173)\n", "Rank 000: Var sample_preparation_process_details created (158/173)\n", + "Rank 000: Filling sample_preparation_process_details)\n", "Rank 000: Var sample_preparation_process_details data (158/173)\n", "Rank 000: Var sample_preparation_process_details completed (158/173)\n", "Rank 000: Writing sample_preparation_techniques var (159/173)\n", "Rank 000: Var sample_preparation_techniques created (159/173)\n", + "Rank 000: Filling sample_preparation_techniques)\n", "Rank 000: Var sample_preparation_techniques data (159/173)\n", "Rank 000: Var sample_preparation_techniques completed (159/173)\n", "Rank 000: Writing sample_preparation_types var (160/173)\n", "Rank 000: Var sample_preparation_types created (160/173)\n", + "Rank 000: Filling sample_preparation_types)\n", "Rank 000: Var sample_preparation_types data (160/173)\n", "Rank 000: Var sample_preparation_types completed (160/173)\n", "Rank 000: Writing sampling_height var (161/173)\n", "Rank 000: Var sampling_height created (161/173)\n", + "Rank 000: Filling sampling_height)\n", "Rank 000: Var sampling_height data (161/173)\n", "Rank 000: Var sampling_height completed (161/173)\n", "Rank 000: Writing sconcso4 var (162/173)\n", "Rank 000: Var sconcso4 created (162/173)\n", + "Rank 000: Filling sconcso4)\n", "Rank 000: Var sconcso4 data (162/173)\n", "Rank 000: Var sconcso4 completed (162/173)\n", "Rank 000: Writing season_code var (163/173)\n", "Rank 000: Var season_code created (163/173)\n", + "Rank 000: Filling season_code)\n", "Rank 000: Var season_code data (163/173)\n", "Rank 000: Var season_code completed (163/173)\n", "Rank 000: Writing station_classification var (164/173)\n", "Rank 000: Var station_classification created (164/173)\n", + "Rank 000: Filling station_classification)\n", "Rank 000: Var station_classification data (164/173)\n", "Rank 000: Var station_classification completed (164/173)\n", "Rank 000: Writing station_name var (165/173)\n", "Rank 000: Var station_name created (165/173)\n", + "Rank 000: Filling station_name)\n", "Rank 000: Var station_name data (165/173)\n", "Rank 000: Var station_name completed (165/173)\n", "Rank 000: Writing station_reference var (166/173)\n", "Rank 000: Var station_reference created (166/173)\n", + "Rank 000: Filling station_reference)\n", "Rank 000: Var station_reference data (166/173)\n", "Rank 000: Var station_reference completed (166/173)\n", "Rank 000: Writing station_timezone var (167/173)\n", "Rank 000: Var station_timezone created (167/173)\n", + "Rank 000: Filling station_timezone)\n", "Rank 000: Var station_timezone data (167/173)\n", - "Rank 000: Var station_timezone completed (167/173)\n", + "Rank 000: Var station_timezone completed (167/173)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable terrain. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable vertical_datum. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Rank 000: Writing street_type var (168/173)\n", "Rank 000: Var street_type created (168/173)\n", + "Rank 000: Filling street_type)\n", "Rank 000: Var street_type data (168/173)\n", "Rank 000: Var street_type completed (168/173)\n", "Rank 000: Writing street_width var (169/173)\n", "Rank 000: Var street_width created (169/173)\n", + "Rank 000: Filling street_width)\n", "Rank 000: Var street_width data (169/173)\n", "Rank 000: Var street_width completed (169/173)\n", "Rank 000: Writing terrain var (170/173)\n", "Rank 000: Var terrain created (170/173)\n", + "Rank 000: Filling terrain)\n", "Rank 000: Var terrain data (170/173)\n", "Rank 000: Var terrain completed (170/173)\n", "Rank 000: Writing vertical_datum var (171/173)\n", "Rank 000: Var vertical_datum created (171/173)\n", + "Rank 000: Filling vertical_datum)\n", "Rank 000: Var vertical_datum data (171/173)\n", "Rank 000: Var vertical_datum completed (171/173)\n", "Rank 000: Writing weekday_weekend_code var (172/173)\n", "Rank 000: Var weekday_weekend_code created (172/173)\n", + "Rank 000: Filling weekday_weekend_code)\n", "Rank 000: Var weekday_weekend_code data (172/173)\n", "Rank 000: Var weekday_weekend_code completed (172/173)\n", "Rank 000: Writing sconcso4_prefiltered_defaultqa var (173/173)\n", "Rank 000: Var sconcso4_prefiltered_defaultqa created (173/173)\n", + "Rank 000: Filling sconcso4_prefiltered_defaultqa)\n", "Rank 000: Var sconcso4_prefiltered_defaultqa data (173/173)\n", "Rank 000: Var sconcso4_prefiltered_defaultqa completed (173/173)\n" ] @@ -2054,23 +2522,23 @@ "Rank 000: Loading EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions var (12/174)\n", "Rank 000: Loaded EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions var ((3,))\n", "Rank 000: Loading ESDAC_Iwahashi_landform_classification var (13/174)\n", - "Rank 000: Loaded ESDAC_Iwahashi_landform_classification var ((3,))\n", + "Rank 000: Loaded ESDAC_Iwahashi_landform_classification var ((3, 30))\n", "Rank 000: Loading ESDAC_Meybeck_landform_classification var (14/174)\n", - "Rank 000: Loaded ESDAC_Meybeck_landform_classification var ((3,))\n", + "Rank 000: Loaded ESDAC_Meybeck_landform_classification var ((3, 30))\n", "Rank 000: Loading ESDAC_modal_Iwahashi_landform_classification_25km var (15/174)\n", - "Rank 000: Loaded ESDAC_modal_Iwahashi_landform_classification_25km var ((3,))\n", + "Rank 000: Loaded ESDAC_modal_Iwahashi_landform_classification_25km var ((3, 30))\n", "Rank 000: Loading ESDAC_modal_Iwahashi_landform_classification_5km var (16/174)\n", - "Rank 000: Loaded ESDAC_modal_Iwahashi_landform_classification_5km var ((3,))\n", + "Rank 000: Loaded ESDAC_modal_Iwahashi_landform_classification_5km var ((3, 30))\n", "Rank 000: Loading ESDAC_modal_Meybeck_landform_classification_25km var (17/174)\n", - "Rank 000: Loaded ESDAC_modal_Meybeck_landform_classification_25km var ((3,))\n", + "Rank 000: Loaded ESDAC_modal_Meybeck_landform_classification_25km var ((3, 30))\n", "Rank 000: Loading ESDAC_modal_Meybeck_landform_classification_5km var (18/174)\n", - "Rank 000: Loaded ESDAC_modal_Meybeck_landform_classification_5km var ((3,))\n", + "Rank 000: Loaded ESDAC_modal_Meybeck_landform_classification_5km var ((3, 30))\n", "Rank 000: Loading ETOPO1_altitude var (19/174)\n", "Rank 000: Loaded ETOPO1_altitude var ((3,))\n", "Rank 000: Loading ETOPO1_max_altitude_difference_5km var (20/174)\n", "Rank 000: Loaded ETOPO1_max_altitude_difference_5km var ((3,))\n", "Rank 000: Loading GHOST_version var (21/174)\n", - "Rank 000: Loaded GHOST_version var ((3,))\n", + "Rank 000: Loaded GHOST_version var ((3, 30))\n", "Rank 000: Loading GHSL_average_built_up_area_density_25km var (22/174)\n", "Rank 000: Loaded GHSL_average_built_up_area_density_25km var ((3,))\n", "Rank 000: Loading GHSL_average_built_up_area_density_5km var (23/174)\n", @@ -2090,13 +2558,13 @@ "Rank 000: Loading GHSL_max_population_density_5km var (30/174)\n", "Rank 000: Loaded GHSL_max_population_density_5km var ((3,))\n", "Rank 000: Loading GHSL_modal_settlement_model_classification_25km var (31/174)\n", - "Rank 000: Loaded GHSL_modal_settlement_model_classification_25km var ((3,))\n", + "Rank 000: Loaded GHSL_modal_settlement_model_classification_25km var ((3, 30))\n", "Rank 000: Loading GHSL_modal_settlement_model_classification_5km var (32/174)\n", - "Rank 000: Loaded GHSL_modal_settlement_model_classification_5km var ((3,))\n", + "Rank 000: Loaded GHSL_modal_settlement_model_classification_5km var ((3, 30))\n", "Rank 000: Loading GHSL_population_density var (33/174)\n", "Rank 000: Loaded GHSL_population_density var ((3,))\n", "Rank 000: Loading GHSL_settlement_model_classification var (34/174)\n", - "Rank 000: Loaded GHSL_settlement_model_classification var ((3,))\n", + "Rank 000: Loaded GHSL_settlement_model_classification var ((3, 30))\n", "Rank 000: Loading GPW_average_population_density_25km var (35/174)\n", "Rank 000: Loaded GPW_average_population_density_25km var ((3,))\n", "Rank 000: Loading GPW_average_population_density_5km var (36/174)\n", @@ -2112,29 +2580,29 @@ "Rank 000: Loading Joly-Peuch_classification_code var (41/174)\n", "Rank 000: Loaded Joly-Peuch_classification_code var ((3,))\n", "Rank 000: Loading Koppen-Geiger_classification var (42/174)\n", - "Rank 000: Loaded Koppen-Geiger_classification var ((3,))\n", + "Rank 000: Loaded Koppen-Geiger_classification var ((3, 30))\n", "Rank 000: Loading Koppen-Geiger_modal_classification_25km var (43/174)\n", - "Rank 000: Loaded Koppen-Geiger_modal_classification_25km var ((3,))\n", + "Rank 000: Loaded Koppen-Geiger_modal_classification_25km var ((3, 30))\n", "Rank 000: Loading Koppen-Geiger_modal_classification_5km var (44/174)\n", - "Rank 000: Loaded Koppen-Geiger_modal_classification_5km var ((3,))\n", + "Rank 000: Loaded Koppen-Geiger_modal_classification_5km var ((3, 30))\n", "Rank 000: Loading MODIS_MCD12C1_v6_IGBP_land_use var (45/174)\n", - "Rank 000: Loaded MODIS_MCD12C1_v6_IGBP_land_use var ((3,))\n", + "Rank 000: Loaded MODIS_MCD12C1_v6_IGBP_land_use var ((3, 30))\n", "Rank 000: Loading MODIS_MCD12C1_v6_LAI var (46/174)\n", - "Rank 000: Loaded MODIS_MCD12C1_v6_LAI var ((3,))\n", + "Rank 000: Loaded MODIS_MCD12C1_v6_LAI var ((3, 30))\n", "Rank 000: Loading MODIS_MCD12C1_v6_UMD_land_use var (47/174)\n", - "Rank 000: Loaded MODIS_MCD12C1_v6_UMD_land_use var ((3,))\n", + "Rank 000: Loaded MODIS_MCD12C1_v6_UMD_land_use var ((3, 30))\n", "Rank 000: Loading MODIS_MCD12C1_v6_modal_IGBP_land_use_25km var (48/174)\n", - "Rank 000: Loaded MODIS_MCD12C1_v6_modal_IGBP_land_use_25km var ((3,))\n", + "Rank 000: Loaded MODIS_MCD12C1_v6_modal_IGBP_land_use_25km var ((3, 30))\n", "Rank 000: Loading MODIS_MCD12C1_v6_modal_IGBP_land_use_5km var (49/174)\n", - "Rank 000: Loaded MODIS_MCD12C1_v6_modal_IGBP_land_use_5km var ((3,))\n", + "Rank 000: Loaded MODIS_MCD12C1_v6_modal_IGBP_land_use_5km var ((3, 30))\n", "Rank 000: Loading MODIS_MCD12C1_v6_modal_LAI_25km var (50/174)\n", - "Rank 000: Loaded MODIS_MCD12C1_v6_modal_LAI_25km var ((3,))\n", + "Rank 000: Loaded MODIS_MCD12C1_v6_modal_LAI_25km var ((3, 30))\n", "Rank 000: Loading MODIS_MCD12C1_v6_modal_LAI_5km var (51/174)\n", - "Rank 000: Loaded MODIS_MCD12C1_v6_modal_LAI_5km var ((3,))\n", + "Rank 000: Loaded MODIS_MCD12C1_v6_modal_LAI_5km var ((3, 30))\n", "Rank 000: Loading MODIS_MCD12C1_v6_modal_UMD_land_use_25km var (52/174)\n", - "Rank 000: Loaded MODIS_MCD12C1_v6_modal_UMD_land_use_25km var ((3,))\n", + "Rank 000: Loaded MODIS_MCD12C1_v6_modal_UMD_land_use_25km var ((3, 30))\n", "Rank 000: Loading MODIS_MCD12C1_v6_modal_UMD_land_use_5km var (53/174)\n", - "Rank 000: Loaded MODIS_MCD12C1_v6_modal_UMD_land_use_5km var ((3,))\n", + "Rank 000: Loaded MODIS_MCD12C1_v6_modal_UMD_land_use_5km var ((3, 30))\n", "Rank 000: Loading NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km var (54/174)\n", "Rank 000: Loaded NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km var ((3,))\n", "Rank 000: Loading NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km var (55/174)\n", @@ -2154,23 +2622,23 @@ "Rank 000: Loading OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 var (62/174)\n", "Rank 000: Loaded OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 var ((3,))\n", "Rank 000: Loading UMBC_anthrome_classification var (63/174)\n", - "Rank 000: Loaded UMBC_anthrome_classification var ((3,))\n", + "Rank 000: Loaded UMBC_anthrome_classification var ((3, 30))\n", "Rank 000: Loading UMBC_modal_anthrome_classification_25km var (64/174)\n", - "Rank 000: Loaded UMBC_modal_anthrome_classification_25km var ((3,))\n", + "Rank 000: Loaded UMBC_modal_anthrome_classification_25km var ((3, 30))\n", "Rank 000: Loading UMBC_modal_anthrome_classification_5km var (65/174)\n", - "Rank 000: Loaded UMBC_modal_anthrome_classification_5km var ((3,))\n", + "Rank 000: Loaded UMBC_modal_anthrome_classification_5km var ((3, 30))\n", "Rank 000: Loading WMO_region var (66/174)\n", - "Rank 000: Loaded WMO_region var ((3,))\n", + "Rank 000: Loaded WMO_region var ((3, 30))\n", "Rank 000: Loading WWF_TEOW_biogeographical_realm var (67/174)\n", - "Rank 000: Loaded WWF_TEOW_biogeographical_realm var ((3,))\n", + "Rank 000: Loaded WWF_TEOW_biogeographical_realm var ((3, 30))\n", "Rank 000: Loading WWF_TEOW_biome var (68/174)\n", - "Rank 000: Loaded WWF_TEOW_biome var ((3,))\n", + "Rank 000: Loaded WWF_TEOW_biome var ((3, 30))\n", "Rank 000: Loading WWF_TEOW_terrestrial_ecoregion var (69/174)\n", - "Rank 000: Loaded WWF_TEOW_terrestrial_ecoregion var ((3,))\n", + "Rank 000: Loaded WWF_TEOW_terrestrial_ecoregion var ((3, 30))\n", "Rank 000: Loading administrative_country_division_1 var (70/174)\n", - "Rank 000: Loaded administrative_country_division_1 var ((3,))\n", + "Rank 000: Loaded administrative_country_division_1 var ((3, 30))\n", "Rank 000: Loading administrative_country_division_2 var (71/174)\n", - "Rank 000: Loaded administrative_country_division_2 var ((3,))\n", + "Rank 000: Loaded administrative_country_division_2 var ((3, 30))\n", "Rank 000: Loading altitude var (72/174)\n", "Rank 000: Loaded altitude var ((3,))\n", "Rank 000: Loading annual_native_max_gap_percent var (73/174)\n", @@ -2178,21 +2646,21 @@ "Rank 000: Loading annual_native_representativity_percent var (74/174)\n", "Rank 000: Loaded annual_native_representativity_percent var ((3, 30))\n", "Rank 000: Loading area_classification var (75/174)\n", - "Rank 000: Loaded area_classification var ((3,))\n", + "Rank 000: Loaded area_classification var ((3, 30))\n", "Rank 000: Loading associated_networks var (76/174)\n", - "Rank 000: Loaded associated_networks var ((3,))\n", + "Rank 000: Loaded associated_networks var ((3, 30))\n", "Rank 000: Loading city var (77/174)\n", - "Rank 000: Loaded city var ((3,))\n", + "Rank 000: Loaded city var ((3, 30))\n", "Rank 000: Loading climatology var (78/174)\n", - "Rank 000: Loaded climatology var ((3,))\n", + "Rank 000: Loaded climatology var ((3, 30))\n", "Rank 000: Loading contact_email_address var (79/174)\n", - "Rank 000: Loaded contact_email_address var ((3,))\n", + "Rank 000: Loaded contact_email_address var ((3, 30))\n", "Rank 000: Loading contact_institution var (80/174)\n", - "Rank 000: Loaded contact_institution var ((3,))\n", + "Rank 000: Loaded contact_institution var ((3, 30))\n", "Rank 000: Loading contact_name var (81/174)\n", - "Rank 000: Loaded contact_name var ((3,))\n", + "Rank 000: Loaded contact_name var ((3, 30))\n", "Rank 000: Loading country var (82/174)\n", - "Rank 000: Loaded country var ((3,))\n", + "Rank 000: Loaded country var ((3, 30))\n", "Rank 000: Loading daily_native_max_gap_percent var (83/174)\n", "Rank 000: Loaded daily_native_max_gap_percent var ((3, 30))\n", "Rank 000: Loading daily_native_representativity_percent var (84/174)\n", @@ -2200,9 +2668,9 @@ "Rank 000: Loading daily_passing_vehicles var (85/174)\n", "Rank 000: Loaded daily_passing_vehicles var ((3,))\n", "Rank 000: Loading data_level var (86/174)\n", - "Rank 000: Loaded data_level var ((3,))\n", + "Rank 000: Loaded data_level var ((3, 30))\n", "Rank 000: Loading data_licence var (87/174)\n", - "Rank 000: Loaded data_licence var ((3,))\n", + "Rank 000: Loaded data_licence var ((3, 30))\n", "Rank 000: Loading day_night_code var (88/174)\n", "Rank 000: Loaded day_night_code var ((3, 30))\n", "Rank 000: Loading daytime_traffic_speed var (89/174)\n", @@ -2218,139 +2686,139 @@ "Rank 000: Loading distance_to_source var (94/174)\n", "Rank 000: Loaded distance_to_source var ((3,))\n", "Rank 000: Loading ellipsoid var (95/174)\n", - "Rank 000: Loaded ellipsoid var ((3,))\n", + "Rank 000: Loaded ellipsoid var ((3, 30))\n", "Rank 000: Loading horizontal_datum var (96/174)\n", - "Rank 000: Loaded horizontal_datum var ((3,))\n", + "Rank 000: Loaded horizontal_datum var ((3, 30))\n", "Rank 000: Loading land_use var (97/174)\n", - "Rank 000: Loaded land_use var ((3,))\n", + "Rank 000: Loaded land_use var ((3, 30))\n", "Rank 000: Loading main_emission_source var (98/174)\n", - "Rank 000: Loaded main_emission_source var ((3,))\n", + "Rank 000: Loaded main_emission_source var ((3, 30))\n", "Rank 000: Loading measurement_altitude var (99/174)\n", "Rank 000: Loaded measurement_altitude var ((3,))\n", "Rank 000: Loading measurement_methodology var (100/174)\n", - "Rank 000: Loaded measurement_methodology var ((3,))\n", + "Rank 000: Loaded measurement_methodology var ((3, 30))\n", "Rank 000: Loading measurement_scale var (101/174)\n", - "Rank 000: Loaded measurement_scale var ((3,))\n", + "Rank 000: Loaded measurement_scale var ((3, 30))\n", "Rank 000: Loading measuring_instrument_calibration_scale var (102/174)\n", - "Rank 000: Loaded measuring_instrument_calibration_scale var ((3,))\n", + "Rank 000: Loaded measuring_instrument_calibration_scale var ((3, 30))\n", "Rank 000: Loading measuring_instrument_documented_absorption_cross_section var (103/174)\n", - "Rank 000: Loaded measuring_instrument_documented_absorption_cross_section var ((3,))\n", + "Rank 000: Loaded measuring_instrument_documented_absorption_cross_section var ((3, 30))\n", "Rank 000: Loading measuring_instrument_documented_accuracy var (104/174)\n", - "Rank 000: Loaded measuring_instrument_documented_accuracy var ((3,))\n", + "Rank 000: Loaded measuring_instrument_documented_accuracy var ((3, 30))\n", "Rank 000: Loading measuring_instrument_documented_flow_rate var (105/174)\n", - "Rank 000: Loaded measuring_instrument_documented_flow_rate var ((3,))\n", + "Rank 000: Loaded measuring_instrument_documented_flow_rate var ((3, 30))\n", "Rank 000: Loading measuring_instrument_documented_lower_limit_of_detection var (106/174)\n", "Rank 000: Loaded measuring_instrument_documented_lower_limit_of_detection var ((3,))\n", "Rank 000: Loading measuring_instrument_documented_measurement_resolution var (107/174)\n", "Rank 000: Loaded measuring_instrument_documented_measurement_resolution var ((3,))\n", "Rank 000: Loading measuring_instrument_documented_precision var (108/174)\n", - "Rank 000: Loaded measuring_instrument_documented_precision var ((3,))\n", + "Rank 000: Loaded measuring_instrument_documented_precision var ((3, 30))\n", "Rank 000: Loading measuring_instrument_documented_span_drift var (109/174)\n", - "Rank 000: Loaded measuring_instrument_documented_span_drift var ((3,))\n", + "Rank 000: Loaded measuring_instrument_documented_span_drift var ((3, 30))\n", "Rank 000: Loading measuring_instrument_documented_uncertainty var (110/174)\n", - "Rank 000: Loaded measuring_instrument_documented_uncertainty var ((3,))\n", + "Rank 000: Loaded measuring_instrument_documented_uncertainty var ((3, 30))\n", "Rank 000: Loading measuring_instrument_documented_upper_limit_of_detection var (111/174)\n", "Rank 000: Loaded measuring_instrument_documented_upper_limit_of_detection var ((3,))\n", "Rank 000: Loading measuring_instrument_documented_zero_drift var (112/174)\n", - "Rank 000: Loaded measuring_instrument_documented_zero_drift var ((3,))\n", + "Rank 000: Loaded measuring_instrument_documented_zero_drift var ((3, 30))\n", "Rank 000: Loading measuring_instrument_documented_zonal_drift var (113/174)\n", - "Rank 000: Loaded measuring_instrument_documented_zonal_drift var ((3,))\n", + "Rank 000: Loaded measuring_instrument_documented_zonal_drift var ((3, 30))\n", "Rank 000: Loading measuring_instrument_further_details var (114/174)\n", - "Rank 000: Loaded measuring_instrument_further_details var ((3,))\n", + "Rank 000: Loaded measuring_instrument_further_details var ((3, 30))\n", "Rank 000: Loading measuring_instrument_inlet_information var (115/174)\n", - "Rank 000: Loaded measuring_instrument_inlet_information var ((3,))\n", + "Rank 000: Loaded measuring_instrument_inlet_information var ((3, 30))\n", "Rank 000: Loading measuring_instrument_manual_name var (116/174)\n", - "Rank 000: Loaded measuring_instrument_manual_name var ((3,))\n", + "Rank 000: Loaded measuring_instrument_manual_name var ((3, 30))\n", "Rank 000: Loading measuring_instrument_name var (117/174)\n", - "Rank 000: Loaded measuring_instrument_name var ((3,))\n", + "Rank 000: Loaded measuring_instrument_name var ((3, 30))\n", "Rank 000: Loading measuring_instrument_process_details var (118/174)\n", - "Rank 000: Loaded measuring_instrument_process_details var ((3,))\n", + "Rank 000: Loaded measuring_instrument_process_details var ((3, 30))\n", "Rank 000: Loading measuring_instrument_reported_absorption_cross_section var (119/174)\n", - "Rank 000: Loaded measuring_instrument_reported_absorption_cross_section var ((3,))\n", + "Rank 000: Loaded measuring_instrument_reported_absorption_cross_section var ((3, 30))\n", "Rank 000: Loading measuring_instrument_reported_accuracy var (120/174)\n", - "Rank 000: Loaded measuring_instrument_reported_accuracy var ((3,))\n", + "Rank 000: Loaded measuring_instrument_reported_accuracy var ((3, 30))\n", "Rank 000: Loading measuring_instrument_reported_flow_rate var (121/174)\n", - "Rank 000: Loaded measuring_instrument_reported_flow_rate var ((3,))\n", + "Rank 000: Loaded measuring_instrument_reported_flow_rate var ((3, 30))\n", "Rank 000: Loading measuring_instrument_reported_lower_limit_of_detection var (122/174)\n", "Rank 000: Loaded measuring_instrument_reported_lower_limit_of_detection var ((3,))\n", "Rank 000: Loading measuring_instrument_reported_measurement_resolution var (123/174)\n", "Rank 000: Loaded measuring_instrument_reported_measurement_resolution var ((3,))\n", "Rank 000: Loading measuring_instrument_reported_precision var (124/174)\n", - "Rank 000: Loaded measuring_instrument_reported_precision var ((3,))\n", + "Rank 000: Loaded measuring_instrument_reported_precision var ((3, 30))\n", "Rank 000: Loading measuring_instrument_reported_span_drift var (125/174)\n", - "Rank 000: Loaded measuring_instrument_reported_span_drift var ((3,))\n", + "Rank 000: Loaded measuring_instrument_reported_span_drift var ((3, 30))\n", "Rank 000: Loading measuring_instrument_reported_uncertainty var (126/174)\n", - "Rank 000: Loaded measuring_instrument_reported_uncertainty var ((3,))\n", + "Rank 000: Loaded measuring_instrument_reported_uncertainty var ((3, 30))\n", "Rank 000: Loading measuring_instrument_reported_units var (127/174)\n", - "Rank 000: Loaded measuring_instrument_reported_units var ((3,))\n", + "Rank 000: Loaded measuring_instrument_reported_units var ((3, 30))\n", "Rank 000: Loading measuring_instrument_reported_upper_limit_of_detection var (128/174)\n", "Rank 000: Loaded measuring_instrument_reported_upper_limit_of_detection var ((3,))\n", "Rank 000: Loading measuring_instrument_reported_zero_drift var (129/174)\n", - "Rank 000: Loaded measuring_instrument_reported_zero_drift var ((3,))\n", + "Rank 000: Loaded measuring_instrument_reported_zero_drift var ((3, 30))\n", "Rank 000: Loading measuring_instrument_reported_zonal_drift var (130/174)\n", - "Rank 000: Loaded measuring_instrument_reported_zonal_drift var ((3,))\n", + "Rank 000: Loaded measuring_instrument_reported_zonal_drift var ((3, 30))\n", "Rank 000: Loading measuring_instrument_sampling_type var (131/174)\n", - "Rank 000: Loaded measuring_instrument_sampling_type var ((3,))\n", + "Rank 000: Loaded measuring_instrument_sampling_type var ((3, 30))\n", "Rank 000: Loading monthly_native_max_gap_percent var (132/174)\n", "Rank 000: Loaded monthly_native_max_gap_percent var ((3, 30))\n", "Rank 000: Loading monthly_native_representativity_percent var (133/174)\n", "Rank 000: Loaded monthly_native_representativity_percent var ((3, 30))\n", "Rank 000: Loading network var (134/174)\n", - "Rank 000: Loaded network var ((3,))\n", + "Rank 000: Loaded network var ((3, 30))\n", "Rank 000: Loading network_maintenance_details var (135/174)\n", - "Rank 000: Loaded network_maintenance_details var ((3,))\n", + "Rank 000: Loaded network_maintenance_details var ((3, 30))\n", "Rank 000: Loading network_miscellaneous_details var (136/174)\n", - "Rank 000: Loaded network_miscellaneous_details var ((3,))\n", + "Rank 000: Loaded network_miscellaneous_details var ((3, 30))\n", "Rank 000: Loading network_provided_volume_standard_pressure var (137/174)\n", "Rank 000: Loaded network_provided_volume_standard_pressure var ((3,))\n", "Rank 000: Loading network_provided_volume_standard_temperature var (138/174)\n", "Rank 000: Loaded network_provided_volume_standard_temperature var ((3,))\n", "Rank 000: Loading network_qa_details var (139/174)\n", - "Rank 000: Loaded network_qa_details var ((3,))\n", + "Rank 000: Loaded network_qa_details var ((3, 30))\n", "Rank 000: Loading network_sampling_details var (140/174)\n", - "Rank 000: Loaded network_sampling_details var ((3,))\n", + "Rank 000: Loaded network_sampling_details var ((3, 30))\n", "Rank 000: Loading network_uncertainty_details var (141/174)\n", - "Rank 000: Loaded network_uncertainty_details var ((3,))\n", + "Rank 000: Loaded network_uncertainty_details var ((3, 30))\n", "Rank 000: Loading population var (142/174)\n", "Rank 000: Loaded population var ((3,))\n", "Rank 000: Loading primary_sampling_further_details var (143/174)\n", - "Rank 000: Loaded primary_sampling_further_details var ((3,))\n", + "Rank 000: Loaded primary_sampling_further_details var ((3, 30))\n", "Rank 000: Loading primary_sampling_instrument_documented_flow_rate var (144/174)\n", - "Rank 000: Loaded primary_sampling_instrument_documented_flow_rate var ((3,))\n", + "Rank 000: Loaded primary_sampling_instrument_documented_flow_rate var ((3, 30))\n", "Rank 000: Loading primary_sampling_instrument_manual_name var (145/174)\n", - "Rank 000: Loaded primary_sampling_instrument_manual_name var ((3,))\n", + "Rank 000: Loaded primary_sampling_instrument_manual_name var ((3, 30))\n", "Rank 000: Loading primary_sampling_instrument_name var (146/174)\n", - "Rank 000: Loaded primary_sampling_instrument_name var ((3,))\n", + "Rank 000: Loaded primary_sampling_instrument_name var ((3, 30))\n", "Rank 000: Loading primary_sampling_instrument_reported_flow_rate var (147/174)\n", - "Rank 000: Loaded primary_sampling_instrument_reported_flow_rate var ((3,))\n", + "Rank 000: Loaded primary_sampling_instrument_reported_flow_rate var ((3, 30))\n", "Rank 000: Loading primary_sampling_process_details var (148/174)\n", - "Rank 000: Loaded primary_sampling_process_details var ((3,))\n", + "Rank 000: Loaded primary_sampling_process_details var ((3, 30))\n", "Rank 000: Loading primary_sampling_type var (149/174)\n", - "Rank 000: Loaded primary_sampling_type var ((3,))\n", + "Rank 000: Loaded primary_sampling_type var ((3, 30))\n", "Rank 000: Loading principal_investigator_email_address var (150/174)\n", - "Rank 000: Loaded principal_investigator_email_address var ((3,))\n", + "Rank 000: Loaded principal_investigator_email_address var ((3, 30))\n", "Rank 000: Loading principal_investigator_institution var (151/174)\n", - "Rank 000: Loaded principal_investigator_institution var ((3,))\n", + "Rank 000: Loaded principal_investigator_institution var ((3, 30))\n", "Rank 000: Loading principal_investigator_name var (152/174)\n", - "Rank 000: Loaded principal_investigator_name var ((3,))\n", + "Rank 000: Loaded principal_investigator_name var ((3, 30))\n", "Rank 000: Loading process_warnings var (153/174)\n", - "Rank 000: Loaded process_warnings var ((3,))\n", + "Rank 000: Loaded process_warnings var ((3, 30))\n", "Rank 000: Loading projection var (154/174)\n", - "Rank 000: Loaded projection var ((3,))\n", + "Rank 000: Loaded projection var ((3, 30))\n", "Rank 000: Loading reported_uncertainty_per_measurement var (155/174)\n", "Rank 000: Loaded reported_uncertainty_per_measurement var ((3, 30))\n", "Rank 000: Loading representative_radius var (156/174)\n", "Rank 000: Loaded representative_radius var ((3,))\n", "Rank 000: Loading retrieval_algorithm var (157/174)\n", - "Rank 000: Loaded retrieval_algorithm var ((3,))\n", + "Rank 000: Loaded retrieval_algorithm var ((3, 30))\n", "Rank 000: Loading sample_preparation_further_details var (158/174)\n", - "Rank 000: Loaded sample_preparation_further_details var ((3,))\n", + "Rank 000: Loaded sample_preparation_further_details var ((3, 30))\n", "Rank 000: Loading sample_preparation_process_details var (159/174)\n", - "Rank 000: Loaded sample_preparation_process_details var ((3,))\n", + "Rank 000: Loaded sample_preparation_process_details var ((3, 30))\n", "Rank 000: Loading sample_preparation_techniques var (160/174)\n", - "Rank 000: Loaded sample_preparation_techniques var ((3,))\n", + "Rank 000: Loaded sample_preparation_techniques var ((3, 30))\n", "Rank 000: Loading sample_preparation_types var (161/174)\n", - "Rank 000: Loaded sample_preparation_types var ((3,))\n", + "Rank 000: Loaded sample_preparation_types var ((3, 30))\n", "Rank 000: Loading sampling_height var (162/174)\n", "Rank 000: Loaded sampling_height var ((3,))\n", "Rank 000: Loading sconcso4 var (163/174)\n", @@ -2358,21 +2826,21 @@ "Rank 000: Loading season_code var (164/174)\n", "Rank 000: Loaded season_code var ((3, 30))\n", "Rank 000: Loading station_classification var (165/174)\n", - "Rank 000: Loaded station_classification var ((3,))\n", + "Rank 000: Loaded station_classification var ((3, 30))\n", "Rank 000: Loading station_name var (166/174)\n", - "Rank 000: Loaded station_name var ((3,))\n", + "Rank 000: Loaded station_name var ((3, 30))\n", "Rank 000: Loading station_reference var (167/174)\n", - "Rank 000: Loaded station_reference var ((3,))\n", + "Rank 000: Loaded station_reference var ((3, 30))\n", "Rank 000: Loading station_timezone var (168/174)\n", - "Rank 000: Loaded station_timezone var ((3,))\n", + "Rank 000: Loaded station_timezone var ((3, 30))\n", "Rank 000: Loading street_type var (169/174)\n", - "Rank 000: Loaded street_type var ((3,))\n", + "Rank 000: Loaded street_type var ((3, 30))\n", "Rank 000: Loading street_width var (170/174)\n", "Rank 000: Loaded street_width var ((3,))\n", "Rank 000: Loading terrain var (171/174)\n", - "Rank 000: Loaded terrain var ((3,))\n", + "Rank 000: Loaded terrain var ((3, 30))\n", "Rank 000: Loading vertical_datum var (172/174)\n", - "Rank 000: Loaded vertical_datum var ((3,))\n", + "Rank 000: Loaded vertical_datum var ((3, 30))\n", "Rank 000: Loading weekday_weekend_code var (173/174)\n", "Rank 000: Loaded weekday_weekend_code var ((3, 30))\n", "Rank 000: Loading sconcso4_prefiltered_defaultqa var (174/174)\n", @@ -2382,7 +2850,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -2411,7 +2879,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 24, @@ -2455,6 +2923,7 @@ " fill_value=1e+20,\n", " dtype=float32),\n", " 'dimensions': ('station', 'time'),\n", + " 'dtype': dtype('float32'),\n", " 'standard_name': 'sulphate',\n", " 'long_name': 'sulphate',\n", " 'units': 'ug m-3',\n", @@ -2482,6 +2951,7 @@ " fill_value=1e+20,\n", " dtype=float32),\n", " 'dimensions': ('station', 'time'),\n", + " 'dtype': dtype('float32'),\n", " 'standard_name': 'sulphate',\n", " 'long_name': 'sulphate',\n", " 'units': 'ug m-3',\n", diff --git a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb index 4db4033..25321c1 100644 --- a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb +++ b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -665,7 +665,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, diff --git a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb index 4913af1..1e4815c 100644 --- a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb +++ b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -436,7 +436,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, diff --git a/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb b/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb index f1496d0..9049f84 100644 --- a/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb +++ b/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb @@ -49,7 +49,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -874,6 +874,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('station',),\n", + " 'dtype': dtype('float64'),\n", " 'standard_name': 'latitude',\n", " 'long_name': 'latitude',\n", " 'units': 'decimal degrees North',\n", @@ -957,6 +958,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('station',),\n", + " 'dtype': dtype('float64'),\n", " 'standard_name': 'longitude',\n", " 'long_name': 'longitude',\n", " 'units': 'decimal degrees East',\n", @@ -1351,6 +1353,74 @@ "execution_count": 9, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable ESDAC_Iwahashi_landform_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable ESDAC_Meybeck_landform_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_5km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_5km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable GHOST_version. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_5km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable GHSL_settlement_model_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_5km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_IGBP_land_use. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_LAI. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_UMD_land_use. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_5km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_5km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_5km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable UMBC_anthrome_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_5km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable WMO_region. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biogeographical_realm. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biome. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_terrestrial_ecoregion. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable administrative_country_division_1. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable administrative_country_division_2. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -1643,7 +1713,21 @@ "Rank 000: Var altitude data (71/175)\n", "Rank 000: Var altitude completed (71/175)\n", "Rank 000: Writing annual_native_max_gap_percent var (72/175)\n", - "Rank 000: Var annual_native_max_gap_percent created (72/175)\n", + "Rank 000: Var annual_native_max_gap_percent created (72/175)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Rank 000: Var annual_native_max_gap_percent data (72/175)\n", "Rank 000: Var annual_native_max_gap_percent completed (72/175)\n", "Rank 000: Writing annual_native_representativity_percent var (73/175)\n", @@ -1651,7 +1735,51 @@ "Rank 000: Var annual_native_representativity_percent data (73/175)\n", "Rank 000: Var annual_native_representativity_percent completed (73/175)\n", "Rank 000: Writing area_classification var (74/175)\n", - "Rank 000: Var area_classification created (74/175)\n", + "Rank 000: Var area_classification created (74/175)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable associated_networks. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable city. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable climatology. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable contact_email_address. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable contact_institution. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable contact_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable country. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable data_level. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable data_licence. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable ellipsoid. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable horizontal_datum. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable land_use. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable main_emission_source. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measurement_methodology. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measurement_scale. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_calibration_scale. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Rank 000: Var area_classification data (74/175)\n", "Rank 000: Var area_classification completed (74/175)\n", "Rank 000: Writing associated_networks var (75/175)\n", @@ -1775,7 +1903,29 @@ "Rank 000: Var measurement_scale data (104/175)\n", "Rank 000: Var measurement_scale completed (104/175)\n", "Rank 000: Writing measuring_instrument_calibration_scale var (105/175)\n", - "Rank 000: Var measuring_instrument_calibration_scale created (105/175)\n", + "Rank 000: Var measuring_instrument_calibration_scale created (105/175)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_absorption_cross_section. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_accuracy. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_precision. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_span_drift. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Rank 000: Var measuring_instrument_calibration_scale data (105/175)\n", "Rank 000: Var measuring_instrument_calibration_scale completed (105/175)\n", "Rank 000: Writing measuring_instrument_documented_absorption_cross_section var (106/175)\n", @@ -1803,7 +1953,91 @@ "Rank 000: Var measuring_instrument_documented_precision data (111/175)\n", "Rank 000: Var measuring_instrument_documented_precision completed (111/175)\n", "Rank 000: Writing measuring_instrument_documented_span_drift var (112/175)\n", - "Rank 000: Var measuring_instrument_documented_span_drift created (112/175)\n", + "Rank 000: Var measuring_instrument_documented_span_drift created (112/175)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_uncertainty. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zero_drift. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zonal_drift. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_further_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_inlet_information. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_manual_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_process_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_absorption_cross_section. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_accuracy. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_precision. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_span_drift. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_uncertainty. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_units. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zero_drift. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zonal_drift. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable measuring_instrument_sampling_type. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable network. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable network_maintenance_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable network_miscellaneous_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable network_qa_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable network_sampling_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable network_uncertainty_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_further_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_manual_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_process_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_type. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable principal_investigator_email_address. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable principal_investigator_institution. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable principal_investigator_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable process_warnings. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable projection. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Rank 000: Var measuring_instrument_documented_span_drift data (112/175)\n", "Rank 000: Var measuring_instrument_documented_span_drift completed (112/175)\n", "Rank 000: Writing measuring_instrument_documented_uncertainty var (113/175)\n", @@ -1987,7 +2221,25 @@ "Rank 000: Var projection data (157/175)\n", "Rank 000: Var projection completed (157/175)\n", "Rank 000: Writing reported_uncertainty_per_measurement var (158/175)\n", - "Rank 000: Var reported_uncertainty_per_measurement created (158/175)\n", + "Rank 000: Var reported_uncertainty_per_measurement created (158/175)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable sample_preparation_further_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable sample_preparation_process_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable sample_preparation_techniques. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Rank 000: Var reported_uncertainty_per_measurement data (158/175)\n", "Rank 000: Var reported_uncertainty_per_measurement completed (158/175)\n", "Rank 000: Writing representative_radius var (159/175)\n", @@ -2002,7 +2254,36 @@ "Rank 000: Var sample_preparation_process_details created (161/175)\n", "Rank 000: Var sample_preparation_process_details data (161/175)\n", "Rank 000: Var sample_preparation_process_details completed (161/175)\n", - "Rank 000: Writing sample_preparation_techniques var (162/175)\n", + "Rank 000: Writing sample_preparation_techniques var (162/175)" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable sample_preparation_types. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable station_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable station_timezone. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable street_type. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable terrain. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable vertical_datum. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", "Rank 000: Var sample_preparation_techniques created (162/175)\n", "Rank 000: Var sample_preparation_techniques data (162/175)\n", "Rank 000: Var sample_preparation_techniques completed (162/175)\n", @@ -2100,7 +2381,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -2927,6 +3208,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('station',),\n", + " 'dtype': dtype('float64'),\n", " 'standard_name': 'latitude',\n", " 'units': 'decimal degrees North',\n", " 'long_name': 'latitude',\n", @@ -3013,6 +3295,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('station',),\n", + " 'dtype': dtype('float64'),\n", " 'standard_name': 'longitude',\n", " 'units': 'decimal degrees East',\n", " 'long_name': 'longitude',\n", @@ -3061,6 +3344,14 @@ "execution_count": 17, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_providentia.py:376: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, { "name": "stdout", "output_type": "stream", diff --git a/tutorials/2.Creation/2.1.Create_Regular.ipynb b/tutorials/2.Creation/2.1.Create_Regular.ipynb index 07581e2..8e04055 100644 --- a/tutorials/2.Creation/2.1.Create_Regular.ipynb +++ b/tutorials/2.Creation/2.1.Create_Regular.ipynb @@ -80,9 +80,7 @@ { "data": { "text/plain": [ - "{'data': None,\n", - " 'dimensions': (),\n", - " 'grid_mapping_name': 'latitude_longitude',\n", + "{'grid_mapping_name': 'latitude_longitude',\n", " 'semi_major_axis': '6378137.0',\n", " 'inverse_flattening': '0'}" ] diff --git a/tutorials/2.Creation/2.2.Create_Rotated.ipynb b/tutorials/2.Creation/2.2.Create_Rotated.ipynb index bbb2c07..66b271f 100644 --- a/tutorials/2.Creation/2.2.Create_Rotated.ipynb +++ b/tutorials/2.Creation/2.2.Create_Rotated.ipynb @@ -81,9 +81,7 @@ { "data": { "text/plain": [ - "{'data': None,\n", - " 'dimensions': (),\n", - " 'grid_mapping_name': 'rotated_latitude_longitude',\n", + "{'grid_mapping_name': 'rotated_latitude_longitude',\n", " 'grid_north_pole_latitude': 39,\n", " 'grid_north_pole_longitude': -170}" ] diff --git a/tutorials/2.Creation/2.3.Create-Points.ipynb b/tutorials/2.Creation/2.3.Create-Points.ipynb index ba45c3b..05b8580 100644 --- a/tutorials/2.Creation/2.3.Create-Points.ipynb +++ b/tutorials/2.Creation/2.3.Create-Points.ipynb @@ -233,9 +233,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -248,10 +248,12 @@ "Rank 000: Dimensions done\n", "Rank 000: Writing station_code var (1/2)\n", "Rank 000: Var station_code created (1/2)\n", + "Rank 000: Filling station_code)\n", "Rank 000: Var station_code data (1/2)\n", "Rank 000: Var station_code completed (1/2)\n", "Rank 000: Writing area_classification var (2/2)\n", "Rank 000: Var area_classification created (2/2)\n", + "Rank 000: Filling area_classification)\n", "Rank 000: Var area_classification data (2/2)\n", "Rank 000: Var area_classification completed (2/2)\n" ] @@ -801,11 +803,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable pm10. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable pm10. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -818,14 +820,17 @@ "Rank 000: Dimensions done\n", "Rank 000: Writing station_name var (1/3)\n", "Rank 000: Var station_name created (1/3)\n", + "Rank 000: Filling station_name)\n", "Rank 000: Var station_name data (1/3)\n", "Rank 000: Var station_name completed (1/3)\n", "Rank 000: Writing station_code var (2/3)\n", "Rank 000: Var station_code created (2/3)\n", + "Rank 000: Filling station_code)\n", "Rank 000: Var station_code data (2/3)\n", "Rank 000: Var station_code completed (2/3)\n", "Rank 000: Writing pm10 var (3/3)\n", "Rank 000: Var pm10 created (3/3)\n", + "Rank 000: Filling pm10)\n", "Rank 000: Var pm10 data (3/3)\n", "Rank 000: Var pm10 completed (3/3)\n" ] diff --git a/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb b/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb index 073d11e..53c4db4 100644 --- a/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb +++ b/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb @@ -401,7 +401,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -414,10 +414,12 @@ "Rank 000: Dimensions done\n", "Rank 000: Writing station_name var (1/2)\n", "Rank 000: Var station_name created (1/2)\n", + "Rank 000: Filling station_name)\n", "Rank 000: Var station_name data (1/2)\n", "Rank 000: Var station_name completed (1/2)\n", "Rank 000: Writing sconcno2 var (2/2)\n", "Rank 000: Var sconcno2 created (2/2)\n", + "Rank 000: Filling sconcno2)\n", "Rank 000: Var sconcno2 data (2/2)\n", "Rank 000: Var sconcno2 completed (2/2)\n" ] diff --git a/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb b/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb index 1366fc6..a764a7b 100644 --- a/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb +++ b/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb @@ -367,7 +367,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:365: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -380,10 +380,12 @@ "Rank 000: Dimensions done\n", "Rank 000: Writing station_name var (1/2)\n", "Rank 000: Var station_name created (1/2)\n", + "Rank 000: Filling station_name)\n", "Rank 000: Var station_name data (1/2)\n", "Rank 000: Var station_name completed (1/2)\n", "Rank 000: Writing sconcnh3 var (2/2)\n", "Rank 000: Var sconcnh3 created (2/2)\n", + "Rank 000: Filling sconcnh3)\n", "Rank 000: Var sconcnh3 data (2/2)\n", "Rank 000: Var sconcnh3 completed (2/2)\n" ] diff --git a/tutorials/2.Creation/2.6.Create-LCC.ipynb b/tutorials/2.Creation/2.6.Create-LCC.ipynb index ea8bfa1..dd8e923 100644 --- a/tutorials/2.Creation/2.6.Create-LCC.ipynb +++ b/tutorials/2.Creation/2.6.Create-LCC.ipynb @@ -84,9 +84,7 @@ { "data": { "text/plain": [ - "{'data': None,\n", - " 'dimensions': (),\n", - " 'grid_mapping_name': 'lambert_conformal_conic',\n", + "{'grid_mapping_name': 'lambert_conformal_conic',\n", " 'standard_parallel': ['37', '43'],\n", " 'longitude_of_central_meridian': '-3',\n", " 'latitude_of_projection_origin': '40'}" diff --git a/tutorials/2.Creation/2.7.Create_Mercator.ipynb b/tutorials/2.Creation/2.7.Create_Mercator.ipynb index 453bada..9574d3e 100644 --- a/tutorials/2.Creation/2.7.Create_Mercator.ipynb +++ b/tutorials/2.Creation/2.7.Create_Mercator.ipynb @@ -82,9 +82,7 @@ { "data": { "text/plain": [ - "{'data': None,\n", - " 'dimensions': (),\n", - " 'grid_mapping_name': 'mercator',\n", + "{'grid_mapping_name': 'mercator',\n", " 'standard_parallel': '-1.5',\n", " 'longitude_of_projection_origin': -18.0}" ] diff --git a/tutorials/2.Creation/2.8.Create_Global.ipynb b/tutorials/2.Creation/2.8.Create_Global.ipynb index 6ee8f61..10c546d 100644 --- a/tutorials/2.Creation/2.8.Create_Global.ipynb +++ b/tutorials/2.Creation/2.8.Create_Global.ipynb @@ -75,9 +75,7 @@ { "data": { "text/plain": [ - "{'data': None,\n", - " 'dimensions': (),\n", - " 'grid_mapping_name': 'latitude_longitude',\n", + "{'grid_mapping_name': 'latitude_longitude',\n", " 'semi_major_axis': '6378137.0',\n", " 'inverse_flattening': '0'}" ] diff --git a/tutorials/3.Statistics/3.1.Statistics.ipynb b/tutorials/3.Statistics/3.1.Statistics.ipynb index f3566e4..4b136a9 100644 --- a/tutorials/3.Statistics/3.1.Statistics.ipynb +++ b/tutorials/3.Statistics/3.1.Statistics.ipynb @@ -32,8 +32,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 571 ms, sys: 93.9 ms, total: 665 ms\n", - "Wall time: 10.5 s\n" + "CPU times: user 653 ms, sys: 68.8 ms, total: 722 ms\n", + "Wall time: 9.96 s\n" ] } ], @@ -52,7 +52,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -282,8 +282,8 @@ "text": [ "Rank 000: Loading O3 var (1/1)\n", "Rank 000: Loaded O3 var ((37, 24, 271, 351))\n", - "CPU times: user 311 ms, sys: 1.5 s, total: 1.81 s\n", - "Wall time: 11.3 s\n" + "CPU times: user 304 ms, sys: 1.58 s, total: 1.88 s\n", + "Wall time: 11.1 s\n" ] } ], @@ -341,8 +341,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 106 ms, sys: 104 ms, total: 210 ms\n", - "Wall time: 3.03 s\n" + "CPU times: user 106 ms, sys: 86.4 ms, total: 193 ms\n", + "Wall time: 2.75 s\n" ] } ], @@ -367,8 +367,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 74.8 ms, sys: 21 ms, total: 95.8 ms\n", - "Wall time: 260 ms\n" + "CPU times: user 66.2 ms, sys: 19.3 ms, total: 85.5 ms\n", + "Wall time: 277 ms\n" ] } ], @@ -425,8 +425,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 18.2 ms, sys: 8.24 ms, total: 26.4 ms\n", - "Wall time: 402 ms\n" + "CPU times: user 17.6 ms, sys: 8.64 ms, total: 26.2 ms\n", + "Wall time: 422 ms\n" ] } ], @@ -478,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -490,7 +490,7 @@ " datetime.datetime(2022, 11, 16, 23, 0)]]" ] }, - "execution_count": 21, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/3.Statistics/3.2.Sum.ipynb b/tutorials/3.Statistics/3.2.Sum.ipynb index 53bf070..081acb2 100644 --- a/tutorials/3.Statistics/3.2.Sum.ipynb +++ b/tutorials/3.Statistics/3.2.Sum.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -53,9 +53,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'var_aux': {'data': array([[[[1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " ...,\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.],\n", + " [1., 1., 1., ..., 1., 1., 1.]]]])}}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nessy.variables" ] @@ -69,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -78,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -88,9 +105,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'var_aux': {'data': array([[[[2., 2., 2., ..., 2., 2., 2.],\n", + " [2., 2., 2., ..., 2., 2., 2.],\n", + " [2., 2., 2., ..., 2., 2., 2.],\n", + " ...,\n", + " [2., 2., 2., ..., 2., 2., 2.],\n", + " [2., 2., 2., ..., 2., 2., 2.],\n", + " [2., 2., 2., ..., 2., 2., 2.]]]])}}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nessy_2.variables" ] @@ -104,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -113,18 +147,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'var_aux': {'data': array([[[[3., 3., 3., ..., 3., 3., 3.],\n", + " [3., 3., 3., ..., 3., 3., 3.],\n", + " [3., 3., 3., ..., 3., 3., 3.],\n", + " ...,\n", + " [3., 3., 3., ..., 3., 3., 3.],\n", + " [3., 3., 3., ..., 3., 3., 3.],\n", + " [3., 3., 3., ..., 3., 3., 3.]]]])}}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nessy_3.variables" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank 000: Creating sum.nc\n", + "Rank 000: NetCDF ready to write\n", + "Rank 000: Dimensions done\n", + "Rank 000: Writing var_aux var (1/1)\n", + "Rank 000: Var var_aux created (1/1)\n", + "Rank 000: Filling var_aux)\n", + "Rank 000: Var var_aux data (1/1)\n", + "Rank 000: Var var_aux completed (1/1)\n" + ] + } + ], "source": [ "nessy_3.to_netcdf(\"sum.nc\", info=True)" ] diff --git a/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb b/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb index 0fce038..04d031c 100644 --- a/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb @@ -50,7 +50,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -87,6 +87,7 @@ " fill_value=999999,\n", " dtype=int32),\n", " 'dimensions': ('lm',),\n", + " 'dtype': dtype('int32'),\n", " 'units': '',\n", " 'long_name': 'layer id'}" ] @@ -181,7 +182,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -211,6 +212,7 @@ " 2000.0,\n", " 3000.0,\n", " 5000.0],\n", + " 'dtype': dtype('float32'),\n", " 'long_name': 'Mid-layer height above ground level',\n", " 'standard_name': 'height_agl',\n", " 'units': 'm',\n", @@ -248,7 +250,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -313,7 +315,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -397,7 +399,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 22, @@ -427,6 +429,7 @@ " 2000.0,\n", " 3000.0,\n", " 5000.0],\n", + " 'dtype': dtype('float32'),\n", " 'long_name': 'Mid-layer height above ground level',\n", " 'standard_name': 'height_agl',\n", " 'units': 'm',\n", diff --git a/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb b/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb index 59189fa..11d12ec 100644 --- a/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -81,6 +81,7 @@ " fill_value=1e+20,\n", " dtype=float32),\n", " 'dimensions': ('rlat', 'rlon'),\n", + " 'dtype': dtype('float32'),\n", " 'long_name': 'latitude',\n", " 'units': 'degrees_north',\n", " 'standard_name': 'latitude',\n", @@ -122,6 +123,7 @@ " fill_value=1e+20,\n", " dtype=float32),\n", " 'dimensions': ('rlat', 'rlon'),\n", + " 'dtype': dtype('float32'),\n", " 'long_name': 'longitude',\n", " 'units': 'degrees_east',\n", " 'standard_name': 'longitude',\n", @@ -193,7 +195,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -234,6 +236,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('y', 'x'),\n", + " 'dtype': dtype('float64'),\n", " 'units': 'degrees_north',\n", " 'axis': 'Y',\n", " 'long_name': 'latitude coordinate',\n", @@ -274,6 +277,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('y', 'x'),\n", + " 'dtype': dtype('float64'),\n", " 'units': 'degrees_east',\n", " 'axis': 'X',\n", " 'long_name': 'longitude coordinate',\n", @@ -308,7 +312,7 @@ "Creating Weight Matrix\n", "Weight Matrix done!\n", "Applying weights\n", - "\tO3 horizontal methods\n", + "\tO3 horizontal interpolation\n", "Formatting\n" ] } @@ -327,7 +331,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -364,6 +368,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('y', 'x'),\n", + " 'dtype': dtype('float64'),\n", " 'units': 'degrees_north',\n", " 'axis': 'Y',\n", " 'long_name': 'latitude coordinate',\n", @@ -404,6 +409,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('y', 'x'),\n", + " 'dtype': dtype('float64'),\n", " 'units': 'degrees_east',\n", " 'axis': 'X',\n", " 'long_name': 'longitude coordinate',\n", @@ -511,7 +517,7 @@ "Creating Weight Matrix\n", "Weight Matrix done!\n", "Applying weights\n", - "\tO3 horizontal methods\n", + "\tO3 horizontal interpolation\n", "Formatting\n" ] } @@ -530,7 +536,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 19, diff --git a/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb b/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb index e52d406..56a70e0 100644 --- a/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb @@ -416,8 +416,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 1min 48s, sys: 1.42 s, total: 1min 49s\n", - "Wall time: 1min 49s\n" + "CPU times: user 1min 49s, sys: 1.69 s, total: 1min 50s\n", + "Wall time: 1min 51s\n" ] } ], @@ -481,9 +481,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "Creating Weight Matrix\n", + "Creating Weight Matrix\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2267: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " time_var[:] = date2num(self._time[:], time_var.units, time_var.calendar)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Weight Matrix done!\n", - "CPU times: user 1min 49s, sys: 1.9 s, total: 1min 51s\n", + "CPU times: user 1min 49s, sys: 1.85 s, total: 1min 51s\n", "Wall time: 1min 52s\n" ] } @@ -501,8 +515,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 202 ms, sys: 26.9 ms, total: 229 ms\n", - "Wall time: 228 ms\n" + "CPU times: user 205 ms, sys: 23 ms, total: 228 ms\n", + "Wall time: 227 ms\n" ] } ], @@ -602,6 +616,39 @@ "execution_count": 19, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1888: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " value = getattr(var_info, attrname)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1552: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " units = self.__parse_time_unit(time.units)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1554: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " if not hasattr(time, 'calendar'):\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1557: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " calendar = time.calendar\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1559: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " if 'months since' in time.units:\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1563: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " time_data = time[:]\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1751: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " nc_var['data'] = self.netcdf.variables[dimension_name][:]\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1678: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " lat_bnds = {'data': self.netcdf.variables['lat_bnds'][:]}\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1682: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " lon_bnds = {'data': self.netcdf.variables['lon_bnds'][:]}\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1718: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " c_measures['cell_area']['data'] = self.netcdf.variables['cell_area'][:]\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2098: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " gl_attrs[attrname] = getattr(self.netcdf, attrname)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -609,6 +656,15 @@ "Flux units: m-2.kg.s-1\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1937: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]\n" + ] + }, { "data": { "image/png": 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\n", @@ -640,8 +696,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 6min 12s, sys: 2.3 s, total: 6min 15s\n", - "Wall time: 6min 16s\n" + "CPU times: user 6min 12s, sys: 2.87 s, total: 6min 15s\n", + "Wall time: 6min 17s\n" ] } ], diff --git a/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb b/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb index 3b60ef0..3bc762f 100644 --- a/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -65,6 +65,7 @@ "text/plain": [ "{'sconco3': {'data': None,\n", " 'dimensions': ('time', 'lat', 'lon'),\n", + " 'dtype': dtype('float32'),\n", " 'coordinates': 'lat lon',\n", " 'grid_mapping': 'crs',\n", " 'units': 'ppb'}}" @@ -114,6 +115,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('lat',),\n", + " 'dtype': dtype('float64'),\n", " 'standard_name': 'latitude',\n", " 'long_name': 'Latitude',\n", " 'units': 'degrees_north',\n", @@ -184,6 +186,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('lon',),\n", + " 'dtype': dtype('float64'),\n", " 'standard_name': 'longitude',\n", " 'long_name': 'Longitude',\n", " 'units': 'degrees_east',\n", @@ -226,9 +229,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2602: UserWarning: No vertical level has been specified. The first one will be selected.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2998: UserWarning: No vertical level has been specified. The first one will be selected.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2612: UserWarning: No time has been specified. The first one will be selected.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:3009: UserWarning: No time has been specified. The first one will be selected.\n", " warnings.warn(msg)\n" ] } @@ -369,14 +372,14 @@ "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", + "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:73: 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": [ - "" + "" ] }, "execution_count": 10, @@ -445,6 +448,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('station',),\n", + " 'dtype': dtype('float64'),\n", " 'standard_name': 'latitude',\n", " 'long_name': 'latitude',\n", " 'units': 'decimal degrees North',\n", @@ -528,6 +532,7 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('station',),\n", + " 'dtype': dtype('float64'),\n", " 'standard_name': 'longitude',\n", " 'long_name': 'longitude',\n", " 'units': 'decimal degrees East',\n", @@ -648,6 +653,7 @@ " fill_value=1e+20,\n", " dtype=float32),\n", " 'dimensions': ('time', 'lat', 'lon'),\n", + " 'dtype': dtype('float32'),\n", " 'coordinates': 'lat lon',\n", " 'grid_mapping': 'crs',\n", " 'units': 'ppb'}}" @@ -681,7 +687,7 @@ "Creating Weight Matrix\n", "Weight Matrix done!\n", "Applying weights\n", - "\tsconco3 horizontal methods\n", + "\tsconco3 horizontal interpolation\n", "Formatting\n" ] } @@ -841,9 +847,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2602: UserWarning: No vertical level has been specified. The first one will be selected.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2998: UserWarning: No vertical level has been specified. The first one will be selected.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2612: UserWarning: No time has been specified. The first one will be selected.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:3009: UserWarning: No time has been specified. The first one will be selected.\n", " warnings.warn(msg)\n" ] } diff --git a/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb b/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb index e3fdd56..f1d7542 100644 --- a/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb @@ -61,7 +61,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -296,14 +296,14 @@ "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", + "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:73: 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": [ - "" + "" ] }, "execution_count": 9, @@ -501,14 +501,14 @@ "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", + "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:73: 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": [ - "" + "" ] }, "execution_count": 13, @@ -737,24 +737,24 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "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", + "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:73: 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": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -766,7 +766,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -792,7 +792,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -904,7 +904,7 @@ "[366 rows x 2 columns]" ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -924,7 +924,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -955,7 +955,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -980,7 +980,7 @@ " dtype=float32)" ] }, - "execution_count": 23, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1006,7 +1006,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1016,7 +1016,7 @@ "Creating Weight Matrix\n", "Weight Matrix done!\n", "Applying weights\n", - "\tod550du horizontal methods\n", + "\tod550du horizontal interpolation\n", "Formatting\n" ] } @@ -1029,7 +1029,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1050,7 +1050,7 @@ " 0.00347976]]), 'dimensions': ('station', 'time')}}" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1068,7 +1068,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1180,7 +1180,7 @@ "[366 rows x 2 columns]" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1200,7 +1200,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1245,7 +1245,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1254,7 +1254,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1271,7 +1271,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1436,7 +1436,7 @@ "[366 rows x 5 columns]" ] }, - "execution_count": 30, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1448,7 +1448,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1478,7 +1478,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1487,7 +1487,7 @@ "-2.1082139034511727e-08" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1498,7 +1498,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -1507,7 +1507,7 @@ "1.1416417144971547e-08" ] }, - "execution_count": 33, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1525,7 +1525,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -1703,7 +1703,7 @@ "[366 rows x 6 columns]" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1715,9 +1715,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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3mlrOeVEhorZRY++sQ+tgi1KlRKFSYCw3kpdRQF5G7cU3RFEEwaLwdOkbTPjwUOycdJgqTGQkZJGdkmvxRJ5KbVh1TyudJwqlgvtmTcZkMlNRZqQwpxBJAjdfF0Ku7oxao+KFMW9bZa6LGXhDPyY9cT0n9p4h5kgcB9YebVpOYh04utnjGeiOR6AbngHulsIrooixwkTMkbMc2xrdYvl4Vyxt8bxoyfcqK+xPh1fKNBrpoJeXVcYSEhOteqzknDKZywqtgy2dwv3x6+aDm59LlSa7g27qz+GNEez75zDHtkaTnZLbeqXVm4EoiPhLWcTyHhU8D6hQKf+md5do1BGNVMgayLgZowgI9WXrH7st/cG8nNE62KK1t6H3yDCCewawY/k+kk9fCFcURZFeI0K5ZuJV3PzkeBRKy7FPi8vk4Lpj7P3nkPzC0EbkpuehUCpIO5uBodBAxHbr5+y0NjpHLS7ezvh196Y8WV3tcxMTyeYHXK0wl9bBlkc/nkZ28oUiPYIAHoHu6By05GXkk5eRj7HChNlkxkanIaCHLxVlRkRRwLuTJ37dvOk3JpzPHvuewxuPW0Gq5mM2mSnVlzapJYLZfOHlLuZIXI15aW2ByWiqDANVqZU4uNpbQk8FWDp3FYailvEu7Vt9mH2rDyMIAj0GdePu124DSao0aJnNllBNs8mMscKIoaiE4jw9BdlFZCfnkBKTXu8cBdlFFGQXcfrQ2SrLFUoF3fp3YtIT49Da23BkSxRHNrWPc0xGRsZ6yEqZTLtFpVFx1dhwug/oWhliYygqIS4insMbI8hOya1SUcve1Z5DGyKI2B7dViI3GT/BEQdpG4lswoTIxNG9GNx3NH9ErLDqPEqVgofm3EPooG7s/ecQgigScziOfWmH6w2VMZvNHN0SxdEtUVWW9xjUjUE3XsW4+0ehUitBgOzkXA5tiGDX3/urNJ2WaRkqyo18+8KCthbDqhTn6zm6JYrwIaFod62m9JLTU+AY9ljy5cySiRQplzyU2GIkSHBAJWgaPFdJUSmRO0/i19WbguwiDqw9StKpVPxDfBh919Bz9xkBQbAocLZ2NhYZBCgrqSDxZDIH1h7ly//8bL2+XTL1UlFuJCetAe02rIgkSUTtPkXU7lOVy8KGhNBnVBgqjeWVKuVYKtF7z2DvYoejmwMjJg8iPjqJ3SsONmlOk9HEib1nOLH3DABjpw3nvlmTQRAwm8wknEhuVo6bjMwVRzst9NH+lLL2GC7X3ukg4VqCIODoZo93J0+uGtsLtY2KvasP88ubS6r29mrI/jaitH17wUFwoOe5v8ODutVedbGR14hKoyI4PIBew0Jx9nLE1s6GZ4e9UWfOTalkIF4yUAF4IuAuuNQqT/Se09WKDHTqFcCQmwcwc9EzBIT68t2Lv7JtaSsVmbgCMZYbcfFy6nC9oFZ8uRatvS29g0V2x/xDWfnEc58UYMe7eAkumCQTh6V8CvgQ6A+kkCX9hz6SAa2obdA8kiSx/pdtgKVlQL+xvRg5ZTBgUbx2/rW/ffd0a8j97gp/toqipf+aIAoU5RZTXtp05Vljq6b3qDBcvJwwlhu5amwvspJyMFaYEAToMSiEHcv3V/ZnPL49mrH3jeDtFS8RPrQ7D4Q916xrdcOCC7mJoijw2KfTKSkq5cDao00e84rFWtdFY94trH1dNue9pqb5209WU8shK2UyMhYEQaBL3yDGzRhVpeKU2SxRmF1IZlI2f37yj2z1awaege7c8PC1mIwmKsqMxEUmsvG37eRnFdKtf2dueWo8K75aV6NVP8dcyAkCKOU9wJks/sJd+oJwwaPB85+NSORsRCKPzL0XQ2GJrJC1IEFh/ji6O7SL1gktgaGoBMVJCDJ/TjrzMaPF06mIp+6/jVWfbSTenEUB86DSpOGLgd85w0R6Y1HKPALcCArzQ+eoxWQ0E7X7VK0NzfOzLH2t/EN8QJIwGc1NCv+TqR1bOxtsdBqK8w2t4lnUOWqZt/sdTu47Q1GeHhcvJxzc7Pnv9e/WuZ1KraTXiB70GFTVSDbk5qvZvGgXfUaFkR6fRVCYP4vnrCAhOrnKnH7dvOk+oCsuXo6YTWY2/rqdTx78xqptFcxmiZKiEuIjk6w2pkzjcXJ3wLuzJU8p7WxGo1olyMicR1bKOgKXQdNdlUZF3zE96XFNNwQBzhyJo0vfYP4z9PXGD9aQ/e1oFuJG7oPOUYux3MiCt5ZW+8xGqyZ0UFfuf28qktnMN8//yqpvLvTviUVJKd/A+dAwbiObPPLNSxtVnn/mov+Qk5rH7CmfNkp2mcZx89MTmPfY91VygKxGO7p2/EUX/AEwQqEtCUdSefD9u/jwx1Vwqucla2soxQsoJ7CHH2OnjWDn8n2knc1EqVYybsYozhw+y/41Fs+CUqWg61Wd6DmkO45u9kTuOsWhDRH4h/ggSRJah4Z53No1bXRPVKmV9B/Xh869AlGoFCBJGIpKKdGXYu+kQ6VRWaqGRiaypYWqvOoLDPx33LsE9/RH66DFZDRxNqLm5uoX89ofzxBzJJ6Nv+1AkiSM5UYc3R2oKKvAzknLP99u4PDG45QUl6KxVTN4Un+6XdUZQYDCnGJSYtLYtnRPi7+g/zp7GXe8cCNKtbJKg2tJkshNzycjPou8zAIST6R03PDahlRlrgVRFOjWvzPBvQKwc9ShsVVTlFvMrhUHyE7Jrfe9yj/Eh9mrXuH4jmhEQWTADf3Y+Ns2SopKKcorZs33m6xvZG7su147upe3Oe24T5mslMm0GKEDuzJgfB8ATCaJI5uPs2DWkspeMTc/Ma4txeuwqDQqwgaHoLZR1fj5dfeNJDs5lyUfrcRGp6Fzn0Ae//Q+Th6IZdvS3ZSXBXNeITuPidvJYCENUcmUSpEP1r3Gzr8P8Pf/1jR/h2Rq5ZlvH2H9z1taRiFr50RsiyZiWzRZ5AB5wMVVGCXUYiGhA8MZO204X/7n5yrhunHHExl911CmvTkZsOTsnDkSx78/bEJrb8ujn0xjw4JtRO48Sd/RPZk2azJvTPqwVfevvaJz1OLs6YhHgBs+XbxwcndEFC33C2OFCVEUKpUvSbIc28Mbj7Pog79rDZl2cnfgiXkz2Lf6SIsV6shKyiErKadR27x/9/8IGxJC+LDuAChUCopyiln1zQb0BQZcfZwZOWUwXkHuVJQZObj+WPVw+1agoqyC399dXuNnbr4ueAa64xXkzqCJV6FQKUiPz2TdT1tbVUZrYuekIzDMD2cPR2ztbXB0dcDWXoNklshOySU9LpOkU6nkpObWPxgw7oHRjJs+iiObj1OUp0dfYKBb/05kJmVblDLA1duZgB5+qDQqclJziT0aX7l90qlUHu//Mu5+LoBAfk4hZXqLEuYZ6G5pqC5H/rQvOqpSJghCCPDHRYs6AW8ATsBDwPn6wK9KkvRvc+eTqYF2WgZ/wkNj+PrZX1rsIQtUl1UQqy9rp97DSmo63oJw7qfx38VDc+7m7/+tITU2o0br+NwHvj43hVDl5aHbVZ2486VJRH6zjZRqVb1PYkf94XF2TlrmrH+d32b/yZ5Vhxotu1U4f8za+/duDSSpSsjUlWgNDZRsyeI5SvkBUABgZ/stk68Lx1Gy55vnF9SoDGxeuLPG8coMZez6+wCdewcRGOrHiX1nWPn1+hrXbfdY6XzwCHBj1J1D0NrbUJhTTG5GPllJOez++wB5GQWV9xGFUlHZMLomRIWIi5eTpVpliDfuvq4IokCpvowvn/6pRZ4VKrWSe16/DZOxukxqGxXzX11UoxLlEeCGXzdvBFEgNz0fd39XPPxc8fB3o3PvQEwmifzMfA6ujyA9LtPqcluL7JTcSsVi198HALjpsesYdFN/9qxsWuGR1qJL32AG32SpNm42mVFpLK0n9PkG4qOSSI3NwFBUQmFOEYYCPaIo4ubnglewB4NuvAqPADfMZqny+s9MzCEuMom4iASMFSZUGiXlpRX8+/0mNizYjr2zpdqxyWjir4sMik6ezixO+Y7f3lkGQO8RPfjp9T84vuNClVtDUSkJJ1IB+P6lqu1tgNa7N1+Bz4CORLOVMkmSTgF9AARBUAApwF/ADOBTSZLmNncOmcsPryB3dI5abHSallXKrE0j0nJstBpeW/Istna2lQ/1spJy/vr8Xw6ua/2Ea42tmqmv3MKelQctClk9XPoicvrQWU4fjEEp5SKyBTOjzn1Sgq/75zz/yAOolKoac5fOjzXu/lFs/WM3JqOJzn2CyE7OqZI3KGMdht9+DRMfvQ47Zzse/3wG3z6/gMKcK/M42whaekmZxDCBMrxRkot/SRkxq5yIacJ4xgpTrQrblYJnoDvB4f50u6ozSpWCjMRsNv66vd5KhyajCaVKgW8XH5y9nPDr5l3Z5FkQBUwVJvIyC0iNzeDgumON9lw1CUFg9F1D8Qry4GaXGag0Kpamfcdvs/8kJSa98t4VEOpL31E9cfJ0BCAnNZe4yCTMRjMIkBCVTFZyK8jbCuxfe5Rr7xne7pWyW54ax9wHvkGSJGy0GmztbcjLqD0U1Gw2k5mYTWZiNhHbqlZhFgQBr05eBIX5MWBcH0SFSFlJOTZaNVXqiEmSxUOWnEtRbjH5WYX4dPYiPioJV29nwgaHsP6XrVUUMpnLjCsofHEMECtJUkJHTTpvN7RjL8BVY3vRc0gI7931udWbazaJxuTcNTDqxNHNgcUp37Fv9SGeH/lm5XLPQHdm/fUi+VmFxBw+W/sA1rZmSWbCBncj+XQKRzZF1LpOjfNfcky64IyCd8jiK8zosCWFoCwNi9/9q14xFr233NL3zNsJzwBXeg3vgau3E2lxmWxbsofifH1T9k4GCBsSyp3/vRkHVztOHzrLu1M/pyC7CFcfZx79ZDrLPltdJaTmSsJBtKMfACWA7bkfmabw4k+Pc3zHCRKik/ntnWW1er0uxtXHmVFThmDvoqOizEhKbDq5afkcWHu0dRSvS7C05hCoKKvARqfh2NZovlrxC3ZOOjRaNZsX7WTRB3+jc9Qy/PZr6D6gCwnRyWxftrfOl/7LGVcfZwJD/XDycKDHoG4sev/v+jdqY2KOxHPvG7cDoLZVc3RzJMe2RdecF1fPM1XC4jUMCPVFqVYSF5nI6YNnSTtb1YApigLu/m64+brg6uNMp96BDL/9Gj5//AfOHDpLeWlFq4enXtiJpufNyVzCFaKU3Qksuuj/JwVBmAYcBJ6XJKmamU0QhIeBhwFs6ADJ1FcwOkct108fib7QwC+zqheY6EgU5RazYNYf9BrRg0+2vY0kSZU3aqVKiZ1j657L4x8cg29Xb7Yv3dPssQRBoBMudAJADw3KJLNgrDCSlZRNVlL2ucEsNz7vTp5MfGQsds46fnlzScdNNrcy/iE+3PvmZDyDPEiLzeCb5xeQcsbS0NtGq2HUnYPp3CeYiO3RxB2vv3DBlUifUWH0HhmG2WRGFAX0hSX8+ck/bS1Wu2TgDf2I3nOatT9uadD6Ayb0pdewUHLT8yuru7Y1Mxf9h+RTqZSXVhAyoAt/zVvDxw9+Q8jVnekzOgxJgu1/7uP5Hx4les8ZInedZPufHa86bO+RYfS/rhed+wSjtbehVF9Gamw6Dq72LPrgb3LS8vDu5MmA8X3R2KqJj05i/79H2lrsKqz9aQu+Xb0pLyknNTaD8GHduff12zCZzORnFnJy/xlOHYitcVsndwcG3dQfD39XJMniHDGZJI5sOs7v7yzDt6s3vYaHct204ShUSirKKijO01NWWo5KpUTnpEOpUiAIsGP5PiJ3nmzlvZdpUTq6UiYIghq4CXjl3KKvgdlYDBSzgY+B+y/dTpKk74DvABwElyuhO0L9tGMvWF1MemIcKo2S5Z93/NRBs9nMovf/YtH79XuPKmlBa5ZXkAc//Hdh08qit0KflLSzGSye8zf9r+9N9wFd5NCPmjj3PTh5ODDtzcl07h1EQXYhi+esqNIHTueo5fbnJlJRZmTf6kMtVrGuo1BRbkSpUlBxrsCQjbbhTaUvSxpZZdHV25mwISGEXN2Z04fOsvq7jXWu37l3IIMnXY3GVs3hTcf54ZWFjZerEfI1lr//t5beI3qgVCuJ3HGS+MhEwod1J3xYKBqthqLcYlN+34MAACAASURBVDITspj3xHz0BbU0tm+MrO3weX3L0xN4ZO40ivP0ZCRm88QAy2uZSqPiue8foVOvAM4ejePumbdgLDdSkFPEpMeu48zBWPIy6u+fJooitz4z4ZKqpAJms0TKmVRO7Itpfp6dZOadlS+z7uctVJQbGXXnYBAEFn+4gpKiUhxc7QgfFsq4GaNYPGcFGQkXEqH7ju7J7JUvs/TjVWz/c2+NBqv4qCTioy60ERAEATsnLWpbNcZyI/oCQ/uI9IF2eY7JtAzW9JSNBw5LkpQBcP43gCAI3wOyabKDs/C95by7+r94BrpXuUFeTgji5Rl2GxuRwLQ3J2M2SwiChKiwvECknElj5/L97abP0rEtUTwy914STyTLuWaXMOWlmxhyy0BKCktY8dU65j0xv9o63a7qxOi7hvL7u8spyi1uAykvP6J2nSJq16m2FqNdYqPV8F3EXF4aO5sdy/bVGZblEeDGTY9dR+LJFBa+91edzecbi6u3MwMm9MXdz7VyWUpMGjuW7avW5DkozJ9htw2sNEAJAlSUGfEL8SErKaeKXFoHW256/HpO7D3D0o9XNath9OWCKIpsWbyLxz+bgaO7A7/OXlb5mbHcSMT2aHw6eTJt1h3EHIlDAHROOk4eiOHGx66rMpYgCqSfzWDzwp1UlBsrl+sctfh39+XTh7+9eGVEUcC/uy+9R/bgumkjEAQq87UkSSJ6z2mObI5sUEgswIfTv2D0XUPRaG0wm8zY6jToHLQYCksoyC5i51/76dw7sNp5e2RzJDfaT8MzyJ3QgV0YdecQ8jIKWPHl2lrnliSJojw95Mkh9h2eKySnbCoXhS4KguAtSVLauX9vASKtONflxxVg6fAMdKekqLR9Kzb15JcVZBXy8da3Km/yRTnFxByNI3LnSaJ2n8J40YOp0VirT1ANcm9fupftVA/B6dw7kHtev524yEQ2/d4GxQsu2eeKciPzX13Egx/czfxXF2IobKXKnO3p+qvluzebzOjz9Whs1dz27A2MmzGS4ztOsHP5fgRRYNz9o8lIyOKb5xc0fs6m7r8VPBmNvh+08vd2cV+nDkEDv7Orx/Wh37XhfPHU/DpzEf26eTNuxihyUvP4bfayppf2rkWuITdfTadegaz7eSuZidmVy4PC/Jny0qQq3n9BgJz0/GpKoVKl4JXfnmbjb9tIOpVGk2jDa8Qqcpxj2G3X8NofzwLwYPjzJERd8BJJEqz5vm5P6KUEhfkz5eWbUSgVaLQayksrMJvM5KYXVLtWzSZIiEqsMud5RFGk14geTHvzdiSzxOrvN1VWhawRQSQjMYdFH6yoUz4Xb2e69A2qcu5Y9lUiPS6T9LhMtizezfDJ19ClTxCnD12S692e8rQa893LeWTNo50qZYI1EhYFQdACSUAnSZIKzi37FUtVRgmIBx65SEmrEQfBRRoojm22PG1BhVROvFSAAQ06yggSHFEK6gsrtKeXwvpowsWu0qiYt2s2Tw9+rYpFrS7mbnqDF8a83ei5auOJeTPYs+oQhzfUUujiUhrwnXgHe9JnVBjdBnTBt4sXCqWl5LYkSWTEZ3H6YCxHt0SREJ1Uz0iXUM8xnvDQGBxc7Fg8p4YHUhPCgEZNHULnXoHsWnGAE3vPNEbShtGQ8/siGe1d7Jj+1h38/OaS1vH4tKfrr4HXl2egO4Mn9WfEHYPJzyjg3amfNfjaqoaslNVKh1PK6kFjq+beNydzbGsUB9bWXSV21NQheAd5sPjDFQ32bjSGW54aT05anlVyusKHh/Lk5zP49oVfObzpeOMHqO9ca62XYCuc8wsTvsHd35VXxr/HwXVWyhMTxGptVKrQCLltdDbc8vQEFn3QvGIjrj7OjLlrGP3GhvPOlM/qLCTl6uPMnS9NYsCEfmz6fQdms0ROWi7xxxMp1ZdaeupVGMlKyrEYHq5gpWyjeckhSZL6t9gEbUx/rVY6GBJilbGEo0eteqys4imTJMkAuF6y7F5rjN1wIVq5V9dF85VJpRxGgZ5fAV+ySCRbepx+kgm10M7yF1rgQtba2/LgB3fxztTPm/7S2JI049xIi8sgLS6DNT9urrJcFEW6D+xC2JDu3P3arbieK/uMAMV5eg6sPcr2JbsprE3haI51rqY+XPXs45aFO9i2eBfjHxzN4Jv6c/pgLDuX1x2u1GA5Lv27AR7Botxi5r+6iAfem8r3//2dUn0LNNZsT4pYA/hg7UxsdBpyUvNY9c16jm6J4q95awgK8+ezR79v2HfVBsqXVTzjtc3fCLmaLIfQ/JfM2mhRha8JnndHN3umvTmZhe/9VW95+wkPjSEzMZsti3Y1R8pa8Qx0R1SITVfILvl+jm+LYtYtH7Eg9guObYvi8IYI9q892vAquO3F82CFKIq7Ah9lzoY3eH/NqxRkFXJX4GOUl5Y3Ty7JTLVbUBOPX6mhHLWNqvJ/lUZFv2vDcfd3xWwyc3RzZJ1tXdQ2KkbfNRQ3HxdWfLWOJXNX1rquq48zXx38AGcPR9LjMpn/ykK2Ld2NKIq4+jgTHB6Aq48zgiCgVCsZPOlqbHQ2iAqRpFMpbPptB1JjeuU0hPbilb1SuULCF61Cv2vDCR/avfLiN5sl4iMT2bf6MBXlRrw7eeLbxYu0uMzKKmRtTQx69PwFOJ5bEkAxP3GWO+hO3UpZipRPCnaYcEJDKl1RYC/oWlxmayAIAmPuGUZAd18WzFraLipvtRZms5noPaerFGA4j1eQB6OmDuGVhf/BwcWeeU/8wKkDTemYZF3MZnNlEn/YkO7c9/YUzCYzgmgpHR1zOI4Da4+2WrlfQ1EJS+auYuIjY6/4anh9RoVRnK/nv+PepUufICY9OY7pb03BZDKhUCnbrgSzTIfi5ifHNdg77e7nyqH1DYw6aARKlYI+o3oyeFJ/vn3hV6uOnRaXwYM9n+Pprx5kxjtTmfHOVLYt3c07Uz616jyXAy+PtUShvLdmJkFhftXD9toQja2aoJ4B6By1aO1tufPlSWz8fQenD8aiUCqYH/UJ7079jG79u6BQCFWUQUEUMBnN7Fl1sN4qoZ9ue4ueQ7tjMpp4uM+LxB9PqPzMbDaTlZxTZ++50IFdeeyz6RTm6Nn19/4OW+FWY6um98ge9Bregz8+WgnZ9W9z2SMrZQ0jdGBXfn37T8znLIyiQqRL32Due3sK0btPcdertxK95xTdB3bl2xd/vZDA3coWBHtXBzwD3VHbqIg4dBZKHS9Zwx3HTiFMv/86spJziD0aj62dDeWlFeSm55MRn0myMZczTMV4riilHhMRTKc/hWgEm7oFaGMvQECoLxMfGcuWRbvY+Ov2NpWlXlo5HCo9PrOyMqPWQcvslS9zbGsUC2YtqX/j8/JJ0rmfOkIVG3LO17K/UbtOErXrQolflVpJ6KBuPPbpdBJPJHNk03FSYtKbNHat69Qgb0ZCFlnJOVx773Drn0ftKaesnu9qxuw7mTnxfQBijsbz8YPfAKC2UfPU/+7HP8SHpFOpNW/cTKurvbOOq8f3xT/Ep9Kzs3jO33UWRbB63mgjPMdV5q7tuF68Tj3eKkE0IypEgsL86d6/E66+LpbjcOkuSlRZVlpcRtSe05w+GGup0tba51gTnnnrft7K3TNv5fCm4xxYU7cBZuG7y5k2azKJJ1PYtmRPs4tkaO1tufu1WykpKiVixwm+ePqn5oVE1rL/CSdSqCizRGwYK0z89PqSln0/aO0onUZuG7E1io+3vsWOZfv4cPoXzRCs7nkacxzKDKX8OHMRT3/xAFdd14sPp39VJaz+8atfwdnTkYXvLW/yOWLnpKXn0O48dtXLxDSxf+OJfWc4se8MCpWK6+4bwYjJg1j2ySoUKgUuXk54BLrj7OmEg4sdGq0GSZIqcyBL9WXkZeSREZ9F7LF4SopKCQzzQ5Isnj5BEIg5Eld7wZxWeKd18nDklqfGYawwcXjjceKjknD2cLwylLJ2SrtTykSFSP9xfVCplXgFeaBz1CIIYDaaOHs8kWeGvY5vV2/++GglOal1h19Yi/Nx1K7ezoycMhgnDwdy0wtIj8+koqwCdw+JzMQKQHXRVqVknY3h5zfy8Ax0JzDMj9LiUmztbAgf1p0bHhrD61+swph0cZcABSV8jLHbc4y/eiBxxxNJiE5ukVj+pqBUKQgZ0IV+Y8IpztPzzfML2o1s7RVDoYHnR77Jwx/dy5wNb/D6jR80P4ykBagoNxKxLZqIbdG4+7nSd0w41947AoVSROegZeXX60iITm6Rubct2cNDc+6uO1+hAzNwQl/S4zMpzq9enru8tJz5ry5kzN3DalfKmoFHgBuv/vY0a37cTNKpVPy6eqN1sL1s86yKTYXkSSXYKzQ4KZyrfa5z1OId7I5XsAdeQe7Y6mwQRDAZzcRHJrJ/7VGykusoPnDJWD0GdWPKizehVCtBkji5P4Yjm4+32yp/6fGWQjH9rg1nxjt3oi8wsGXxrmpFEoDKojxBPf25/bkbUduoMJvMJJ1O5dT+mDrDy2rirpm38seHKynMafmqq/8d9y4OLnbc8PC1/HzyM7Yt3cM7d37W4vO2B+yctDzx+Qx8u/mAJKFQiETvOc0/365va9GqkHQqlf899SMBob58vnM2scfiebTfy4ClanBzIqHe+/dVXL2d+PKZn5qskF2MyWhizfzNOLrZM+GhMRiKSsjLKCAzKZfYYwkU5RRXK4Bjo9Pg4umIZ5A74x8cQ0CoH8e2ROLu70pOah7FeXoe+vAeSopKObj+WBUjaWsR2MOvsoqlm68LnXoF1tr3rcPRTj1lVin0YS0cBBdppMONBIcHUGYoIz0+q/Y+Iq3AiDsG0bl3ECMmD2LT7zvIzyxg198HqsXiF5iLiCCUMj4CRMCMhqfoSxx2or1lpRqsSPvQUST9XG15kP3NjPQNonOfIAK6+yKes/qazRKZSdlo7bXYO1tCHAUB0uIyObzxeN2VjJpA596BDJzQz/LCcW7+UwdiiNx5kpLi5pdYb/NCH+dpJQt375FhPP7ZdD5/7Psawx4vZsJD154r9NG8RGhreIvmrH+DI1siUWtUrPx6PfmZBQ0fr4HWvj6jwlBpVPUWHmgUtXnpWsuj0cB9v/PlSQyc0K+KxVQCkCSK8w0U5+vZtmQPB9cfq3mAZnrKuvQNxs3XmbSzmXUq3o3xjjVWqatt7JrGqclTJkkSkaZMcqRxVHADCnbjJCzmKltPxk4djk9nD8xmCX2+gbS4TNLiMshIyK5+H1Mo6pVVa2dDaXEZZrO5yrEXgNBruvHge3cy78n5pMakt0yupBWxd7FjxB2DcPG80CBekiRK9WWUl1ZQXlZOqb6MxBMpltAtScKvmw9hQ0II6O7LqYOxHNoQ0aBwyJufHIe+0MCGBS0fWWGjVfPI3GmMuXsYtnY2SJLEdco7q6/Y1h70mmiGh+TNP59nwIS+GAoMTPZ+uOEbtvFxeGPp84Re0xWAUwdimXXrR00ap8+ontz/3lRSzqQz574va1/RWs+uRh43v24+zFr+IpsX7UCpUlq8aoLA8R0nOLyx9uI0gijg4GpP9wFd0DlqSYvLIC8tn35je+Hm44Kx3Eh+diGZidmVFbCL8/ToCw2VCqMgCKg0SozlxspINICeQ7vj182bnNQ88jMLOHM4ruMX+rCzkw6Gh1tlLGHv3vZX6MOalBSX1vvC2tIoVQoemnMPu/7ez49LFtHjmq4seGtpres7ivb0NJ8gjhuowA0VWXRCvKCQ1YJaSgEu9bDtRVNUSPLpNJJPV7UUKZQK3HxdMBQaLP00zuHT2ZPBN/XH2cuJgFBfTh+IRVSIKNWWiz493lIStqLsgvU2bEgIfUeHo1Bc/IIjYDaZLUqgIBAflcTyef+2+IuFrZ0NtnY2aHQabHUavj40h6yknFqbTzq6OxAU5g/Asa1RlcvtnHUs/3R1i8raHI5tjeKZoa8zd8tbfPWfH4nafXn0TjJWGPnnm/WAwAPv38U3z/1CWYl1vX0R209wz2u3WVcpu0xYPGdFtSqbWgdbRk4ZTN9RPVn381Yid7acFTXmSBwxR+JabPzWINWcQ4b0ChLXAmCiBznSGNQDPibmaBzrF2yrtk1DlUz/7t689N0jAGh0GvT5BpzcHTixP4Yti3ZyYH0E3Qd0ofeIHkx54UZWfrMBv27eDBzfF41Wg8loYsfyfcRHNrJCazNRaVQEhPpia2dDRnxWjbkzRbnF/PPNhirLBEFAo1WjtlGjtlFha2dDYJgfkx6/HrPZzNK5q1gz31L4qGu/YK69exgOrnbnIq4t4VuCAIU5xcRHJ3P6YCy9hofi5udK6emWV1LVNmpWFf1KTloe93Z+skP3Qwzs4ceUl27Ct6s3ZpOZnkO6c4vrjBq97u2Ztyd/DMDynJ8YcvMAbLSaJrVe6D0yDDsnHT5dvFiW+QNT/B5tXhsbK5MRn8nZiITKKs5OHg7YOdshmSXCh4UimSXMJjMF2UWYTCaK8/QU5hQRNjgEs1ni6NZI0s5m4N3Jk4DuvkTuOEniyRRUaiWO7g54BLihtbfFZDIT0MMXrb0tjq72aLQazGaJirIKbLRqlGplZWPxs8cS2BWdLPe9bCe0O0/ZHT1nYCgqISclt4o23xoIgoCds47r7xtJfHQSB9dZLNON8ug0wjqvlwwcQ4eBTwBfBHbhxBv0xRmxidayuVveYuYN72M2mSsrIQZ09+WFHx+r3J/uA7rw7w+b2L3yYJXww/MNh83GWm5iVopx7je2Fy/88CiFOUUU5+mpKDdSUW7EWFZhKebS2QtjWQXPjZxllflqpY2sg7Z2Nny+6x2eHPhqraGMVvOUWYF3V7/C+3fPozhfj4uXE7c+O5EfXv6t6kpWODfueuUWBFFgydxVVQwITaY9WsGh1mNl56Rj0E1X4d/Nh1J9GduX7a1mmKnEGvvWyIqZ9Skx1gp5vHie+sY8v26ksoS00r+5NBHMkTsYoLrI9njRfnYf2I0bH72WTQt3c2TLOeOO4uJjYpl7nX4B703/im1Lq1YJHD99JM98eT/R+84Q3NMfU4WJhOhknhv9dqXcksmExlbNsNsG4tvFq7JggSBA6tkMDm883iJh+NfPGIlngDuxx+IxFJXi28UTD383clLzWPHVumrr2znpcPJwQBRF9IUGCnOKq12DX+x9j6Nbo7h++kic3B34/d3l/PzGH7XKYO9iR3B4AM9//wj/fLuBpR9boZhPA8/76bPv5O6Zt2E2mzm+4wT2znbYOekoKy1n5sQPSGtk6GWzsWK5/eG3X8OLPz1OzOE4ctLySD6dxoJZS3HxcUIUBDKTai9c0SiZGktt+9BAr1TvkWHM3fQG+9ce5c1JczAaG15d+GJEUeTu127jrpm3UphdxBTfRngML5GpKfM3Zuxvj3yIm68rt3k8UPmRjU6Djc4GlUaJnbMOe2c7EqOTrV5ITWOrxruTJ8Hh/jh5OOHkbo/JaGbJRyv5p/jXju8p693bKmMJu3d3bE/ZdfeNIDMxB2dPRwQBTh86y77Vhy2J1Fam37XhXD2uDyVFlhAWs1lCX2Dg4PpjxEe1vFVTJ2i5WiojgXsxoMAVM97NUMgAkKRqXozEkymUl5RXevvmrHuNo1siq+WDtVZ+mL2Tlo2/befHmYtbZb72RklxKXGRSfh08SI+8vKq5pSbnk9ZU5vH1sPC9/8ioLsv1947jDU/bK5/gw6Ag6s910zsh183H4rz9OxZdbBVwrs6Cq4+zkx6YhxzflhO2hkzUDX8UKCCSx9zY+4exks/PY4gCETuOsVNj4/ljudv4I+5q8jOKOTGhyxNurf9uY8vd88mNyOfvxf/S4lUjqvCEY1gS/erO/PMl/eTEpvOzJvnWpqgm2p+RpWVlLPxtx3Vlvt28WLIpKtx9XHGWFG3N83Vxxnfrt74dvXCxdOJ3Ix8kk6mIooCFWVGS/GohKzK9f26ejP/1UWV/x/ZdBxXH2ceeHcqvl29Aeg9sgeeAW6YzRJFucXkZRYgmSW0DhbrutpGhSRZFEiAM4fPUpRbzGSvhxAEgeXZ89m3+jAn9tXc97Aot5hbnhqPT2cv+owOt45S1kB+fn0xP79ueb4E9QzggffvZtlnqxlxx2AWnJ7Hk9fMbBcVcZvC9j/30ndMOBpbFR9O/6pyeXYDcyHbK3M3vQHAzBveb9Y49i523PvmZGbd+lGbpr80hJevf4elad9ja2dTGUpdqi+jVF+GIAqVKSktkeNbVlJOfFRSlXfdUVOH4BHgBtFWn659IZfEbziGwhJWfb2u0ksWNjiEu2feikKpID7aUhUuL6Og2fNcP30knXoF8uPMRVYPxWoMKkFNF9xadU59gR57Z7vLLsShQ9GOPNSN5eS+Mzw45x7iIxM5sed0/VUaG0HiyRTG3T+qxs8qpDISpEJKADdEvARnhMukb4uoEPEK8sAvxIfAHv5oHWwRBIHCnCIOrD3K+l+qh9fJ1E5AqC/Db7sGQRT4/Z1lKPSZKPkaI09WriOyAQ9Rz50vTmHklEEU5esRBIHwod3Z/+8RXr9lLk/Om8FV14bj5O7A28ue5/ThOLYs3Yt3sAcLY+aRk5NDZ4+7KDbfh4lQNMJifBTHCFN05ezxRB675rUm70NKTHrltaNSKxl911BG3zkEk9GEyWipCHleIcpJyyPlTDoH1x4jJy0PN18X/EN8MBlN2DnrCAzzs/T9EgV+e2c5CSdSuO+tO5DMEpIk4ebnineQOycPxNJrRCjlJeUc3hBBenxWHRLWjiRJ3OJ6f73rvXX7x3j4u/J7/FdMeGgM/36/qUnzNYf4yERev2kOAOt/2cZHG9/gzpdu4q3Jn7S6LNbi88e+Z+7mN9taDKsyVjGFZZnz2WD6g7GKKU0ao8egbsxZ/zqrv9/I3lWHLHmf7QDPQHe6XtUJnaMWURAIGxaKvZOOinIjZrPZKjn61kAu3Nb2tDul7FKidp+qzL0J7OHHiMmDcPZ0pKykgj2rDja5b8Spg7G4+bow/e0p6AsMHNoYUaUka5OxQhPUBlGXO722z84tFwSh9nLrTZ0TGryP+VlFXHffSERRYPnna8hNz2+cHNaipibMMnUjiBxYF8GBdRH4dfMmbGgo4x8aS9rZDDb9vsMq+Yc1PRiKzXoi0GLgF8CPTDaQLr1PH9xqV8ysFX7SyHE8AtzoNaIHPp29LJsLAiaTifS4TJJPpbL62/UYikqqj10fTWgW3ODxaqEheVdNteI2ZOxL11GqFIy+ayh2TjqWfrKK8jKLd8pF4UIw/5BqPkAZ/dFpTjHpVldeevoL7J10zJz4gcWTJIhoHbQYCg0ICgVfPrugMmSx17BQ3v7zGfavO8YPMxfzw2t/cLQ0nQLzEs73oCyT+pJk/JTdu7cy7thIPt/6JqcPneXbF3+jouKi49DI862i3Mi6n7dW/i8qxDpfkDITs2usmOgV7METn08nNy0fySwhCJZc5ANrj7D/3yNtUhUyMykHSZKY/NyNaGzV/DVvTf0bNaJFQkO55enxPDp3GqWGMm5zndG+7vtNuLaFhtfdqWWAZoQbWnM7wNXbkYmPXEdBdgFfP/9Lo7e/9ZkbuP25Gzm57ww2Ohu+fHJ+8xWyZp4fAaG+zI/6jBP7TiOIluv52JZIBFFkw4Lt5GXk4x/iw3cvXujV15jQbZlmInvKmk9CdHJldTBbOxuumdiP0VOHUJxvYM+qgxRkFVKYU9ygstrxkUmVoSIqtZKrx/fhgfemUpBVxJ+fduxGttF7TrEg9gvGipPbZP5jW6O4p9OT/HzyMzr1DkZrb0NRnp51P29l5/J9bSKTTOO5uBiNb1dvbn92ImobFflZhRxcd4zEkylNGremfLIzVGBgIedvWRLXkYtIuvQB3oJrk/fBWgSHBzByyhAkSSIjIYuIbdEt1r/P3tmO0MEhJJ9KbXRZ8suZgFBfbn5yHJsX7iLyfPnoi14sgxQuBIiljHtc5JZHnufFke/y7J+zqr1cGQprjhCI2HGCzKQcrp82nCWfWAoG6c2+nFfIzmPkMZKMq/n40R8QJVij/4XCnOI6i0E1lqZarNPjMvns0e+tJoe1uC/kPyw4PY8+I8MappS1AGt/3MLjn05Ha2+LsbbeUJcJXfoFVxaLuJzpdlUn3l75XwpziojccYIHw56rr61gFVYbfq80UPz46u8U5RvISc9v9XoENTE/6jNOHojh6UEzq3947r6VeKJpz0gZKyArZQ1FalBoV0lxKVsW72bL4t3YO+voP64PDi72OLpZKh7a2tmwbekeTu6vP268otzI7hUHObD2KI99ch8v/vQ4Zw6fZcfy/eSmtU4vtEZTlzWtHs/B0k9W8/BH05i7eVblR+HDQtm6dA/v3z3PikLWjrHcSHpcJq9OeA8A72APbvnPBG5/9gbMJjMH1h7lr3n/Umpo3dDSjza9Wdn8sSXx7uxJ2eu1J8m3LyzVOOs651LOpPHbO8sAS0z/4En9GXf/KFZ8ua5Krkt9uHo71xieXIoPl96uJMaQzft4N3j0emiCFV6lUXH7cxMpztfzyxt/VH2hsLZ3/Nx4tz47kc69A1HZqHhj0kdNL4rSDPla0jt2yQaVf2Yk5GA2SSScSKlcLlxcwl4yo0BJtx7dMOSWXQjPvmiMKuvXIMtPs5byys+Po7ZRU15WAdRU9KgUhQRKhcAPR+Zw8kAsv87+s/U8L3U1lG9nuHg58fwPjxJ6TVe+evbnuhUya3vHLhmvpMjAvz9s4voZo3hvzUzemDSn7SvzNXLf3l7xEoMmXsWRzZEsePvPFhKq5Zm96r9cc8NVAMy67SN2/bW/SeOkxmagUivJTMpm5/L9lBrK2PfPoaYLZsXiHiu/Wss1E/vzR+r3TPF5qFljtSgX72dTIqhkrEo7VMoaT1Geni2LdlVZJooC4x4YzbDbBhJ7LIHDG49f6K90DgdXe/pf35uA7r5IkoSxYLBbbAAAIABJREFU3Mipg7EolAq69A1mceIM9v17+NKCXi1OgbmQBIyY0OCCAX/BtXnFP2rg0pjtBz+4iykvTmo1pexS0uIy+eqZnwFQqpXc9Pj1vLv6FRRKBUe3RLFk7qpaLdzWRBAEXhg9q8Xn6cgU5Raz7qetaGzVPP3Vg3w046v6NzpHbnq+JdH4EhTUlEeahg1tYxFVqpSMnDKYLv2C+evzfy8ons24Tm20Gm587LrK5s3no4yNFSbS4jJIjUlHMksc3nicMkMZV1/fh0c/noa7vytxEQnViiGJCpEti3d1GGuswzmD2/m+iTXxv12zObg+gudHN63/4aENkQiCgNbBhvKsCpwV6eiNMUCXynU0wjsEqRwwGs3oHLVEtXELl/aGKIrMXvkSLt5OuPm6svefQ9wd9ES7yJv59OFv+PThb1hZsIA/Ur7jNvf68+LaE+9M+YzV+l/pO7onL42d3dbiNJlew3vwvyd/YGUNlUAbw0PhzyGKIg99eA+rin/ju5d+ZenclVaSsnn878n52LvYM+rOIVUKeci0A+RCH02gmc39zGapMqk4qKc/975xO0qVyO6Vh/AO9sDVx5mCrCIOrDvK5oU7axzjyKbjJJ1M5cl5M5q0C42l2KwninyKuRaJVwE1ORwhR3qevri1qAfn5zeWMOXFSSxJ/RZHNwd+e2cZv7aRJc5YbmT5Z6tZ/tlqRFHk+hkjefuvF1CqlexZdZBln/xTtVxuB+P3+K8xGU2Ul5azZv5mCrKtWwq3Tqyk/KttVEyfPYVljewbJ0kS8dHJhA8N4fiOE5XLvSlCzxJM3HFuiQlbXiQQu+r3isbkCjYyr8LB1Z7rpo/EwdWebX/sZuNv22vfrhHHMuTqzoyeOoQ/P15FVkpV77xSpcAr2AOfLl4IgkCpvozIXafYv+YoCdHJqG1UNT7wBUFgxjv/Z+8sA6O4vjb+m92NuwseIBDcg7tDobgVt+JepEBxirVIcXcvWqAEL1YoVpwEAoQQ4m6b3Z33w8azm2wU+L99vhBmrpw7O3KPPacnVw7e5M2/73SWJT+R2sOmk9cs8XpKZVJ6TG7Pxsm7SEjI+Nv8cnEWKlHE+4kPO+cd1Z6bkfr30RTBZqhPfHQ8VRuX58rvd3EzckIRO5owZUUUoguGwl8Ul4ZjhDWoRLo6DWf1jXmMWNGXtWO3Z72enOIrsV67VCrKhvtLuX36HiOqT8te5wLKkwTYMHkXEzYOR6Yvy1tvmS6elpx6YwQJ8jg5Pi8/Urh0nsUHaJxHZ+Twvtw2Yx/th7fg2zFtmNVhCb6eWkp/AGXdS9N1YnucXByR6UlRqVT4vw0kMiQSEwsTytcri6mlCdHh0dw7/yj7wuTgvjO1NKFW22pM3zMWP29/5LFylAoVFrZm6Bno4evlh56+HqWqliAyNIr9Phv51qp/ygBavhOfJY/s/2tu/X9KWe4gkUowMjXMFsWpnoEe7m2r0rhHXT6+9lczcIXGcG7bJe31f1Lhr6MFl98Uq4rmITbEURaYk+pMVcIYSbC4EVvBOt/mV8gVDKk4CSsHC7pN7kDfWV0/m1KWGiqVirNbL3F26yVkMgmdxrdn+eU5JMQr0oS5xUbHsn3mAcICClCByQd4qNR5KUMrTQJg2cXZeN73Ts6TfHHHK7GQ85cLQxMD+s7uyom1f2otAJ4ZLuy+xrClfXj/3DdZIS0iWCKKm/jEQRRYYIgvpZGiL5jktfgZoG+oj2sNF2q2rkpMRAweu67mKTlNw661sbK3YP2EHeoD6TYJigSlxmLySYiN0pwfI4oi22ceYOCCnsRExOSYae9LQLdJ33By/Xl17UUNmyiZvpSxDebkep6E+ATm9FzFsMW9uPL7XSSClMrGziSofJGLbzAWTEGR9j1sbG5M+bplcj3314xx64dSu101PO+/Yd2EHRxfc+5zi5Qpzmy+QI8pHdn3fgPKBAW9inz/uUXSGYPKTcBDeZC1fy9ilPuMzy1OjnBi7TlOrD1HmZql2PFyNZcPXGdR71XJ52UyKd2mdKDN4Ga8febDqXV/cvfcw+TzRco4Y+Osfg6XDVzL8BX9aTe0OT/sHINKqeTO2Qfc83iE7yu/fCES2++zgQeXHnNp/3UW91mV5px9EVsadK3N0URugjG/DaZ+59psefILfx39m50/fS1pC/8hryAIgiWwBagAiMAgURRvaWr7BSplAkXLF6FF34bEx8gRRRFBEBBFEXmsnNioOE6uT9yUarE2tB7UhDI1ShLkG8LDK09Z0HMlEonAjeN36Di6NWPXDkEeK2f54PV5XpAvDXS0PESIUTwggQQKAx+B9cD3JMVNqmhJMOvyhjg/E+tdEpHKsguzeXXvjXYPhLYx8zmvQaFQcXj5SQ4vP4mppTHWTlbJ5xyK2fHTkUmE+IXx6/CN2aP7/8LyMTpa9k8O1Rxb50eKlS+SfK5Jz3qsurmQn/uuzl0B1Hxas3u7ari5l+bUBo/sK2Sp7rc9844waFFvNkzciSIhsQi6YEVRACIBc53Gyc6cCBLMrEwoVc2FkpWLYWJhDIA8Np7Xj96xa84hlEkEAXl4/T55B2DtaKlVJo3HUyMTWURR5NjqMzTsWocTa7+sjbI2q3B6D1rncW15cccrJQwzTQ6EQLuhzYiOiE0/SKo/U9pr8tRZOVpRtUl5SlQogohaMfvr97uIqeqOyRQCMgzJkGMmqihaxplZ3y7NYrWZQBfvan4Wss0B9A31mX14InoGMuwK25AQn0CvoiMyNsxPWXP5DPZ3HYORqSEnI3Yz4tcBbJ2+D3lcLnOYdVlvLtkNtz79FaVCiUd6L31+I6fsy5n0e3nXi6mt5jN27VDqdqzJ9D1jCfQNITYiFq+HbxngOlYji6LPy4/4vPwIQKuBTajdrhqjak7lzaN3OJd2pFLDcrQe2BRrJ0vsiqh3TkEfgtk993Aym7fO60qF/nN70LJ/Y17de8PPfX/TmFIR4BOUrJCBOoxxzeitGJsbs+bWQjqObk1UaDReD7yZ120FiCpkMikNu9clMjSaV/+8Jj4mvsDz6REkWeaO/8+g4MMXVwHnRFHsKgiCPmCsreEXp5T5vw/CtboLe+Yf1RiS02lMm2SWRL83n7hz5r7aepoKziUd2bf4GIGpKturVCKv/nnDm4fv8PcO4Mbxuwxd0ocNk3cTGRKV3E4QBAyM9ZHHJRRIzYYEUc5jrElgN5B0k9wENgHD1TJxBwsK7gZ6fseTsjVLcfDjZlaP2pzjJNz8RFRYTBrF6/1zX+6ee0iVJuWZdXAiUj3NzFSiSiQ8KIIg3xD83gTg8+ojbx69/SI8bDbOaiUz9Ys+LiY+TZHTl3e9cChmx4z94/nn3EN2z8s7xrfcwNLOnDaDmxIVFsOO2bm3BMZExnJ4+Un6z+vB1ul780DCrFHUrRDthzXn+vG7nN16KcUrn8+b4Ff33lCmZilKV3PB8/6bPB3bwtaMas0rUrdDjS9OKcsKFeqVpd63Nbl9+h6PrmquZtr/p260GtCI3iVG53gz0W9WFzz2/MXNU/dp2M2dcrVKsX7KHp373zr1D1O2jqCL/ZAczf814o/o3bx7/oE9848S6BOccaP7lSAhLoGHl5/QeVw7Oo9rh/+7QL4rMfJzi6UVbQY1pahbYa7//jcB74Oz7vAV4L7HvwyrNInfg7djYKTPsAoTs8WO+e+VZ/h5B7Dq5iKmtZrPoytPef/cl9MbPZLbGBobULlxeVZeX8C75x/YPecwf/9xj7iYlBIuZd1LU7t9dZ7fekWJikWp2rwSP7ZZiEKhxLW6CzVaVaH98Bb4vw9kUuPs14iLiYhhcPkJyf+fd+IHtjz9FWNTI+yK2PDh5UdCA8JxKGaHfVFbRFEkLDACz3tvcCxhz6kN5/PNA23jZEXDbnUoUbEIj678r1eOTkQBKWWCIJgDDYEBAKIoygGtGregC318QcFcsBbdJS0ynki3KRIEASNTQ4q6FaJB1zrIY+UEfAjG0MgAaydLdQ2bFaf45B2AiYUx0eExGilSzaxM6DiqtXoDL4qIolqBjomMw8zKBKVShVQqoVwdV/7YcpGrh27luaLmpQrgLbuBwunOTAWWAAFY0IcagqX2ekyprs/yS3NSiCpStV9+cXa2CCxkMilHArdhYmGMKIq0NeiFQpnzOjzJSC9TM90S8pPu09zm1clkEpxKOlCkbGEKlXLAycUBuyK2GJkaAurQu9GaQkIKwDrde0ZnBi7opblwpob5By3sRaVG5ZnffQXBH7PJEpoNq+bCP2aw+LvVWr2PZtamzP19MnO7riA8KDJ7cmRxXet8U4PYqDgeXn6SvXF1RarrMHLlADZM2vVZCmgKgsDIlQNYP3EnKkUqI1MOFA3H4naUq1uG4uWLEBMRS1RYFN5PfHh6Q7eNs7Zcr4LId5BIJbTs34jCpZ14cdeLG8fuaixxUqpaCbpN+gaJVEK1phXoXmgEYmrSF13ub6mU4uUL025IU7yf+OBWsxSHVv6Bz8vEMNFUv4OoEvmgDMFXZYISSwzwpbREhhnq8Nn1//xMWGAE09ssyvU1yFLuLwB/xOxjx6z9HF5xKuPJL8yrpyuKlHFm9a1FdLIe8LlF0QxBgqGxPoMW9aZwaSfcapdGkAiYmKuN7kd+Pc3GybuzGCSPoYuXV8d7YOTKAdRoXZVBZcflSBQP1WFEUaS1Xk+tNcpk+jJqtqpCzdZVaNKrPvI4OSqlCnNbc/QN9PDz9uftk/cYmxtT1K0wZlYm+Hr6ERkShdejd5RzL82oWtnMlcwCW57+SjG3wuyac4jd84+mkdXU0phi5YoQHhTBxM3fY2xmiKmlCXfPPWTH7IPZ/+5rweBFvXhw6Qn3LzwG4ILq0D1RFGvkyeBfIGpYWIj/1K2bJ2MJ5869A1IXjdwkiuKm5POCUAW1l+UZUBm4B4wTRTFa03hfnKdMF4iiSExkLC/ueCVT3tsVtSMhLoHO49tx9fAtqreojLWjBREh0dgXsSEuJh6vB954P36fXNsnMjQ6mcY7CaaWJljamxPoE8zQJX2ICI7CyMyIPjM64/PiI14PvPN0LWpfoKak3XAEemDPJ9wEa+0KWT5BoVAmJ6auurGAs/ID7J53JE9r8egCuRjPMzGaaIoioMJMfE85wRKpkLNbV6FQ4fPSL2XjlQ7LL87Ojbg5xrmEg0ilEhRyBSdCd9CnxMgsQzC3/bifsu6lGbt2KD91ykX4VC4RGRLFlYM3sbS3yL5SlgX+/uM+Q5b0ISE+IV+t8XW+qc7dcw8/i0IG6nfaH5su0Gvat9w4fofI4EiCdSjHIZEIVGtRCSsHC+yL2GJoYsAHTz+e3/LUSmD0pcK1ugvN+zbkzJaLnNt2WWu7XtO+pVabqqydsANBIuHE2j9RqVTZotuX6ctoM7gp3wxrxl2Px3zyDuTMtita239QhuCp6oUC9TsxGiX/qvpTU4jE3MScklWKs3TAWp3n/9oRExnLzZP/fG4x8gxr7y7BtboLM9sv/tyiZIq4GDnrxu/AQ6mORggLCOfOjQfUalOVrhPa88k78KvziIPaCNxpbDuW9Ms5+/P0NgtZfPZHyrqX4pkWRlSFXMGtU/8w78RU7l98zMIev2BoYkilRm7UbF2VJf1+y33R6WyiWKLnc/e8w2kUW4VcQVhABGEBTwGS650ZGuuz5PwsDvhs0GzAzQF2zD5Ev5+6Jitl/y+Qd56yoCwUWBlQDRgjiuLfgiCsAqYBs7Q1/p9A4Ht1Ervfaz8qNXQjKiwaPQM9Hl15ypt/32HtYI6VoyWDF/VmSb81GWLH2w9vgWMJe8ICIwjxC6Vxj3oIgsCuuYep1NCNUbWm54vcjsgI4DAqeqY6moABr6glmGIgOGY9SHqFLY/ZdMbVm8np6L30nd2VfQuO5K7wZjZkEkWRR2IM4RwhqYBrDP4oxD5ULeiCwflp/RUkTGu1AHlcAs9uvcJDeZBjwdu5duQW87v/kmnXkI+hmRdL10WZz4P1XNp/g3ZDmyUXd9cZWcinEmHTD3v55vuW2BSy4dqR2ykn8+J3EFU06l4X20LWafIAPkdc/dunPkj1pBR1K4y5rRk2TlYgiqhUIp/eBiCVSTGzMk326gqCmpXw3oXHPLn+kiDfWzmvWfaZIJEIVG5cnmrNK+LnHcD6CTszv5+BTmPb0KNwIjFDNn+nyk0qUL6uK4271ubK0b+Z8e0KgnxDUhqk844l4aPKKFkhU0NKLCvwFntRJsaQweUnMG79MEb80p9ZHZdq3RRmia/AszRy5QBUCqV2xryvYA3p8ccmD1w3Dqdxz3rqMjhfIhKva8WGbgAs6rOKy/uvI9OT4VzaifbDWzB69UBOrT9fcIpFNp4/URQJJ5xoFNhgjKGQklajUCiZ2HA2Px6cQNuhLXAu5cjZLRd1JsXY934DPi8/sqTf6iyfvZ2v1gAwtYU6SiciJIoLe/7iwp6/dF6LRmi6Flk8C90mdwDAY/dVnac5GrSN+Oj4PPXKKxVKXtx9zZDFvdkyfV+ejfvFomBzyj4AH0RRTGIOPIJaKdOIL04pG/FLf/YvPpZjAo6zWy8hlUkxMjVEoVDRsl8jGnevo+bMEEWuHLyRQSGzK2JL++EtGVH9h5QNQQFtymwEK+zEjQQhR0kPwBtjfqQyRhgIRgUigy5ob9IHD9Vh+s3tzrYf9xfInJFiGFH0IkkhU8OBSBoQL977oq5PbvHw8tPkv/sUH8kPO0Zl6ZUtX7cMI1YO5MymC2mOW9iaM37jMMysTfm5328EfQhJc16mL8PS3jzD8dxAIpVgZmOWZ+Olx6kN52nSsy69pn3L/p+PZ9leJaoIJxQBsMBaa9hr5/Ht8PX0S6uQfUa8fviW1w/fpjkmkQg4FLdHIVcQGRKVJg8iP2BoYkDx8kUwszbF54VvvrA2SiQCjXrUxbWaC/+cf8T2WQd19lLGRmd//YVLO9J+eHPuX3zC8bXnsbAx4+Dy0xrD2jVBqZFYxp541LmrPi/9mNx0LkN+7s2UbSMZ6DY+2zJ+Lfjr97/pOLrN5xYjTzFhozp/++DSE59ZkqxRrFyRxH/VKQ8KhYr3z31ZN34Hnca0Yc6xyczu+PkiJzRBKSp4SAQR9EFJDfQ5hp14lbKp3s2Prz+nZ6FhAGx7vhLnUo7UaFWF+x7/ZqpkSiQS7ArbYFvImiMrTmJqaUJUmMbIMACe3HiBbeH8Y7LODup3dgfg9indC15vnrqHuh1qMvfYFKJCo1IMVJCmxEPF+mWJjojhzb/vdRr31sl/sHa0xMnFAbyybv8fdIMoip8EQfARBKGMKIovgWaoQxk14otTyg4uPZExBCqbscpKpUhUuJqNSyNTYyrUbF2Fuh1rMqHhrLQW2vTtdWUi1AYt8wuChArYESru5RPbMUagMBbIBOMvw+KYap1DK09m86PllKpWkh0z96kZGnMLTWtMnDOeBJSUzHBaiQtybmLAZ1LK8jlnIsAnmEsHbmBupZnu3aGYHRO3jCA+Jp4ZrRcQkYqoJolSP9gvFBsnK/a/W5/lfC0k3XQXTsN6y9QsRYOuddi36Hfdx8kBLh+4iWuNkvT5sTN7F/6u9RkMVUXyAiNiGI9APMbsoryowkximqadpb0Fegb6/H3moXqsNKx+uc+ZzCuoVCJ+b9IxbeawHlpW+G5mFwA8H3gTERJJr+md2PnToTyllTY2M2LQwp547L7G5f03st3//bMPVKhflifXX6S5DqJKM+Oic0lHWvVvxOZp+1Embu7O7/mLpj1q47HnutodmzyG5m+AAX5EkQDopZLkBjao28j0Zfx8dgbWTlYMr/pD1ov4HO/2PLpPHv/1ElElIpHKcu+R+QK+cT8dnUzgh2B6F/06aPHPbLrAuHVDiY1MZBxNdQ1f/vOaOu2rfx7BMmNZJIxQ9gPqyB85lfBjPzZsxR6rDO2HVZxExzFtaNClNoMX9+bHtou0voNUKhUdzPtyNHAbc49PJTYylgCfIA4uOc6VgzdpM6QZH1585PntV9RqV42W/RtzYc81rB0tM3+vZfc7r4mFOosxbBIZpJOfIx3e68fXnEsm/DgTu5dTEbuQ6UsTOREE4mPiUalUhPqHU7RsIW7/cZ9ZHZYAauKn0tVcqNigLEqFiiRb5dtnH1DIFRR2dcJjl+5eu68aBcu+OAbYm8i8+AbQWvz4i1PK8qOmhDZIJBKqt6zMqhGbC2xOTRAEAWvBkmTbzRfwodKEt098+Knzchp3r8vau0t4dOUp01rOz104YyawEiwxFPcTR8M0xw05i0lmlOj/ozAyNWTS1hFYOVqyasRm3mcSKmjlYJkcb16qSnHePvuAU3FbRq0exLRWCwDY5fUbTi4OyYpctpSzVGg5oDFrRm/LUd/s4tU/r5HpSVMUs3RQiSpeYEQ0RwABEYiiM8/oSK3E8hpJaD+sOSfW/Vkgcn8tMDDW5+65hzy+/gKABp3cs3wnK0UFcuIwwBiJDhv/vj91ZdfcI0QE5yz/0NjCGDMtBgtNaNa7PrsX/K4uZ5CYd/b60TsadqqptY9KVBKmCkImSDHDitLoE0sfYvgFKITAVSyZi5NgS6NutRm65Du2zTzw1eXxZQWZvgyVQpVG+Wr/fQsEQSjw3Jt8g6g2Yo1aNYjLB67nPPS0AND8uwZM3TWW14/eavTqPb3xkuKpSqh8KYjEkSSFLAkqevCJbdhraK9QKJOjF87E7qPb5A5snLxL6/grry8gPCiSXkXUHs8B83syfHl/xqwZgiiKWNip9wuiKPLx9Sfqd3anSc96hPqH5VuNOiNTQ0ytTLArbIOvpx/hgeEZ2tg4WWU0uGUDbY36ZHreuZQjO1+uot2w5jgWtyPUP5xX996wa+6RlPIugEulYhiaGBQ8UcznQgFT4oui+BDQiTjli1PKsoXsWP60WCwMjA0oVNop04ryOZ4zp/3yyhOjac5cWktvnrjLzRN3WdRnFVN3juKs/ACPrj5jctO5eadMJo4jQ0YRvHjHTORMAuQYsIjiBCHBIvP5CionKK+9ZkljpMur+f6X/lSs78aO2QfSFNFML0cLaQ88lAeRpCI98EoMh/N5+TFZIQPoV2o0Lfs3Zsr2UQD8mXCQPfOP8ObxO8L8wzUTa2gq3KtXsK+RZ7deoVKqGL6sL7vnHyEmVY2qMDGYGCaSVONPDSkxdCCa3zFNDIV1LGFPXHR8mnIYeZJ/p8s9kBf3Zh7f30kkGdtmHqDN4KaUdS9NiF8ob/59p5V1URRFXokhBFESBeXRF+5SSOVPUUlGy3cSJBKB6PDYHCtkAMsGrmPBqak4Frfn2JqzqQRK5TVLKicnCMj0pcRHqymVBDFdGKtSmcE75q8Kw0tlTQxTkBCMKXupgJSagpx3Yj9ikWCDSL0q1anUuwKnzjxko0tPiqgMMUnnjf0csHG2IiYiVl1SJov7xNBYH4fi9im5oKKKImWcadClNrXaVqNQaSc+vv5EQpw6V1GQCAiCQL9So7P/vssq1zmX7H3ZRVKo19yuy6lY342ZhybS7LsGdLbRasT+bKjSpALLLv7E8zuezGi7UPM3ANg4aSedx7Vl5oFxrBiyUWNZoc8BIX19PwASkJDWu1SqSnHW3f0ZQSKQEJ+AiJpUZve8Ixl6u1Qqxs/nZ6GnL+Px9edqD3XiODtmH2LH7ENZyuWhPMjIVYNYN35HFgvQ/X3r5OJA98nf4Ov1iaiwaIJ8Q/hh5yhmdViaEqKdeE+/uOPJz33X6Dy2rjA2N6Lt4GaY25qxfPB6Hl15mmkY+pt/3+W5DP8hZ/i6lbJEmFgY02vat4iiyMW913n71EenfqIocnr9ecatG4rnA282//D/xEqQR1jSfy2eD94yYkU/ToTtQFSJ+Hn7U6pKCbweejOimg5hPFmgmGCJnXgPHzohAYpgiqFgkWW//xV8M6IVbYc24+zWS2yYuDPL9kmsXLri/M4rnN95BQ/VYSRSCf3mdE8+N67ejzpZjcPzswC7Fry444Wv1ycGL+rNlml7kzcfAgIZCvwCAgmJ59T45vsWOn20/z/i7NZL2BWxwdrRkhd/a08ueCcG48tMVDQBIEGE97LVmCrOYC3R/IxKpBIMjPQ0ntMVDbvWxtrRknPbtTM0JkEURaQSCd0mtefh5afI9GUUdnXi25EtCQ2IQN9Qn/hUOXoKMQFPlSOx7ABABUTQlWd0pIZgTCnBjiJlnJi2awxTflrH+KlWKMVfgSgCmEMZ1VMctKy9IDBoYS+6TerA22c+SKUSFn23hrdPMn4PO41tQ7thzZFIJCgSlFjYmRHoE0xcdBy2ztZcOXSTdeO24fXg7RfvEZNIJBwL2YHXQ29EUcSusA3OJR15duslt0/fo1zdMshj5Vzc+xdBH4LpOa0TtkVsUMQr0DfSp3BpJx5cesy8rsu/2HprSzxmcXztOdaO2ZppO5VKxfj6M1l5fQG/jd3+xShlNgQRyV1EUrzTevxCkVRbUKeSDnSf0pGtM/bqlNu38eFypraaz32Pf9UHsqE4WdqbM2C+OpIkLz2jnca2xdjMkM1T9xITmWIsLFKmEGVqlOT5356A2iAwbv1QPnj64f8u73J27YrY0KCzO2bWppzZfJHAD/8btezyBQUbvqgz/ieUMpmeFCcXB5YOWEuX8e1oNbBxpm5Y9/Y1KFurFIIg8O6ZD+e2X+LmCR3pfTXFDedTfkfSeKaWJjiXdMD7iU+W7Gr2RW356fBEgj6G4FKpGMEfw1jUZ1X25syGJfP3lX9wbttl5DFxKBQqJBIJhUo7sOXxL3Sb8i2Hl5/MtaXTWDClDNm0QGfXGpvULRelmEQEYokCBIyFLOTN4poYGBvQcXQbrh25xcjqU7NkpEtCkqdMlznS9EsVumhpb8HhT1tSfaxE9YVJN17Fhm78cmUeH70+ERMZy8Elx3PPBJkNRIZEsXvuYTqNbYuevhQEgdeP3uJ5bBclnnkCAAAgAElEQVQRyg6kFGNPwJg/MEZNRFKySnGdniWNyM4atF3/vLD857MnONAnmECfzD/oUSb2qKKbpDkWpxiJD6fQlkavSFAS6BNMoVKOfNQStpNVPbRDy07SdkizNB5S0F5fbfO0PZhYGDN+/VAAts8+yIVdV5mwcSidRrdk/+IU4hh/VSixpGfaNSBWcKdsowQq1nBj0IJe9KkxgjOPK6OkY2IbM+Ss4C2dsE8dJltQoeiChFXX51OujiurRm3h9AYPBi3syeZHyzWGJTsUs6OYW2GO/HKal3c9iQiJStncFoCsedl+95u1GJsbManJXIzN1MykhUs7Ur+zO5Gh0fw2eisulYpRpWkF7Arb8Pz2Kw53S6mvtu3FKsrVKcPTm8uzvZSCgkQi4dtRranUoCzDq0zR2s7Q2ICV1xfw6/CNhAVoN5ZJJBIGzO/Ot6NaExcrJy4qjujwGKa3XZRpv5zCBRvimUoI5VFQFgNuURh/LARLAKo1r8DkLSP5++x9zm65qO6kw33y4s6bHL0LD/ttJsQ/jMsHbnLl4M1s908NmZ6UZn0aUNStEA8vP9XoxTy2+oy6lEfbqhQp40ypqsXZPvMAVw/f1jBiRhgY6VO5cTlcq5dEIpUgSASuHr6VbHBxb1eN8nXLEPQhmOvH7hDwPiiLEf+fo4DDF7OD/wmlLDwokgNLjtNz2rdEh8dwduulTNsrE5To6ct4evMlT/56jjxOTrUWlWg9sCkeu69y/Xc1c6WZlWm2at/kFgZG+lRuUh6FXMGDi09wLuVInx87I4oqHv/1ghotK1OnQw2mt1lEsz4NsLA1S07UjI2K5/Ffzwn1D+fIr39gV8SGjZN2sf/DRk5H7ybwfRDLL81RNxYEzK1NsS9qS9DHEPVLOPVmOmnJYuLf6fdIggCiSLk6rlqtTM9uv6LvrM64t6midb0lqxRPkSkP4FDcjqjQaKLDM9b3KuTqhO+rLEJUBYFSVYrnqFaZgaMB/fuv4f6dkoAKY/E8FZBgLNE99yU1EuLknNpwnn0Lj2bdWAvOxu2njWGvbPcLCwjPkuGvzjc1mLprDAD9XcfgWqMk/eZ015nCOK8QFhiRTDAiCAIulYsxqlct9l0ciH9wM1SKWIxUJyiPBIlEQulqJWg9qAm/jd1eoHJ+jVCKSpSiAj1BX+N5qb4RZCA5kyImMhJqw4W9f/HN9y05tPxkhnPhqnA+qeIwRoKTYIVUSDuWa3UXll/6iYt7s0dfHR0ew95FvzNt1xiKlyvMR69P3P3zEX1mdE5WysysTLCSyiEgfY6siIlFJN9OaY+5xJrfxm3n7uPXKJibYR455VDyDBmar1l+ItRfnbNi46Te6EaHx/D+hS82zlZY2Joz4tcBGJsZIdOXER8rZ2rLBVRtVgG32q7U7+xOn2IjClzmvEBYQDjT26rpwWMi1O/+V/fepCGi8n8XyK1Tmg2vL257UrZ2aZZfnpN8TN9In7ObLnB2W+Z7iYJCR8v+nAjbiUul4pm2m310MvcvPubM5gtalRX3dtWYsm0Erx+9o2+pMcnEah1Hteaw32baGPTCqaSD1lqeOYEgCJTDBoXogxwvDDFGkqiQVWlSgZ+OTGZ0rWn4vPyY5ViW9uYMXtQbSPm9s4vhVafg3lZN+nEyYiffFR+VhjArOxi8uDfXj93hzx1XMm23/+fjlK1VCjNrE7b9eCBteRcNUIdey6hQvyy12lTh+u93OLj0BAlyBYIg0Hd2V8rVcUWpUGJmaVpgrNj/IX8h6GqBLwiYC9aiu9BM/Z98tgRLZVJca7hQvm4ZDI0NeHT1GU+uv6BBV3dKVCiKTE9KyKdwmvepj1RPyvfVpuabLFYOFnQc1RorBwsu7ruOoYkBZWuWws/bHycXRyKDIylfrwwLeq6kfqdaFHZ15sKeayn1dUQVxmZGVG5cnmbfNcTn5UdUShW3Tt7l9cO3fDOiJW0GN+Pctkuc2nCBpR4zKeTqxKYpu5m8dQRL+q3R/oLI5HfY/nwlhV2daGPUJ5mGNUukshwvvzSHyU3n6HiVssbYtUO4duQ2Dy8/yXBO41wa1rbswmymNJ+XrXlFUSS0qgP37/9Kip0jATO+pZZgopWOXfNg6uvTdmhzzK1NObAka/p3IHkt7m2rsuCUugRGTok7QJ1fdnLDedaO2crCP6azuM/qZJphmb6MY8Hb2TJ9L6NXD+bVP68ZVWsaTXrVJ+hDMI//eq6TrPkJURRRmsZTpWF5KlWthExPhiCoCR7unntIfKw860HyVKC8r22Xs24p92Jqj1Tq4ypRyVNlKNH65RD07DDTf0qhiChatG1AoE8wFRu4Ye1owY6T5/D4eyZQNLmvhLOUZQXOEu11BAuVdqJ684qc2uiRIoso8lQVSKDYDQU9AS9MmEdlwFiSUs9o1sEJxEbHsXxQ5qyi2tZpZW/Osos/cf3YHRp2rU1EcCQPLj7GuaQjRmaGxMbF0m/MPnz916G2SD0F1gPlMDWRYG/5F8sX9ME3wJ+xUwsh0jLNvMb0pzZROhGe5Boa5nBvV41pu0bTyWYQRqaGjPy1H60GNkUQBA4uPc6WaXsz9Jm+dxzy2HhWDNlQ4PLmBs37NqT75A5EBEeqc5rTIxfPnLm1KSNXDcShmB3vnvtyev2fybm5BQH39tXpNKYNVw/f4uyWixQq7cSOl6u1/oZJ8FAdppvjEKQyCV0nfoOVgwXGFibUaV+dX4ZuoPXgpjgWs2NWx6Ua2ZN3ea7G0t6ChPgEzG3M+MasL3Ex+fuu7Dm1I6WruTC/x684lbCn+5QOhAWGI49NwOvhW9oOaUah0k6IKhXy+ARsna25d+FfBEFg2cB1WY4vkQgYGBtg42xFbFQcEqmE0E9hKBLUxhcP5cFcrdOxhD1NetTFxtmK9RN3pSHQSIKhsQH953bnzeN3XD10C3lcqigNDfdpx9GtsStiR0x4DN5PfbilpVC7Ywl79A30eP/CN0eyp0EqOS6IR+5lURD5q0YNa2vxnxYt8mQs4dChPL1W/xOespxAqVDy/LYnz297pjl+7fBtrqVyKRuZGNBlYvs8n79Gy8pUaVoBlUJJWGAEx9ecJSI4Mrluzp0zDwAwtTShYkM3rh1Vy3T92B2N48VExnLr1D/cOq0ufGlgpI972yo06l4XhVzBP+cfMXBBL5r2bsCr+2+Y3GweiCr+vfqUH3aMptf0zszpvCxb8c0D3caz8cFSjodsp71p39xcjq8akYTx4kV30j5OesTQhSgOYoZlgcgxbt0Q2g9Xv2hyo5ABrBy+kYlbRlCyUjEMTQ2Sj9dsXYWFp2fw9NZLPHZdY/TqwbjWKMke73VMaTqXtsOaExYQrpPFMz8hCAKyaEOenH3Nk7OvP6ssXxueqUL5xBaQFwc5RESrEO2H0350c6ysLPlzxxW2zzqImZ0JVcrO5MnLJihEd/Q4jQ3ncBJsMx0/KjQKxxL2lHUvlZyzFiKGESAOREnSe6Qm0RzhldCRXu6l6TCyJSUqFqVk5eIaWTezgpGpIS37N8bSzoxprRcS5BvCjtkHMbMy4fsV/Ti3/TJeD98SGRKFvSqCMLoQw7eInAH2ARKioiEqehALV4+nssIQUw4QSW1IZIIVuIItH5AIn68G0su7XphamrDUYxY/tJjPiiEbWD9xFz2ndtRqSb966CZzj/3Ap7eB7F2Qc898QaKseymGLulDn+KjdDcIZgMRIVH83HcNFRu6Me/4VMrULJknOdK6YsHJaQT5hjBxU2Umbvqehb1+RalUJbMIasOhZSfY+HA5cVFx/HP+Ib6en/jg+YlqTSswbv1Qrh6+xaQmc7Ves36lxyb/7aE8SJcJ7XP0vGUHB5acYP7JqXgoDyKKIpun7kGRoKRU1RLUbF2FgPdBDKs8GUhbhysrVKhfltrtqhEfKyc+Rk7QxxAMjPVBBOeSDijkCqQyKafWn6fHDx0RRZDKJKiUKhLkSqJCo4iJisPvjT9Pb7zEyNQQ+6K2KBKUuLetip6BHnr6MqQyCaIIIX5hWhWyIT/35sCSE2kL1WuBsZkRJSoWY+X3WbOCf/IO0Ola/AcN+C988euERCYlMjiSXtO+JS5GjpW9OUqFEscS9uyccxhzGzMKuzpliwpZEAQ6jGrF6Q3nuXNWM4tSEqLCorVaSTJDfKyca0duc+3IbSzsLFh0Zjq75x3m7NbLaRJQgz+GMrXlfCrWd2P55TkcWXGKE2vP6TzPmDozORa8lfOKA3zyDuDNv+/Zt+j3vKlhlglCxCi8EYjHARmBFCcH+UF5BBEVsTEZvWEiRogUXJJ8UvgSwMnI3XQwy7mifHbbJc5uu4RDMTvW3VvCmtuLCfYLwb6ILf1Lj8HPW50P1ELSjfPKQzgUswNg+8z99J7RGf93gf9/6p38D6FF/4b8e9wTgounOirhY8BYZo9fhqt5Ybwfv0cqkzJi2QB+G7MNmXiAKPZghQEmErss5wgPimTTD3sY8Wt/5HEJvHn0Dj8xASXpQ20N0Ld1w9zWjPi4BMY3mE1cNgtHl69XhhotKqOQKyjiVojQT2HYOFslb446jW3LjtmH0iTE20jMqS2q8BXX8pLpiKT+eOvz4nklDOJPUlUw4qXYiWickRCLLaGU0FBzqaBwTr4fiUTg6Mo/2DAphT48JiImg0LmoTqM9+N3WNhZYO2oNholxGX9DlWKCt4TRgQGGBJPCczQFwyy7JcdrL2zGNfqLsklPVLD0FifRWdmULGBG2Nqz8gXhSw1Bi/qw9qxW7mwJ3vhsjmBvqE+f8SovWBJ0QcAO16t4cf9E9j24z72Lz6W6Ribp+5h89Q9aQ8KEi4f0K0eoG1ha5Z6zCL0Uxgv7njRcVSrfFfKzK1NObftErXbVeM7l9GZ5kPp8ns7uTjQqFsdYiNj2TJ9X45kksqkmFmbYmlnjlNJB/rO7kpcdDyBH4KRytTXMyYilgS5QmPRe4lUgpW9BTVaV6Zo2UI6KWSW9hb0mdmFQJ+gXKUt/IevG1+uUpZDooa8xp75Rzjw8zG1VcRAj6jQKFQqEWNzIxp1r0NUaDSlqhTn5om7Om8YZHpS5LFyyE5YW2bIgpY9PDCcUTWn4VzSkYmbhuHn7Y99UTtMLY35c/tlnt325PH15wyrNIlfrs7j3bMPKSGAWfwO8jg57UzUm/+K9cvSvF9Dfvt7Ef9eSxXCpiVE1q6I9hCnzNYYpYrkMc4kYA0YAU48xpM7jx+rr6m2e0RbgeBc3lPmWGHMXqLpTOqEvLJu1zF7UXCbtF1zD7Nr7mHOxu3DyMQQD9XhnHvMEq+J//tgutkPoUX/Rjy48JgAn7QfzEKlnRAEgdHu05MVtT3zj1C+XlmGLu3Lthn7UqyHBfzsflHIC7rv7FL2CxJsnKyIiYzNMkew46hWWDlYUrmJGwv2vtXQwhav5x8QJfFIJAK7vNZw9fAtWvZvhOcDb57eyD5r3cbJu2nRtyHNetVn5cb9+HkGkb6WUXRIAOBITHgM/ed0Z+MU3RhyRZWInoEeDTq5c2z1GbpMaE+wbwhBviGUqFiUl3dfg6giKiyKKk3K4VKpGDIDPbZO30f/ud2p2aoK9x7fp8d3RijS7QNlBmao4pToC3pUlNgCckAKZO4hzPMQ1nTjSaUSAnyCqNGqMmdG7KGtUe9Mu3vsvsbRX04jkQi0HNiE8RuGpeT5pbrXkkJBlaKCe8oYIlgHuAH+BDOWKmJ0jnNn02P+yam8efQWu8LWrPxrHnr6MkTA1MI4eVN79fBtXCoVo34Xd17czcQLruMzFyVG4k88hgg4YYkkVR7j6Y0edBjdhk7j2mPtaJHrmlaGxgZsf7mKC3v+4vntV0zeNhIjE0PmdF7G/JPT8Hn1kUFlx6XpM8B1TK7mzA4R2aaHy9Ve5J8O0XFkK0pVLZ67uXXA4nM/4lrdhW0/7s8VQYUgEShevjCNu9fl6KozRIZE6cYJoOGaqEQID4ogPCiCd88/cPv0vUznBfU7x6VSMRr3qIuoUhHgE8zz2578uf2KTvM37lGPA4uPEewXqlUunVBQZVm+ZvxH9PF5YF/UloZda5MQl8CfO65kuTHRBkWCEkWCMg29bExELOe2XqbDyFaEB0WmUcgEQaBa84oUKuXIyfXnNY4XERyZHKJYUPj4+hMbp+xmqccsTq0/z91zD+gwug3fjGjFlGZziY2KY/mgdaz952eW9FvD5f26WdeS8Pj6Cx5ff0FkSBRdxrdj+ZD1XNp3QyubWk4INQDeEE8CesB8QM22JfKe3/YOY0jNujkaMzcQBAllxWie040YhiKgwpjNLFk4ivXf7UAeJ6fv7K68e/Yhy+TevEAbg56A2hqeK8UsESqVij+10I/7evoxp8syfvt7MW30eyYXEn964wVh/mF0GNmKY6vP5Gr+/5AzyPSkLDk/kxd3vFgxNPN8ofhYOYgiwb5hSOKeoC4rkPJ50GMDpsQRL8ZioDJiSMVJGJoYolQo6TyuLRFBkdkOWVUpVfy54woSiYBrBXuem8wlNDoplwukkts0bWRLVFg022cdZPmln2jau77OUQkyPSkmFsZ0HNWazVP3II9L4Kcjk5LzUGwLWVPY1ZmE+ARe3n2NjZMlv1yZw5vH79kyfR83T93BQBRQ0JwUY4uKkiX/xuS+WbbWWhBoIenGecUh5PEJyZEKMn0ZjXvU48LuFK/1r9fUObPmNmaoVCpUKjiz+QITNg7H3NpUK+HBW1UIEWwESicecSCGXXjSnsrkjVKmiE+gRb9GzPxmCf+cf6S13Ym159jjvRav+95cO/J3jmn7X4jB+NOWBL4D3vKeRVQW4zEW1HmMF3Zf5cLuqxz03YS1U+4NbAv+mE5cdDzl6rji5GLPot6rcG9bjQWnpyOKYgaFrKBhZmXCoWUneHztOY+vPcdDeZDpe8eyuM/qfJlP31Cfex6PKF21BB57ruV4HAMjfTqNbYO+kT57FhxNzhcrSEgkAqNWDmDNmG06l2VKgnNJB2wLWacoZP8h//GfUqYj8lCD/+b7Frz59x2NutUhLDCcq4eyoD7VcW6JVEK1ZhWp3b4apzddyFAHpv3wFkikEkSVipb9G3F+p/qDaF/Ulpb9GmFoasjJdRmVtWTkxtKRhdcs0CeIIRUmMmbtEI6tPsPaMVvZ5fUb6+8vJTYyDqlMwm+jtzFt9zie3nhFwPvs19DYMm0fnvfeMH3PWC7uua416T6NvNkoyhtDAtCFJIVMjaJExZQnXpGxLosgEUAQECQCokrD9ckDC7aVxIzaoooQcQkCAlaCNe07NaN9dDPCgyKxsM24iUsfZpQobOK/2fSiarCGXthzjebfNczeOJqQxT0VEahm71Kki6f39fpEYVcnipUrnFKc9j8UGBQJSkbWnJYm5EebgeTcNrXSPWrVQMpJpTxSdiWaKYAzEjah4gWP+RF98RZW4i0qRFkTFx2PkakhlvYW+Hpmn6kt2cIM+D4JoKQqEm+9zgimbojyAIqYhhJ5RcZTPlGnQw383wUyaEFPokKjuXM2a4NWUsmD9y98kenLGDi/JweXniAmMhapTEq/OT3YMn0fEUEpYb8eu67i3q4aDy89RiJIcRUjeUV3ohmMgBxjtrJ580IOTjvO/YuPs16khudFFEXeEYo/xsQiICEaU2S4IMVSyJ1yI0gEfF74Ur5uGTxUhxET6fmn7hzN1ul7ObDkOBXqu7FmzFb1N0hQ02s7lXQAYPq+8cxovwRBQ0H4yDgTEEunO2pAvOCIIE159kVlyt9pPBWavlnpro/HnmtUb1WZ6XvGsGPOIU5v8MjYJxFDK05ij/da2n/fMokQGABrJ0tCP4Xx0esTq0ZsRqFI9xskyhGuCucTXVAwKvGEI9Ec4QXtqZZK1l+uzsHQxDDXxq32w1tQqWE5Wkq7pzn+z58P2bvwaDKRUp4jk32NTCZhys7RvHn0loeXn+H3xp/Bi9T5TwDtTPryR/Ru1ozaQlRYzpgOM4Nb7dL0mtaJnXMOEfQhbXifrszX33zfAhsnK87tuJpSIFmDpzf7SPGYai3Tker+7Ty+HSc3nOfd8w9pvGe6oNf0Thxd+UcO5ST7exhN7dN853Muyn/IHb48pSwPsXfBURr3qItMT0qTnvUI8g3h6Y0XuRqz/fctcChqyz2Pf1k3fkcyMYdEImBhZ87A+T25c+4B13+/Q9uhzbC0s8DS3oIK9crgWqMkR389nUxB+7mgVCi5sPsqI1cN5Ogvp5nX/RfeP/uAPC6RfUiQEOIfzq/X5vHyrhenN3lku4bN1cO3Gbd+GD/sGMmyLJjSsgt9YgHnDMcFHIlXfMrTubIDiSDBVkjJqdGUEyGRSNj1ejUORe3oMr4dTXvXZ9fcw5luPnKKJf3X5o1SlgWWXfqJ68f+1njutzHbGLFyAJsm7864OfoP+Y4kli9dNyZRodGUr1gWw3+98RNnESLGEMh3KFmsHo92+HMPY3EKJQU7hizuze65h5Pfg7mBlcSMEVM6cnzdWWJi4hBiUz5PygQFTXqoveB9Z3fF8/6bNDmUmtBlfDv2/3yM2u2q03FkK/b/fJyIYPW717W6C3f/fIggwA87RhPsF4pKqUSlFAn2C8XYzJBRqwayftJurMKjCBWXIkHAUrAm5l0MS87P1Ph864LnhOLHHEQaJB55Qgj7iSaOiuJTLIWce+HG1fuR4cv7MchtXBrP5ZjfBjN4cR/6zVXLbFdYHTqelL+VlFO372ftbK8GQjwQCmny5kRkhJJEdpJb3Dp1jxC/MJxLOlCjeaVM34ujVg/E3NqUme0Xa2TOa9i1Nmvv/sybf99hX9SWs1svEfghhGFL+uBaoyTVinVD8X5Qul76WLrU5LzXAgRBIMAnCFtna+Ljcs9AeGH3NcatH0bF+m48vp6WoTYsIPN7Ob/QaVxbmnSvS0RQBCN+6cfj6y9YNjjley2PkxMaEM4uzzWMqDEtTwsdA9gVVpPi9J/TnT3zs5dHJQgCo9cM4s8dl3n1j/Z6ZZ1Gt8bQxID9S7IuSJ1T1GxVhQ+v/Pj7zP0c9V/5/WaGLO7Nxsk781iy/6AR/4Uvfh7ERcdxbtsl3j55j76hftYKmQZvg5OLA0OX9OHdMx8EQeDvP+5zev2fabpValSerhPbU7JKcRb1WU2TnvVwreaCMjEBtFHX2vi9DdC9joQusfC59O48uf6C57c9mbJ9FJum7EIer0gz780Td7l54i6u1YrTZeI3jF07lJ/7rUlmS9MlTn1cvR/Z+uRXlg7ImrY2OyiDLbfZjkjqYp8izvZPMTMooftAGuSWSPOvLl3N1lWYuX88G3/YzZnN6gKZ9kVs2Pt2Hf3ndOf+hX9Z/N2aVPIJunsSUyOxrYfqcN4InsXcUWHR1O/kTsOutTOEZ4qiyLFVZxi+oh97Fx7L2ebjC6GTzxVyuoYc5JGl/JnRSyFIUtpqsuLumnuYct3K4/XRFP+AEijwRYWUlKKFANUJwZKSif8rVr4IIZ/CdFtPFjAwNiA2Ij5DGYlnt9Qsub+N3U6ZGi4sOjODEdXTlimxcbIiQa5A31CPet/WIi46Hl/PT2kt0InXKkGewJDFvXEu6cijq894eOUptdpURRAEqjStQJfx7Qn1D2PTo2VYOVgwreUCnt58hW0hKyrUKwOo2elC/cNYP3Ents5WHFt1JsVbrOl3EyTIxXhCqJpKIQOoADgTTxu8+Z6qubh+z269Yly9mWnmBFgzZjvrJuzibJyaSKLn1G/pOfXb5GZJSloxt0I8vvpMo7erBIYEM55YtqLeOojosZhigjLNPShIpRn6akfaOnRWjpYUKuXIiXXnWTt+R8brmDhPySrFqVjfDQCJnh6CJCNJyV+//82jK09xLGHP6wfeTNkxCkWCEl8vf0pXd+Gb/o14MD8AKJamn/+bd7SU9Uz+v6GxPmvvLOac/ACt9XuSE9g4W7Hy+gKCfEPwfvI+R2PkBw6vOM2gBb3YPHVfilE2Hbo7DcNDeZBtz37lwytfRtWajiIh9+9kQSIwYdNwjvxymgNLjmd+r6S7D8rWLEnz7xpwcr0H71/4prnn0rePjY5XO36yiPjISlbNUM9buUkFNalIOjmzet9aO1pi42xFeFAkhqaGn+fb9P81v+w/pezz4cUdr2z36TS2LVYOFkSHRWPtaMG8rsvTnNfTl/H9L/2Ji5Hjed+b6MhYXj98S7nargT6BGFoYkh8jFz3OlOfAUqFkt3zDtN5fDtEEbbOyKg0vrr3hsV9VrHqxgI1OUk24PPSD6VCyZ8JB+hkMyjHhR7Tw0QwpZh4n/fMRcVoIBID5jCwcxWinkfkamxjc6M8kTE1ZDIJM/aNQyqT8uT6c26fSkkaDvAJpoW0B0OX9KHzuLY06lYHqUzKizte3DiuufxBVjC1NOFYyA4g99T46WFobJAhN7Or/WA8VIeJ18Le5vfGn63T9jJ0aV+uH7vD8789s82i9x/yH2VrlUJpKGfHqTjCInekOnMZ2A30y9BnzZht1O9ci+6TO2gsBp1dyPRlGJsZpWGIBegyoR3TWi/kXqLHfs7RSfSd3ZXd844AMHRJH4pXKErg+yB17ubR2wR/1J6f8emNP5f3X2fH7IPJm5KkMPP0sHG2YuVf83Asbs/zO6+4fPQ660btoKhbIVoPasqMveNIiE9g2LJ+nNt2MdN6X3FEI8ddw5nKwGsUWAP5kw+jUqlopd+LImWcGLt2KF4Pvdk97yhzjkzCwFgfN/fSnNl0UWt/Q8GYypJgvFTtiMMBPUIoKlFiK7HIlVy121XDyMyQexce025wUwC1QpYJ1t/9mZ+6LOfWqcw9ExEhUUSERCGqxDQGr+O/neWnkxPYvGEWfoG/kWJweIQ1fqQmbbErYoNjCftkI2tOsPbuEs5tu8SOWQdyPEZ+IdA3hLZDm3J8jXbW5QNLjhMdFk3xCkU4Gb6Ls9susWb0tlzPHfwxlPqda/h7fwIAACAASURBVNGiX0PkcQlEhkTxfXXtNWHd3EvTsl9DgnxDWD9pt065hOd25D8LcEJ8zjypvaZ34unNl7i5l2bj5JRUBkEQaNS9DoVKOfLg0hOe3XqVV6L+hy8cX65SltfWcW1IZSVwrVGSeh1rIIogj4tn2wy1VbFm6yoAlK7mQvDHEEKSCg8KAr//eppgv1CuHMgi+TwvrBHZtZrrAF9PP7ZM20vvHztTumpxPO+nhAH8Eb0Hr4fejKs3CxCwdLAE3usuC9DGqA8rLv/EidDtvPznNaPdZ2RLPm0oJdjgJN7Gl8vIgMKYU618Oa49z0ikIapEEMUs47tbDWicvULPOqBiQzdGrxrIrrmHuXH8LvOOT9HYbvPUvWyeqr7fKjcuz7ILsyhZuThthzTDwtacwA/BFCtXGNCuaPWa3olBC9WMa4oEBW0M0lOMZwPp7qO6HWvSeXw7Kjcqz+UD19k6fR9F3Qrx+uHbZC/J7EMTaWfcR+NwcTHxbJyyG7farnQZ3w4DYwNe3PHko5c/759/yBj+lp/Pf3bH/l+wJGax5ubfNcDIzIj1B08RFpme4bAJMD3V/x9iTRigDtW9/vsdRv7aH0EQELUwreqK8zuvsPnxcq4duc0fmy8S5h9GVFgMO386lGYtMn0ZTi72DPm5N6JKpEbLyszpsoK2I5qweNUuorBGXy+eeuWdqV+rGsoEJbdP30fPQEaHES0xMjWiRKWibHiwDJVSRVx0PHfPPWS/hvC9MjVLYVfEhtGjl7F97XOU2GFAPCHPXvL+Bz82/aB+btff+5kW/RqzYuimlM7prPPGogkGXCGW9M/wHaATBgQCOahzlokXIIm8I8n79f7ZhzTFlsu6l8LQ2IC53X7RvMlNNba5ngXVAFCCoAPxhSYPgyBgamlMnXbVmLxxKCqVivCgSGIiYpFI1XM1/64+F/dpIppSeyZUKhFJau9IFvlq6fOan916RQ+7ERRSRSCadkdmWY2QD55Y8R7XdHXmVt9cyLUjt1nSf23W7wItz5m1g2WuUyd0Qg7eVStHbGbwgp70nt6J/q7j0hCaJSHJYCuTSbh29A4zD4zXWSnT5mUSpFIGuE0AYMzqAbQf1oKYiFiNbQ1NDNj8cCkOxew4uuoMoYHhfDerM4ZGBmxKKqadDU+YLjliWpHeGyYI2sO300QuZPSaCQL88+ejDPmE1k6W1GpTlVUjNtPnx84UKeuMmZUpl/ffyB4ZyP/Ctyu/8J+n7MuEIAi4VCpKo251kMclsH3WQewK22BsZoChiSFx0XGYWBgzZMl3hPmHYWJhglQmITo8BpVShVRPmvUkXwGuHLhJlwnt6DalAwlxChDUCpuppQkHfTeyd+HRbOeVJWFSE/UGYO7vk/FQHuTpzZfq4tW5hIlgiiumuR4H1MVlO49ry4jq0/JkPIAxvw2icGlnxjeYrfFDpw2PrjxNEz6TGh7KgxmPpQpTvPvnQ2a0WZh9YbNAi76NqNyoPAC+np/Y+mwlsVFxWNqZJ3+QOlkPzHQMeVwCj6485dGVp4Da6lm5cXnc21Xj4NL8i/f/D5phaWdOjdZVsLK3wLW6C0sHrcOtdQWe/56RbELg0/+xd5aBUSRdF356Mpm4JyRACMHd3RZ3d3cWFodlcXd3WFwWd18sOCyysHggQCBOhLhnMjP9/ejITDzIt/Jy/iTTXVVd01NdXbfuvecgcgaF/kFIDCUOiNHEYCIzQSUqOb79LAMX9uDtw/eZCtznBHKFnEPLTlP+h9IsPj9V8ppFxhEdEYsqIRGXfbcREDG3MSPI+xNRIVHI5DIOLDpB4z51mbhwD77sB/JCosiFl5vx9bpE6TxOtBnahFrtqnB2swsvbr0m0CsYjUaDobECh8L2bHu2IkOjbO6JX2hcZQjXHo8ApE2VBEReMYTqYjAKQSIc8nb7SNGKWYdPywV98ohu+HAQDcnP+DUgCiOmU1SHvCh3yGhuCPYLxTa/NaIo8uL2K7ZM2MPGh0uJj4nn0eXnHFp6mu1TDjBq3UDunMg4L/RLYGiswMzGjKW/T8HC1oxAr2DiY6S50MPVl6ntlvH4mjQfJDN1/NC5OhO3DsvEKJPwS5N5rLo2m8FlJxAbHY/CSIH/+8Bc989SZo5lLJwOHEsf59FEhaSXaXnw+2PK1i2Z67ZB2tDtPL4N0RExPLyYtSbp3wV9hZxX99/SvkoLilctkjI/ZwSVSsPYTUMyNZ5yizJ1SrDg1EQ+ugfQNe+PmebbK+OUTGuzlACPIB1CqSUXprHcZQYfnntzZvMV/NxTc8qrNi1PiepF+cvl+WdFS+UUoigi+8wFvo/bR0rVLJZubIR8DOPwstMMWdKbJ9de0PXntpzZfJmWQxrz6t4bHl/JAcnQd2SOf3BOmfClO5tfE+aCtVhD1lT68I12yqs0LU+JakXxeuVL0cqFcC7rxI3Dd/nkE4LnSx+6TmhDTGQcBkYKbPJZERoQQakaRVnYa63ORGRorEh5uXw1/H/samSRC1anY3Us7czx/xCIRq3Bzz2AkI9hGYojZsvekwmKVnRm0fmpWNlbEhUWQyfbQZm3lxNoXXPMxsHcOnafp9fTv1RWXJ2VrSG46LzkxZvWatHn9SUJlnnMmXdqErdP/MnRNCFd805N5NW9t0SHx3Jl780ME9SzwpmI32hn0V/nmFwh50KctFuYjnzgK3iFjEwUnIncy9wuK75s4ZbJ+Bi1biCXdt/g3V9ZaA790/ANvNZf5Zo6xTMQNE8yoK3sLeg1rROnNlxEJpOYSUP8w+m2vgODBpmRkNBQq1YkzRpP5ekjT4Ii1gOFgWgUjMeUt8RSCRXOGHKXVjXzUK9alRwJ0KftX4ES+VhwdjJX9t5m7/xjOv11KpmPmMg4Oo1thamlKZvG75bCaEUNJ4J38fDiE46dvMjWk01RqxvotGtGF2rIpM2zwuWdaDmkMbb5rDGzMkGmJ0OjEREEcCyWFysHS7ZN3o/HC28WnZ+a4jk3FXoRQ1ohWn8K0otisjwpR5INo6UDNnJl761Mx0CgGIYfMmJRo4cSC+QUwQQD4fPCpy+rj6T0dXjVyYQmRXQko3LT8iy9NJPYqDhOrPkdQSbQe3pnnTa0547MGHN18sWSGBorNy5L3+md0DeQk6eADRY2qUQloiji7ebHq/vumFubUKFBaTrbD01tQ6FI7UBS26WqF2b1pWnERMRiYiFR05/e7IK9ky2zO68CoEytYiw8MwllfCIWtmZc2HWdNcO36zwjolrNuE1DqN+lJoYmBrx77MHiPuv4+D6Q9iNb0LhPPQqVcUQUwd8jCAMjfd4+8mDVj5t0QrHX31tIiapFCPT6RJ/Co7K8P2nhoj6Mn3sAU1ssxN8jKLlCpuW/GLmYIxr2qMPgxb14+9CdDy+88XT1zdH87qI+zN3TD5ndaUXmuVY58FqN3jiEuu2rsfHnPdw6ek+rahJzoVZeo47HSWsMOpfOR8fRLbmw8zpVm5bH/akn987+hUNBG3pO7ci6kTvoN7srJ9df+KZkKr2nd+LY6vOStEgWKFG1CI161iE2MhZBgL+uvOD5zVeZlpfJhJT1aunaxSlXtyT3zv6V3sj8ytpkVzRH/hJFsWqOK/zLUNXOTnzUoUP2BXMAYfv2r3qv/qeMMrsCtiw8N5UH5x/jXKYAM9stwcDYkDbDmpKviD1hQZFcP/SHDr2zXF8vY82LbzGx/s1GWaNedfF65cv7p565ayeT9rJCtRYVWfT7VGa0XcKD80++ilE2esMg7BxtmNVhebpiLurD7F94QsohyQJr/5jP5Kbzc20sJaPdiGa0HNyIJX03ZEgDX7C0I9VbVcbASJ8fOtfkl0ZzM9UFyggu6sPcOfGAuV1XpTsO38Yok+sJXFAe4s8Lj5nbeWWmCeGf0zaAnlyPhj1q4+Bsx76kxfg/Hv9yo8yugA3NBzRMud8pCyFR5IUYTLCmF2raI5e/wjHPr1QqZsnpWyPRiOW0WlsMdCFVtwoMGMOO1fU4MP10CvNjRrDNb03t9lWxsrekYGlHCpVz4tLu60QGR3Nh57V0/c0M4ZowCjQrjb2VKWcO3+QjLpDG22TICOoIwTqhyRIdegJGCOQXrNATJANjy9PlFC7nhO9bf5TxSoZVmgTAXY0psexIc3UleWlOGS2jDOBkyE5MLSVv49fO6cwMW5+vpFBZJ2a1X8q9s4+kjZp4KeQsLjoeA2MFXq4+DK3wS0qdy+ojnFx3nmc3XxPkE4z7Y4+Uc7kxyi7FSLkw7597kZigYtXIHXi98pMKaW3oTdk9HD19PRb2StW8ysgoA5DLQN9QQZ22lanWtBz1O0t5eKEB4SgM9fH3CGLThD243n3L6PWDKFrRmYNLTtFnRqeUTcQCJSQNumGVpxAbEc1R/20YGBugVqkJ+RjG/d//Ys+cozremQ6jW9BpbGvmd1+F+xPPlOOnw3bz+v47JjdfkOX9SQuZTMbJkJ0YGCvweOnN0v4b8XzhlWn5L0YO5oi1f8zHNp81Xq/9WD9ye6qxmEP0m92VNj81ZVaHZTiXKUCxyoU4uOSUbg5nDoyyReen4VzGEUs7C1qZ9E05Xr1FBUytTSlftyQf3wdKuaqZGGXabYsakdZDGmGT3wa5HuQvlo/FfdfRpM8PvHvikbN1zWeiZPWiWNpbcv9c1jmOI1b149ef96Tr9xfju1GWK1S1sxMfder0VdoStm79jxtlQuOv2mblJuWp3LQ8hkYKEuIT+W3WYUrVKoGppTHFqxRGpifj8m83cy18mmvkZoH2N8UBy/X1GL6qP7tnHyEqO0PhC40ygB+X9KLFoEaYWpqwqNdabh7NRkcum+sYmxsxZEkvqjarwPapB7h9/EFSV0UKl3dizvFf6FdsTJZNzz42gUNLT/PmYe7CHfIXy8ukXSN4+/gDG8fsyra8fUE7FpydzJQWC7MkJEgLF/VhQj6G0qPAcJ3jW58up1A5p29ilAEUrVyITQ+X8Pu2KxxcdCLn1MjZjAmnUvn5eesw1Co1jsXy0tt5BKpEVZZ1/nH4Alavz24vk/uaHdtd8gJA30CfcZuGsH3qAcICI9LVC9eEEyQm4mBhQp/+HTh69yG3HqaVtpgGpPUqh2Br2IHt66bh8yKAu6cfptCtAyQKSnwtIVZwIiY8AjO1D2UN8zBixQBaDG5EG9O+ZIS0CxdRFHEVg/lET9R0QybzQF8znQS6AbqeZHM6UV2mn/L5mcafYLoj0gfwwpjZVECJicwUmUzGpcSD6Z6jJ5pQQjiLtsGnx1YqcRxLmWVyJ+k+qT3dJ3fg7aP3VGlagXbmfXMVuvwl6DimFSPWDGRUjan0ntGJWm2r0da0TzpinmS4aI7S0rCnjo5dKktnJkaZ1vHkxfGodQNpO7QJb/76wPSOK4nRssXF+NRrF69ciDWXp9LKVttTlvq7aIcTiYlajWhEmvWug01eSyxtTLm07w4fnnunCpMBhgZyZhwYTdWm5dm3+CR7F6YJQ00y1OwK2NBvRkdWDt2a7rslY/LuETTuVZdWRr1SZDwuJx5iVodl3NMiacotarSqxIg1AzC3NefDM0/cHrqzbWLa/E3QiGp8CCMcBYYoKYRpSojsF0OQcdR/K5Gh0Qwu83OOq8nlMhaen4ZVHgtio+IwMjXE65UfQT6fsM5rRcFS+TE0MUSlVPHyDzc2//xbjiVQDvtuwcjUkKiwaMysTQn2C8XvXQCffINpM7QpzfR7IMi1xkkmc2Lyb2lkYsCmR0tS8tXkCjn9ZnVm16wjWdaDzOdPnXGSwfX15Hr0nNKBfQvS0PonPU8yPRlyfanM/oUnUClTx/dXMcqSYGlnTsvBjdA30CcsMIJ7Zx8R8jFMyvXNxbvpinjsv22U5ckjPurcOfuCOYCwefNXvVf/6pwyEwtjqjaviMdzL7zd/HBwzkO+og54vfKl3YjmCDKB6LBo7p15xOTfRnFqwwX6ze2G92s/PF19uXv60Rcnpv+XoEpUs2P6QQYv7MnJ9RfwfZt7QdjcYNvkfWybvI/StYqz9o+FxETG8ujS58fdF6nojFNJRxyc89B3ZpcUo6zl4EaMWD2AM2mkDDJCkHcwzmUcc22ULT4/lYlN5mdqrARoIvBFH1M7J4oUExg+vx9LB2zMlUEGEBEciU0+a2QymU5S/tCKE3FRH+bIxy10yzcsV23mBAaG0o526x+b0PrHJjoegF1ua3Esno9fx+3i5LrzuWp32PK+rByyCZ83/gxZ3PPfZ5D9S5GYkMipDReZfWwC4+rNSnfeUmaJJVC0qDNxkfE4F7Tk1kNPwFmrVEaGoQnB8RZ0GbqEH6xtGfJLH64evIOXqy+iKPJUE0NEyDGSda0ieY8qbhhG5kbERWWfpxKticITJbEkEMVQRCQjTqOpSAInkdMcFbWA4oAKfZbhRCwgMQT6aPz5RH9gcFKL5YnlCG60oQrgWCIvIC3ktI2Vkih4RheimQoUQc5+7PkdS1mqLiGAUylHzKxMubT7BlOSvCrfCha25sRExqJSqrDJZ0XV5hUAmLh7JAVLSaRAy67OYm7nFTrzjGUeC44GbOfirmu6BtlnoFLDMrQd2oRrR+5hYWOWtKjN+J3adUxz4nPJ4JuMy/uT8svUGTNTxkXHM73dcnpN64BjsbyZtvPJJyTFIMsMSwf8SqOedZiybwz7FhzH86UPAZ6f+Gll/y8yyh6cf8KD80+wdbRm4dkpdJvQjl1TD+jkSGlENX8RQQSrgEpAIMGMp4IYjukXCouDNK6vHrhDh1EtclXvdMQent5wZWhFXaIqbSNGYajAws6Mcb/+yO+x+2lt3DtHhll3x2GY21mkbAQvuTCVBxeeYGQieTVzi7iYBII/pgpRq1Xqz2onN1Cr1BgYG2R4rkL90lRvWZHo8BgiQ6Iyjrz6SihVsxhGZka8uP2aIO9g6nWqQb6iDnx47sX1A7cpU6cErx+4fzUm7O/4+vjHGWWWduZEh8ciyASs7C1wLuuETCZgZGqITX5rLGzNkevLSYhTojDU5/Kem9RsW41GvX9AJhOQ6cko36AMRqaGkvYWMkwsjFnafwN+7oFfHlf8uR6vvyPc6TMQGxnHr+N/o/O41gR/DOXagUxYJbPZzc9peAdItPtqlZpKjcrmzCjLJASzca+6XNl3i8nNFugYLH1ndeH57dcp7IZZwc/dH4dCdtmWS4sg7+AsDTI3WqJiLOGfwPeTkmaN+1JVSEAhZDyRZ4Yu9j/ioj7MpcSDDCk3IV2IpJW9Za77npOx6Xr3DU31urPu3kJKVS/K0OX92DP7MPGxCTgWl4S8bx27h2HaF1NSyJiQQVKtgZECU0tjfNykUCf/D4FUalSWJ9de5v47/J34Bzy3GSKLZ7TtT82wd7ZLYeHL7BltPbQJ57ddpc/PzbnzcAEfvNYApkgLbzcgCtAWO94BzEeFHddDJ+C6ZBtxyqKoNWpM8CaK8aQKDWuAewRhxdLdZ+mnDiUrBGsieEUZlCwAlgK90pRQoI8zBfiJCMzRIxYn9LCWpz4THho10CdNPX2iyA/EsP3FSrZO2pvOWDGSmVJdVBMgziIODfYYYCrY6NxXU0sTSlST1NtGrhnI1L1jQJDIpHzffmRgybFZfr+cwEVzFFWimk8+weQtbK9z7t7ZhxxbfY5abapwbstl9s0/ztT9YzjoswVBEDi39Qprh29j+JqBfPINYeWQLdIYyYSlMDvoyQQqNiiDMj6RZcNSQzt1vF9asHO0ZlDlaQgGGYcskhm9ufbGaWabqEmsjQeXn2PUqr4MnNuVXbO1dBq1v5cma0Y8gJ4FR/Lz1qFMPzCWgqUciQ6PYVy9WTn2RGeFYN9QCpV14u6Zh6jUos5v4EN4kkFWMam0PXH8hjutUo5kiQye+XmnJ1GiWjEEQSKQeHHHjTZm6eUtssKKwZuYuHN4ymZFyn3Qup4yXsknnxCmt13CuM0/cjRgOx2sB2boXZW6l3qvokKjyVvEngGzu1KpYVmCfEIpWa0IR1ae0/WSQZoxo+3l0hrHWqHKMj0Z+kljMoX1ULtPWuNV5z2lfVzbs6W1caid9/bm0XtK1SzB6/tvUtsQNdRpX4Vfx/+Wo+iGL/Wa3Tv7F6533zJ20xDe/eWBY/G8BCXJhPzQrTZVmpanQoOy3Dx2nwbdaiEIAsp4Ja/uvsHCzhzb/NacXHcBvg6Pyz8b/1Cij3+cUdZ3dlcu7LhGu5Et8HjuxdtH70EQ+Pg+kEeXnmFoZkTV5hXI42hDQmwCjXrWRZ2oQtRokOnr8/jqC55cffndA/YFUKvUHFlxhmotKjJkcS+Orf79mybJqpQqnl5/SfuRLdg2ed9nt6PRaAj0+pSO1nlW+2X8+mhJjtrw/xBE8apFsi+YC/iiQIX2gkxBLGvwFHtTXMi9AdhUrztjN/3I9hcreXD+CTPaLmGfx0aAbz7uf643kx5TO9J3Zhe6TmhLZIiUj+HvEcjK63MRNSIBXto5CpkvYmzzW/PiVmqS84Xt1xi2st+/zyj7F6J41cKsH7Uj3a5t6VrFqd2+mjSniuD73h+7qhZM7TMXlToBI1ogIx8KoohGSSKTgVZIHrSTgA0kyR+LHCQw8hfACrAlhlrAWcARqAfMB5ohcpSrV0VuXD2FoxBCcb30DHgAHihQsjLpkxUQBOh6RfSIo0iK9yr9hoeICeCPrscPRKSd41f33jJ0WV/aDGtKfEwC+ob62OazRqYnw8BIwftnngR6fiI0MJybh+/SfmQLhnaZxHtRg7meLaMmL2ZU9y6UqV6S0TWn4e8hMQK6aI7iojn6RTlmvWd05sNzL4ZV/CXLclsm/Jby/y8N5wCSV23L0+W0GSrlnv6+1YVDvptZP2oHD37/67M8ZscCt2FsZsTcbquzLbvh5kwiQ6IJ/xSpa5R9A2z4eS9zj4ylfpca3Dz2ecREoQHhzGi3jEsJ+7l79hGrh27JlBkwt6jftSYh/qEUr1KEiwlS+HB0eAznNl9m3oYjkM780ieevOR0pVyubkkMTQ1pNaQRTiXzE+wXSvd8P0onPzM1omGP2oQFRuR4nKweupXa7apRvEph3j3xyLLsiFX9KFWzGEUqFGTv/OPsWXQSP/dAnXzE3KBQuQJEh6VSzIsaEYXRtx1zAM9vudGwey0do6x+11q8uv/um19bG5EhUczvthpTSxM0ak2K5uOre295duMVn3xDGL6qP0+vv8TBOQ9/nHpAxzGtKFevFH7uAZjbmEL6dPj/Hv7LRpkgCJ5IW6ZqQCWKYlVBEKyBw0hvP0+gmyiK2cZqrR+1gx+61OT2sXuUqFaUWu2qpiTuajQiwb4hPLn2UiLjyM0E83fsZmfi0cl0ZyQbrRUdfMu8s6RrPrzwGNe7b+g0phUxEbGc2+JCYkaTcgZ9ydGOj1a9KS0WsfD3qZyO2MOVvTdZPyptUn0qEkhARMRQMM5R2xGhMQiCoJsgTBp2pySEfAzF1OLLw0S0oSIjPZ+8xJPxjnJOsHb4No6sOMOet+t0qLDTUel/5XwnlUrDvvnH2Tf/ODKZjKb9fsDjhTdvH7nTcUwrAE5mIUKqjTEbB+vkclZoUFrSyfuOXCHT3fssfu8Di04yYF53VEoVEcFRmNuYpoiW75lzhESlihBNJG9FU2LEhogUA6YA9sjZjQEHSGQJUBe4g+S5WgQU0LqKDGnqHwEUTe4UMAMwQdLjqpX8LVDTGF9xO2EqDTLiySeLJ78sVTNKiYNW272AFcASkg1/A4M/sU0IRjIMQSUm8lqMIErjhIgMU7wwREMi65LqJj8bf6LHe9SiE39eeEqZ2iVY2HMtb/9KPxZ/WtmPTmNb8ejyM5ZcmsHwEQtwowYi0wkONufcuQtcPDeD+uTT2a0XRRFBENj4cCmja0zNkeitNjqMbkmhck44Fs+Hc1knGnSvTd7C9ni5+nBx5zUdpsW0MLc2pV7nGhibGREVHkN8VDythzZl7YjtzDmeauCtH72Ds5tddOraF7TDOp81fu4BNOxeG/cXvrxOYn7rVHAMzfvUZdymIUwy0Of1ww8sG7adcC3jRZBLS4zCZQswutkSySCTZ7zs0Mkj08m50fKAaBODaI/7pNy1ZC/GnM6r2XBvHs+uuzJ2wyDMrEyY1n6FRFKk87xoEZcI6d9ZwX5hVGxQhqP+2xhSfiLeSV79zCBov2IyIHNwLluAsZuG0i3f0BQDR2GowLG4A+O3DuNlQiBbtn0iWQMwGXLCSEtgk3pRiUxEEATkCjnPbrgm5Xz5MrfLqpQyn4u5J37B0t6S3oVGpj+ZGWGFqGHn9ANs/HMxzfS13kk6hB3Q7ec2dBjVgt4lxhHsJy0PBX19BH19RFEr1DWz9USa31JPrkfnca2JjYrD3MaUqLAYRAR83wWQr4gDHz0ziGbRMv5EVer6RseDZqR17/W0xmnSuLMrYIMmUYW5rRQ1YGCkoNsv7fB67cvNw6nMkhndh2+1Nk2rfQakRPNsnbiX4lWL4PanO/W71kQZn8i1Q3coWbUowX5ZRyx8x7fF1/SUNRRFMVjr8xTgqiiKSwRBmJL0OXOpdi08ve5Km5+acufkn3i+9P6KXfyO3CI2Mo59C46Tv6gDgxb15PX9dzy8+PSbJK9Pb72YvIXy8Ns7ibb4+OpzOufjxDhcSSSWeojIMRJvUQYZJrJUIyqjBWrTvvUAKVk5uxj3j+4BWNiaZVkmtzAggGhU6D5uj7Egc3a6nMD/fSBN9bqz5ckywj9Fcmn3jS9qL7fQaDQ61xRFET159rp95tamLHWZwZOrL3Vy0MysTXOdY/cdnwf/D4FsnbQPub4eRmZGOsQ+gkxAI6p5ozElhsNI2lybSTZiVIwkgnjAB8kgqocUypgRoYQlqQYZSeVHAQsA7RAqDTAVNbuIRGIzjNIcQSlup5CeZJjJCdEqbwd0A4ZjbW2Gg4MerVvnY+lSyahY0HsNaw7+TigHH7rXWgAAIABJREFUIam9OMIxoRNyVKj4GWkPUQ9wRslvPBQnol54kAHzujFoYU+mtEiv97d5wh42/7wbkMb7dVSIaJNKtEJFEF5swRnHlKMt9HtQsXFZFp6byiXV4Rx7zAqUyMeWZyvxcfPl5pF7XD/4B9MOjMXMypRnN15SrEphBi7oyfntV7l75iF2jjac23w5pX6bYU0ZvXEIT66+oFveH1NJP5IWhee2SPerSd8fmLB1GKPXD0YURRb0XEPv6Z0pXM6JIJ8QbPJa4uHqw8gKztw7/4TTW67QfXxr3B594NzOG9TvWJ3gj6EcdFtJSy0ij2Sc+PUKG1ym4uMeyJ7lv/P4lhtx0RkTkHwNaDQalg7cxIH363jz8D1G5kYc99vEx/eBGJgoMDYzIjE+ET/3ACa1yjyKok+xsSBq6DCqBZseLWZ41ak6hlnv6Z1oMaA+yvhEzmx24c3D9wR5B2dqJHce24qYiBgdj5MyXsmH596MrjmdBDGewoWC+eCxjOT3hZx1OJJAZkZZzykdMDQxoK15/y/OEUyLyo3LUb1VJVoa9s513Qvbr9JmWDNqt6vK3TOP0p0vXqUQgxd2p6VJfzQ5j5rNEhUblObeuSfkL5IHY3MjopI8ZjeP3af1kMYcXH4mmxZyD+u8lgyY04X3Tzyp3qISzqUdCfD8xMl15/+xBk6iUoXrXcmjd2LN7ygMFRQq58TF7deyqfkfwT9Yp+xbhi+2Bxok/f8bcIOcGGWCjMjQGA4sPJH9Ff4O71duvFmZIDO2nwy9S38HE2MG1/RzD2DLL3spVaMYnca2IjQwnAvZPMCiKBJNOKJMxBwrBEGWrQfN3yOIgSXHssN1dTqj7CUqIjhN8sspkQm8pAM1kvI2EEGj0iRdI9ULZpPXEp+3H0lMyN4Iio+VchVzC3tnO+p2qpGhzktR9IllEHGsAmyB51gwkQKCdbqyn4ORNaax8NyUzPP/kvGNx5KoEZFlk3ORp4AN889MZnGfdek2XPIUtCM8KBI9ud43T8z+R+MbzGuZPXeqRDVRodHpNjNCxVBimQIokQwr3bGjZgAmRv2IiUvO6+qO5AFbRepr5QYQD6wGygBNktqxALyAW0CNpLIuQCeSDSjpGt3wF0/inHQ/8hOFO9tQMQTJuCtCxYoyHN76kPhawxM3D9ruvI+qqiOurirCZU1Ao01Xb0kc3TFiJ2qMEdkFFCTZ0xbNPkIdhhDqH8b9c4+y/R0SSURDiQzOtCGIzToBkhqNhscuz2lr1pcL8Qc5/HEbcn09EuKUGJkZ0tVusA7hQzJ83nxErVIzTItg4Y9TuuLcB7w2U7C0I41710MQYOyvPxIZEsXwypMY8+uP9C8xVhJWzuL7XNl7iyDvTyy5MJ0h5X9h/JahHFlxlmsH/0DPXstrI9ej848NmH90PMd23iJOkBMZHsuoRvMBqN68AsMWdWfLNMl7nxyNsH3WUXbMPUGdNpVo3L0mw+d1xsbBkqU/bef6saTvox2upuVl1MnzUamyLKMdvu31yo9O9kMleRNRQ94i9hgYKVCrRXzcJA/9pj8Xsv72HBb1+1VXfDrNvTq14SLDlvVh65OlzO+1lqFLeqOnJ+OPM49YOmgL1g4W/NCpOk1618XMypTF/Tbw5mGq7mJyvtOqYVu5mHCQUrWK4fbAPd1zaSAY4hziQ+FGk/jobwjKYCoVMGHOqi3oG+qTGJ+IwlCf6PBY3P50Z9PPv1G3Uw3k+nJqt6vKrWP30/+4aWCTz4oek9sTExHHsVVniQ7PnPRh6eUZrBq6Jek7aM0RSe8SnWiTDMbXol5rWH9vEfd+H5auTHLo+5qbsxhTf25qpeQ2tX5f9DLOS9OBKOJUKj93Tj2ieJXChIXESF5ZUSQyLAZTK+PUHGd9rXe8lidW1PbQxmiFi2p7zbS8tcZ5jOk4thUBvmGcWH8BuUKPhNgETm3IWbRIWnzN/LJcXBRlgoo3jz6kfM6Eq+e/hf+4USYClwXJ/79FFMWtgL0oiv4Aoij6C4KQJ6OKgiAMBYYCGJJFONp3/CPw+sE7Xj94R9ufmtF3VhfunnmUof5HtCYGV0Ri6Y2oNsKYg5SUKbHENNtr5CvqkM7jEitGEUMrdHcL9YmhHxHidiwFKWRJlsGDVqSCM0PLTcjxd9R8Rl5W/2Jj2fpsOfHR8Ty6/EznnJnMhKpiOB5idwrXLEO4qzs20VbIhOy9SjnGP2ASVSWqKVOnZKbn63aqQe/pHZnTcXlKro02ru2/TeWm5Sn3QymMzIxQGOgTFRbN3rlHM2jtO74ljE2N0YtVo1LpAxkt2ryoWtISr+Dh+Pi1Rq4XhTzxBTY2XVHJnBHESD4Gv0CkJ5Kx9RKJYGMDcBzJu3YdqA9UB96QnrgDYsmPBk9k6OEos0KuOUqeRh+IidHH68EdzJ+ZoxIMEAQBURS5HRJC5KVlwH0yymfUUJwY5gF/kDavDKzQt3VmYMmxOYoEkCNHwD+DR8+NzOh2VEpVipesctPyPHZ5TpM+9TgXux9BJhAeGMHjqy9YOWRTitfjj5MP+O3teiY2nkuQT3C6NnsV/Cnl/94zOtOwZ11WDdnElmcrUMYnEuKXM++zwkBBWFAE/h6fmNQsvZcwGce33eD4thugFdIlJuk6ntt1gz4T26YYZWnxx7knPLvjxtpLU9k660iqQfaNoK03mWJ0aW1ODa8+na7jW7H75QpG1p6J+xNP6naoRh4na06svaDT1iefYPIUsKVJr7rM77mW9890N5Vun/gTRA2VG5dl1uFxhAaEk6+wPaEB4Vg7WBLgFYRz6QJ8eO6F24PM2X1l0TK44Ue1YnnpM6cPggBrftqqwwgs15cz/dB4fnuzlnxFHdJLoWQAhaGCJRenIYoiZ369hLGZEUsuzSAxIRGZTIahiQFrR2zH7YE7y1xmUKFBGQDunn6YbduZwe+dP3HR8ZjbmhGZJifP/30gzRS9uKxMK8r++fB2+4hzGUc+fgjEKo8Fgd6pz8vLP95SoX4pnt18/VWu5VwqH60H/MDh1RcoUr4Aw1f24+Ku63x45vlV2v+O/018LaOsjiiKH5MMLxdBENxyWjHJgNsKkk5Zyon/h5jbz8I37EuKgOv/1w7JF+Ds5svoyfVo/WNjilcpzIUdWoKvosgrNERxhuQhFk1XXmu6UFNPRJZNDlqjnnUB2PFqTYqWihoRUUwfVqjBArWWVyw5VyP5Xu56vYaP7wNzrJmSti9aB1P/z6D/m/5aQsHSjiy+MC3DF2Th4oXp0bIirYY0ZnKzBYTGZJ4D8q+EIOPirhs4l3Ni44NFvLr/lo1jd6ecHrNxMLaONoysNg2NOuMQm9CAcK7svZXyecah8bnOvflXI3lcfYM5Jrdzi2GMEZZGuwmOboZEqvESKJt0VoWt1Qp+GTmQWq2r0LfmcEiE3iNGEewbwrktLrwQvRGZjhQwAZJHqg7QE4E4RKYBvyAFURzE1PQJ0dG2pDXMREKJ1kQRI6rwQoEGR3yvvWTfuV9o3GA6bc1TQyDDxVAikSHlmTkAj4BgQFsi4jpSOOZdpHBLbTIQDd4vXLERcha+LBNkWIkehHKD1KCQaGTMpQj5sq4syHh85SUIMq7su82VfbcByYvRaWxrLsQf5MCi4+yacYglfddz+OM2ukxow6/jdmfp8W7Usy6ja88kNjKW3oVG6VJfZ8H+tuXxMhKVKub3WIsgExBMU0PCRRuL1P+1We20Ig8EQ8lA6/1LG1rYDk3N9dEebzKBhp2r0210MxYN3oL7M28dz5eO10XIxOOuvWGWjXZU5mM99TqCTODoqnO8f+bJnCPjMDI1RBAETCyM8Xbzp1qzctTrVAMLWzNiI+OZ22NttrT4j6++pE9RXT3M5v3r03NyewaX/4UAj09aenCa9H1N+i5+b/1Y2m99htdQqTTM7SKR3kzZO4qjAdt4++g9wf5h3D31J4JMhkatoXGvuhQomR9za1MiPkWya+ZhnU3DCzuvAzBkSS8UhgraDW/GvFMTiY9V0tasr45RmytojbUVQzax4OQvmFiaYG5tSkxkLEv6beD1g/fY5LNCFEXkcr2UTQiRnEdJpM0LL1OzKCc3Xqb1oIbERcdJ4yVpLD249IyBc7pIRlkm0go6OWpaZbQ9aIgiFrZmmJooCAuMQBmvpGg5JzZPPSR99UwYJnU7/j/0Xvsn4r8eviiK4sekv0GCIJxE2voMFAQhb5KXLC8SVdZ3/IegVqk5s+kyZWqXYOiyPnx47sW1A3eIVkcRSwt0h5dAgnwYXZb4UatajfSeHa2XbaKpivatJ4GegjaLm/ND1WogCPT4cQNv3w9Aewe8kNM5tuxYhb6+Atv81pSoVlTSO0qaiBUG+igM5Ky4ml6LydjcGAMjSWRRG3aO1hmWzwoqlRrfd/7M7bxC57jCUMGGB4uwzW/FibXnOb3xUkq+webHS1nUex3er7NOHk8LC1sz8haxJ8AjiPCgyFzV/ZYwNjOkWb/6uD/xoFKjcoDkuVxxbRaPLj9j3cjMyVsywoIeq/lpVX/0DfRzFHr6HV8PMkFG4dhoYmlDLF2QQhDVSGyHKuTEYVvUktBP4dSvX48r+26ze+ahlPohmAJt07RqiR4WKChIHM2Sjg0CQBQXoKd3FrW6BFAFKWxyM1CRl+JfxNIemAdIfrtObWZw/pIcF/VhlPFKhledwtuoaPBeSqrxKALDkbxipRFYhUgpJAr/7khkH9NInk/s7X9lZNeWPD34lEitHLusUAkHXjGdT+RBgylGeFIJM/Q+0wuunVN5+bebKf/fOnqPDqNbEfIxjMPLz6ar16B7bWq3k+bJ0jWL8ejys0y1iJzLFkAmE/jw3Jt19xYi19dDkAlY21viVDI/vu8CiPvMNaOPeyC/3pzJzO7rCQkIx9jckHkHxxAWFIFj8bx8/BDEuKaLPn+x/42gbUjZO9uxx20Nc46OR18h58dKE/F+/fGLwr+v7L2FY/G8TNwxHFEUsbA1R2GoT//iY7KvnA2W9t+Iha0Z5jamFCxTgHqdamBpZ8EnvxDObnbh9b23bHiwiJE1pmVYP08BGyo1LJvp+S/F0+uujK49A5Dyup1KOzLn+C/ERSdQqGwBDiw59dVy4YxMpTwyCzszIkOkZ7hht1oUKe+EWqXhw3NvmvSsw9WMyDdyiLyF7Og7vSPPb7nx+Lorfaa0Y9+Sr5+r9h3fGP9Vo0wQBBNAJopiVNL/zZDenmeA/kjblv2B0196re/4Z8L17htc776hWOVCDF3eF9MCBnQb+IHINAzCapWarZP2c0KWXsQ5eUfJTxOOO/VIZD6g4OxZF/IwlsIYkkA4hnQmnjGAHGM2YeEbxrSWS3TagJzFZtfvWpPiVYuk0y8TRRERDaIIHoQSjiUCidgTS36sEGQZL7jGbvqRyXtGYWppQvinSIpXKYxMJmPNT9uo1qICFeqX5vrhuxwP2k50eAz5ijhQqkaxXBtly1xmItOT4eBsR0frgZSqUSxX9b8mZDIZzfr9QMshjVGr1EQGRyGKEP4pkhXXZiHXl5OoVHFg0cnPav/cZhdaDGrI2U2Xsy/8HV8VFjJT8mgC8aQwkpereNKZSwSEfaReg9voy7yw1LhSSrBKk3UmIJlPacOVE0ikfLprxcTURs4j4DVwCin3rCMy9hFLLUB3k0TJHEb068WbgDooDBVsf7ESQ8NBpBpkyX2YB7SgAOZEoSacOUnnSgORwFhsbTWULGmM6rk7Bay6MT1oB9FhMQwqNY6IYGnTw8jUkNMRewCJJOXEmt85vfEigiBQJsUrFg86DJGfh+S5y++df8qxNw/fIWqaY1vANuWYfUE7mvStx4C53XF76I73a1+MTAyIj03A1NI4Xa5Qo1516TC6JbERseRxsiUqLBpVQiJj60gL5r6zutJzcnuqNivP0pG7P6vvwxouommnKszeOwLPNx9p1qM2iwZtwcvNjyD/COJjlTo6T/8ktB7SiFFrByLTk3HjyF0W9dnA+Zi9bH60lJ8bzcXtz89nhlWpNGyfejDls7m1KSuv5W7jLytEBEcRERyFzxt/7pzQDQkVNSKRIdEUr1I4Q0bRqfvHsG7Uzq/Wl6ygSjKM+hUbw/SD4/B758/uWUfS5Yk17VOPOu2q4OcewLZphzJpTReFyznhmaTbqacnMTGKokiJKoXYPPkAlRuVpUTVwhibZsJimUMULutEoJcUFlmtaTk2TT6YwhD+Hf8i/FeNMsAeOJlE/ysHDoiieFEQhIfAEUEQBgPewOcLtPzThZdzuYOWnQDl35Ls+RXw7rEHEcFR1O1YHX3lOWAgpNC+ixizHes05BYJYhwCMvRRoNFo8AASMQeuAs0RaUYQrwnGGzUD0OM6ZkzECRPyYI1cZp7SVkaJyNK/GdP2hgZGSHHzWmPHVwzHF0sScUTFc9RsBEoBEME5YllDcVGL4l7rOmuHb9P5bk361KN+t1qM2TiYIJ9gLPNYULJ6MRRG+oR8DOPDSy/u+r3Dt6gjMVEq1EGvKKYxRV/IWlNlw+idVGpcjk8+wVRvXYUhi3vxySck48I5eS6yGL8FSuRlxqHxGBgbEBcVR9FKhXh17y2JShVRIVHYOdmmhJ/M67oef4/0DvGVN+bkvC9p4Pv2Iy0GN8p1vX8lku9PZr/HV5jjciIOrP2MmCBDJuihEZMJLTyBv4D9qACVBuJwRS6OoYQs1WCwEKMJYT0wVavl+9gQQDSPSF2WByB5rMwQKYNcfgaVah5QBNiOBlegGulfVXIES2scjDoTF6/AiECUZKQvaImJviF9mjan64J2dGg/A2+f6UjGYkFMec9PDX5g8KLujJu4lfn7XzJ79zMqVhRZd382brfeU6BkfmzzW6OMT0RhoM+YWtP57d16Rq0fjEajobk8+3wekLxZ0w+O55NvCHaONhxffY7NWppiALOP/YJlnqRwQa1x0GtGFzRqDY8vPwNRww9dajLzyARiImPx9wgixC+M5QM30WNKB1bfnMv7p54kJChRJaiwc7QhLjoe96eeTGw8T6KF10KyOO++Rac4d+gBiw6NRiyUP+W8zD91bhG0iTa0qO1b9apJdHgst848xuX4I1yOP2LFqXFc2H+H2+elcLkUuvHMQhZ1QvgyDi/LNhwsl2kPopaQ9Kh1g7h17B6L+21MakqgtVk/Bi/sycors1AlqkhMUDGj4wrc7r9NbUT7mjm4/vQDY7C0M2fP/OMZd+oLPHI6kgEp91bNuycfaNKnXjqjrGjlQhSp4Ey1FhVoNaQRd888pMfkDpSqWZyr+2+zcvDmLEPIddcqGcv+pKmQ8u/pXy8zYetQbB1tCPEPp1LDMoxa25+YiFh83nzk7WMParaUNA+1x4ZOKGGSaHiNlhUpWa0IB5ZKHivP137kLWKPQ0E77v3+GIAn112JDo/hwwtvHZkFnZBY7ZBFnbEmHTcwUlC8UkHCAiMI9Ajighb7cAp5iNZzof28ZCt/JBXK+Ph3/M/gi40yURQ/ABUyOB4CNP7S9r/j34Ug72AcCuWhvL6CRwntiaUrIsYYc4iSMiWypHyNaE0MrzRq4qiFQDzG/AnEE89QoCXgCowBFiDSATVXgXKoKUcUzYnmJ/J+IVHGi1uvMTQ24JDvFsxtzLhw6Tb9+nkTHTEAKYfmCckGGYCGNnziDEXET+gJ2T862rkiAHGaWHzMBAzsShL4wR0N/kSzA2nXHiAUuzITqKmw4/0zr8z7fceNF3dS0zY9XnozfnN6GuovgWUec3a8XEVUWDSv7r1l98zDFK1cmPtn/0Kj0VCgRF709OV4vvTB0FjBjMM/8+PS3szTEpI1NFYw/dB4TC2MWffHAjxdvfnz/GOsHCwJC4zg1d03xMcqqdOxOi6/3ci0LwmxCRgaG6TSeX/H/xvsBWu8xMVEUwUwRpKeTKtXVIYw7NHO1SmHAw84RxzvkZgX32PME8riiBc+eLINNYORAikWAcZSbZUSG5seqNVxhIdPB/yQDKhApP2/ZATi4aFGqZQWtpFEIoVLxgFGWuV+4+ef2tKg9g+cW3IZR5/nKGmPElNMiKKwYEHN5lXoP2Q1t27NQ2KGBG+fcDzejGVCj1asGpK6MD0Xs59lV2bSwao/IIlCN+pVj2sHbpMVZh/7hYqNJS9eL6ef+KFLTUZtGELn8W2Y1moh6kQ1iy/O4MaRuzw4/5gl/Tbo1B9UcixyuR7Hg3ciipI3f2KTeTy97gpIWlJbniwjX1EHXv7hxkf3AEpUK8LysZtTCCJyYpBHhkSjVOYuVLj/pDbY5bVArtCjee86LB+zl5GLuiGX63Fm241ctfV3QhmXgJGZUbrjO2YcZscMibhk2aVprLk+iyHlJ+Kr5cnMDRwK2nFk1TluH/88YevcQK6QM33vOGzyWbFzRnqPk/tjD85uvow6Uc2NI3dp+1Mzjq48x93TD2nzU1NOhuzkL5dnOvP614LrvXfcPvmQaXtHYWplQnhQBEsHbuLtI4m1cvrBsVJeWDao3LAM1vYW/DYv1ch1KGjHhd030dPTo0mv2ry48waNRuTt46yFrLNClablaNa/AXdO/oljsbw8vvYyxSj8jn8Z/us5ZX8b/imU+Dqnv0BwI4NE/8za+yd70A4tOUWfMZ2RLT5JNIfQoMEcSwSkXTxR1PBCA9FaRCAJ7EUSnm2Q1Ep9oBwSW1tldLWOShCONWRBsa/tHcsKQ8pNSLnfwcXNiYjYnnTmFVApXflEKpDAOYxzwCKpDbWo4ikCMVFHICo5fOIs8CepRpk1rq+bMHxRImM3/cjhZaf541TGzFfWDpZM2D6MdSO241QyH06l8mdYLrudW1EUURKHHP0UQ9PC1oxll2cSExGLWqXByt6STuNas3nCnpR6Pm+kBUnt9tXoO7Mze+YeTUmATx6zK67PYf+C4zy7+Yohi3vh+dIb/w9BuN59i7WDJfW61KRM7ZLY5LMiX2F7fpudMWObsbnx/4ZBlpsdch3R1kx2qLNrLxPhV+02ZBoZFUU5brQnjvwk4J2k8ZWmKfTRNsrkMgNqi/kJFj3x4glKbBAoygv8KCmYYCoexZs9hNMZUYd1V0FYSCdEvJCEqW8BbYC5SMZgGeAFerKJKJXai0xzYDACHRCZChQGDmPGae5tzM+9jU8BMJaZUhZTfDShBGCOu3Ve1h87xx+37Ek2yCRY8vptZbbO24WxFvFHG5PeuGiOst9zE72dhzO/20pmHpmA+5MPWYYhn1jzO3U6VqdPoREA3Dp2nzsn/uSS6jChAeEMXtybF3des7hPxsQOCDJUapH2VgORK+SS9Adiym81u9MKilZ0ZtaxCczptIKI4CgsbM1YfnUWY+vMID5WmRIilrdwHgK9QtAk/eaCfuoyQLSzwtDcGIW+Hspk4gWL1O8vRKTGpFeuX5Kf5nYmLCiSyV3XAdC8Zy02ukzGOo8F07utw+NJqmcmxVP2tcgPclE+7fi+nHgoxWOYEKckMT4RA2MDTq47L3nv0ogcJ2NS80WMXj+ArU+W0t5yAEqlGh3iEH3tJZWWMHVSmZ+W9yVRqeKPEw9A1Hz2e1w73E/7mjLr1CgOMTKKOcfG4f7Ygw3j9xDiH57hemL71FT2w6fXXVP6dHH7VV7ecWPbsxUULO2IV1JooHT9rPuX2Xyic31Rw66kPFRdkhcZ9TrXwDavFeMbzEnfuNZvU+mHUpStVYw980/ojAe5noAmPgHPZx78aWFI/xkd2TXzSIpkQk7XacWrFMbAWIFdfisKlXOifL2SvL73hkNLTkqC8GhR68vUSW2n9k/UmYNzJ/Pyb42Y+tfgu1H2Hf8rCA0Ix9jcCEEQMMuAHDpUDCUmKS8sFW+RKLO1YY0kUHsPiUI7H8nGmYxEvvbw9feOIZVEpCLSgrCsThkFjzAk/W5qdvAVw4hhHbqU/m2RNNVTodKUZd2U8ZwydGHXq9XMPjaB9pYDdCi6C5TIy85Xa0iIU9JjakdK1yzGhjG76DG5PRd3Xc8x+UewGMV7DEkQq6NHABbia0oLVqy7u4BZHZanvISNzY2ZcWhchm1Ua1GRy3tupmMka9ijNu8ef+De2b/I42RLdFgMv2+9kqI/FhYYzvtnnjy77kq7kc2p3b6azgtTGafkw3MvCpVz4tHFJzn6Pt/xbWAoM6IiRkAc4Rp9nrAdNcO1SkRigh9go1NPEATCRYhkBRoaAhBDDHFiF6oLFhgCD0XbdJw/GuyAZC/EeGAmUjDGEQQeU6GMAeExlfH0TDu39KZP33c4ONzl9euj2EUK+NxJv1nhqQnDg59Q05mIEPC7FIPE0BiP9vOpxhklynRCLU1lXXHRHKV4lcLMPCJJbexwXUNTWVcKly/Ih+fpvdwv7rwmPCiCZS6z+KnyRKzsLanRpjJuf75j0u5R3Dn5JyfX/p6uXkZIJkVIu7CMiYjFxNwYKwdLIoKjqN2uKoXKOtFmWDOOrT5Hx9EtsLa3JCwoArsCtsRFx5OvcB6WDd2e8uw5Otvy9P4HTtycgrdHMIumHsXPLSBdH35e3RsTU0OWjNjFB9dUY/TSwXtc2nub4pWcad2/Hr0ntESVqObkry5fRK3+NdFpXCte3nnNzw3nYmZtSlRoNB1GtuDUxpxpS60fvZsfOtVg/LZhLO3/a66uXbFBaYZVnvQ53f4s2OazYka75TkqW6lxWWq1rUrtdlWJCo3G0NgAYwtj4mMSqN6yoo5R9q3Rf3YXlg7M+t4qDPWp3rIiWyal5oTrK+Q0798AfQNpbVCgRD4+vg/k/TMvilQoiHsG8j2ZwTa/NR1GNcfrlS/mNiaAjGUDNvLk2svP+Urf8R05wr/PKMuGmvxb44s8YamN5Px8Dnaz/4m7KBZ25sj0ZDpinslQISJileaoHGlRpG3w7EcyxFYhMaltBQogwxw7YtOU/QIk3W9FXBCSEVgAidRgHxJ1dm1Agx67yYsfMplW33M4HmPfiJdtAAAgAElEQVTQQIZCswZpPh3BRjBFpVQxtOJEhizuxbYXK9k//1gKfbGevhyNRuT28ftsHL2Tai0rMWbjEAI8AilfvzTGZkbIFXL2zjvGg6R4+rT9U4rxuOFIPNtINkTjeEv1ppvIV8RB5wWsUqooVUPbU5mKDaO2M3R/f+yDSvLHoTsUEE34cWY/qraoyM/1Z4OoIcgriPvnHtF7Rmf2zDmiU9/T1Yd1I7ana9fYzIgCJfNzdd9tPvlmki/3v4CvHA2QeQ5I9vMMgJXMivzq4wSIH1HSE309NwwVW9HEqfHQfMJJsErxuIqiyCccUgwyCSZEM40AcS55BStszS8RFNkbbUbV4sUvI4oi796B9IxPAtYjw4XSGGLzyggP8SXSnKDl1cOFdu1q06VLSy7uukHzAfURBIH2VgOxsrdgzokJFCzlSPnyE1G/7KzTJxgHnAR6phw14hjmmSqOQbdJ7YmPSeD57VcEeX6ibsfqtB3enJd/uOH+xAOP515UalKeIYt64+8ZyJPrrljYmrHownRuHb3PXy7POb/tGi0GNUCt0hAdHpOjd1pG7yBBJrDUZSbHVp/F+5VkJF3YeZ1HV14yYnV/jk1uz+aJ+zm5QSLMEZI0xvpMace8kxMwMDbA3MoEX79wvD58Yu8aFzr0r8Oc5d0Z2mhx6oVEkQlr+hLoE8y+1UmETTIZxKd6skWlkjf33vDm3htElQpDYwVjNwyi04imiKKIobEBbx97sH7M7my/K2TxfsvoXmXyvCR7lvT09Og9rRMDSkqbTMnaWSfXX0CmlY8lqrREhDNwcGjUGp7fSq93pU1iImgJDif39cbR+6y7u4DJzRcRFx2fYURHRvT4AHqmqdEZgm3qBojKIXWMKs1Sr2mqEjG2MafF6NZcPiqRf2g+pc6l2rlZYzcOwjafNZ6vfBhYcny6vEPpK+Ri7ZOJJ1/UZE/yUrZuMfIXdeDdo/cZFxBEBEFgwOwuqZ7NpGs6ODtQqVEZjM2NMDTWp8PIZqjVGnzefMTLVZnSr0xSFlN+pw4jm6MwUnD3zCPK1i7BjhmHM2QATkvJD2l+d63wRiGTtVxG10/+Pt/xjfA9fPE7/tcQ8Slzb42tYIUR24mjntbRLgjMR2RR0udopJwubYr5CejJhpJX844Cgt1X73NRwYpIcQBRwkQ0Ym0MDWshxI/HCFtkqMmPBmvBjNeaT0Rij4AKa0IogjVCZro6SciDPgGcRkMXraMicvlrVKoowBBD/d3kSXyAQZIQdlx0POtH7+SPUw8Zu+lHnEo7smPKfjxf+tBcvwdqUYWHGMafp+9w4q+/cIhWI0QIIMio1bYK805OJCI4klMbLlK0ojPWea2wdrCkX7ExeItRxPMr0qJWiUSskshbbz0u77mp03dLOzNMLU2o2LBMSg4LSAxiiVUt6N03lviEWUAoAUxlbHFTfq43E5VKgyAI1GhdmcpNy+tokGWH2Kg4HbHU7/jnoISeLU7qp/iLN/BVy4iK20MUBQnGjSBxHFUwQi4o0KBGrZMHloxSSZsUAvaRAUTRi3hhPKKoj4PdLqz8vbF1skVdeAofPpRDCvGdioY5vOYgIeJOCmHAe8OpRMXPAoyRyZ7TsOEFOnVazc7pBzm45BQrh2xi5uHxrLoxmyIVnAEYXOZn3F9lRBVfFhmz0VAfEDFgGQXx5w0awkUHQI4pvpTCHLkgLbp+6FyL+d1X4vPGny7j22BmbcrkZvPRV8hp2r8B7Ta34PX9twyr/H/snXd4FFX3xz+zPZveQ0IJXUB6BwuogHQQGyCIICJNREWqXRBUuqAgIE2KCEqR3gWkE5IAoYWWSno2m2TbzO+PSbK7yQaCL6/l9+b7PD6G2Tt37szcmbnnfM/5nnGkxqXRc1QXug/vwO2L8XgFeDLo05dIT8pErVEyZ9ji//i+3Lpwh+fHdqPniE6s/nwTMSev4envSUZSJiNbTeZunCy3X7NJVVp2b4ZoE1Grlcwdvx5TnpmcrDxsFeR3jzLTyIbvD/LVGrkwdcVqgTz9XHNqN6pMQIg3M99eVeZFTX6umRmDv7NvECV+urOwzEbZw4KnnweP9W4uixSVseRBaVCplexYeuCB91v31VZux8Tz1jeDmTHowVi2B0V+vpWBL8xn/sJXi4yyQigUClr1ak5eTj6dBj5JxdoVGNlCFuX5O5y8CoWiKFd59qFPmNR1Wqlt1RoVb3z5CntWHS5SQSxEo/aPcutSHLWbVefzrROYP3Ip7j56fIO8uXmhdKavUbt6NHiyLho3DTaryJXT1wmrWQGfQG++G7f6oZ1nOf5B+IcaZYIrJuPvgpfgJ7VUdPhzOz8Ig1aaB0IoJTejlDZ/CcriXfkH4rXPX2b5h65zhAASxUyuixXJYziQi555BHOXJHzIoyFwHhgFOM8HjWYbbd0Xczcjn3i0iLijJ4Vagicawc46PajKnH2bjbtkkInIFz+MZt3oTeQbTUX3/ayYSjofAQcBK6AhhH08qgi697EkifNSCmlMRKIDkI6OCTQNusujvVthNObTsU09WrVsxojmE1328dzbXandrBpfvDIfUbJxVsgmUxwFHAeUaFTR1LZmE1yggucX4sPYxcN4pEUNWebfJuJXwZeoIzG8N342u4/NAq4CPwB9AC1K5WLq2uIJVng7HfuD9WNp0qEBfQKG4OnnzhtfDSBPbeSlAXGYbI4iIxKtWr1H8OV0wutV4onnW3J82xnO7Yu63+0ox599b5XSh6v8slJzVEtVHXPswnnfKFsqSdIm5HyuQsRTmQFUETzJlAxcRyCXvTgyWkoWEcoqMqiImRqouEKPlyvRoUMrVr2+tsjosUoWTkhG8thZYv+OFQ/y0ju9WLnhEEq9J57mHJ7v/gzV6lahZdcmRW2zUrPJN5rw8vMoEnHwEbqTxRanPhVsoA7zyUUFSFQUPLks5XCX76FI1TENH16imeDLiDmD6DCwHSe3n6V2ixqYcs2smbaJQz8dA6B938eo3iicK6eucfjn4073oFC05rszM5jcbTqZKdlFYb2lwWWuYCnfvJpNq/LB2rdZ9dlGDGnZnNp1niZP16dum1qoNWrSkzPZuvUCSqWAxWzDXMnO/GtupMh/WKwMntSD9r2bkZKURXpyFsf3XuTMnvNMWjQEpVrJz9/s5tiOAmXFPHt4taOanaPKXWGujc5NzaqY2Yxs+0GJRbVLlMIcuZynxZ6RVdfn4+6lx81Dh80molAICAqBzm4DSrRXuLmOvCg8B0dm6ZfUpQgKAUN6Dhl35TqXmSkGtizay5m9Be+6UpT3Og9uR1jNCiyZ4FCKxfG5dcwRC6tAVn4KRrMRP9ENnVIu6m2rZHd2pDewM2g5FR2ekwKNjCPjXudydDyLpv9G0vVkAkK8mTi3P6m37mLIMHJ8ewQnHSIqnFlC51yvonEVjNHxvJzalvFdVaF6ML0nd2Tb4QiS7qQxbtKLNKpblxfDhrncxTfYm/6Tn2PDzG0k30op8fugT19ixUdy7ljlOmH0GN6R1Z9tJLMUJ7EgCDw3pgtmk4VdPxygw4An5BDkqNuc3HnetarxfdhYJ/VFx0LS5lKua1lQyPD9RWvAveJPZyRJavaXHOxvQLPQUOn00KEPpS/h008f6rUqN8ocUW6UPTS89tlLLP/op3u2sUoWkqUMlCgIlHwIr1uZzdcjSMtvgpzkn4yc62GHl/tsxr6r4Ouv/DHmFeagZeHJyzQX3FEUZCD/WaOs8Hq/9vnL9Hm7G908Co4hKMgWszhFIyS8kMOd9EAEAhN5Ai1qhbZkf45dSxJJUjopgAaRcMEdnWDPWFkSNVMWHrkHZh38mHfafUyDIQ2ZszQIUZRrLclJ5WlotYN4IbwySVdTeeL5VtisNj7e+B65hjx2Ltsve+a1av74/RQrDoeSb72JXBjYPq/deIVWQl4Jhcnvzs5g34+/02HAkywat5K1u/Zzm5+Rc//sCAn6mJVz2nNo7XGiDl8stYhtOYrhbzLKDGIOVy15mAhBRTqVsBJUzCh3te9Jq0QWJR0vWtoDLTHRByWnENmKxBKgAgo248l0jLyKlcHADiAHL89cvvrciw1jfy3qR5REjkkh5FNc/CKLp5u+Q+U8L3qO6MQv83cW1fR6ZsATjF8+kuHNJ3CtQGnNy88Dv1BfvjszA6VKSROhMxepjolpgCdq1VHat9/Am906sXCsLFFvlkyckB7BxHSnIytZQlN+wkuQr0/9x+vQtncLDqw9wuVTJcOtpm2fzIc9Z2C1WHmkZS2aPNMA/1BfslOzESUJv2Afjv92lshDF53yRu957e9jlK28PIeti/eyYea2ojbtXmzN3bh0Lp2Q2WdFJXuuXWlG2ewt7zC2xyxwlFnPk1f6Or2GNz97nvA6Yayds5PjvzowMfcxyiSbjZGzB3JyRwSndkeWes5F+JNGmZuHG5szfuDM3kgmdZvher8/aZQVLra9Az1p0K4eGjcN1R+tROsujdG4afhq6CIiHBxRjsdcfG4Gb7WZ7FxA24VRZpUsRGAgO78XVqkxWsUvhOgiqONVpcxG2cutG9Lc5sWW1X8w+N1OaDUqBIXAqjm7OL/Frv7oGHb5VxplHT7pxJezUkjLGAVo0apXE2xdTW1lQInmfhV86TexF0smrpEdpS7QrHNDLt+IIeFSEh744O6lZ9CnL3Js82kiDspRHtUaVsEv2JsGT9ZDo1Vz5JeTBFcJoGr9ymxfso+E68klztcJ5UbZvx7NQkOl08NcG/4PCuHjjx/qtfr/E774APHlpT1sD6Jc9lByy7j/h+Wfmkd2v7GUZus7tlUp1IQJMsNUq2l1Jq0fxdo6H2EPWXwXSAEKQxWTUBt/Y+XS6hjzFjn06o2BT4mXJlBJKPkyL+34pUGlUfH4861Z9J5dbRBJxIgJiSxwWqg1QmI0SUylkhRq3+xi/giCQAXBnwoujvnGl68Q9XvJHAVX6DuhF3tvXEIUrxaMpfBe+GMyfU2dAYdYNdnOXqUnZzK47jsYM41O/fiJR0ngBSAOOY+uMLfsddKkTwkqCBGtXDeMl97rgUKp4MkX2jCuw2dkpWTjjoAs0NLKqV9DynVm9IsuCunsM7YbvsHe3Lxw54FCGP8S/JPqxdyvTtnD6NtBEQ6lEpOUR4TZizx+Rq4pKJGrmAni3hKGWfFnR0UmsqKYoxTbXkz0B14HwEZzoDd6+uCJnmAk4vHBSltgPLK4jxfZhp+YtfAQMbb1dFC+xLOD26PTazm14AD5JdYv16hVK5R6gdVYMuFHugx9BmjMr/N3sHfVYV58rwfDZw7k3fafAJCdnkN2eg4/fLCOIdP6UT04DH1yLDfohQUVVT20fD//O3Is2aR75yFm5KJFwFpULNsOGw2o3/kiKcfSUCgEmnZsyA+T12LKK5aDU3APf/1mB69++hKiTSQtIYONs7c5tRUEgXpta9NvUm8sJivbFu0hPSmzoIt7f4+KF9wtxKt1xjJz/4dE/X6JmJOyoXh08xlGzhuMm7cH5w5eRHKzG1pWd/syILe1bKx5n0/HbBWRvD3JC7fPA/0F2fjNt8Gcd35Eo1Mxdd1oKtYMYeO3+wo6dMgbynNhaAoCt2MSGDa9H9ci7xSxGGVaqDqqDgoljSvH/KW8nDz6hY9g0bkvy5TD45QP5mYXfCmsNSXo7E63QkMtK93I7xtkJnQfsOSrnTzaoipjF75OUKgPSz9Yz6b5u8Am3/PqjcKxmm3k51lBUBTdY8HD3X48rXycKxmxpOfJzgwAk9iUuNwVBGq3EXDL/n4KzHEwqh+155qlNlDQtm4Vjl+/w/4GVvbv+Q3fK/b9vKvYv1dCogPrlOcw78wlc8vk87/3d9TJGCnFCBZFK9M+O0iW2f6dNVkGkcQ1qtgi0SntDsuaTavz7GvtWTRuFeY81wZZkpjF/N+iyZX6I0jJuLOeBll5LBy7gjY9mvHqxy8SUNGP2xfv0Om1pxjZYiKmPDPdhnUg4VoS+9ascdlvyZO7d86/IJViqJaWX1aK4VuO/yLKc8rK8b8ExZ+o3RFYyZ+vP1hEXt4gh60fAzOBu6jIIIC71BF8+MOl8nRjsh9QcrY0vLtkOFGHL7H1u91O231xx/Uj04FsZE+sKImAhOIBX641Glflw54ziBczSESPDXfcSKK24I5WsHtws9NyOLzxBOduXEAuFVDcOVCbA7sW8NYII9kZOZzefZ75I5eWOJ4o2TCjRla52wVcRpYeb4dAFjXqV6FFk2bUbFwVtVbFz7O3cfNCHG16NCOsRghZKdmEKf3I9p1LfGojClXrBI7iLyUgOBQJ96vgi81iJTf7/jVnyvHXItacSZ60FnuRdwGT+C7WahcZMLBzQbiXAkkUMWQY0em1qHVqLCYr9WMus3jrVIymD5DnoYjAbCS2FDtKBZSEU19hA0kkTjIDy3FmaD/g+rUxpKWlMXRGf87ti+ZG1G169a7Mmo3HsEltCtqZ8BA+xTfwCaZ+vw2fCkEs3/gbj3qHMXz2IH6cuok3Gr7HiDmDmHv0Mz59YRZpCXIuVbUGVRAEAaVKgZvgTl3khbCUKfHh1CVs3eZHesYsdLrL1K6xiUpZp7h5Z7DTmXhoNtKpy5M0+rw+Gp2aGQO/KWmQOeDkjghO7ogo9XdJkog+EkP0kRjcPHT0ndCL84cuEn81kaoNqnDzwh2SXBRkvx+2frcHTz+7lL3FbGXe2yvo9353km6lkPiQfHrmfCvjes3m/W9f4715A7gWFUeVGkH4BnmRnpxNviGXOi2qM2fUD9y8aH9xb128j8GfvIBPoGepoWUPA6nx6Xj6euDh7UZO1l/z/ok+eYMhj39O8xaVGbtwiGyUFcDNQ+cy7M4Vsi3+UMyFZ+MV4nJ/IqAkke0S0/ceZtsbA1hz8jzWexSB/juQTy5mOpXYbuYF0qRjhBXonvoGe9PhlceZP3pZqX1ZJDM3VZXIMdtzM7PpywWxJ80Ueo5tOc2xLadRKBU891YXMpIysVms6L3cqNWsGrvvUSezHOX4K/H/2yh7QEbsQdivsuZdPMi+9/PO/x3s2IOwc4VtOwx8gmNbXMsf36s/vU6LQLqDRLYn8DECH9OS27gV5EpppUSK+18V7CFAoS7q/14KciVQ7LqrtSr2/ViyGKxOcEMtxVFSg+kcnghEShkYqIaEBr14hTqocVOUrZ6ZUqXgSk4ysbyKlf4AGDBilF6kBdaiUEK/UF9S7qSxfOt0nn7uM4xGZwU6iCblfCxDG76HaBOLvO7FESNlkMoPyPWcCvEBUBOVeik1wppz/fxNTm4/S3aaPSn+zJ5IRswZRPNODdF56Phl2XZ2p/YglzCU5OJPKtWLKWt+//6qMl2DctwHZVHmKkv9skKIEvmSBijOLgvcibWyeuovIImk2rK4Ihmx4IMWBaHkEq72w91bT+e6GlK9x3P1UhLZydcwIWHGijN7BmDDN9ibZ155Au0fB/jtSDiOIbMAVttQhvWaQs7JLGxWG+1fbsNT/R7j95/Hk0oAInqUXCZXMjNt3hFAIDH2RaAS13gD97XubEhczJIJP7Lw7eV0GtSOdXe+46XQobw0vhdP9X0MgPdXjGZy12lF0vJpZHJ4TW0stpcByM+vwfnoNnjRHa0wBZM0HtCgYiGB5tMsfiuWVt2a0qp7M+5cTnB9f/4E8nLyWTZlHZ1ff4rhs15lw6yt9BjekfB6lQgI86NCtWDSEtJ5o/EEl+p4hZBEiRqNwvl+grPnX/DxZsMPRxkyqQcfX40oWqRXOGYPL9amyI4tm7cbkkqJzdsNTYb9WJK7nb1wFDf6csyPPNOnGR6+7uxZc5SkmymEhAfi5efBr4v2M3nFm/w0eztHfj0NkkSjJ+ug93TjRtRt1yfhgl3pP6k3z4/tyundkcwY/B1Wm7xdpVHhE+hJanwGuCh8bTFbGfJFP+YO/14et9KZLS6CY16lC4bPkdFw7sOBbcvMKvr7xLYz3BjRkYk/vMm0V+Ri4N3e6MCe1fZoAaEgZFJQO9a7KuhPcOVktCD6uYPWIdTSYaxeMRkFXSiY+9EQrFaR/gvXIeWKKAGzpwOTWMlu2bk5RsIm241GR/bQJUqpqSo4hrw6hkYWnFKVuhWJvXQTN+0Nivs0lJxHjwoBePLF1tRoXJVlU9bec30kVlJhuDmq2FYduTTGIl1BLcjjEW0iP8/ehkqtZM6Rzzm7L4rVn23EnO+iYHpZUmJcbXdix0op6FZKHbyyrrHK0rYc90E5U1aO/xXUf7wOe1Y+WJiaxWShacOGuPMtOTyLfWpm48sx3Bxk6MOxcIlxmPkUcEMhROIrfE3wfUIXy4rNC3YybftkunsUr5sGVcnhGssRGVSwJQtPPiIVBemsAeRwzHzyiaQXLSTpvsqMALmGfFLUflgt/R22umNkKvHSO1QWAqndvAZ52XmY88207NiUlSuH0a/veEzmzwENkIgn7+FncCc1J/2ex8skDGeDDGAE8AZWSx927V5NkKLk9cw3mpg1VA4dLfxANFb5IYmFC7uAcinffxG8BTMpXALqOGy1oBWSAX8ybGlESFokRgMVMLObKxhQWaKpkGYjJ9VIrKcbRrEPZrWExbIL+Bz4zKG/aBTc5vn3JrJ5/i4MN1NRcIuSsySVK0euEO5Vkb4TX+RG1C18grxp5FeJnMxc0sUEzuGFxGzgEcAITAY6kqN6jZgbv7H+y800bF+PrDQDkYcucmZvJCuvL0DrpmF4s/FY8i0siZ7Fjvy1dFC8AEAiEhbbC8XG4otIOE2kM9ymByJQETXeBQzwo489wrwR3yPaHv5c37FkPwnXkuk3qTduHjpmDVvMt6e+4LnAIfSf9Bzfn/8SpUqJOd+Mt78nu1Yc4ue528lwcMBc/OMKbXs24+jm0059Wy02Io5eYfKwpzh/K4FdkVfLPK7+gx7juRdbsHL+HratO1ni970b5WNJBtmJk5aYKYcJAe90/IJFf3xKxerBNH36UbJSsxlUf5zL4/gEeTH3wEdkpWYjiRJXzt3gTkwCLbs0pm/4KF6Z8hxzDn6Em4cWm1VEpVGBJOHh645KpSQ7PQd3Lzfee+ZTRs8bjFqj4tsxpbMs/01M7PIFa28uYNHZ6RgyjFSqFYpviDf1H69DWkIGokJJtUcr4eHnTuKtNE7uiSL69C1EUcRfm022JRJoUNSfWv0NFcMr2sv4FYNGp6Lv0Ha0fLw2qw5GsP3oJXJCH06qxcNChapBLImaCcCrAz9l1aooJOoX/JqGv24dvZ7rSr3WtTmw7igH1h27p2NV7+VGzVYVOXzTlRErP58anZrnxnRFrVWh1qgwZBh5v+Nn5dEb/6soD1/8e1AqI6Z0ck243PdPs1kPnL/57/R23IuJykjKxMPX/YFkh6+du8GAD59n5fLhTJs3hsizQQjk4c5ZHhWcy7cGKrzwUFziuq07VknHwDfbEr3EH0ly8NSVpjInSaRIGaRgwQMFFQVflMU89lGHL5GbnVekkuaIyoIPaulH4tmIiAd6kqmKmjO0ptAgk6HDyFAypG/xK2SO7uFJF602LJKfi1/qY0Dkja9e4bkxXXitzlh0eg1bvt1FjXpVWLGwLYuXv8e1C+lIGQlUF7xLCHTYz10kWcogFRv5VHbRwhNoi0QDtN5uSFkl52apH8d/qyH2Tx/3w6rLWLhvMbatitIPS8CH3Er+BKgL3MWNt6mpkMNRIyULEr8ChTkvjyHxBXeAEAQuIGAwrMf+KekLDAImAo2B68AZDIoFLNu1ko4dG3J3SSp+YiSppGJn6Wy4M4dAwY+eI59l09ztZN7NonaLmvR5uytn90Wz9OheJOtMZIOMgjHNBrpittZFWU0ec1ClAN76ZgjzRi3lwNqj3Iy6Tc+Rz5JwNZFVsQvITjOg99Yzat5geo7qzOuvT2Xp0lzAAHhQqCYpYMZd8KAOMtut02vpPaYL62dsRu+llw2ye9wT7wBPmnRogJefJ3k5ebh5aPEL8eXIphNcPXfznrfr/MELRP4eQ+3m1ZiwYhRfDPiG/FwLS6esZ+mU9YyYNZD964+SazAxau4g1t74hl7+Q2SxEEnk6K8neWVKHxJvpnIj+o58u33lcMbjZ26zdaWR1zo1p46XPy++34YqVQJQKhVcu53KzG92o8q3ISlA1Cmp7O7G8wPb0vUFWU6+evVAlmwdg1KpYHCbTwpun2NejMM7omC71WYj+XYqz/Rry4fPzyL+WoGYQjFGrEL1YBafmYFCqWBgrTEAPNWvLT2Hd+S3JfvJN+azZKLMACr8Hd6XBccU02SmqEL1YCb88CaPNK/BoZ+PY7ZIdrEbR0GPUkLtBcdcs8K8KpuD0IVjH+4Of2sdxJ6S7qL31JF4M4V3nvm8aHPV2sEEVwkkqHIgWl83Dvx6moQcGzXrhvLUgCcYM7sKf+y7yJ7NZ9h68EPSjbWw2B5BpzlGJf98Gvg3o9uyDlTy8karVCEkmBAEiL5wgR9X72fmnBW4TwO94I8f4OtmH5PN2z5WhdmBBVM75Orp7d9bhSPLZXKoRVd438rA3gsKAZVGhX8FHyatHs0jzWvw49RNPNWvLV9+Ppy0W59w+pwRFBoqBefSs8Uz3Ii4zcF1x0oej5LfoIEfvsDyz9bhxmzycMgDx4inKgo13rwypQ9bvt1Navy9nZUPivtG4TjO7/9Eh8BVTmkptSXL8e/G/2ujrBx/D6KOxND0mfoc/OmPMu+TlpDBqJaTAPjgm8G4B+lQq9V4ecrSxYIAVetX5kbUbSRJ4tadW7jp3AgKDEaSJN5fFHHfNatFNHNSuk0uLYFxgJHb0kh06BDQ400G1fDl7QVvoNaqXIc0ABUEn4JIfyvgT65kQCS0RDuRygWhXPdH7RY1qP2okjMRzuGIGvVe3p7wEh0HtuPi8SsM+3IAviHebJrzG/NHyd5fBXKpaxSl124TJZEIKY0MxiPREXgLyFOpYcAAACAASURBVAUcDd5VQE/cmMOQ4c+zYfr2Mo29HP9eKAUVT/gHED54H38cW0rdhhUY//7XeHv4oHXXoNOMBMm92F6DyGE3BimDXF7A+TOiAF5ENm4CgPZAPqLYhEMHLuBe8yAAjwpeREt9yaEeIp64cZZHEFAIWjLuZuLhoyfzbha5Wbmc2XMejU6DVekJ1pbFxiIAVYBp7Dn6PRePzuexb2vx4rie7Fx2AIOYRRz5fPDRAqZ8MgSb1caLocPYY1tPz1Gd+X3jcY4t3Y1AJBLtgAwgD4FOhGiywOKFf6gv30fOQqvXcj3yJoOn9iMtMYN103/h7p2Si7yG7erRuntTkm+lcm5/FBlJmbh5uqFUQPy1JMYtG0lWmoFz+6OJOHDBZVHaQlw+FUvSzbv8vukkei893oGeJF5PZuE7K4sWhOM7TeWDtWPYnL6M7DQDYx7/kPiricRG3eazX99jxqBviToS49SvKEnUrRRExya1WLXgAHO+3gFAt74tmPFJHyaPtyvnzlg8iCWzd7Fu2WFSL9wp2r7j9hxeGPkMGxbsLXX8jjizL5pn+rbF3bOkyqFvsDef/vwOtZrJ7P36r7ei0qiwmq0cWHeMAw4L9PvBw9uNr3ZOxD/UF0NGDt++s6LM+z4shFUNoP3gNqjUSvasOer0W2zkbWIj5bBNZYFhKYUEkJyYyZF9F/H11NGgeVUGju7I6A97kZWdhVXM5dblSrR6sh7pqQbmxpznWLx8L2qtMhIbf4VbiS2w2uT6XiphEeHeh6jhG/7XnXQx9J/Yk+dGd+alsGFsTl1KVpqBO5cTeCF0GIEV/dF76Tm44Q/Mx9Opb7MBuWCAQ9dO3LdvRxizc8nPMlNbkcVtbX9yrL1RCil4KH5jysjeBAYEYcozP3SDrBz/cpQzZf9d3Ff++T6KOcW33w//K56JP5M7d3rXecYsfN2lUVaW61ZobBTH1/s/4vX247iMijw6oCALd36ngVKPRqG9J7uZKmUTjR9WFiEvHKcB8ZgYhYk+gEAmVzEwjBZdmtC/ynDEsiRGCwrc8EIn7SOHETgaVDp+wJ+SGdmSJHFdSiWNQCS0uBPPsBbv8vl3g+jXdzwZhg8AT5TKk3ioprNnaQCHZx0lqHIAWjct1yJu3n9cxRAvpZPOF0DhonYysrplF+Qwxh8BHXrlAmqo4/Hx9S2lJ4fzeFBZ33KUDWXIFysVf0JWP+VmOh3dwzhx/Dgxx5MY9O1ZfIK86D6tI5KLDEowI+LNJRIpKTQD8vOlBh4FfgaeASDf0o7zF5dTVRGASlDTSPDDKt1GREQjyGyUIAj4BHrz/vKRSJKEUqXgl7m/YcjMBTEDiMHOlBXCAGiwMZJk4rlzK5orp67h9lgwR461It86jHhDFsMnTaXjqn1F12BKty848ttx4jXVkcxLHc7lNjVrvUtkxBayUgx4B3ii1Wu5ejaWt1pNkp95Dx0dBz6JT7D8fCtVSiRRJCDMj8MbT7DovVVIklR0rKyULPneCAq+GvItAM+N6cKjbWuj1qq5eyeV6xG3qNE4HE9fD66ejSU4PIjgKoEgSay4PAeFQiCwUgAJ15PYumgvj/VuTv22j5BvNBXVyvL08+CHi7PJyTSSEpfG643H039iL65fuINjoJbXTQvXIxPZezOS0/tiimIFtv/wBw2qBrNv/0SyM3OZ9VVfAoK9Obr5XIn34ZmDl2jWrg4dXmxJSnwG6+fsIPLYVWdGQLLPx5/nbCfuaiJDPnuR3auO4OnvzvalBxn0UR/6vNWZmcO+5/XwQLR6DU2efpSX3uvOnBFL2fHDQfvMCrSrC2a2rVT0t1uOjdAQXxZM74uXnztHtp5jaiN7vUeFYy6c3sEodFiYSTkO6rRaB1nzAmVEwUny3eFbY7InRSncdPQd8RS5OSaizt8m6mQsok10GrcrRUpRZz/erVAtt+IT2Lo4AbdkmZ1q1KQK9WqEMObNZVjNIjY3dVH1vLgWGm6vDMBqe6uoD6s0mvica1TxzEVtst8PVaLDLFCryTVncfzOWcxiKAK5hHjaaOhVz37ujrlhjijM3XN6n9jZtoo1Qug3vhdXz8ayKWUpKo2K4zsjmf+OnF/8+ujOzBu5FKvFhqBUPtC3xPE7r1QrCakaxBN9WtL59WfIy83ltw07qd2gBmsmebBt7n50eu09xXj+qyhtneS43Sn398+xX+W5Zn8C5UZZOf5XoNbKMdulwSpZuCplYiAIBfkEY6Si4Hvf3Cu9j5aL6MllA4ULKDMGomx9aKoqvUaYKIlcwRMrax22LgSGAs87bKtJFkPYtf0gGp3mnnWDHCEIAjWkPGJ4jXwmAXo0zKMy11ALJY2yGCmdBL5EQi5tkUMmN/zf5+kOrRn4+G5+2t4LA6B09yM9+zP252Wg5zsaXMpEryjOWriGJElkS5lYsOIr+JKOGrtBBhACLACGAJ3Qu2XRuVkGLevWIyj4CXYtP1im45Tj3w9XeVGeT4Ux9HUFohQMTmGGADOAaRi4gcA8YDD2BZkE/IQcvrgQOA0EA7sRuINHsUyywkLRhaj/RB1iI2/x26I95GTl4uHtRp+x3Vjx8Qbq4cNp3kfiR+RQWwl5Dnct2t/Ei6RJx9myfDsnaYKZsQW/6MnOn8/BmJdpKElM6TGdz7dNZMaMpRya8CTOxmVlstMq8POsrfgEeXP3Viqvfd6XEc3GF7XIy8ln88JdFIcgCJS19uemuTITLSgEKlQLJqxGCAd/+gNjppGazaoRGx3H3dsliyvPPvgRr0x+jvVfbyUtMZN2z7fi919P4ubhRtTRy5zdGUGbns3Y+cNBBI2aXxfuYfiM/lxPyObgziiy0uV38+r1xxk5tD2nf7vg1P/0D39l7vTt5BXU0fKRRJcOqil9v6FijWCq168ENhtvzx7A4JYf3vOcb12K55EWNbh44hq9R3Vi+Jdy3u7JnRE0fKIOKrUSnV6LIcPItIELmLRyJG8vHMLF41cZ2/5Tp76efawOO49c4tiadxBFCQn44bNf2PTdvjJd/4cN3yAverzZgX2bzxIXmwKW0hnQB0XE2VtEHStZBw/AlJqIxVCyvmuetRsG82L8NK6/GVabhcO3ooE1QA0kJBINP5Cbv5zWQY3+1Di9AjyZ+uv71GgSztDGE4i7kuhUXgBArVGRn2vGavnPnXqiTWTT3O28/H5PFo9bxY1omYFs0rhpUZviKQjlKMc/Gf86o6xMyokuims+aGHDB/I2/FV5Kf9BTsn9Yp8finelYHwdBjzJoQ2uw00kSeKclE0Wa5EXa5DNVszSPKoLrnKq7Pht+wFyGY3zAsoTI80xSxdR49qrZyCTXIoXCswA6pVoa+MJIi+tJSu1dJnmRCmLOHRY8QfpKko8UOCOPzfQMRQRJWGCOzrBt8TcsElW0qlZZJDJ8CEqugsttV3wV/hRU/DkpFSJnGy5aK4I5NCJi/TgXhUKTVI+cVI2+ZhIxohIIGAFKQFvJCALnJg7BfI9eBkhbwtZx2zsPlpSdbLcC+cCZVFCLECGZCAWsBCMmruEI+EveJbsp4z93XMsTptdPPOObQt+9wvxJteQD4ICSZJI5C7HfwnFZOkFPIUs2hGEXC/wENAHOf+sMhI7gJcK/tMCe4BAAnzeIC2zOhIvQYHstUQmyd6v0atjc/745TSii7lkzDQSVjOEnCxZOCYn0yiX2JBEvAVPmkkZRNEZC2GIZCMxFHiuaH8lF9CjJIVczBQX6hEw5DfGzBEuHInBYray8otNQPcS49B5+iEBideSefUzWZXRw9eTnMzSnU1Iop1AKYzWKKVeU/F7kxibTGJsctF+F/+4hqAQ7O0c7lts9B0q1gplw8ytAIRVD6bZMw0Y2+4TuSCxQmDX6iMIahWCUkn63Wzmv7MKz/AK9BzYlqTbaezaG0NgiDfJl5JRphvsAyn4Tpo1miIz2+BgkEm5dqVGQVAQF5NAXEwCktmMRqngszUj+KDfwqI2YjFWKD8nH51ey4pPN7Lq819457shhFYPocWzjYg6EkNOphG1Vs0jzavT6Mm6RftNHfQtgkZNhZY1WLJ+JAAKhcCHIzqTacznsanfARB01gwNqwCguWsfq8JoH4ekd6g35uAIVDg6BZ2YjIL75miYOhQIRhDoOvAxtDo1Z/ZGEVdgGDjVa3Ps23HfYgYLgDbDoV6aY3FmB8++2dveh1+GJ3fUF7CYujn1o1ZEovMIdDpfwcEQiri+B5gC1Cj8FRhMlmU3Yl4+CoWi2LrJRX6Uw89vLXydtt2b8tuyA7z1tJxDJ2jUTkZZ9dohdHn1cfas/cOp7psjC/mgrNlTL7dlWr+5Tu+TQxuO8dyYLkWOj/8YpeT2uvoeOj3bpdUgU5Q21x6ANSvlG1RqPv19op7+p77t/2Chj3/mqMrxr8bhTSfpPPgpvAM8S/yWJmVgYBSFBhmASHeSqVBQ40uGUczhgphGhJjNbTEFURIxZOViFxywQ8INqcADb5XMmCRnRSUVKgSK1/rxA66V6MvT4yjNGtUosb0QSVIWV+hBFlsw0h4jY8hmB5lsJJ5VpKGimhCArpg4yR0pk5OiheOSF3mkAs6Foi22ehgLziFRyiaf0cWOrCGPRlgl12EYSWIWJ6UwbrCCRKoiMg95gfwL0JQsRORcH8cX7yagFQq2ESyklEklshz3RtMODXh5Qq+ifxukHKKpTQY7yGEFGWznAg3JlAz36OWvg0anpu/7PfllyW9EmhPZb8ol2uKGIefxghZewFdAP+S50w+5nh3AYaA10BaoivxMzwG+QqV+Gg1GcKpD5ENKzhjOXb5Ex0HtXI4nNvI2odXs7walSumUc+Ut+PKYUIH2gkgwZpxVIzMI8lyHN97oUCAQW6L/wAp5zD08jV/Sl7NwzDJ8skQ0LCzWykSlsHTS4zN4dshTCILAkV9P3tsg+wux7sutmHJNrLu1kG9PTmPplHVUq1+Z6gXGCEBYjWA8/ezlOHoMe5qhU3qiVCoxm+RFf26OiZCw+4cplxW7fzyCh7c7PV9vX2qbjOQsIg5dZJdxJbXbVOHdCfMZOWE1vXpN5nrcTRRKBRqdGr2nGwqlgv7VR/Os92DSEjOZtXsiS38aydKFe3m2zWf0+WIlTd+ZQ7tJ3z20c/izCKjgw6bFB4g+4ZrN+m9Cq/fB0/80OM332/joD6HX+JS6X6ZFANq4+KUWZunBVAnHLXsTdy89L1cfw6qpv5b4vXq9ivQb25n6bWqyYPw6Ys7ceKD+74Vbl+IIqRrktO3K6Vj8K9w/+qYc/8NQKB7Of2WAIAg3BUGIEgQhQhCE0/dq+69gyhy9jWXKEyv0TjwsRuyfotD2sJTYHgAPUrutcHyGtGx+mb+Dll2bsHvFIacm2YiINC+xq5VwbEShQEOaaOAiNTAxDfAglZOkShP4+LEWuM1YSB7LnPZ04xhK3DgvppNNQyR80EnHqUQ2+ajwQokbm8hlCFCYT2ABkoFvgWHI/onzNG10mJbNx7PMKdTRjji0WBiFzF9dgIKi0TIqk80o0qRvCXBgpO5IGVxjCDb6FmyxAe8DE5DZB9ApNxFgkz2K8mwvqVwpkIfgwo8iSiI38MHEQmAH0B94suBXX6AncnHoZDx5Cr1/I9LTYlFgQoeeYMFAZYUzS1kqO/ZPeRb+bpRyHVp2beqUv3ADCya+xM7uCpiZyk268OcChP5DOHpUVVp6jujEj7N+5Ui6gRxpIzKTeheYhzxvChEGJCEzZwDX0TADM0OQ9bm3ApWB+oCeu6l10GmyoZgPwWZriNl9HWf3RrkcniCA1s3OeAdWDiTZRQgfQF18UDOSDIKxosBKHHnU44zGgK85A3fmksOT2MVsrlA9LJHsJCMrP/6JbYv3oVd4UVE8SjzjMPEaKmU8oYHLmTtzPJE7YjizNwpDxh90ff1pWnZpzInt50q/tvepeVnau9RJDbg0b7qDxz01LpV1X23hrfmD8Q325ottE7BZbcza/yEndkbgE+CFUq2kRsMqPOv1GgDhdcJY+8Uv3L4sa6krvD3Jy8gm804q4VUDuBkj11uTvApy+6wOHn51yfwqAIz2hXthntbY7jOZuPQN2r3QkjkTN3Djd/t9FgoWMOO7zmBDwnx23UoiJu47iPMDLJw48RVLFml53K0ZWr0WjU7N2O+GElQ5EBGR8NphzPr4V3ZtPocAxGSmURgcYQyXx5uRY587xvb2vwPO2+e9W6rdyBcs9u2aLIflkOPz7epZd2DBqtQMITs5E/JNdqVGQLKWIvLkYCgI+TKDp3RY3Cnv2qM0bMH274jJ184sWfX29j6Hb9GMIGLc3yIjTzbCvJV3qeNWBdIyEDztxrkja+atFkmzHMGRaZZxGbXNHUm6v0hVhWoh9J/UC59AH9ZO/wWNEkx5Zqd5otVrGTi+G9NGLiXLkI1W7Y6gEcDgWp25rMyNKIlkSelkS+kk3SxZkDslPh3vQC8y72a52Ns1/pP6sw9UD9WRDCxDDbRC1qzUa1JGFcxy/K1oL0mS64+ZA/4VRlk5/n1IunGXToNkj6kkiaRKGeRJIh7YULIdG0Od2muIQYWcXxKLEhNzsS9kW5DJOE6cOU91ErjOq+QxFIEM3FlMXYWSaFsWKUV1wkTMTOYClYGuKDiFB/OBDkA7ZGW4y8hf9CX48QugxZccFEf8UKlLKfgI2Cj0POZQaFA5QqQtmcx3ysBJxMvBIAPZ7HoPWA68g4fHL/TslsOQnpOY1n8eoUp/rOGruHa9scM1yMCDCygF/4JrKpEtZWDBihol+UWhWieAjwr+zkc2/roiCy4cRu29gCf0RjKz3LAbqA5J6OX4jxBxIBqFw8fPgidyWJ8jVKD2A+vfL5Ris9qITLpJjjQfe2hrEPLcOAI8VrDtFL7CTpT8jogOL4WBQEHHaevPSHyDbJBdQp7XM/D1jEApxZNbzChTsZWUP25gVbj24Hv6eRTlozZ4og5te7Vgx9L9Ltv2HtWFSyeucuz8aSLN1RA5SKZBvvbZ7KAy06lT/y2ioyQEzPgSj/qsHwnXEvFyYPGrKfwIk65wVxpFqyfqs3TPSoyZuZwwRLD6s4288G53Tu08z9jFw3i54psPfI3/G0i5k0ZWajZDHn2PxREzcPd2R6lS0vLZRnz+yjckxCaz8JhcK652s2qkJWYWGWSOUKqV1Hg0jDbPNmDNnJ0PZWxfvLWagBBvxs/uB5YezB61jLgrzsd++42viLk8ETliAUBNUtJEBvbqRrMCdqd282p0GPAks95ejdVs5aerszi8xzn/7e+CSq2kRv1KNHysNk/0aMrYzjPuv9N/EQpBSV2fakjKQkOnIkIppVEK0dC7EftTZyIL8tRCdjQuwVsRh0JR55779h7ZCa2bhpxMIwnXk5n5xve89/1Qnh/blY+fn4XRms0FYxK5Ni0ehhxGjE7iTmoFMjIeQas8R1VPK6GUjKS5H0xSHmZMWCSBy7iTy1tErbSisf5BPcDTIec6INT3gQyycvwP4R8cvlhulJXjvwKrxcbtS3G0fqkxs388RA7vIlEVDSvRsJw8aiGzOWYEvsCTeCRCERAwU4Hiqm4infj9j3cIUXgTJBhJlT5ChYCf4IeIiIGq2OuEbUDOE2lTsG9lsmlDCC9jZCcGtIA7aow0wB/fIjEOHdUaVCE1IaPU89KShAETstBASe+ckr344SxeYCuoe+SMEHRswZudrNz4EU2ajOGdMfPIbVEDX3+JoY+FM3t6f1KzWqMgBQ/+oF5BHlK+mMcpMjFRG/BB4BICJ5DrQ4UBV5E/skuAMVCk09Wd9Kx6nMx6nVqqoBIjKsd/jmObTzn9251MMkjGMVwXMgjyzYcU53nydyDmVCy6ID/Irlrsl0nIYYoVUJFMuMqED17csFmxoifVlkw8WiQaA1FAJeRQwgkoFB8Qpo+jS/fWLFr/CRnZ4wA3BA7iyw/4CKWXbshKNRBz8hoDPnyeq2djWTZlDRnGdNSSGrUgMx8qtYrmnRvxdP/HCazhz/LoC4h8geM7Q6QzqkqbaaL0xFu4WbDVH0TYvGAXnYqFT2oFHZUEHfEHExj7+Ie8OXMgqQnpdBvWga5vPIPeU8cXA7758xf6IePkzgg8/Tww55t5ufJIvAK9Cazox60L8diQcPPQkRCbzKpLM0mITWZyr5ku+zm1/yLtujdGp3edj9vyqTqkp+Zw43IiVmvZWfLUpCzG9f2WYDeJt78ZTF6OiXMHLrBlsSzC8dvPZ3AuMA4gYCGI1Ve/5uaFO0zp9RWXT8Wi8JTfezcuxrPh4HjuJmYxuOe8Mo/lYcI/xJslxz7m6LZzHN8dyc41R1k/e8ffMpb/FBqVjsf8wjmR0Q+LVEFWX1Tn0sC9ZK51IcYuGIxWr6FWo3AObTxB5OFLXD4diyRJiKLEpy/NIcOWwemsACS2AF6YzHGkXRqMLCrVHosIlzM/xEt/CQ9VSSEsV7BJVqKkLLJpi43qiOxE4mWgB2YrmOnFRXrSQpIQBIHQGiEkXE9+GJepHP9f8fCMsoBiIYmLJUlaXKyNBOwWBEECFrn4vQj/OKPMVWKzU3iHqwRc7h9i9WeLQf+v4EGp7XslrRdiz6rDpNXSY2AjhayMmanIuU3bgFlARSQGk8iT5Eqf0wRvVLiqJxJJ7ZqhnNuTiEJSElTARQmCgChZkZy8btHIwgOOqEAeIbQURExSPiI2dFQoEXOuUCpdF4WVRHKkbHJIQy6QOw9ojhz++AYy+3UeH77HtxjzpCUeI1YcHzeBA1RHQwVFAF91X0BcFQ9irn6JzFrZ+P3gV4Qb06gm7ECJGq2DCMpZ0jHxLtBbHhoSEiOQle5eBl4F1iKHZ1bHGdXIxpmlcPVs/FeTfh9AJOMfe/wy9lEdL3JUQ8i0zkFOqL+JB6MJuCvQY2QnfIK80ejUrP9ys73Yeml9l7a9tDauwmKKbbt06jpVm/gQce0CJYVv6gHd8FRMwV/pR4S5GibpK2TFw+exhzduR36W3wUqo+cqITmBnFsXTV1bNrHK3lhREqgUCRNCiwrzlia+dGpnBGf2RJKsyOaayRMTXVFxDR8pkufbtuLZV9uzf80RJnebzh1lGrl5NbAzLnakJJm5HnezxPmHVA3GlFeKMp6g4MqZG4BA/8l9uHrmOpvm/kbrbk0Z9OmLVKxdgW3f7XEY9r2fE0mUSBMzSMCGSoAqgg53hWfpIUtleA8XHlMQBLR6Lfm5Zgxp2RjS5LA3URKJSImjTo23UKjUtGrhhc1sRqFw+Nzny4p0N8/f5IeTVxg4qSdSjhG9RsH3Rz8iJSmLoDA/ku6k4e6pw02vZcvK39n4rQNr6ThWp2+0/dyS4tOZ0Hs2DR+vzSdrRjPi61fYt+4Yw179lLu2a9hFJgAkmjzmx4Y523ljej96vNmBzQt3F4UEDm/9AUKgP6uOTmHY6Kf57MyZoj0tKfLxbQ5+DpvWfm+yqjkUfrbaG7nH2cVAyLOHY0pG+3apQOBDUCpJv5nPoZ9P8OvifdyIjqPgRuASjtvvp8iZnmn/29chZNHPzrKrDfY5q7/i4BB0UHl0UjosPH6+g/qgh70cgBToizu+PBVWFSHNzihJjuIsjs+oSoXey40N83eRdDOV7PQc+bxUKvS+HjTr2JBOg59mwjfLkViG/XtXEfgeGIDMvKsxi+O5Ye5Dc59g8o0mBIcQ2aIQUId3wiUpg1R+hIIKoTAQmIpcoL4SoCSPruSyhUohlXnurS4smfAjkig9lLA95/XOQ04jeYC0lDKJbpVlTC6+WU7XqXxZ/CBIlSTpXhpsAG0lSUoQBCEI2CMIQowkSYddNfzHGWXl+P+FW3Hu2MPkQGaXGiPXMRoLDkWXs6hKrPQaoai4zgqsvFrwiwEPJtGn++ecW3iG4lALGnREYCoyfATknDFnJkIgH9CgFUoqXgEEVQpg+q7JLH5/dYnfDFIGJ/FCYlXBMRYBewkiHZFfEAvCH6vgV8LQq4WaSPqRy0ygIgJ78WUaIYJsWN4wp3D96ifYwwiVZBvHc5ujNHMQDLFIJmKkDHIJpdAgKzwzmIGSDmh4lDwUyKGanshvV8eXtIiCPChFqbIcDxdqQUMjq5XbDMWABg/MVMEblaDGw8ed3csPYszK5YX3urNssus8xv82Kir9CfGeQlLWfOQFVB6yuMeLQDOMYkeuWg5hkr5ErglmAHo59NAV2RGSAujxVFiLngEPpRf1dfacFlzI72vdNAhI6Ny1GLNyMedb6D2pIx98E4/RNAWQy7QnEcO2E2/RpmszKlQP5kb0HZLupiAz5DuQa+4VIh+15TYIJYUsLhy7TO3m1fl447tEH73Mz7O2AeAf5ktOnoFfE1egKlDIk0QRjU5Ds05yBqCXv6eTUXY/xIipJDIAGwNAyiNF+pyaRBCqvLfSrCsEVQ7g2UFPcvNiHBf/uMqN6DuMWzacKT2+dGoXbUklSZoH1AML7DwaS5M6nzB55Mts+maXy0iA0GpB9HzjaYbP6Mven44zc/Im+49WK1VqBTN4Qne69G2NKEpEn7jO3LdXlnnsd+PSuRuXhkIpEFo9mDaP1MXN9jXRFz5DDgM3ERI8nclTnuPrnt+h02uo16YWmxfuRqfXMGb+IHwDvbgRm4rVbKXnq485GWV/FURRYs1XWxn+ZX9WTv2V2Og799/p/xG+GbeGdn1a0PiJOrh767lw/Condp4nP8fE4U0n0VRWY6ISJZeWVZAdJ4eQ6xa6ERwezPAPX+bs/ou4uanYv/4YpuLxzgXIphJ2g6wQbyKnALwLgIQFAYGeIzvx/fjV5BvLpfDLcQ/8heGLkiQlFPz/riAIvwAtkJWySuAfZZQJgoCgKlhIl8KIOXpuSmPHXHoQ/r8xYn+D6McDsScFYzJkF2e9LgNNkfNVQov9VoVsvKmp0KAUfySBjdjwQE8ytZUehIaH8NU+uQ5O4aKvYYFscmtVD6JsA33RpwAAIABJREFUfTAyFllsYBZgryukYBvB5ODKo65SKZm+5wM0bhrO7Y9i9/IDJdpEkofEJigKRZwIvImBrrR1CH90BQ/BneaSiVsMJA+BACBECChiDAwoKVkQVyCkdkPSb0dwK09EREkuaZhYD3xKSXhhIxAJBQoSETmK/Mz/gBw2UnCuLKQS4t9T8uF+zM3DfkYflFl6kOM/QB8qQUU1pyxDGRu+3sKwma+y6N0V6Nwd5s5/ML5SvcKF+zqyJQUfpaCwILpk1mBnRD8SbLWQDbO+QG0AzDxLvngW+XNxCWji4gDNgWh0wkaqe/gj6GVDTOumITg8kMaP1cLLx12WjRclBAGsJgsKlQKdm4a87Fyy03Pw8vMAUeTg9Qukpr1d7BiPkO9RhfxcM7cuxOHbKpicg36QMxqYiVzyoSdwBQWTqF2Yy+di3m2au51azavTunszNs75jRu2DC5bgzApWlO14tuMGtuc8eNfpVqDKlRrIKsaJsQmU6FqEI+0qEnMqesFXZd+T/IkI3dph41BBVs8MDOdW+JLhKoFfIN9MOdbyM3J5/HezXHz0LF79REAajUOZ8jnLxEY5ofZZEGlUhJWM4Tzhy5RrUEVRs15De8AT8LrVXS65yYpjwypFc6sZzWiLj3KoqmrGP7hq1yPvM2F0zcQbSI1G1UhvG5FgqsEkpKUxRcjVnB42zlQOlyzfBO3Im/zUb8FRbLwK85MZfaO9xnXcxZWczFBCEfpeIXAyogvuHsnjbljltOmaxMsFhsfrX4btaeCjz/+luuxJvz9If/ULb7ucQUA7wAPEq4nM2bBYJ555QluXrhD7WbVebS1FZVGnsN9RG+O7pCFRIwN5G+JJtO+qM+7bWeZUhrbmTx1jn0NobxtV+W1pZUStl747BSwgUlX4tnw9VbGznuVUW0+cGJ5nPdzeMc6ins4LAaLooAcZPJFL7sTU5lvv5ZqB8EbKdtBJMORzXKx0BS0dgec4HCv8ivZGTm1zt5GmeBwnHw7ayYICrIzctmy5CCSzUbl2v/H3nmHR1F1Yfw3u5vNpvcCBAhVkN5BlKICoghYQAFBwQaKYKUqKiqCDVA6WBCUIoJIExAQ6T30FkKAhPSeLdky8/0xm+xssksSqvrlfZ48LDN37tw7M3fm3vOe855K1GkaDWoVEjB73DL25mUiq1hIOIcgpCKr/sjn16h+xT+/gLijl8hKyaLzsC6c3R9L3LFLJdpvb5WLbVooSm5vwcYa0iUrVpvVaUHmyqunLJ4+ZYLTvFN1U+tzgot3mOAu9N3dXLiUup080krXePl34zbGlAmC4AOoJEnKs//uiutJHPAPW5RV4L8HFZeBi8iS2SC7q8xEfnEXZ3GsmMnikBiAhDeh5FJd0CEIoQiCQGZyNqO6yHEIBZKRtGAVlRo1JiwMHnq2I51SjKzb8DEWScImSSRxGDMN0RBLOIlE4Vr+ObhSEDaLjTe7TSg5wbDDTCSUiA0LwuwUK+QeHoKW2sqJueIlG4CNVI4jLyYLIWEwnibWNAgrfZA/cAeAOcgfOD3O6QH2AI9jYggyi3YR6ICc+HcUsojDYWqTT6gbkYUK3F5YzFZWfLmG/u8+wdo5m+5YO47tPEuvlzrTtkdzxn99lLSsD5z2e/AX3kIWBikfqAksBro7lRHYSYDmb+7yCaVj13to0vFuLAUWTEYLqVez+XttDFmpuU6uVhpBwmqxIUkSksLF6tsjk/nxlS24MnKoNJ5UvasSW5ftYOXeHAwssu+ZBuwGnkRLFi0IxEu4dqL1KYNm8OQbPWg2qAnbF1bCnPRc0b5Jk5YREzOa06cFcnM9iYjI4fWhD9Jn4CN89dcHXDqVwMUTl1n88a9cjU0GoNn9DbmnVyvO7I9l+y97iDUkYOYuZOGdLhSKphTQGhO7+PboPHQ+nlw6nUj8qUSM+Ua6DuoAkkR2Wh4z3vqRK2eSGPP9MGrcXaUoGW/h5ErnreXnizN58dP+zB/7MwAmyYCFZiX6aqE1GbnbmDN2KVXrVqJRuzogwLkjl9i98TiLv1hnv9lli3N8tsV42nRrzIzNY/Hw1DBz/C8c/vtsiXLdB93Hjt8PM3/CL2C2cHL3edBo+OHj3/AP8cVqNOGVa8IASGbHBHLF1LUsiZ8NwMYf/iIrJYfE80lYbPDL9A10f7YDY755lhP74xjbv3hKgxuHh1ZD1XqV8QnwJrpBFCd3neHiycSi/eMWDefN+93Oq/7zaNaxPq26NuLHyb8XbbNKZqyqJmDzRZ5zvof8fTcif4PUQFV0Hl/QoOZRPBN1rJ7zJwBH/zwGQLV6VWjfswW/zfgDfbZj4RnslYzBmA1OrvfTkL1BvgXWAU05x8NMmj+bKpKE6jYZqytQgVIQAayyEwka4GdJktyqKv2zFmUCDoZMGS/mJvagVGv/f40dcwc3lvXbJYFaFOPg4nwRggeXpNnIC5oQ5NxgBcgLkAXI8ViFmIKe/ujt23LYS740nrZB1fj25NQiy2OBzchByQND2rec3hoAGKle/SPWrBlEzJbT9oWVSDXJhoUDaPBA5cKNqRDBVYJJv5qF1VLseVFcS7WUio0CnJX0jGjIgDIuzJyguGdRQhDJ0tvk8T0ye2hGy2hs1ppYpb6Kg1oBMch5oZ5BDpS/G9gILEW+ngAzkGX+FyPLHfcEPiWCJKJUVa/ZrCJLYjkSeJYbt5rlLUMslcu2XC9uoI7k+FQWTlh2Y3WX5RoWlnHxzszNzOfHj2R3NV1BMio2Itrzi6mE4zzYJZFPP/6art2Gk5r1GbJB4DAOxuwId1U6zltDXyEw1I+YHWf57rN1jhNo7VZ4nSdoHZN+q94AGo1sT1e4NQaE+XP6eDLwMxSxTADZWDJO8tngWYQ28cOk6qOIfRCQx8UwGjHVvYBAsWu1Ytp6jqnyMdtWO1+TXCNLl3ZAds2Eixcl3ro8Cq1VRdv7W5J0MY25Y5cwdPIAGnesz6/T1lOtXmW+ee07qjaozFHfVJINPZDTXngiq59+BbxJWGQWo6cMxzfQhxdajuHKGYcyoTKhLqKEh683YVEhDG1n9xDw0BSNTZPRyjN1X2dVyjzOHIjlwB8xWPX+eLIJYzGZcx2r8ZP8kCwWLp+8TMKljKJ9Sul2IdBueFLGISktygoWbN+WU+zbcopPlw2nbtPqRYsyjc3KPd0a0e/1h/AL9GZ4hw/BYi1qd+FXIjcl21lCXvHOWRI/m4ObjjK2+ySnfhS+n+bGXGD32kN8sWEsfYe0Z9lUeY6jXNz7Bzsm8P47HXWL2Y74KVsxz5vazWowYPzjZCZlkRSXSlpiBqf3nKPzU+3pWzWEyrUiuXjiMmlX0rmrRTRJsVfLzWxLTs+gzEQVphQAUClc+DwzHPL4TuyYsj430vuFzIfT/c13xMp5Jii+1wrFYSnIEZ8t5CnYE6MJvyAfnny1C+cOxDJvjLO7tVpQg/Uqsmv/W8hjJxJIRoWeml6eqMW3aVizOj0HPM5PH60oypop2Wx4aDU8+cbDnNpzjnqta3Ny91l0ERoyAlRE5GtIOHs/MAKoA2xBdolshkzr/I68AGzP+StaVEygilDSO6G0OWNZYrbcy+YXytaXYQ5W3u9ReXQS3KBMqTf+n3CbmDJJkuKAJmUt/89alFXgP4ca6iDSrHEYmIPsvhCJwDQkViDHEmxBnrScQ37JhgNvAq8h0ZZM2pORFYMh18CrbcYBcEHKx8ByHBLeXly69AETJkwk+u4oYmPiAdnFUVtCjrwkVCqVLO5xDdyFwHHeQrbOaZA/BG9Sz6VbRfmgFjS0wIcL0jPoCUCDnuFvduGzua4UElsDF4CfkD9+Q5At8d/jYB2r4I0RKz2wUBUVmTSspqND1P2c3nv+httbgf8u6mkj8BGmkmqbhdbLmw73VeKxLvcTFhLOYw0i2R07DKOkwy/iBIa8IFIvZ3LfvRE0jbyXTUv2FMnZoyt93LnD8UMnyczqgBy79gUyyxQHrKFu1UD69u9JVItw1g2JI7dYDm4N8WiF8p3bYlZTXO1VdrN+TvF/gcSro9i0fx7JZzKJ2X6K3Ix8Pnt+DlXvqoQx30SB3oQoSqw5uItkauKsLvg0MBWBjTSolUXlahG8dt/7TgsyV7CarWi93LNXhlwjr907gW92yqxNN90AqqgvEm+bgdWef1HND0SqT6IVIst6ScqF2GNX6PvqgyTFp7P998PM2DiKgBA/3hswk/N7SrJnZUFmcjbV6lW5ZpkTO89y8cQVnn33cfqM6E5ORj45aTm80dlx3QVBIKxKEPqsfIz5Jmo2jOLJFweRk57L0s9/R6tV06JrE6LqVKJK7UgSziXx5QtzSiQKP3/4otMEW+Ohxmq58yktbidqN65Gp8dbsXTqBvJSSrp6qgQ1wR6XMJqPIfElsjfMVnTCB7QPrItG5QEWKz4af7LTckscf0+vVtRsXJ1K0eEc23GaDK2BZX8K5MWOQmbN5wHfISvDjsTx/T9v338J0CJyDxmouPbTU4H/a1RI4pcREkX+0ZK72LEy1XMTrN+30rJ+m1CaL/UNVu74Xaim5kKhyAMPmqkMnBf7YSSSgEALPTrWZslqNTncC2wDMpHd8grT6ZqRX7rTMNMRPTsAMObLVkUTvpTMraUl/oKZVcfkoPcuqj6u2+3qvkqi489NHyOEMGzSCc7TFRuVUHOVukCYC2vc9UAjeHCXXSq80b2Nqd+qPoavfoMisZNC7AE6AwJa0ghFQxK+SE5uoLGEIlFXVQUQCQitSp/hPdmwYIvLvimvyXU/H+WJfSqvuuCtxJ1WgrwJcLLGXifzWBjnIgDVCKeaBwwY05tfp67l+21LWfjecro914lu/TpxYudZGt9Xn6adG1C5RjijHvqUvasPOrE8xcVuCiEFOEQ/9I0cDLNXksOCn59rxdsnB6P5feTE1LuQBXKeoMtjZ4k/mYC3vxfetjXk0heHS1MOfqzBVx3s9jq4ulaB5JNpOYXMOhfC1YQ7lEO7LxCfLIs7qLy9kWw2Ei7LE1TJbAaNhrQCCXjAxfEPEKJ+maEDPmBcry+KDEFOMUaKRL9IEjXqVSY5IRshxMH0F7IdUoHMCp0/doWTe85Ro2FV0GioKYQTpPqDy7bfAYEojYYQTQSC0jXRKX5EwbRk2yfLnm6EgFzc1/t6NmfPH8dpdX99Rnz2NMZ8Ey+0Gk9+tgHJrFC5tI8vpzeMk8qyY/O345fy0pT+qDw0buNiRKOJl5u+Q+en2zN28WvsWrWfrs91QrLK5xQzsxn6+QBqNq7O1bgUrGYbVouVz5+fTbX6Vej81D1YjGbOHYrj95kbS425UbbDUqBkp2yuf7t9tyjmNoXzHAXDpypQsIcKRUine6a8D8rr48T02J8vxT2QRMdiU1CwlE5GFGX+RPuzUa9FDZrdU4f545Y6tVsoNrHt0qgZ6YHfsf3vLKxWLd7qBO7WVkFjBbAQGhnAwDE9mTv2J/KsGVSvGc29D7chKCKQhHNXebX1WCRRQpJE9koq9Ci9CIYDicgG0cIFmQ3ZTVJCVmQESMRbsJV5znOz4sscHkMl48yuWbfTdsU9dvHcl6mt16vseiu9YypQZvyzFmUV+E/CW+VNE5U3YIF8eKBjW9b/vpscKRQ5NuUCjgUZyAG8g4E3UZOCTvKiwGimWr0qXD6TiJeQD1IGzgszC4nHYxjbfRITlr9BdMNqxJ+4fFP7YUODF/7Y0KJDi+9NYMlcwathIIMHn0SkMbKq3EPI0+VD+LIIHavRYKE6HvgKUdikD8mkBxa6o2YXASyhthBMtfpV0Oq0dHrqHjYs2EKiPfalAhUoDcGRAYg2sShgXrSJTkmcd66Wle/eXzqSYV88w2v3vu+ynvLCy1tL18fuwfuFL8ggCVlx7UmgAD91H0JUT5BgSeSH93+hnuSJTehDvlQDEPAljgZq32ufwAVqekSQI44kx/YMVjrh6bEDT+1RcvXO7soq1mO5kg0e187xJ1EFefJYHDF4Cyq+WbSGls83ZOTIp3m+wbhr1tXmwQYkxKVes8zQKf1p0K4uT0QNK9oWpA4iSM0td03yD/YlINiHqa//WJTHTCpwraBXVvR6tStDPxvI8Hveddp+f7/2jJgxhKS4FEKrBHNs+ylaPdQUrU7LxL5T2blyH+0fa82yxLmMe2QS3gF+VK1bmVHdJjlfB5uNC0cvceHoJUTLf13R4OZArVaRm+XafVKJ3kO7MGbkl3hIHmjVNqK0WrzUcmynIAj0H9OLtdt2sT1HIE89kDNJZ9k9bzl1zH5OMWBGDBTwkIsz9AOGIXuKBAEJONRfuwNmvHmTaFXZ8p9V4P8YFUxZ6ZAkqfSX5K20Zv8H2LHSGJAy5blwV1+5muHO71og9UoGT3fvzFfrZ2NlHbJLYHHUACKxMZIjTGLG5EW8u/QNXmo2igCVQKJtGBLfIgf6mvBkBDXx4OCmo+xafQCNh8bRf0kkT8pFTwGBki86wcvpTO6s+kpckbKI5Xls9ANAjxUDz9BKMpTbXaoQoiRyliyypcqIaPHhCvUFL35Ycoh8U6Es/2/AeCCbcA7TUKhaIoC5kRBGvriZLNbgjwdhXpE8/2l/rpxJxCfAh+/G/YwoSmWKPbyl+ckcJyl1u5P/u7KIrSyW6P8DlJMRK7rHyuuk9EZQvHd9A3T0e/tRvn13qVMdTlZau7X/k37TWHD8S1mtT3lvlKyMom5B4fJl1Tmeu5S2chzL3inDWbJiP9VaNcd0bCT56WFIeOItxDJ6RG/iL2RxYm8cKh9vPPGmmcUHSbKzCebi7Hmxvsv/cfz0kd8DGo2GVn7+ZJnXkSWuIsjTB09VFQ5Zn0FfMAGoi5rfCNF9T4RvXSdWQVD0WTDI7dAZUzCINkDJvqUDC7hsnc7lPS35a0886zdOJsSWj7eHIt7Vft1adriLAcMfxJBvYvyQbxFDHCJDgj3XlJAvn++R5zsTs/MsBpsKlZ+v06LISRnQzSRE8FS8v3zt4ihKFsXNWMRqZczMQehzjVhNCjZGeayrcelmDNdrV5fgSkHUb3sXgiAQdzoJQastihN7+bNn0Gg1DG0+Go1Ww6NDuzB50EzMJsf5HgsZwqrM75ix91PmvLmQyQO/RjSbb40iXuFudwSD4Eb5zmns2hknxTVWsmNOY05Zn7vhr7y27u5bYd3KWDO96z6K+TKzdnxzDGFh99L6wQbs2xDjYMgUMaJBkYFM/OoHzhjGIdEekMi2LiLPYwkPdWvB8eQUPpw+h1MX7iFfL4uzmMyQxXEE4XXqqh1J5bVo0XChhAigipOEh4aTlROMTUwiJDQIdf5SgqPqEH92MDqSqIMWD6H8xhm4Od8/5zqUeggKBU7lrVGwaU7GAxcMmlvFRXdwlytPqdlgZ5Zvy7f/n4SKRVkFKiBj+/LdPDWqF80TrrD/mBeQQUkJ3d+BJ4C6GPmezcdf51zb8VglC3G2KkhMAqYiv/QsiBxHK5S0YNskG0elTHJ5Aitt0PI74dIu7iKoaDGm0qiw2a79QkrCv2hBJkODkS+5xDPUKUPcmiucIotkZlMoPW7EQIz0OJYsb0Wp3vY/Cxq6ulWU8hV8qRYZRfcXHiC8Wigrp63j0qmEWyOkgax+eQk9FiQqoyXoGkIqFfh3oGbjatzbqyVWi5Uln60uU54fq1VE43HzGOMsvZE53/+FWq2lfrOWeF7OBklEnR2N2qLF5CKPUVEy6hs8d5A2lCCtw22vXY1QrmR9hL6ggEiNFyGedctkwLnL15eYXA8kNiGL7QjI6SmmAoU5RqM5dWoKEareNNU5xk79ZtV4YfQjpF7NZtxz8zG6ydukxKpvt9N32ANybKx4e40Tnt6eTH/rp5tS18ivB1OjYVX2rDvCE9VeKbF/1lsLGb94BCDH2q36eoPLepIupPDzp7+xc+W+m9KuCsjYumw3T73Vg/TETOIKE2crkJx2lXP6NvYFGYCAlUGkanfx7R+BmKzvIcdZDi12ZCOyJOcwAI2gJVA6QbKTYSObGlGrqZXiicF6GU+1jlFTBtP12U7s+m0/U56diTG/4jtUgX83/nmLsttt8f4vsGNKlDIJv2nWkOuc7EuiRGZSNrPfWMi9Axpw6MSv2MT+yJK5I5CFPn5EjjMrfBkLJCQ0J7BgBVmYMDIWqAJMKKrXwhT2SWepK+foK8J5KZNMvgOiATDTkqssI5j5hAuyZV3n44WlwHLNPtkkV5a3ypjcmiyvDZtkJZv6FC7IZHij5zW0fF6ivMAeAhHk51LBADoKqOg//gm+HbcEtVooEaheWMYVyvtMpEt5nKEaJj4BAkllIZHiEuqrSomvK8eYcseIOVmUy+MDXxbFx38p8xbdsCqR1cNo0bUJK6evJykuRd5RigqlktF49OUupKQnM2/SD2gLvIsWH24ZBsV9sIkSHr5e2JRjQWGVlYIVLI/Rcc7Av+KKfns1lFVBM67m0vuJlvzyxxEAdHZLvLlOAH+dT2HG3CFsW7KHv9ccIbRyIBa9ibiTCZhNFiTFgsrp/AoZfmXcm0qrpVrdSGo2qU5+toFje2IxKfZrrCI1gmuX6LotwMG0CyaHLV9lP2eEV1WacIh4ow2LzR9PIRWDKGGSViEz39HIAj3eGKUwBJ0nkdVDeHPqQCrVjmDY07PJzTbI98qev07SOK6t1c5mSeEyuzhnxQGadWnAL+e/oEevqWjTHPF56B15plC6FRod250Yk0Lrsdr1vXSCKJFwLplHB3dk/5qDpZd3nKTEpko1wkhPzKRGw6p8+NQ0l7FrR/48IR9eqCroavxLIhu/38aImc+z89c9is3lUCi113PDKI+SnrIrkusxpywjSK7ZX1dxZ5JyoV7g2tDi9o4VG//Lv1rHi5OeJuObTeSk5znFKabnZ5On71Siijx9M+SE0Z7IFF/JaaeEGkl0dNDTS8uYQX3ZEPM1p+PVSKIaf49kQhPNiIInOsEbRPh8yGwun7lKlTqRtHm4KX8t2114Idz16OaiHErC7hi0MrFm1wun96BjnLtTNP+/QYXQRwUqUBLnfoulVa1zHLwQgVXshJzsOR45z9Zop7Le3ploCzzBaipRjwwNZr7gIq8gKl7uWYRQuCArhEgfkviWQl5N46HGZr32i0nHVfRYUQ4Zga0uUgKXDVYs2IgqsV2kNgGY0AXMIzvnBeSPWBJ+TCTSTZ41nwBvnnmvD3vXHpbFUG7hS1aSJC7giYm5FDKbNl4glUyqiX/ho/K7dgUVuKmIrBFOj5ceJDMpm8o1I5wnYGVEl+fb8+XPa0hMb4FIT7zYQB21kdAyxmWkJWYy7NN+zBh7DWn/MmLkG4v5euYg9h6N50qSs8KbwWDGZLTwx5I9tH6gARnJOXiH+vH40AfwDfAmKyXbUVjpOaZ0MbS783l6adH56Lh8LpnTR68QGOLDk8MewGCTWPnjrhvuR6QujEidPBE6oYdM26fAffa9R5AZgwl4avVMXDyO4Ah/Jg/7njdnDZYXZOXEpE/XMHlSX778vB8TX1lUJobtZsBsMvOHPeH1jeDzTePJTs/j8crFWRQZBlHPkdR06tUZRWjramTtiaEyrp/P6g2qcmDDEaZseo/RXT9yWaYC1wdJklj6xRq6PdeZFd9sdNoXoPZBI2zHKjUqdlQccqoXkBdnvwLKVC9X8ScZQQjHN9Cb5z56mrQrGRz7+xS2fVnULSongItQgT++28ob814u9RtegQo4oWJRdgfwL7V8u0VZVOtuF0o5v7s4JeX2AqMZv4vQlrMcVp/HYPsGqIassmQECi3S6dSrG8/Xkz/l77UHeOfTrzGIPyjOZkRWaQvGRHcuJV4q2mNymZrehohj0iKWgSmqjQcGBmLkC6AyAlsJ4mMihdCyX3/FNdOiw5MDWIoV0bKYOoQwZHIwM755mbhTmfiSSW0CHK6Linrue6ItdVrU5OdPV5GXaQ/EvpHnoZRxYsVCAQ0pLiFuoT8pbKCmdO2EvWVGeVm90vpc3rHzT2HQS1ERTLmURtyxy1SuFUF2Wg7J8WnXrK44uyAIAov/3Ed8+ncUKprlM4QztkG0UxegQREbpuh7WJ0qPNCnDVG1I9n393meH/Uw0xfuLEpjlVvTwSbpshzn9LrqWHCoFAyNNtXB7H4/5y8mjXiEeXO3sT9bLuNvg9Hv98JkMJOjt7D59xi5sM3Gvh3nUalVeCiMMaXF1oiiiKUwDkrnSWJ8GicPxXPvk2149rUuGPQF6FWwf38cV5Oy0aY6tPeVcXGSp6JuJVNnV7AzW/LJsDbFsSADObfSDgIDpjBp0jOsW36AfVtPARAc7s8H3zzD9GkbSU/OwRXUBfbz2xzXLzYjmydfnkeTBlGs3DWeVX/E8NWCLXhfddw/dYqjPkn5HCsX8oX3RCg9ibQkiSz+fA2jZj/PrrWHi7YLFsW7X1Fe0LhgSOzn8/TSMqLDh7L7pSA4WfgtkpkjNg0G1pAe68nZWAgM/JWGTXeSvTfdXpHcB78ALx4d2hVRlJj+ynwHq1ZeD5HbPXdwyr/qZsy7I3+cYiaVgiYu2u0uz6u7eGPl2LFvzk3Pwy/IByTJKX7RV/Qm3GsdVw2dgIb2Q35HIhmJQpf8Dsgx5O8CPdFwEH9WMnpkf0IjQshKzWXltHVlF6WSRKxmC8GRgU6fpPLkI7uRe10mBra0OmxuWDPlq7o833SlQmlZhGxced5U4I7hv70oq8C/Al4qXyKtGVziT2wMBsYCr6LGCy02WrdT8caQJxnb52t6vdCJyZMeYMr0F0lM6gHkA/uQk7SCmsv4+zdEFEWGThvInjGL0BuO4py7byEP9mnG2FdeAMBD58HCD365Zhv9VD60koxckgZiQkUoEpFCaFE8S3khCAK1JD1nGEoBHwL+aJhDJXajE4KpEhqEOTsLFTZEwIbNeYIMPPzigxjzTXw37ufbtjBXo0FNSsn+cA7v63TlrMCNYf2CLajP3m9nAAAgAElEQVRUAlqdGxnza+DR1x5k7NcnwYl1EDDyNkm2t6iqKZkYvfvz9zPog76c3H+BXWuPULNZTZbO2sLNCmeKOXKJEcN/5Ktpz7D/tR9p1DCKr78awJiRizm8/6JLxyTRJlKgEHxwkvXWlN2Vacfmk+zYfBIfPx3qKgFMmtSHPbtjUekLSEnM5lTMJa4klVws1W8YRa1KAYREBJB8JZO0iykc3X0egy0Xk9jGxZkaE2R8l98+aEDHQZ35YO5z+AZ4kZiQSVxcKiNf70ZwsA9HYy4zb9YWF8e7xtGTCfR9dQGLpz7HVwvKftyNQCrNXdENHh7ciRHTBpGVmoOnj6fbeLjLYhYGvkWphJmd/QQbty+hjaezaFNuZj6XTiewetZGJ7XQCtxcnD0YR72WNTlz6KLT9rah0RxLfZuMAh8ELESqzSRZrWSRAhS+Sx4jhAFUZjfeaIkMqorO04vvigkLlRX6HANXLyTT9pEW7Fix98Y6VoH/D1S4L95mlBJL8a9CeS35/xC4s1S5215TFYyfuIxElhNaNZwa/mqMJ5NRCQKqfWq8XtHQuXtDlk1cjqBWUx8JW9B0krMmA/2RzWRptGqdSJcnXqP7Uw+QlZaLzfArsBZYB9QCjgO+1KwSxdsPTCytE47fggqt4EkdIcypSLmUC4vdszAhEH/pKpelp7EgEYUOf1UoBlHPkBGrSUz6HlldMpNsXqAlZlSosGHD3zuAsKqhLHx/+W19FlSCimDpIokcBprbtxrx4XPCC/NF3QwrsxslRncxZdcdK1lextldnrs7DFGUMBmKxYu4sr4rr6tKjc7XE4vFVeyCjuK9Unl7U69VTd6Y8xKDu31O8hXZvXDrKXmRrslzuBanN9cV/Q487fjMeClDPhXxKKIiQbJab8Git5CXZSDQX8drwx7gyd7TyM4ygFaNEKAQwlHePrMi1is1w7FDGVehjJ/yl2PdbOGOmLeCELndRuCLN7uzZuUh1q05glpvJrJKEI1aRNOjfmVEUeJibCqXY1No37k+sWeTOLQ7lvTkHCpXD6V6VCCvfNKHuHPnODB5J6LY2+laCmwiyrsqs3d/SFJSDjMnrSXlajaWEEffVGaRx/q0Ys6C59l74AI//LjDaeGrUgQZeSc4mMaMPBuxsalos61YfRXXNUmxUHUXX2JnPpzyT7nJjyWo1eRlm/AJ8GbqH6PZvuogv811XggqlR0FH7lvG65MRxIlBJXAO90/5aFnO7Ji2jrn4xSsmkESgOoUh0UIRtDZZfjt8ZGCWs3VuFRGfPM8SbEpHNx01N6f0uNzSmXhy/KuuNmxaG7epU75r8qUG61wkxt2zM353cWx7V9zkJemDCAnKZOrF1KKjstPMVOTEGpoJAoX0ZFqM2fE/uRKlVFrBBrVV6E9EYlKUOMb6EO/sb3ZtnQ3N4L441do80jz0gsWdbFkTtXrOdYVbkaus5J1lqIAWpb76nxAeZr130TFoqwCFbg2wlRBhAEkmhk27nkWjP0ZQ64sDzzl2ZnM3P8ph/88TmpCJgICd+UFYhXeRS9FAzb8uITHAX/e6zmFmfs/ZfEnK+n3XC0W/vgUohgKXAV6ULfuWJ57biQHvt5/x/paCE/BizrFJPpjMZGa9BlQOLENxsB89ks9kWiLSAjhnsfxWrrptrcXoB5BqHmbTCIQ0eHNFeqjc6sMWYF/Jjy9tUgF4M1xCnDOyaVjOpXUgSWOybfHOxUuyG4lDuw+z8Pdm5CRqSc42EdelN1GGAxm4hXuoMmJWSQnZrFp/XEAKlcNpnbtMBbP/4sCkwWVPY4r4WIaCccvsWvDUVJzLiCK9YD1yHmUALYgkYZVKmBYh4lM/XM84ZUCSbmaTXGs+uUAq345QM9+rZk7ewifTllD/Olru3ZZrQVs27aN6tVELl4WUalu3WdeFEXe6P4Zd7eI5oWJT5ZYlBXH9LVvMXP0Egx5RvZuPEZ+cibH/j4t73SjbBmqVpNi/QuJzsozo1NdBSJLlP+g7zSi6lbiq83vos/R82rrsehzjSXKVeD6YTFbmfPOYvqN7sm+DTGcP3zRbVm1oKGBOgyw0LB9PQLC/Dibe4Fm9zfikRcfYMeqfcQecX98WbD889Uc3nIMrU7rlCKhAhX4t+HfvSj7f5sE3ok4sjsUW3Nk6wladGnMjl8dssYT+3zFhF/eZHhbOeGqFi3N1MFIkt2dSJLVFK+clc3xr0x9ji9enE1t/ze5kh2KSAAt2wt0aVCDGvWquT5xWRSzlAp2pSkAlvE+6bw9iYgOIyslh4I0cCzIChGCifuAyQAkZkpszBtAS8mKWrjBYVzOeykIAnUJAazI7qO3Vob4pjBiZTrRdY6vOzRGzJIJM2Z8BH8EQUWVOpG079mSFVPXIhaPJylsS7G2RjeIoqGHjhhLb/Q8i0gkXsKP1PJMx1MbiuAtGwyCwv0ZNft5QiL8+OyNn5Bc5L+xBDtYnsrbHffJoFDDya3tUDH1SXQ8txYFo2P1kZmZ9acv8uOUQSQnZ3M+IRPs8VuSItjfI8PBEAlKdUGltdrLMZakYIcQjcVf3m71dbTD5uE4rlJkAIlnU1CZbKgUk3rRX74mSfFpJClj+JTX3K5e6K0LRJ1X3e6E/J59ZwsgDE8PMzZJYN1vR3j13Ud56ek5TvmDlFix/gibd55h0oTH2XU4jkW/7kNjVMSoZsgsZVLGZWJPatjx9yOo1dHotLO4u0YwgX7hbhc9SgieLlxglXnHPErGmoVWCmT4VwMZ89QMBC8vRygwOOV0wyYSEhnAmvkOt0IlIyaZFbnOFFVU8gjjqvgJ2ZIXotQWyEDHO9TReZWop7COhDNX6Rs9nAFjerEobiYnd5+l+f0N0ecaGVhnZNHE3YkxUXq5umSZbiC3Y2nvkzK8e8qS/8o5EKm0OsoCN+cRJKw2G+cPxeHp5QGS6JatUW4/vecsXZ7tyKR1Y7hw9BKLJq6gVpOSLGjxOkrzGJIQGDHzBTb+sI01s8tgrHQXw3cT2FAndrGUXHFlhcv75i4+0E2ZCihQ4b5YgQqUDzt+3cfIWS84LcpSLqWxbv6fjF74KlOenVm0vVC+uzC2wZhvIjcznzc7TuDSqUSqSZ5UJReRbLbuXMkjPgMp+MxCy65Nitxbqt8dxagfXiU7LYfYw3F8f53+7eVB/3GPo9V5IIkiJoOZ5PhUWj3UlKQtO9lzxAwoJ0g5yK6MRb0mz/IOR3iZBoTjJdwkgY0K/ONhk6ycsOaQSxusRKNlEwKn8biSxtHfLxPupZHXykD9tnWo1SSatXM3l6hH5+NJTloeXiof2mi8yJG+xyJZCfWpglpwrKSad6rPkHG9mDVxNacOxcsbva4vN195EBrsS3aOnuHDfwTN7f+Aenlryc11p/ZaNvh6BIGwGqTFQA/71ovAOnyifZgy800EnVZekJWCnFwjr779EyNefZAv33uCdyf8ikWhOGezmbmc6oHJNNv+f9Ab25OYOZhqUdXRp5Rk4m4G5u18nxnjfyE300UaDjt03lq6PdEK/6Dyv6cEQcWEV/uR63+I72d9hjU3jxoaf7w0QUiShF7Mxiqa8VeFUHzZueyrdXQb1IFmnRvw+5w/8fTWsiptAd+OX8LKYnnOCr8fZclFVwEZTTrezbwxZc9TZ7PaaPdoS9Yv2ELDe+sT3aAqa+f9ecPtkCSJI9tOkHH11jP4FfiPoGJRVoEKuMd9T7TBkGvk0OZjRduObDvBkE/68fusjaQnZgKwfv4W2jzSjFYPNeXAHzFu6/MN9CYx1iFIIQgCatSINpGvd3/ExD5f8fHvo1n+xWoWvv8L03Z+xKKJK1CrBfqNfYxaTaN5t8fkcvWhVtNoOvZph0arwWYVyc/WkxibzIkdZ8hOy3Uq6xfsS7X6VZg88JsS9bR6uC4J6RO5cmUC8sLMiMBIJMaCk5tZENkM5KAUTx3pBJGqku5mFfjv4bSUQxo/IstMF2DkGPAm5LXjyKl4BEbQRrDip/bn3sfakJWcTVjVENKuOOKsVGoVA8Y9xvcTlgPy+AgUQqhUI4w35r2Ml68OD08PREkiMNSflzt9hMFl7Nmtw7m4VLKzDISE+JKWc3tdFwEy0vIIDPYmO/P6zy1JEhpqY2M+ULjA8we+Iqj2Z7w3YCb5QeUbt1MXbKVt8xrMmT6IaTM3c/JMotze3Cvk6V8sVlogPaMtXR4NY9O8LAx5N7bIdIW4Uwm8M30gjwy8F1EUEUUJjUZN7UZRaD09OHHwIqJN5OBfp+nf+oPrOsfDgx9gRIcPqGP1AQ95YWe06TlqyCXf1hOJELxUv3CX2kCowu322fcex6g3MSjojaJtv83cSLcxncluHE7iFQ/y0s5jsiUj0RANVgKEK9ytCrxuDwRvf2+6DOzAuvl/Mvyb51GrBb58cd511fVPxt3t6nJ6//lyHVOvTR2y03KJblCVz5+bVTIO9gaQdCGlaJ5QgQr8W/HvWJTdCWn7f4qcfjkSUAL/WpfOeq1rY7OKTouyv3/Zy+HNx3l6TG92rNzLuYNystmpL81j9MLh7F9/xG1Qrc0qYjWXlIPtpu3HhOVvMGH5G7x+33s8+WYPNlqWcGrveboP6cyJHad5PGQw7694m6FfPsuctxaWqf2PjXgYBBU/TFheJLGv8/GkVtNoug3ujLefjivnkgiPCiEgzJ8dK/cRd/SSy7pOrT9HZVGPiR5YCEQtpJHvoaLAvBIwI7uUjAGWAs9SQCXi6UOEJN0aK295x8LNGC+Ca1ehcrmOlqHuco8vV3WU5biyjEs3LoZORSSJHKJw5P1ZArwEFOYGikZiOUelLrzSsR1n9scSVbcSao0GBBWCSqBh+7to36sVq2Ztxmi0gFqNylfHs+N70+y+esz+4FfOHbksV6d0v9EqhDn8HEyZ1UfebvFzlNXmOsaeZ6bDLU2tSLaMzeFyo1V6DSlccfbsOMeDneqzfNk+xX5HWVHJ2Cl+K8VAJK3CRU6RFDmxk+y+qFWIKVrucxhPDqSkUO/phqzdd5rgU4461AZFMmqFPL6TCofdlVGwWPFUZVFgc2bCPDTbObMzjlBNJKgdz3eR3D0gejjaKmoV40GCfYcucu58Mq+/9ABDAu5DsMHBA/sYPjy/RH5glZDL6kXxDH3nUeZ9uhabVUQIcIibkK9guOzWY0mxTQgLcexXCKUUJqN966Ep/JE5nwlvLyMvx4gqW04foNFqsCqTVxe6Mipd/2pUdew/44grUnnJrolVaoXz/qJhbFm+F8nDw0mA5Lg+kxzbrxS6euvFJzjDQNr7a1ELHqBW0e3ZjswevxxViMPFOub8SX7oJ2JjquIqLQBqY6ETRukKojiEJhq7qFM53mdN72/E5D/Gc/bABV76fCAZSVlERocDAl++ONf9gW7ed+7SyTi7dJfyLrpJqXSKi2Pc91gr5o1aXMQwluZCp/PRMfjjfggCxGw96bwguwnfjPXzFYxbefp5vd+AMsDpG1WG660U9Ci3eEcFyocKpqz8eGzEw9RoXJ0tP+0kqm4l1t0EmrsC/0yc3nueAqNzgG5QRAB93+mJ1WKjU9970OcYSTyfRHZqLsGRgby79HW+Hb+E5IupTsc1f6ARSBLte7di128HSpxrYt+pPP76I8w6MBlJkjCbzCycsIxXpw8mIMyfT9aNJTA8gBbdmpS6KOv01D3c1boO237ewbnD8U77TPoCTu46y8ldZ/EN9CEg1I/dqTnUbFydkMpBrJi61m29fiofmuCDKNnYJ/nZF2SFwzUJGIicZLsSAAXcg4kteFHhxvjfhzJOIQ54rth+HQVEcd/jbVi3YAv+wb4kx6cRFBFAhyfaoPPVMXfUYqcFV4PWtajXogavPzTZSTHvTsNitaHTlZ4z61bAdpOMHFV1Fs4ZpmORXkNO9pSAj3oKIeq6pR16TWTlGHj/8zUAeOTZkCQRjSaGgoIHcbwr8gkLPoxYUJffFu6i/ysPsOjrkq6sN4phT81i5s8vc+Z4Igs+/JX0pByXRrHyYsy8F3iv79ckX0p32m4WTejFljjH3goYxNdIKfiYyroqAMwYtYTxC15kzGzo13QcZ6/EcdQgIPJJsTM9D4wDOgFVyZWaYJHi8RCcY+w0Wo3LfgWE+jFj3yTMRjNDW4wm/sSVool0u0dbMnH1aEyGAnq9+hDje0y+ppfHvwFdBnZg9+8Hy5wOwSjpsdoKMJvMpF7JQKNV07jj3RzbfuoWt7QCFXCBipiyckBhTTi9LxaLxUa9VrWoVKtkvpybbZH/T+BWioHcwrp3riqphKjWyC/uyjUjyE7LpcWDjcm1J0gOighgw/fbGDzxKb56ca5sdbO3b8ikp8nP1nNix2l5m4u2rpy2jpXT1rHZtozki6nEbD1OlTqVGNFuHN7+Xjw38SlqNKrOrINTeKXl6BJ1+If4MWD84+xctZ+5by8qtX/52Xr0ubIb1MndZ4u2CyrhmsHXqVIWeibgPFQrIeddc8hsq7mIB+XIU1WesXOnrXTuWDOpnMH2pdR9U65JeRixckJQCQgI+FgvYyQPOcbQC8gGnF3gtJ55xB65SK9hXTmw8SjDvhzI1bhU9myIIS0hEzQaVIGO3GQevl5cvZQhL8gUHytrVUdsmU3neAYLghy/Rbs4hsXHcT9UVseCT60QpFDrHfdJpVRJUxAqqgLHIsyQkku1CH/UekdZJfOlZKoEhVVaKTqiZJ9MYY4Fp0+C/G9OXUebwpc4hEh0D1vwSbMSeK4Ada7Dqi8oGDFBOUFXLuAKGSWVQFWfCHSanXg3uMiJQyn4a/XUCa2DoJL7qc5WuEfmO347SwSEF/0qCJbHuUphhDeFyHXVbl+L8zH9Mee1BMGEv/oI740bgr9/ED0eb8HX45bTpVdTNq9SJHsOVjw7efZgRCVDeg1J/ELEx1zmuc5T6P5Ua2asfZun238s71AIfYg+8m/Bx7GQUl4/ITCAUTMGkZqQRerVLLr0bcu23w6RkpzrECApzD8n2kDvasEsIGnUoPXA6GHm+1/Xs2jVH/Qb1JP5f79H8+ZvIWbWRak0WngcijyQuoBaPP9Ba+7teg9VakVw6VQiRr2J2k2jMZvM+Af7YdSb2Lv+MOkJmTwx8mFGdZvEsb9OlLg+e9cfoXfIEFYkz+PcwQtMWjeWrh5Pu2j7teFeLt214UBQy9fMbQLh8r6H7O+22s1qEBDqz6aF20s9xCQaOUEBFs+HkSRf+j4/nd5tqvNQz07kZ+bdsm+Ll6+OTk/dw6aF27FZy+FRcSMeUqUlYS5DfdJ1On/cFDj1/c414/8d/7xFmQJn9sdyZn/snW5GBe4Q0hMz+erFuXQZ2IHFn6xCXxhXIomEVgnmxckDyE7L5ZkJT3LhaDzbft4BwISeU1gYO4Oc9Nxr1C5DtIkEV5JdW/atO8Q737/KxD5f8sOEZXR8qj2XTyewKG4mA2u95nTc06N7sfCD5UWS/bcKFiRcyT7LyX4NgC8CpwnkJJFRdWnfuzUH/jjiyB1Tgf8cGqh9iLE9SYHnEEwFDZBdWWdBUfLuudRUGbly9ipbl+5m1v5PyEnLo1bTaO57Qk5kLEmSrFhnn88FBPsSfyrxDvTm2mjYPLosooG3BoJw0+aMDWrVZfjHA7h8PoWfv9mEwXjjLJIreAeEctdj4Vj0OQgqNZVONWX10qM0bl6dnk+24vD2s7S6/276vNyZ1T/swFxwc9uxYdl+Xvvwses69tnRPchOzyckMhCrVWT+xFWcOuhaKt1T5YWP+iBmq3MqB2/1TCJ0kVzMSyTe0IAC2/tAPlNmTmHlLxtJzi5k12KB2ooaM3EIK0mIhj+Jqvw2hlwjO1cdoFPfdlw+k0iv4CEAVGtYjUb31mPk18+ReCGZ4e3e5YIbd/TAcH/eXjCMrJQc3uj0ATP3TWKTZel1LczuNALDA3hgwL3Me2dxmcqfwEw2v0GB7I56OXkwy7YP4sTvp7kV5vBG99UnNyMPb39v7motx6/t+f3gLThTBf71qGDKbgD/IEt9Ee50m8qCm81s3QFW8UJMPBmJmbz8+QAWf/QrqZdlN5b0xEwmD5rBByvfZvLAb3hxyjPkZeRzcNNRMlNykUSJzeIvdNM8hWi3bGt1WhBURXLIEdXDKDCaUalUbChYgsZDw9p5smtPxtUsDm08Socn22G1OJuvvP28yE7NLbEgc2WxLAuDc61kkxGCP/G2BRTwuWKriJoNiOxDQkKlTia4khcdnmzN9uV7afdoC3q+0pX18/7k8hnFRPtmyLwrcb1joAwxDmVJGP2PxG14L2gFHa3Unuj5gVytnrNmI1YeBaoAyYSRShVLNKf2nueB/u2Z8uwsLl6yq5IpVzh+DlaoeZtoxs8ZwgsTejP3211F20Wt49rbPB33R20u+VwLoqNuq6KsUknbw42dxInxUiwUNAIc2nkOQaEyKCkIYcnDteS0MnZMpYiPUpscE3hNgVwm/KCjL97JDspOXWBDbbLgkVuAqkAh1648p8IKLyli7rDI5Qul2ts+1ISpIxdhs9l48sXO/DhNIdttUsTWKO+PQoreI9lx4Sy+coyXTae4xoovudkf8A9AAkyZVkzArvhklszZhk9EENs2nSKschDPjnqEQ7tjObxV4UJmT4Eg2N3SQiMDSM9QMHnK9in7qxiXgiCw4fSnPNRgvFPsYeE9FnIVMWz2dAVtOtWjVouavDtSVvJTpThU9KQwRzyYkJVX9LuRbyBH858g3/o4IsF4q5dzV7CAqIFLhmgKbB8VlTVaFhCX8jg26SLwGfA28AzQFjgKjAemAvEIfEhtT5j8yvd8s2U8R3edpfPT93Bo60m0ft607NKYmM0xHM43sOu3u/EL9iX2cBzFIajVjP1xONENq5KTlovOR4fVIvJys1EsS7DHl5XynnVKGuwUT+vY7C7urLC8ykPBat8ga/bE6w+z8P3lrt0Wi9VhkowYuA/nHAkqUrKfI5JJhAkhlBllbF/7x1qTnpDBnjWHUKkEgiPLKX5VlvOU9m28QylSrhv/NW+xsqDCfbECFbh+ZKflMn/UYnq/1h2tTsu+dYc5sesMkiQxfeh8nv2gL1+/soDewx+iar0qJJy7ipevDkOekVe/Hsw3w78FYK1+MYIgYLPauHohhSq1IykwFDC43kjqt63D7tXO8WcfPvkFE38fTbseLZmy8V1Gd5Ndckx6E97+XiXaeSvgKeioJhzlkjQGM8OATLyZTAGZSHwN1MVmg1MJfzFq6njaCpVZO3czao2age/3YetPO5wXZhX4T0AQBHxtAfgSQCWNQB5ZWKU4AglGJUSj1Xnw5JuPYrXY2PLzLtRBAdesz1pgZduqg2Qm5zJr0YvMm76ZmIPxt6Uv14Jao6Zuoyh2/n3ujpy/rDEzpeH4nvN0ebot52IukXIlk3rNq3PmsGtm5VbBbLYy4sPefP3+byRfzWbeZxto27k+L47twb6tpzm270JRWQ9PDUPff4zWne9m9ker2b35ZKn1N2pdk9c+fgK49nUrsBkQJSs6tV+RhL3NJhIc5scLI7qwoIwxb94aX9oG+JBv24BVsBHgEYbKy5uEvEuYbO8XKy1QYOtAsG4jRuNFYBqwBngXOA6MQqUaSnR0ACtXfEilsCj2bIih+l2Vef2bwQA0v78BXQfex9lDcQwc25P8HAMp8Wn88qXr2OBVqQvYsWo/LzcfXcI4l5WSTWC4P9lp+WXq651G8y6NiagZxvqN67mYF0eIEIJWKJ5L0xk9h3fl6A9qzCW66Il4i/zj5rwpx4A3vLcewE2Ja6xABW4nbnhRJghCVeBHZB8rEZgnSdJ0QRA+AF4ECrNrjpMkaX2pFZbTmtBv7GP4h/o7xfU0u78hZ/bHYsxXBCqUMymwEi7VkP4p6oz/J8jL0rNo4gq0Og869mlH8wcb8dMnK8lMySbpYgod+7Zj1TcbeGXac3Ts05aPnprK+SMX+e7EV9RpWYsf3ltKemIm/au/QptHmtPs/obs+HVfUXzX7t8Pyfe02L2c0HMKG63LaP5gIzbblskLurgUMuzSu+VJIurWoummTCGqC0FEcJIkaTDtujYhO1nF2mNDAKVQQCf0NMMkXUYneGKz2lg8cQUvTHmGOW/9eM12OcH+XJenfeWGO3ZMcU7Bw82rSUmUOR3rKtlrOdt6p8f0dTKZgiTiT6Dsimjf/vjIR/hrwwlSrmSgDgkqYnRe+KQvXr6esoCFYuIcUbcSVxMyWb75FL/vj2PsuJ489kw7Pvp8LQa7AI+oYL80BkX8lj35sk3ruH9igOO3d5rjWqoMDvZHUAr7KFX9fGWDR2SVIOo3q8ZL3b8CLwc9JigUHJWslc1fwcooEzkrczqbHO32sn+ZVAoWTq1wK1QVWFAZzKhyDE6MmKCIl1OyXIJVGV9WGF8iX+OLJxMY+klfAkP9mPf+r7z44ZNcPZ9Cbpbeqe8okik7JWpW3CuNQS4vKpgqleJS+iQ7yuojHG3t1KMJp49epu9LHbHZRL796g8O7z3P/u1naH5PHV56vxczP1xOcHAQwz/pQ4t2tVk07y/qNIpi9yY5VkpUqFrip4gNs0l89vMw9m4/y4uPz7C3X+107c1SAccunCXf1AxJ9Een2009/0gC/MLZeyKBwx+t4KNRPTFU9UUT7DB66RIU8pgaxbXXeSIAflQuStaNTcRTpUbFFUSaoIRaSKW+TyRWyzAyrE2QXcAlYD4QgQdNqZmfx+dDltK1Xzsq1whzEnqJvjsKgHf7z3JKdI0kofa355C0l9doVJgLLEwdsVCO01Qy/JJEzcbVyck2XdNLAorFGCneSaUdByDamVan96o7ZUelN0Kx9+aQSf1Yd3wPX8z7C1HsDQxGJa0gSjpC3WuwXcF+IVQK20xevg1lhKQXcwi1x8DWa12bDn3a8d24JVjdsXjXgdN7zxMZHc7uW+G6+G+a75Xnm/Jv6tfNwD+UKY1vci0AACAASURBVLsZrbICb0mSVB/ZF+BVQRDutu+bKklSU/tf6Quy60DMtpPsX3eo6P8eWg0vTB5A8wcb0evVh+jcr/2tOG0F7hDMJgubF/3NnrWH6NS3HQDrF2ylU992CIKAMc/IhN6f8/eKvSRdSKG71wDqt67DlI3vcWiTrHi1b91h5rz1o5PgxrXw+eBZvPvoZHLSc/npk5UEhPgXyd7fLugEb2qowkjZkoTZVw20K1FGojn5OCYwVouVQ5uP0eaR5rexpRW401CpVfiH+JGiyEtWiBYd6/HXb4fZtuoQW1YeLPpb/P3fzJ0hq9saDGbee3cFK1YcYPoX/endo9nt7gIAyYlZJCdk0aZTvTty/puNHyev4fK5JAB2rY/h7lY1b+v5Tx6+RM9+7Vg0eys5mXqef6s7L73zMKIosWL1Joa9sZhDadU4nJ7P7Hm/kJdt4P5ujdBo1NzbvTHevu5VOf+ImQjAByPdJxI+fuEsmbk/YDZPwGJ9nbz8pZyMTUcU5cm4wWRG5+nBx6N73lA/Qz0j8fGYj5zTsRDJ+HscRaf2JtzTDxgGfARMAmQRMYkgbJKVjOQclkz9gy9HLGLJdDnB9B+Ld7Lgw5VFrvClYcy3L7Nv41GX++Yd/LSE0vA/GR9PmsVPP8Ugii2BakArRL7kCv3JkNwnJF86+TeqZumJCOiHSlgNbMGb/tQhvSgHXM0m0Xj7e7ut43rw1KheVL87ij8X/40h9/bnN6zAvwQq1c35u8m4YaZMkqQkZI1uJEnKEwThNHJww23B6b12txa7RcBitvJ6+/FYzFY6PtWe3Ix8p/01G1enU9+2fDd+ScnK3FjwXTEcTtakf5sP8b8EDe65i6i6ldj4w18l9mk81OhzDEiihNloZsXUddzfvz2rZ27ksdceQlCpWDrlN0z6Aoz5Jl5tM44r7tz4JJFGHerz9oJX2PjDNn6etNJp95+LtoOg4of3l9NvTG+S41P5a9lu+6HXtzgrC2vm8jhJIj8mHUH4E0lytgQL7MafYKdtBzYc5qXPBxJ76AIZyTm4hItYLqXlVPkcS6ILNuAG4BQ7prD8Cx6u41WcoMzV4ybG4vobdp25bW6lf34Zmbyajapxcs85Z9U8O9OSm23guD0HmS3Ur2i3+pg8NjSApZLs6hgZ7s+2v07T85GmPPdMe159+ycSrsqTMMHquMZeaTJrIKkcbJakaKoyr5hTXi+Lgm1QWMmFHMcBE5+by+Rlw9m31mF4c3o2vRxsjcrk+JxZgxwTPWO0wnVTMdbMvnIjPRXzStFT8UkUBJnoEQSckn9ZFJ1TMjdKS3/heRRMy8mDF2nYrg4hlYM4eyCWlyb24cjW45iUec+UMVvKMaBgjgvVJD0dYVdOTJrVWxFDpGA3533zJw1b1iAz10ROej7N2tYESaDdw1XY8mU1rNLLAGTnwKXLP3HPfRt5+umHmT9vO7XrVaL7kI54+XiCJMmn06jIyzNx6kQC27aeYuXKgxDuiy7PjEotoNGo8Q7wIrp2ODbJxI6jFmR2qugiYTC+TEbCPCIDo1GZLLzRbw6zlgwl0CxiyJevee7djnea/y6F8IfKxX3w80EAmumqcvLqU+gtNRAoIMArkYaV6oNKS6R/LS5e/BaT9QtFW0S8hIN4SpXAakO0xyEu+XAFg0Y9ykPP3EvfmiNo1bk+X615C42HGi9fHb4B3piMZkZ0mURuRn7Rs9n43rr0rfWGYwza31WT144iKDyAnmEvys9Gsf1QdgarNBR6D5QlDted6l9YrVC2XsgE3ke2ue8FRgKfIzGYRJZQyJUFRwbS6qGm6Hx1rJ7xh9yGHGgoiWT9j73zDo+i6sL4b7amJ6RBaKEEpCMdlF4EBBQQVIogCHYEK4ogIoqiYsGCFCMgvUvvoffeewkJ6T3ZbJ/vj91k75JdCFXwy/s8PGxm7t65c2fu7JzznvMefsaKTDGKoZQcNfJWT93oXF/sHiC8Whmunorh0rEHGx780KDoffSRxj3NKZMkqRxQB9gLPAm8LUlSP+AANjYtzcV3XsVW/RQP7o3HxGSPI966YHeBfRazBYu56OZ8FOAf4ot/sJ/LfVUaRjjVejm16yx9Rz1H9OlYZny+kIBQf/qPeZ7JH/zN130n8tu+cbxQ8jXnkFY7wquVocaTVTDojZSvVZbFiZEc3HiUHwb94VTgcuUfG1j5xwY+nvk2m+fsuPcnfBM07daQdv2aE385iazUbI6P+5Vsc12gFbYYoZkEcBaNomSBh++Mz+bT97OeJMemseHvrfddMbII/y6aPdeIDX9vu+t+evV9ggWL97Ns+WH8/DwZ+WEX9uy/yPQ5u+7BKAsHs9mKWqti1LRBrJm9iwNRj3ZdoxV/beOlDzsxZeQC1s3eSeP2tdmycM8DO77JZOG199rTvE01vLw9GDvkbxau34lZdi7rYTb3ZuXKT6lbrx7hFUK4cCaOC2finNpYtSp8/TypUbM0kiTRsGFFHn+8LHKWAatFxmy2kJNr5NqVZDIyUtBqA9AXIIgCMFudLYLtG07x5vsd+X7Msjs+Ty+NHw1KVcYqmwEvFOpq+fu0Km/CAy9zJfVDDObBQCremp+p5lnw/ePT2W+TlZZDz/C3QZIY3sUuuCQYhGvTplGyXEi+A7hS7bJkpblmaOq0rE6ngAF3fF4PCjWbVyMzOYsLqjSs8kzy6mHaXutKYSu2/ToVapSledUa1G5VA7VWTceBrdkwcyu+gT5k2cvXSJJEIK7DHGu3rE5giQCi5u10uf9OEFiyGJXqlWeP4Mi5HygeHkJSTApWS9H75COH/wehD0mSfIDFwDBZljMlSZqELT5Atv8/ARh44/dkWZ4CTAHwkwLv3MXtygvgYtvVk9HMGB2d/3fl+hXJTsvh+sX4Qh7GNsRC5RL92zkqDztuwTbs+sd9PPjKPzbQf8zzZCRnsX3xHuKvJPHNS7/S+5OuXDh8hfTEDLYu2M0bP/Rn7tdLmTtuKUuS/uSrPhPZsWRvfj8j5w2jRc8mGPUmFv2wnL9GzgNg2OTX+P3geAZWHVbg2HGXEgodflKY+6QwHtBzBy4QEOKLQiERUjaEKWOHMe7Xb7ma9iOSZCYwJ50IRZm8g4qdY8g18ucnswktG0yvj7uyb/Uhju9wrpV242eRtRK98OJjTHbj3XWH/L5VAiMm1DFyVwNJdhcy5OQRfMCqjO7W9p3WtnG7W6zLJjKWBb3pKrWSAV/3YduyA1y/numcl2dnlGSlAqsLkRpZYIjUcTZGNfV6OmvnOeoHLpiylYGvt2LSt735YvwKEhJsioB5zI1K5xif2VOoWWYUxm3/IczSJ3MlNR6LFUr4eFHCo0Q+S2QOczAjJj81732xlDaNK/HMO08xYMxzvPP8b5jtjjWLt+P+yQ1zsGYWreP4Ji/HZ6W4bO1TaAhw3GviWAEUrvT4lSINKHxWuPh8A1upy9azceFeur/RlsW/b6B9nyfJSM7iyLYztjZiTpnwWVYKa8N+7gqL6zUn/qgrUoVcOJOFL4bNZvC77UlPyaZfs3HIskxalkzBTAaJzCwro8cs4a1Brdi97RySQuLEtURy7HmBCotMusnIjkOX2HFAUB8UutIkOYwT2Xoa2zp1zImP559sODCRzBQTSrUCXz9PiocFsHLxgfzngVXIVXSakwCHemheDTTl9VRHWx8vFHmzIVqDBgPlVcGUDErlmt+XqNQqQotXw+u8UKDazpT9MnQ6w6e/yU+bRzK05ReO2VEo6Dm0A6983oNrF+I5e+gqXy95j0WTN/PqZ9345s2/nGdTuA/GrfiIx+pWYPzgyexeedh2LoVgs9xHL8g3334H7yE/bBlD7IV4Hq8zDIdBlodyQDKS9DN9ejYlSBNI1NwddHm9Pe+3Go0uMzffILsVgkoWyy9Jc69w5UQ0ZauUvqd9usLY5R8z95ulRM19sA7a20LRe6d7/JeNMkmS1NgMstmyLC8BkGU5Qdg/FXAtUfQvo9s7T2PUG/nx1ck079kEL19P1kZu/reHVYRbwGQ0M+2TOQSGFaPLa+04uOEYx7efZt+aI7Qf0JK1kVGc2XeB6NOxvDz2BeZ9s4yd/+znzxM/0E75AgAKhYIWPZs4yebn4de3prFS5zo/4kHnkwEkRiezcrJDlUyhVDBgYFcuH4u21fJT+N7k244+/vxkNoO+6cP1S4mkXC9AXBfhEcYzbzzF2r93EHuTGnXSXRb9ivwjirBSxRj9ZXf27rvIjFm3z5rFpl/jXEJFjJafAA+SclaQ5D2FWiGVXbbPztazasURVq04QsdOtRn2eVe+H7nEZdtHARdPxFC6XDDdX2/DpBHzee71tqQnZ7D70GHicyyoFTLlfYLxVgffurPbRGaajgmfLeXjr7rTqXdjVs7eTaCvldSs40BNoeV59IYUPL0DmDxxI7XrhmOxWHmxZyPOnotnx+7zt33sauVKcvxSL3L1g7DKAfj7zGLCj61YNusgy+bscRJtke6R6uXNoFV6USb85pkW6YmZnDt8hXa9bbnpsiyTaEkm02ylUuvSRC3eSyt77b86zatQvGwQGSnZXDge47bP84cuU7tZVUqEh9y7kykEZNnKeTmFFEpgxRMvrlEFDZ6Ss5NGrVGxbsYWJgz9hZTsTGyRGOILrBk4QCk5kxVflKL7sKcpW7U0X/f9+bbHdD8iTuZ9vfSe56m5wicdviQtwU1KQBGKcIe4F+qLEvAncFqW5R+E7WH2fDOAbsAJV98v2OEtqqLfY6yZtomQMjZq3Ww0OyRUheO/MLwbe1cf4sqJa/nbbltJ70GxZo9qzYk7HHdqXBozPl9A17c74OGt5cD6owz80lGUU5eVy/RR83nl69789k4kZqOZtn2bsXHWdgZ8+QI7l+1zmbxtNltQqpQM+qYP0z6e7ax85ebF9naUGG8H5aqXoV67WvgU887v22qxMWD9xzyP1kvD2f0Xmfn5QnKzb1T0KpiTMHPMIl77/iUiR85zG8ooaYT8IKFekuj5d5oFyfX5Onl37QyDwlv4wXTKC3HzOBLXi6jSZza5aPwv4HbXthvFyVs9+9zdU3lzXDIijOtz9yHl5VlpHSzSy592oXaDCuyMOp2viOdUGyxDqB1lH1Nupo4QDzUpifbaUPaX5oQLibz95gwGDmzObz/1ZdxnS0iIy0BpKMiIAc7blRJXUsEoSJZbeYZE/RUyvM/j4xVCUj3v/H1Z4bZzVmdpqFu2JO071keXoueb+a8hAQmpWXwxaS1mq5UqVcN467mmeHloMJstTF+xj53HLiML69WqFFjfvNMXLoGoxChLErJKiaxSIvk5WBlRiRF3oUt55y/mzQk5YlsW7Kbv8GeQjSaWTtlEWpiB4zmvYpU7Alkk6T+nekgSoT7FnfsDJHvNNOf1JzBp4vNMMG70JR2Omw+XRBH5aS/2K3VcSwnFZ/lodLHdsBpbovHYQ7Ua61FUrUSWUoVXgpk95205h/svxdH3hcbExKcTc9IRzqgQVC3Fmm4I8+mv8OGJChVJzpmFUq1gyLsv4qXyZtrMtdz4RBX78Dvr6MMaLNSdEpadMi9c0MfxbHFiF0V2U1BO1IrKjsJciQz+qunbePG9Tqh91exIvEaWdRhW6tOlyzZatNhDs2froVCrMBlMjH01kitn4kCpJMWQwiWDCaMcgpokyqsk2nv3Q1Iq8fT15LWve5GRksXmebtvyOsVhmRn0Dy8NCyKn4pao+LtJ0dy/qCQW1cI1gzgrJxKDN8DNuGnXAwc5TkaylYU9uMHhPrTZ+RzzBw9n6MZcciUxlaY/m2hp++AppRgA1arlUU/PFz+9vSkTNKT3BREvIdIiXtInZr3pH7ovRnKQ4v/ePjik8BLwHFJkvKSfEYAvSRJehzb5b0CvHYPjnXPcWybI0fhxjpVefAt5o2X74OpS1WEO8M/v61j6O+DqN2yOoc2HnPap8vKZenE1bz+Q3+GNh3FN+tG8v7U11FpVHzYZozbPt9r/hk/bPuC1r2a0Tv8dcBmkN2OMMeNqFg7nLpta7Fwwopbti0VUYLOr7fj0rGrrJ+xhay0nAJtFk5YgaePB+36t2Dc6hHosnKZ/MEMrp2Nc9GjDUa9iRmfL2TA2BdZ/NMq4i8n3vH5FOHhQPUnKnNki+taUq271SU0LIBhff5Admf4ukD8tVQiqpUkJdG1Smlk5DZKrTvOp2O6c2DvRaYtvnVulNGcg9FcvcB2s6ULyRmf4uNVkD3w0Wj4sVdHVEoFL09fhPq4Iy+0WYOK/PVlX3JyDVQoE8yLo2eSmJaNn0bD+31aMahrE1bvPc28zUcK9PtvQ5eVS8kKoZy6dJ4DRzpilZ+27/HDYJnAxbQXCPW5aRd3hXcmLOa7Ic+ARgFDXuTowSPs3DGbrh0a8v3yWuQaXDs95i7cy5uDW3E80I+d2wunYJsHhaQg1C8cgDl/7KRNp1qUr1yCy+cKlz7wbyApxhYSeTTlGhnWv4FQADIzn2P16jL88P0Mur/ZBpVaZTPIgExjGsdzy2GQJ2Azn2VyjCOpLZ0gUBnIL8NmUqtpFT6a8qrNKBNQrLg/k/Z9xbWz16lctwLZ6TkElwrk6LZT7F97hF92jEWhVJAUk0KfCkMKdQ5W2UoqZckzyGzQks0I4hlJuyeeoHGX+mSn5/DXp3PRZeWSjYxNnXI0EI2tCHQu0BpPZuHHvQ07LEIRHij+q0aZLMs7oICjC+C+SODfMe6CqZr2yZz8z1pPDT3f74KHt5bU+HSW/HwHp3mrsTyqbFdhcIf14tyyBHlqmLLMH+/PRFJILsU8Ys7FsfjHVbTr14JBNd7jt31fE1wqkCNb3AsHHN9xmtce/4CB43qxNHU6PgE2D/7muTsc43Wj0ilua/9yS3IydKxZHMWWhKvsWG4hTaujdK4VP4WPSyMvIMSPZ95sz7RPZmM2WVz2nceQ6HNNrJi0nhWT1lOqUhjvTXmNq6dj+eWd6c4Mn8ox1uxMA1M+nsvbP/cnav4ezh++jC4z15FLJo5J9NIL7IvIWrn12os5Y3kGgcg0ijWahJpPsng/FCJnU8zJcKmQWgjclgqmW6ZcWbDxTds7NSrQ1tV+cGYgQ8sE0q5vU34Z8hf4CG/xWtu1TM/Qk5NtsF1HgT1AzElyCheztYk+n0C5ckHs09nYVGuAg2nJy72Ku5zC20NmMmBgMyZN6MuY8cuJT8hEaXT0ZwxwMETaHF9UymhurOmqUBzFM9gfs7+GHCGi7I12dehQpipf7t/MnoRoKA1eOY57amXyZQ7PT8Eqy4zr3JZcnRFJhmydkTFT16FQwOBeTZn1WW9WHzzLwkWOPFVL/rCE/DMfQblQKYFageyhcporpxpoNzzPSpUPIaJmabYu3kcBCKyZpFGzes4uBo7sStSwY1jkbjc0ltBbymGRLSgVKifmMT+0z+KcL5Y/JKGtvrTjfsgsI+QN6mRydEbeHL/I6agVtUHEp6j4ql97Rn2xFACjv5jbZvt/wvytvNOuEbs32J6fIrPlNBbhfpPt96M4vu1RZ+nZ/0kuXUpGEnNUhWeEQnRGiaIg4rMo1/7Md8qPFdaiwPY7PVti3RiD4jNKqeTS8WvIfmUgJdSpmcXSkHkzp7Hn8G9YLNb8a3wxPQWDPAXHvSVhZAxXrF0JtD9jlSolJ3adJbxKKQ4fOcoVi4naretRtWlpzhy4QOW6FZk+egHlqpfhjw9nocuw5Wkt+G45f1/4hfUzthYqFw1AxooFV+GwFanStiYenhp+GvErWqsXGsk2r35Y0JEL9Ab2Ay/bz2cCVbzSUOTe/xDBItwEdxN99V9+xyws/qtG2f8TtJ4aPprxFqd3nyf+ahLlqpWmbJVSdBzUmotHrrDpASvyFcEZolKiCJVayXtTX8dkNJMSm0pqfDq9yr7BOuNcxi4fzqguX7vt89Kxq4zs/E3+315+Xjz3bieqNIyw5XIVAkq1ktTcNI5SAv31SYAaMJPtOZyPBpSlVFAJVFoV0adi2GJXDO00uC3rZmwhrGJxcrP0JLmoOeUKsefj+KDdV3R5rS0/b/ucfWsOs3b6Fpf5Y2aThcr1KrDzn4O88EEX4i8nsnb2vVPBKsKDQYsejfnz03lYzBaXhScTYlIJCPR2sefmuHQ6hqf7Ni1U278it7Ny00lGD3+G/Yeu8KcbNTWFQkVwsRSuJWxHlpvZt6bg4zudwKBG+e3qly7JFx3asiHhDM+uj8RidP8DGptuC1W6Gp9OzUol2X38Sv4+qxUmrtzJxJU7eavTE0wf24fV20+xYP3hW56TxkNF70EtaNf5cWeng8V1eCAyBIT4oNVqeLq3rY6g1lODLktPuaoluXY+3iksSFJIBBb3x1elwRbd3wwRakUiCsnZCLjfiI1LZ9bCvUz5vu9N24UE+mB0w6TdDgx6E2kp2TRsWon9W8/cdX/3C2cPXcbLS4YCj2Ej2WkZaLRqpn3pUIs0y97AjXXdVJhlh2NjyeSNtHmuIQ0G12Xmm37ore+xcaOKjRtPs7XV73w/8jWWTFzjcjwzxyzkw8g3KVu1FOumb3FSInYFpaRCK5/BgIzohPBQzWXnxn2sphEG3kLLToLkw1ShGI9RkiS+wMICoAGwDMikXrUEqujCSbiadItZK0IRinC7kOQHkFBbWPhJgXIjRTvbHw+Taozdq+DhrWXQ1705ufscV0/GcP1iPK16PcnlY9HUb/843n6e6HUG1v4VRWJ0stvu7mW+0UMFd2zXffTiFCqfD3h74gAMOiNhFYuzcdY2dv1zgMdbVuO7TaNpp+h5W8PzLeZDvzHP89s7kTcdq28xbzy8NCTFpHBcTiWBFeBU9sFIKJ1ZsvRLZo9bSt3WNdg0ZwfJsan4BfnyZLcGmI1m/IN98Qvy4/rFeDy8tfiH+CPLcn5uW25WLosnRznmxM5I+RbzpvubbalUuyxmk4WtS/azc9URjHoTsmwlrFww7/7cn0kjFnDlVCx9PuyEyQrHdp3nzP6LjrkUPfyeQhivyHJZ3TBbooqjPZfDmivksYl5HEIem5P6o+C1lnMdLKhV+FxYj/GNuJtQ1Pxj/wvrWSEoK77+fT+qNKhgKwVyo3qfLKNQKdDlGPjs7VlOYgoKnZArKNbYsvet8VDx+aSX+fj1GQBYPR3XxKoVcgvtRorJx7b/lZeaUa9eOJ//uIq4hAyUJsf8aBNykWWZK3FnScxSY5XVeHtnENStDkqtJx4qFWPfaI9GpeKdjSuxBKbah+S4vhmxjlpXqgzHOLqFVaZ1zYp8NGsNVuH2Kb3BkTeUU9aHN15qTr1aZZm37Sgrdp1Ck+24dz1SHfPwcvdGJCdmsm7FERQCtSeJxojA7jopLebYc5ysVkqEB5GakIHRYCnQtnqDCpR6rASjf5pLlnEOYGO2FNJGygVPpVKYTfxE1gjX1b7WJJFuFNaImDuWUdGxphTCsD1ThBww4fp4JObi5+fByLHP8fGgSAAsglqnQm9C66Fm8LCnmPbdagy59k7FR7/wLJBdeaMFxj4v56xDj/rkpGSxY+1x23ZxPeuE54XIYAk16uSMrAKHEdVHxeeG+Exy2u6G4VcAnQe2IIFUvv25LHrjs/n7vD3Hs2VbeyqGV6B3tY/y+z6dfpmrumlACWFEaZTS9iFC4c1lUzp6q5oAhRmfmrU4dFismQZq5UqWLPNkYvc/HWO94Rnn6ePBN+s+JbRMMKd2nyUlLo3fhzqrPuahUt0KFHs8iKlLrpGU9gkQjJLp+DGdDD7EioOplYiiEmMpKwWRLmdzCgk9jdCq0ykXGkPb8pUoW6mUy/qhRXiI4e6dzMU7zEbrgoOyLNd/AKP6V1C/ShX5wNSp96QvqXnzezpXRUzZbUCfY+DXd5wfeuv+2kKnwW2pVLccG/7exuHtRzmjSeWqpMJkUhFAAjWVIagljZtei/AgMPnDWXQc2IrSlUowZsmH+UbNz2/efGFaZAtJ2FimEAJQSir0OXqM+lt7iZ97tzMRj5djZJevMeIHBerwaTDij08xH1JiU/Hw1ubXPMlMyWJtpMPQUqmVBIYVQ59jIDPVFs6TZ1DUblGNfiOeZcHPa9HnOF4Ss9JymPHVPwBUfjycZwa3on7bGiREp3Bi9zkatqvJlwMm06pHIzq93JzwKiX5+aP5lK1cnNfH9mTF9K3EXizKN3vY0ax7QxZMWMk/v69HEegQQpADhBp/qtsP1TDqzSjv4Ht//r2dtbtO8eNnPeg95M8C+yVJonzJKpTxdzwTk7Vamlcrz8fdWzFq70aiom1CBrcTILX26DmGdHwCD40KnWx2227S39tQqRS89WYberaszV+LdrHz0GW37e8W8Vft9IoLgaCT+y/R+Ok61C9RmjOpfdEUq0B6SiKh/lbKB1e6b2O6GXr2fYKNK9wzL31fbcn8v7Y7DLJ7gLWLDtCpe11aPVOHqOW3ZjEfBEJKBdK6R0O8fDzYs/Yop3efozRriJcWYqIMGsUlurWO4LGISrzScJTTdyP8SpJmepVM07dAFeA8PsoPKKX1YG+mEj2zgVASrftRHp1Z4NgmSwvWrP6V3/eOo3i5YK6ejuXdZp85tcnN1vNR27G89NlzbJ6zg86vP0XVRpU4vddZGdO3mA+tezdl8gczqSob8KI3RmRKoeUy/ljp6tRephVJ/EhZIEDyoYksY+QASpMS1XU1T77TEKPehEKpeGA1uiRJQqVW5tegvVMElQwkINSPi0eu3JuBFeHRRVH44iOMm+R9WS1WVvyxnhV/rKfbsA5s0yWSlvAy8AIgkUIyey0v8IQyKF/h6E698w8Vw3Yr9usBMZ2FnROTwcTySevp/Fo71kRu5ofBk1mZNZOVf6x3+50UOYuz+KNjLKDCk1+pLCcRYvTjyObjdHylNWv+dFM+QVKw4Lvl9Pm0G5XqVuD0wf2kkQJORTSzadKmNHEX48hKzcJkMDkrOwoeb+T6pgAAIABJREFULLNZJvGajTXIyyfK8+Ye23eZ2BQ9fUZ04+q5eDatPu7oI8uWh3DuRAzffzCPhm2qUa1+BQxmOH0kmtByxdm6/BAvDuvAxz1+xqpUEXs1mb1bzjDw486snr2L66Jh5iUwZaIqo6BoJhsE9iDLUa9GNhX8QRU90k75Z4JSneg1l8U8NqcaXgWVLwu1ztwwna5UI23NFfZjCPldbnKMblk/yO2QXN8DTtuF8e1bf4xNi/cheWixlnDU+JKVLvLbxJwy8Zp5Oebe6uUwlqwqBaZA2zUXz1PMM8oT3swu6dh2xpjJ1pOXWDztdTZHneb3yC0ApFV35DhlRDjG8nKTurSJqEDbv6ajiEjDy04wNC1tM5a2RVd0jFvhmD+Lp2NMFo2C0cs2MvW15xgwfn7+9pxwIc/O/lWz2coPkzfioVExYkA7Xu7ciF+mbeZirKOGpWSxIBvNKAwmJNEJI973ArvrxBbnKTSKjK/4EiC2VUhotb7UDnuMVr2bcOl8ApfOJyArJZciaJI9n88shKTqgxzj0Ac6jqPOdvTglehYOx6xDnU6Kc2ZZapZrRTT35+VH+SmSrWdg0KpwKpQYM7QYYhJQRKZPze1BJ2eZ65e4AUWfvVfW6nZJIJBH3bk4qFLbFmyH6vF6rQWrbmOumeSeP/mXROBeXdSjRVZfRfPIcBpbdRpVoWqDSuyfOpmsoWctora4lTQyJitV1GiJW1vIgqlAqVWZctnsz+r1KhoGFSSqzkfkmmS8DbnUk4dzIGsRPSsxRbCDlAfi7WgUSaxm+2TN1NZCuHEjjNMPfo9HQe1Yq19HclWmeEz3sJitnJ6zzkuHbvKn5/MpteI7ihUSk7udISC9hrRjdlfLgZAI2mphCCmI+fgWhJAYA0lCS02VtI30IfMlCySY1MpWbEEMeeuu57Le4x+nz9P6col+arXj3fVT7t+zXmsQQTj+/2KPqdg7vl/Gg9T9FkR3OLhNBUfUfz0w9+kZVUGXsTxUAsml9HEy6k3+WYRHhQOrD/K78OmA9DZx33ehCzLnMMLHfOApkBjcpnFeQKwylb2rz2CQqmgcv2KbvvQZeWSm6PH09eDeqElKRn8LpBXQyqZxx4bSQ2fACYM+oOvVo3g6qmY25bZrdqwIi+P6kaJMkFEfr2C3BwDzZ+uhUqtJKxMINUbVaRmkwiatK/FmOmDCQjyRa1V0bJrPfQ6IzUaV2TsnLdYNWO7U/01q8VK5Dcr6fZKCxQ3GDc6UwbR6ZdI1SfwMIU//79ixbQoGj5V698eRgFMWLiNr+dspmXTx1g1bwjDXmvjst1n7VtRs0Rx+sxbhNldofBCwmKFeBehbO6gN5r5csIqRo5bxoAXn2T8hF682LcJv04eQMsONYmLKVwu593ALAhjbFl/gqe71kV1Bwzl3aJ+q6qMnfk6yXHpLvcP/fZFXh3VldjLSWS7KaVxtzi++wLTxi4j+lwcAz/rToeXmt4RW3s3qN28Co/VK8+c71aSna4rsF+SJFSSGkmSMOQamfTJfGo1KVhjT6VQU9G3HHUCw4nQhKFERbYchsMgA9t7QlOUyik4Eg6v48e3FJeKsWrKRnRZucz+ajFv/TgAAI2HmkFf92bd9C2c2X+BEuVteYetejVFn6Pn+gWHeEmbPs3Ys/Ig2ekFlXsBQjCgwNkxKbGXIFxLyjfv0Zhti/Zw9VQM4dXuf4HmPPzz6xpmfj7/1g1vgXnfLMPbz4shv75yD0ZVhEcWeZL49+LfPUYRU3YPkYkMBLrY04AM2UrJBz2g/zg0HmqMehN1WtegTpuaRH4695bfWfzTKvqO6mGrPXYTZJFOLgO40Yuoox/pfEcgIayfvoUPIt/iuwG/YXbjfZ31hU3d7IWPnsVz0VZ2Jb+IDm9eeLM58SvSObzcVv/nzN5z7Fy2r1D5dJXrladd32Zcu5iISq3KD+0A2LX2GM26N6THoBYkx2eQfNn2Ax1Y3J+oZQc5c+gqm5bs562xPQgs7s8/f27FbDAzbv4Q+tUb6XQcq8XKvk2nqFq/PFFbt3I+MwGD1YzFUh8Ln6OUjuGr+Zv6xcuhKvLv/Gu4fDKGVj0bEbVw7789lALYcvQiB9fYZNNnTx4Ei7cAUL9qGZ5sXYnaJUuw+0o0n2+OukkvhUezyuFsPH3x1g1vQFq6juFfLKZykD/jf+jNLz+sY/dNQvjuJarXL8fHP/Vmxd+7OHYxiSXz9tKxa11WLD14349ds145evRpjF8xH66ei2PiJ/NJinXtGMpMy+HP8avu+5gALhyN5sLRaOq0rEqTjo+zY8Wh+35MrZeGnsOeJv5KEvN+KLyq8qaFe+nUvzkRtcpy4fg1t+2sWJBdGjutKFmyD1WqXObMwTjk1MsESjIZcjr+FGPWxV9JT8zgk042Qaru7zzNpjk7uHw8miNRJ0G28uxbHUi5nuoU+eHt70WZKqXYNHu72zGVohiZfEMKWzDQHg1bCGQr4U4RHQ6EVShO/OVE/IJ8qdG0SuEm6B7gXtYe2zx3B2EVit+Tvu43fIv5MPDr3swfv6yodM29RlH4YiHxqFCsLhIlA1CQkM+EiFhPoKxwLWt+ixCrfz1k8d+4HoU85qSD4/l1SCRt+zbDs5B15FKup7Hrn/28PqEfB9Ye5sD6oy7bKVGhII0bR6IgHZV92ZiMZuZ/t5x3p77O0S0niZqzPT/mXa1R0rp3U/yCfAkpE8SBtUdIuZTOY5JNlviNXl14d9J+e1sV1Z+8+Q+ceJ+kJWVhNJhITcpi50rby4oY4rN/zWF26k025ssePnRk90XQaqjRoDyV65YnsLg/Wi8tKBQ0aFON4LAAZJMJKS9s0B6WdXjfZS6az3M6pQwwEZsQwW/Acizyx6Qb2pHl/wElUr3wCfAiOTYNWe9aBRNX97qbQrdOIYu6QnjlncL8CpYrcFfYW5w3UVxEEkQEcMUGikn3YliWxU1Il9DGac3fqUCJOG4vT04djqb5C0+w6Zyr549DmlyV7Qj5sgQ7BCGsGkd/Fk/Hz4JONqOp5Et6lh6jnzCfwrDN9qkyOvQ3MAQ75iy1toRKoQAfBb+N60mlYsGsvXyOjfFnGbNlHWarFd+qjmsc4OX4fCzF5soKC3CIdcRYHOMwpTnCLtXZCioGBrJky3EkYepNXmIha0F0JM22VlU6h0MlOj6OretOYMkxOBU+doIYFioKRYhhu3n3kngfiaF8Bkdo3dQJ6zh3PIZXP+pIRGIWy2bvplTXuiitcj57bdUKohV50vJidwGOc7RoBXW9NNuFUiigikpDhSolqF6vPGXKBBBWLoRjO88xdeQCR+5oXgixSri/TGaKhfqhVikgR1iLYhFtp7BrMazYRdkFcZtYDF58bthfmE7sPMdzQ9o7wpbF9mKoot0pJop7uIWL3+7HW1ajUZf6LPp1HanxGU7rK19CXwxZdgpFlVkzYxt9h3fh/GEhN1EIr5SUShRWCUmhRbZGAgPte8zAl+ReS+XHeS/zx8T5RM63ECP3RyGn4s10Zv24kKd6tKZ4mUCOWyxEPF6ONdM2Op2Hf4gf//y21uk0u73zNMsm3ty4lCSJagRhkI+TzS688cZDcm2QPft2h3yVx8yULPyCfF22e9ixNnIzA77sRUSd8lw4fP9ySe8FzCYzBjeq0vcMj8o79/8JHj6j7BFGKakYV+Xj6BkPDMMmiXsUHyYQIpW4xbeL4A5PPNuAms2rMfn9GU7bv3rxR6LPXKdO6xqkJ10tdH+ndp/j1O5zNGhfi4HjehM5Yk6BNt6SL97yCjJ4BVvRTAAj3szCF4eYwqVjV5nwyiTK1yzLkN8GEX85EQ9v24tN3ba1yMnUMaLjOCxm55dvrZeWF4d35YluDZn39VKSYwof3poUk8LUEfOcDQdsOR+vje1BckIWao0KtUZJdqaeVX/vIDDUn4GjniXhWiqblx9mx6rDbFxkMwqr1q/A+rm7XB4rISOWM3F+wM/C1k+Bj4BEIJT4VF+GDnuKuq2qszJyC+sj3eTZFeG+Ye+GE/T/uItbo+xOEZuYQdXyYew+dncvL2arlaazpoJaZl6nF/hkx3okz7tL2ncFP08PrqdmuqkW93Di3PEYAKZ8u4ZBH3UEYO3Sg/R7rRXTJ935WipRzJdRA1vj463FYraSeS2NuOgUdqw/zvHVB5iwejg/vjP9pn6BwOL+9H3/aS4cvcqqv7bd8VjuFCajGY32/r6mdBzQEr3OwJSRC+64D6tVJik2jbotq3HITe3LHkM7sv/3PaSnlwKGAx6AAeiFt+IkfmE+zFhyDR1/2/oEsujKqK/7siVyD1OOfo9voC9bF+2mdZ9mLPnJwVqq1ErUWjUmuzJoky71SUtILzS7pJU88vPGXKF2y+pkpmRzVChQL7kQrnkUoFAoKBVRgvBqpR96oyw3W88f7824dcMi3D6KmLL/MOyeBgUSjaVQzshLSWEdEmpCyaayFOb2AfbAmbBH0CuSnZZDemJGge2XjtkMschP7UbVbRZE3L/uGEgKBn3ThysnrrF/7REykh0/YjVQc5Ku6HgCUOLJTqqjKHAtrVaZi0evMvWjWZiM5vwE4shP57I0bTprjfOwWqyM6fEdFnsR2kp1K1D6sZIs+PYfPprxNi9XGlJwgE7XSvBc22XPnQo5KxR4+GhJjMtg8R+OF7ngksXo+WZbFk+J4sKxa3j7aqlcvSQWK/R6tyMqlZK46BQ8fT2RtFpKhgfRqlt9JNmKIdfEH/PnIF9v72L2+gDbgB7IZj1r/4qi1wedKVEumENrDvJ4q+psmrPTtdiEUzK+kKwvini4k8cXuxE865J4GNnFWhOZJZVQmNZHqN8lyskLTIbsSvrfnUdelNUWHvpuV7kwLots5owxiUxrKBIWApUpVFKGIrm4r52uvf2zVZZIj3C8WImskDrHNm6lwAqpkh15JpKPoz9jgGPuL6WmER4RwtbLzk4PvRClbbZPoSHQcb+GVXLUMErPcbDYuQnegAQWiaaVHGGG3irHtbTKjvM9k2HLl0nOdoh1WK3C9dU6jplVXsbiKZNVXsb/vKONJss1o6M02J/bRqEIs94IZguSyewk7e4k7uFxY/0pF8gThxHv3Wxdwf1gK04NfDS2O0qVElmt5Oq1VLKtFjxK+ZOVpUeZ67huhhDb8dWZjm0mL4EJVkHDSqX5oGsLvv/sHy5dtF0LtWBYW7OyWPvnJvp92IlIe5FoEFhc+zw171KHxVO3EHvZ1odT4WW9gyV0EsYRX3ZcMWXCb57T2nJivhzPBYXkENBwYrkRrpt9PUrCGnbqW1zbwnE6v9qGrLQcti7a64gSsB3U8VlvZ+GEZ5KTiIgda2ZuZ+Bn3Tm176JNmVItXBOLhfWzdtCgvgcbNjYDHM9UDSMoq/Jl9bptZJneuKFXDToa4hGYxHvNR/Hzzq/oW/5N0suF0HVIR5b9YqtjtmLSevp/8QIGnQFvfy8ObzrOqikbC4zxTlG7ZfV8sZA8PKpGmdVqRQaqNq5809DOIvyHkZdT9hCiyCi7x1BJampI4cKWALdti1A4HNt2imPbXHsf7xb71x5h/5pDhFcrTes+TYmoXZ4/R8wmNT4dT8mT+nhikA8AMlrJ56Z9ZaVlF9jWrdjLPNYggjd/epn67etgMpiQrTLxVxL5oNXnJFxNIvpMLE90bcjqqQV/ROu1rUWNplWYOXZxgX03QpdtwNPb2duZHJ9Bbo4BpVLBvF83AFClTjhmi0zUsoNYrTJGnZ5DW8/Qf3hnUhIyWfDbBkw5ejQeanJ0uUCci6NdBkJRKg5TPsSEJEtEn7uOj78XUk0vflu5D4unEnV2HBXVxfOVR4vgDKVKSbXGEZzac4GD2XGkWGeAPfs003wMg/V9amoKx7Lfi5prN+J8bAqdGt3b3JFH81Xu/qFm3XA++qI7F87GE30lGa1WhcFgZvXqo3TrWo+ZfzsX4l4c+TqrNx7HT61BqVRwPS6NxdcukpSRg0qhQOuh4sNuLXnph3n4XHQverJqykZ+2zsOBKMsDwqlAg9PDUFhAfkG2b+FK6djCa9Skqtn7p3Sn0Kp4OXRPdi79ggnd5+/9RcKiQ1zd9GiWwPWzykYeZCdruP1F5/myoXXuHilM1Yq4MFMyiov4CkF4uPriUJhKiBkmZWSxoCxLzL/i+WM7/8r49Z8ygetPqfLG+3Zv/YIsefjSIpJYdrwWSgUCqxCBx7eHlgtVoz6gkbk7UClURWI9niUYdAZUN9nBrYIRbgT/F/clYElAqjWpDI7lu67/we7RVG+Qn+3CLePu5i/q6diuHoqBi8/L14c/iyZqdnsWLKX+MuJaCX3YR2FOWaFmmWp1uQxRnX5hsxUm+EWUbc8CVdtLztHNp+gw8BWBfqTrQqy03OcJJltO+yeXjHuKI9xsnuHw8oFU7NRBCXKBaNQKjCZLfnsz5k9553yQdbO2U3TTo+z4LeN5KY6XuKMOQbCrMGcYStW+gNl7Ht0wBQ8KE+I6hKai8HESYks/GEVmy+cZP3W/pjNdextj6KzfEBtjzAHS+HkYXXDIYk5ZXo3eWfu1poLds6JeRPZMbHQbVZBo/rG7+YzH+5yaETvvPgSY3H9ud+o7pzZf5H27zxJ1HgrWEU5oFqkyY9jUsWikbRuC+eiVVMsxJesbINTUWCrwAjm5U8pDEJOjpA3ZPUQCuoKU3n2eiKvBDVAVoJByBnTCsS1ye6rUIU48o0ahjiYtYOKMvmfY3UavLRqJF8TMTmODlNyHNckPMAhNKG0J4dZrK5NufJlHMnv+jA1Gi8zxasnEq92yH5rshzn6XfJwVapkmz3ulgOQPbQgEqJrFbdcI3dPMtFptdfvK/y+hNKB6gcTh3Jfh3GTR9EeERxPh86m3OnrlO1blnqNizP7q1nyUzXYTCYCAnyJtZsYxJ7dqpLSJAvqgANhy4mkp6t47HwUEY2a4u3VkOovzc56QZm/r0LzQU9misOg8oqsDuSyna9r56J45m3O7AicisaDzUKlZISZYN47s2nuHIunoO7LjrnlwnzIDJRbiM+XOVjilaHWODZ5LqsxsltJ6nTugaXj1x2Wy4i/3vC9XAqZSE+E2SZPiO6smHOTmLOxzsYbaWba5w3RnEeVEIOs7DOc7P1KO3tXBW0n/LeIo7FTWPBzGV8P3QkwcoA1FIIssnM3LeX4GE1omMuDtdFDl7yflZODODJrg35bWgkFrOFebGTyUnXcenoVeIuxbNlvs0ItN5g0Q355RWunYvFP9iPvasOcSTqhOtzvAkeaxDhsq6XUW9E46G5a4Pv30DsuThbTnUR/n9RxJT9e+gwsDVVGlZ6MEZZER5p6DJ1RH46F78gX5r3aEypyiU5tessp3advW25+jysidyMT6AP8+OmMrDKMOIuO+f9PP/hMyz+cRVt+jQjvFpp1B4aoubu4NzBy5zZd4Ez+y6gKEwCO2A2Wxg44hliLiVybNd51i/ef8vvZKRms+rvHS73qRVaGnj6cFjfF6NcEQkVHtIFanl44E0qKsnx8rti5noOydUFgwygNmnWFuith/FQ3pxp/H/Eqb3nqd64Mn9PX4je9FaB/Sa5BgbreTTKm79ANO9Qk4pVw5jQvhqVyofi5+tJrt7Ia6PmciX2zstxZOuNBPreTgnnW8NguXdFh0X0L9+SBL1rOfeHFUEhvvRu913+32eOxzBwSDuq1ipDlsnC8uWH6NP3CRZtPkb1KiVp1jACvdHET/O3obDbRPtORJO716bU6OOhoXyGlmg3Coo34pchfzH58DeElCyGyWjGZLKQna7jx+HznUpk/JtIuJpMSBnX4hN3gi6DW3N8x1lizsffuvFtwsvPk5zMgjL6IvpUGsri2D/YM+M4F4856uIZc8xUUWZzztKdXLqgIA1v1lMKEwsPHGXRsStkyCmY50Qx8KteJMemMvfrJbTr1yJfql5EiXKhHNt+inV/2ZRN3/x5ACd2nMlXCi5s8eem3RsxfdS8Atu9/b0eSYMMoFaLasReuPfXvwiPCIrCF/9dzPtmGSrNv3CqRczXI4vMlCxWTt6AWqOibNXStB/QCrVWzbrpUS6laRt3rodSpWT/2iNOP1SePh6MWfYRCoUClVrFs2934I/3ZxBWoTgj5gxFqVZiMVroOqQjO5bsZdPs7UiSRIdXWtO0eyOunYnlzN7zDBzXhzV/bqZE+RBW5iXc273CdVpW5dj+K5SvWopjey5y6qggyyx6+0VWyE1uSP7XBM9ygFcIrbxCbF5Ygx5JKlOgfWBxf67Ep5CdW6fAPiNN0bEDT6V/3sEdO8W8KzeKA07bxe+6Yy/s7UVGTPJwraboxI6J6otegiGiFZiyvPkUc0rEl1cxd0XMYxPPQfCs79t0ioNbz/LiJ8+w8f2lGOQGTqfiodiKt7oYSEqnfBm0GrKNaVxOS8SUKvFkbjgdqjXg57nbOXk2Dp3eSEREKLMmvMzZSwm82+1X2zhEJS9h3CphTpS+jnnwjTGTnZpLfY8gTp13sC4Z5Rxzr7LfPhpvR98rNwnnIdyCbSqVJyFdh5Ss5ZK+uMs2x1Md10222HYoBVEQa5ZjfLFKW3h4z3KP85imMgOWLgVCUBodHeqKOz77XBPutzxHh1AnTPZUI0sSslLhfJ/4uFFRVDscDRY/B3uSx1KKOXySyfn+9vLxwHKD4WPWqJgy2fYS/dbwjpSrGMrmraeZOXEAy7Ye58iV67z78Ty8wIlk9o7LW88GkpMTybt7ZT/HXCrEPEmTibDyIXR7rS0Ths7i5P5LNjXDvHvWW2CCxDEqhWeFUXieuFm7kqsXH7EYdKZj/TmxWZIzy6VUqZz3g3PeV94Y3bFjSiV1Wlalfpsa7N9wnKO7zjuv1Rvbi8hTwEwXCm67KQru66MhMyHdiem7EdkpNoa2WdcGXDh4CXDMXyDeNMJCDvNQoiIDOEM7TNHDseUVx5PJS7zfejQ/RH3BrMu/07f8m7R/uSXNnmvM9sUOwyyiTjknIQt9jgGzyUzZqqVp27cZ5aqX5fd3/7ql1LrZZMZkMhFPGplYCUZF0COclqFQKshIziLi8fIUKx5AWsKj5cgpwn8b/xdGmdV69zHVRfj/hMlo5uLRK1w8egWtp4Zn3uqAt78XuVm5HNt6ikr1KhBaNpiD64+Sk5lL31HPEXMujvUztgDw0Yy3mff1Ug5tOu7U76GNx8hIyuKp/i14Pmyw0z5ZllkzbRMA5WqU5ZWv+1CxdjkGf9uHa2eus1oo9OwX6MOb3/Tis/6Tqd6oIp7eWmej7B5CoVAgu0nuHjV7CEunruXIX5vQy8867fOQ1uCrLHZfxvRfgFqrwpwFJdSHiTHOwEJfwIxamkhpTTIKKbTAdxKy4zmVFIbB8iPgzZhvNvPrj4OIaN8yv825K0k0ffEHlvw2mB4vN2XR9B3UalyRV0d0YfTgSGo0qEDspURGTnqZqJVHsNgl4M3+DgPEqoKwUH/OXkgAzZ17FjUKBVVDQxjRrCWDlxfMYbobNA4pR7dytekxf+Fd9fP8oOY0e7oWvr6eRG24/TCv28Ubo7owa+J6t/sn/rKBt95ow8bNJzkfnci6vWc4eTmee8Vblokowe41Rzix79I96vH+ITc7l2LF/UlLKCj4dCv4BfrQcUBLkq+nMXXU3d0jt4IuK5fHm1fl6LbTN2UaYy/GM320a7VHSZLwwebAOiEbMTFC2FuCTL7i4OWPGdf7J37cNhaFQsG66VvoOKgNjTvXY89KG2vqG+hD5foVadq9EaUiwlg5eT0Bof70G92TZb+uwTvAm8To5ALH9wnwdio2naPLZj+ZZPM5MvW4znoC+JnsrBw8vLTo77dk+z2G1WK1MYz9W6C7BatZhP8wipiyIhTh0YYh18jC75cDEBDqT5WGERxYd5TrFx1hEOcOXKT+U7Xp+cEzLPx+ORGPly9gkIGtNlm1JpXpV/Htmx7zyolopnz0NxOixpBwJpbcbD3WvLwJpZLMpAwGN/gUyUPL8in2sEgxB8qJWRIMKjH3wkVdIatVUDZ0p6YmfO+X92dx/VICYepooo2zsdALkNCollFcdQKtZxlHDoqY9yD07eR9FlXW3KioOXm2RTYrz2vvZqxy9g05enlNRHbMR/gsOnTyPOBuclcojCKZ6M1XSFR/ojKnDl6mql84IcZ/SFX9Q7FQX9Qx2firBTbJnkcmyzKXktIwWMbn7zKb25BsiSEs+gABxUrbDmOwzcOr/aaweNkwEq6l8sYnnVnwy3r+ivqEA9vO4uvvxd6o01yNd7zsWrIduWFGXwXf/LURo48ChXDKgWcd1ySlqgpPjYpPw3uhM5q4lJqKuZyZXk1qk603kpqSTFZGOsFhpfhq2WbiLmbggYQlw3Uem/FaHKk7o7EalXhU0hDQpDpoHYaiNku4lkYfxj31DB1mTUchsGMWT8c9ZlWJyoAFr49kZ5Ce6lqXL4bM4tolu9KgcG+agh2Mk0Xr6E9URXS6x/Ly+cTfffHeUEhUql6aCZ8sApWjkZjz5x/gRe0aZVj8927UKBj/emf6vxlJXIRt3rLLOu7vCktcv1xK2QKz5e2FQqmgUo1SNGlVlZwsPcv+3OKcg5k3P+7C2kR1Q3e1+sS8WJGNylvH7tawOO4brtOqaVH0GPY0Mz4XDBmL0xecjwFInja2b8jP/YkcvZC4S3ZGyM0xnc5HNKjMBUs3OCuy2tqGlAmiRuNKXDh0mX4juqLXGdixZC/XzsU55dz9kxpJYnQy6YZ0LmPEhD+epBEheaNGS7ycQgZWglBipKKLkTYgAyundp/DqDfx9OA2rJy8gTXTNtH/ixfyjbI1f25GqVI6CXQolAoObjxGaNlgJr8/s0D4ooeXlrHLP2bZr2vYusCWpzZ77XaymAf2gtJWupFKGab+OY5u3VuxcdaDL5VwN/D08aDTq+2wWq0Ych89Z70kSWg9NY+cMfzQdfBQAAAgAElEQVTQocgoK0IR/jtIT8zI//G7EQfWH8VssvD8h88iuxCy8PLxoHmPJjzt2dtt/2/9PJBn3+6ALMtIksTWhbvJychFZS/yW/+p2qi9PNB6aUiKSeX0fWLHCovLJ2zHr+xRggD1AmJN86hYqyxNapbmypZKXEm5QrbRQHG1H16S9y16+/9CYHF/Aov7U6NRBGqNCrPBxIKf12BWuS4FYMWC3hxeYLtF7kJK0tp8oywPuhwjLzzxJVNXv0dgqB//TIvin2lR4CuE3gU5Ppt8NVitFq7HnCMlx4DaA0pWj8BTUzBkKbCYF690bkLDCqWZcvAAnmoVH7RoyrJ9p+j36xzOLN5Fblx1LIbyaPxmU6yxmmKPV3I7F1knLpG0KgRL9gxAS86Fg+jOf0nYoKYuJbi/avMUC04eR2c2cze6jn2HPoVBZ8w3yB4EMtJzUGlUVK5dmvGRg1g+dzflKocRUSWMrRtOMm1KFH9N2UKX7vX4e/4eWj5ZmVbNqjDn4unbOo5ao+KJp2pQrqqtNMuZI9HMnbje8UL6CEib52ToSIlLI7xqKa6ejr2t70afjXMYZPcRrV94gvBqpfjn9/Uc3XYaT28NzZ5rxFP9WhB3KYEtC/eQk6Hj3MFLLJqxkqNUwMAEQEM6GWTIfVCQTg5jkGlAHJtQ8HuB40jspUfflhiPZ+PhrWX7EiFX/oafmxsVE60Wa34UhivodQaWTlzF6T0ORcrriVryDDIH6nMhTk+1Jx4j9kI8Z/ddKCAy8rAipEwwpSJKMG/8sn97KHeEZ95sT/Unq/DLW9NcKj4X4dFGkVFWhIcbeR7QRyw/70jUCQJC/bhy8ppTUc8yj5Uk8vTPWCxWphz9nnF9JnLlhCPZu/PrT+Hj78Wzb3fgGd+XMJstmI1mkBTUaFoFTx8PZIuFGk9UBgkq1AznyJaTnNx1Nr8PSajfVZh6Wi4h5tMIP7bOeR9CH8L24powinvC0KG9CCrrQ/u5n5GhfxUrVblimE8J7U6q+djz0kSPuDtGTmDQnDzUIkOlclZXKzA+J+U5YU7cfRaPL+SH5OeKiB5mSyFeal3lv9ixYdYOfAK8MOjNmE0W27koVUhKlfOY7GygQlajViRiuCGNR+IE/lYl2gT7D7Vw/rkJKayevo0uA1s4zkVQXLQIKoG6YCVnNu8hM2EE0ABIJ+P6SCIa6/ELssnze4X78u3QZ6hQMohflu+k99ytWNWgUijoU70Wk2Zu48qOfWSd/w6wGZCGlH6kbB5JiEcyWt8gzF4F5y12czqW7F8cG+R66C/3g61R+IaXB0CT7Zi/uv4lGP/NOoKRkERhTiEnyTPZQfGZPYT7JMTmHFDqtLTp0YBvRy9DVrthUZSOsZo9HX0bfR0CLBqhbphSb784bh5bskpBqfBgZmz8iIBAb1565mc+HNOVpJg0ylUMpXyFEH6c9DInjl9j3eqjNG9fg6gtp6lZvTT+m2zH8Yt2jCmhkcPREXrAwZr5qCQGD+/Eyrl72LH2WH6IquypBbXtfnKqx2Z2wTILuWgIColOdfiE9eBUS0xcJ65yQIV1IZvcFRO3jWnVlI0MGteLqZ/MsR9TqFPmshyi/VyRnZ8bbnNU3axjVwqS4m6zCS8/T8pWCePQpuP549Jl5LAu0lYzskS5UDoPbkN2Rg4ftRmDpXlpDPyEo/6kPzoeB4YCNvEkK52wkoC391fk5AzH9roWQ8NGMxk9fixBJWzFAjOSHCy3yWhCpVbli3ncCW4UDNGoXTEyelQYmfLh39RuWY2XRvckKSbFZVmXhwlqrZoXPnyWI1tPcnz77Tk3HhYciTqBt78XORlFoZd3jH9B6EOSJCVwAIiVZbmzu3ZFRlkRinCfsGX+Lk7tPkf/Mc9jMpixWCz0fP8ZxvebyMZZ2/lq1Sf8fmA8+hw93YMG8PfF3wgqFYhSqWBwzfcLhCc0eroOMz9fiNZTQ0jpQDbN20VidAqWh9RDqVQpeGfY76TpZwO+ABjlEcTqIymhWU2gJvjfHeBDhOx0nXuhgRsgSRLFvbPQpa/FSgf71kzCy0zhg+H96fJ8Y3Q6A8tm7yEuJsUmaw4s+zOKZwa1vGX/KVfPk5k0FJtBBhCAQfcL1473oukzFQBY/P0rTJy/jSVRRzF4CgaL1crZ2CRmjOpN/XpnyTPI8mDK/RB18nAiv3kLlUaJ2WIlPUfPd4uiiE5IxZRbsCabbG5L5rWF+NmNsjz89nEPSgb74+ulJesuQnlq1imLXmfklKCE9yCQGJfOu30n22T4geFvzETSOQwgc5AX7374NDVrl2HR0gOMH9uTQ0evUszfi7SbvJAplBIv9n4CHx8tSr2ZyAlryUrXgfnhfE4UFlaLFVmWUSik21KGfBBFjvuM6E54tdIsnbiWls83Yc+qQ+iFMOC4SwnMG7+MfqN7YrVa2R+VgMMgy4OCPIPMgYEoc1oRwlbMeNPkyRKM+/YthjYZSeK1ZGZfmeSU15Uck0pw6cBbinfcDhpVDeL8lZmY6Ze/TcNYwlGhz9Gzd9Uh9q46xPMfPkNwqUCS70Lt9X7jxY+7Elw6kIU/rPi3h3LHyCvfU4S7xIMPXxwKnAb8btaoyCgrwsONR4whuxGJ0clM+3g2YAsjMuiMlH6sFD3e68y3/X8jIzmTtcZ5tOvXAr8gX5726OWynxLlgkmOTcFkMFK7VR00nhpSYpIJDPWjVEQJp/o8MoI32yIoCirc5D658Ba7NRDE7aIxKNYVk2WCwgIw6oxcT/QhzyDLb8pLxOgXUEwdiGQV2S7hs+iFz3W83CB6gNVql9vz2CynmkoiUybm3IkMm8i85Qq5OCJTqLUzI+L8iX24qU0m64RzEOdbzNGx3+uSQjgvD9dS+BGB5VEp/yQhewYqDz+atQ5i9IhPqV67EhlpOiSrzFufdCIpJpV+tT8GtQpdug6L2cq3S9/lox4TndQCM8s5jpO2JQ2sLW44ooTSGsaqaW+g0xm4eD2FRTuOYlWCKtd2PgYP25x8vnAjC4b0okxoEOcKKLNrqFI6lDdGzcWktrWvWDqInwZ3YeHifUSWjOXcOedvqNWH+aBPWzq0b4tKpUC2WqlRqywmo5lxXyxj0tBu/P7zBk4ed7yomL0d8yrmfcnCNTb52+ZZWdwbheZ/7J11eBTX+8U/s9m4CyECwYK7uxUJVrRYgSKFtpQWSilaoBQoxQsFirtT3F2KFXcNJEQg7rbZ3ez8/thkM5vsJsFa+v3teZ4+DTN37tyZnZ297z3ve44Zaa5WWCRIFTMln3uGRKkyVfK5Shg0eVr2drNU7XdQMCIwNWBIUy4fuoUQlwTSZ8PHU/enxtKMeb8f46dJnXHzdePb2bv487dBnFr5F8qnUaRUyJ7A9yhfAXdXe6wszBFqqzh26gGhL+OwDM+sn7SyQKanGpl9naK0vFSV+dzLJM+01D9M+p7RY5yMUYL5MGWSMUnryPR8zyR9/H3oJrVaV+XK4Vs5vNEycp1DzFR2FNP1PwM9LzPpUIx5oGUxbkYUJouW8SLoYSiqdBUhj4L5ZmF/gu4HEXAvJFcfWfmF5kRmjln6nk3K3C8NIpVYIaeKoBX+UF6K5odGP+v2Prj0mGIVi/Lk2jMAYl7F4ur1boOyj+rV5tKRFbziIGrcsSCUYqThIOjPLQ8sPU6vcV1YO3HrOzv3u4SDqz0WVhYEPQzVy04xwYT3DUEQigDtgV+A7/NqawrKTDDhH4JKqdYJhbh6ufBRn0YU8nYl6EEobQe3YEK7X4we6+jmQGK0Vkq5bM1S3Dv/iBcPQnnxIHMi+rpG5dK+Xe0pW6sEVraWPLkeyMdffMSxDedJT1MSGRLzRn027VKbiwdvIqrTDOxN1ou/THh9CIJACafilHACz1KF+XZ0Z1bPOkpi5B5qNS1HyfJe3D77EEtrC374YyB/n7jPxYO36FNjIodDFubZt7WjNfAc8NXb7u0Fdx6EMHPRUfzVSQaPzcLSHRcoUyaDp0/igGzlTTv7lVSuU4HAi9l1Qc9DY5i/8Ch+rSthba9AbrENtbJX5t4YnN2WcPRwZY4f1S5udO9ai2t/B7Blw0UA+vRvxMz5vZk+eTdXLj8v0P2TYtQ3rbn/6NVrH/emqFjdhy5fNKdXlfEFav/ztL1Mm92D1g3Ksev4bX5eOZBBH81CEMDO1oqBvRpw9NZTDl94SIZGxDo6/9Q1QRAoW8mbQP8IBo5qg1KpPSbuVSx7V597q+t7V3D1ciYpNhllmsRI+tJTBkztwZXDtwrcz/tmynxrlOD+xSd4l/YkLVnB8AY/Zp7Y+EuuFGZYe08m9OUkwAqIxZLbqFlJBl/o2mkZKeP9lKlZiqiQbAXF2PB4vEt7Gm3/pvARnPBBRBTDEQRzIHe9a1qygqiQaMrV8eXx1WfvfAxvi8SYJB5f8efBxcf/9lBM+LfxbtMX3QRBuC759wpRFFfkaLMAGEPOFWoDMAVlJpjwLyDmVSx7Fh4GoFbru5SrV5on14xPKJ9ce0bTHvXxvxmIs4cjvtWLs/f3w5IW+Xt85WS/PEu606pfE1Tpau5eeMzgaT158PdTytUuRbk6voQFRlG4mBsJEQm8DAhHla4mLjaFUP9w0tNUmGkykJkJBNwL0ctvN7eyoHytErQb0ITddYcCd4Cquv2WwlSKWzhBhgZRkNafGGbypDVgeklLUlbKTNJP5mRIz4tNWlsnZZ+ktSYSRkCvbykjmDUWIwyWHnsngSBVh5SycHrsReax1hJPLAkLJ7UjECTMoCo2AXcPB76e0AGNUsWjawGc3XwOmaUlM3d9x5mdVxm//HNSk9NRqzMQBIHhcz9Fk+m91b5DNVJS02nz6e8AeJcoQ2zgJNIS16KdMIKZ+Ua+GFKHJYtPEvE8BjPP7OvJYpFsIrI/gxtBL7C2daVs+ZGEvWxDYmJxHJwPMPAzN07ufI4N+qyV/8Pn+F94TmEKoy56nFdR+9BorLC1TqKMdxksY7OD+72rtGpvWXfm8a1gipcoxIgf2tKv9VztvuTse6WRelSaZX+W6S7a+z120QH+mNADx5KOxIRlB5vSGrUs3zEAmVrCmqVI2LEUCRuryqBpm8q0aFuFuWO2a9MH0xTI5TKGT+zEd81+JvWVVlREJhFckdZvSj/vnxccYteSISSlpnPzWiDjVgzkxatYkpMV7N93k5fPo8niPUW5lMWVKDsqRKrV9yXIP5ySpQtTo3FZWvpVpHqj0kz/egOFPJ1o2KI8glKFKDP8/IvG/PkKgqzvo2gkSJK8n2Siin4Tu6JWqdn6615dSpwmQ0NSTDJO7g7EvIrmhRhLAnaYaRQUl8lxkMx5BJlAqWrFefUszGi9Wk6Vx1xjJZu1M8beefsW5u/91zC3lPPj1pFc2neVM9su5mIPLa0tdGIYDoId1UqnE1F9Cg9uROFT1IzPew7j7OU7HDo9EMHSCzN1KI5R4TgKhrOdfjs/LVcAZmNvjSI5t+/k28BCUnuaX4B7cPkJPv+1DxFB0R+k/9fFvVfzb/QfgbmFnJ92jebg8uNGRcdMyAPvLiiLFkWxlrGdgiB0ACJFUbwhCEKz/DozBWUmmPAv4/rxO0SGxNBrXGc2TdtpsI1TIQeS41JIS0pDqVCy9Pt1b3y+CvVK07BTbcKDotgx9wDFK/kwfsMwkuOSSUlI5dtGP1GuUTm+mNYDB2dbXAs7snD4WlISUnH2KYRPaU8cXGxRp6Sh0Yi0G9QMu0zPm/N7r2HjaIsowtY5BygmuJFsOZ5YVXEyxKJYym5R0lKFtSynmpcJb4roV/Gc23uDjXMP68n912yrNfJe+fMuZk/Ypd2ozmDSsv7UblYOhVrDq5dxTJ+2l3ETOtKwdikuXnuOmbkV5Rr5EnTnM9JTnClbyoYRw5tTrHgZ9uwvuGJZmlKGa7mKbFxfhtGzt7Fo2ld4uRWiZ9ff8zzOq1AJvHKW1mB8orn4lwM0aF4eN/c8U/WN4nFABH/ffUHvdjVZvPrsG/WRE2sPjsTKxoKkzJTRLExYPog9q84ScP/11FLn/9iNCXP3cfNBKCO7N+LAvps8CM5WisyrGtHCUk6laj40blSGi8fv06ZHHcICo0hNVpCSpCDl6D0at6vKrYtPWfjDFq2J9L8MV28Xnt1+wbF1Z+k/pTt/7fxbt2h1bP05OnzZgu9/XkYci4CKgIp4zQzKcBkPmaOun6bd67Nu0jbeRpkzLzgVckCVrkKp0C7onNh4jue3Xxhs23FYG46vO6v7d8C5IA6lbubg8hMsHbmOtde2AzDv1z4E3A2ibrs2zOyX+7sybuO3tOjTBFEUaW/TR8+D1cvXg/sXjDNBoigSQzyRsgysNCI+OCEXDC8kAdTrUJMbJ+7mdQtyYd/iIzTqWocDS4178JmgFX/p9G1bjqw8SfBjfUXRz6b00AXAUaExhD0PJ/BeMPFR2QbmKqWa+xcf8eLBv6u8bEK+aAh0FAShHdpVTgdBEDaJotjXUGNTUGaCCR8Agh+F4n8zgN7junDnrwdEh8QSmZmWYutgw4BpvVg9YQtJscmsGL2x4B1LVmv9+jfH29eDpzcCWDV2IxqNhsLFCjF69VdY2Vry+OozbO2tEJVK7p15wMjmUylbqyRymUBSpsJXzPNwYjJ92bJYuFsn7iKYyyns40anr1oRF5HArEFLqf5RRb76pTeiKLJs0jZU4kMsBWf9Gi0zA6qJkKNmy4DXEeipMmoSsn+sdLVzUj8km+z6KT1GTHqelGw2RsqySWvTdGOX1gFJ/dX0VCMtDP9tbqR91r2Q1rZJ65qkDJuEsRDTFAgZGdqALJMRnLJ5GBbWVpzZe4O4hPTs89jZMG2EVr1OVSiboXEo4cTMH7tSv/c8lPYyBCcXinvXx+GFki6dalClalnGDl6rS1qSO2bf2yw1QqcwJc2al+f0qQd4VvBgw+JBaDQiRw/dxjzMkaO7HtCwgS9mSZljl9RjySQCFwZ94aQeYJJ7LxR2Y+Gfw1Ap1URHJCI3N9OqlUoCIZnkeZPW0ZkptNs1cjM27LnC7NGdWbXugi6VL2s/6NeUWUhYO5mEIRatsz/jNfOOULZyEcpV9SE1i7mws6FYOS+mjtiir94pYVHVzhJ/PGkcodZw92oQZUsUomLlosTEpXA/NDttTZ6oPUdhLyc6DWhEeroaubkZmkjt9+L2pWesHr2elIQ0PByb8+zSU06t1qZs6tVYZo1Fer9VhpULRWNMmR7LpO/Jl7NvY/5mkUHRuBd1RZmmZOXYzfQe1xlzS3MeXHpCYnQC569dI1EYBWLFzCPMUfITwXTBQ9ediDpdRYZaY5QREw15neVqk6VImbturkxtX26fvo/fwGa8uB/M1cM3DfYBoFapUaRkp2J2/NqP1KQ0/G/om3cfXXOaseu/4ZgkgJOiRZ8mTOsxL5dKIoC7jxsRQYZtHURR5C7RxDAcjaYzEECc1UTKKZKwFXJbkguCQLWPKrHs+/VGr8kQol/G4uZtWnTLDzVaVeHGsds069WQDRL/PZlMhoWVua4W3dXLBe/SHny9cBBzBi5Bla7CysaSz2f2ITUx7Z3WD/6/wj8k9CGK4nhgPEAmU/aDsYAMTEGZCSZ8MLhy6Ca121Rj8K99UaWrcPZwwtXTGQdXezZN30lS7Jt5kpjJzRg8sw9ntl/m8OqTPBNjScAFc3kGo4fUYOE3a1AqVMS8iiUqSPuCFyzNUKsyeHDZ36B5qiFEBEez8Zc9FK9YhHknJrL5172s/3Uf3b9pjZkgx0zQvm7+/XX4/z1c2HedL2b04szeazRqXwO1MoOfhywt8PF/XXvG8L7NqFulGOcDtSuvjauUIMUsjs/6NOSbkZv02ndsVYW+nesQGZ2ErZ02lbNMMXcAvvu+LRYWci5efc746buxjNUGWceP3OWTHnXexeXqMG3FQOJjkhnVZznLD3ynDcheE73b16RTq6pYWcqxsjDTBWVvg7rNyxMeEktibAo1Gpfm7uXnTF87OF95dWNwc7Vn3bJBeHs68emnS+nYsQZlS3sQGZVIt441sVGLZGRoUKkyWL3mLxSZzI1lYHbgJiZog0lzSzmK/4BprlKh0pkfb525l/5TuhMZEk1USAwnj90kQ5ya+xi8EMUoHcvwvkk/RbICW0cbnD2c2DX/YJ5tH195Rrm6pbl6+CZNPqnHF3M+Y/MvO7lySD+Qe+kfxo0Tdzm04oTe9qU3ZmPvol1IcfF0xhDM5Ga5DKGzEEkcMYxCw8eZW3yJU2xEUf5LfBLNiXmlr5rYuFtdzu+6kuc1GYNSodSzgjEhN46uOU2jLnXYt/io3naNRkNUSAxepTx49Twcb18PGnerx43jt3X309XbhYeXn1K0rBeCIHwQ7PZ/Cv+CJH5BYQrKTDDhA0JseDzLf5ime/kWr+SDb7Vi+JQvgqunMzFhueTs8sUn33fg4PITvHwWwW0xllhWAqVADRMnH8UzYztlZFrPG91qsMSbS8pmSWvUjKmRpSQpEEWRq8fv4jewOWd3XdG2NVa/YQhGZP71mDWHbKZHSDcgiS5VipMyW5Jx6F2btF5Geh5pbljWNUtX+/Vq2yQveqlfnMxA7Rjo1bqJmT8SgkTYgJTsdERNSjYro+8RlcGzmwGEPA7Fr18TOg5pzl+7ruqxaTrWMFlS+5eYHeSnRCSxa+15Zn7XEStrC8JfxePh5YQoimg0It06VGfFlRe69tULu/L30fusnHuEDEftKrssXc3Emd1p3KICL55HMnvkVqwV2Z+BlZU5Ht7OCJlBjyD9TNSGn6Ws+y0aWRgo4etOn3JaMaub55/S/8tmbPjtmB57mSGp/xMlH4+ZUoO3tzO92tekz9BVlCjqxoKpPRn0/QYAvBztWL/xS8zNzRjYbh5hIdpJq0V6OjaZ9XhJKhWlKnpTyMuZS2e1XoGdPmuIAGxedBI3d3t+XjGQx7eDSIiIZ+mYzRAbjyCtcZRcu0xpuEatX7fF2NhaMGNuL5IfhrPl4WFaDmhAjbLe7N56BcV1ST2qpSVZVyxGZwv1CB7aoPnRsxjaDvVj/Tz9ySCgqxMTExNz78uJN1XGlXyWep5m0u+lTCDoUSjFKhYh4E4QAM/vBmHnZEtUSAz2ooCZ7BoZmiZ6XZsTiUzPSzF33ZhRhs9IXW5e1/ng0hP6Tv4EK1sr0vKp5Qp98gq/gc24evgm3Ud3Yma/3ylZpRg+5b31fCZBOzEXBAF3HzdW3ptPfFQCgXeDGVpzDNZ2VqgUrx/sRCKiIac9kjmvwmzwVifobRUEgQoNyr42S5YF/xsB+FYvwaO/n+bf+C1hYWVB9RaVuH7sTi6z7A8ZmgyNQbYTtAFbn4ndWPPjVhDg0t6r3Dp9H9AyaZUaliUtJZ3L+6/T76fuyC3krJmw5Z8cvglvAFEUzwJn82pjCspMMOEDwq2T92jcrS6nt1wA4MX9YF7cD8bK1oqv5vdn/5KjBNwN0rW3tLagarOKuHo5k5KQyuOrz4gMzl4dd/N2ISNDw0v/MBLERBLoA5TS7VdltCGKXZQSY3VM1tuiQl1f7BxtmH14HKHPIzi++eI76deEvJGeqmT/8pPsX36Syo3K8sXP3di9/DTRr7SB/JqLkxFFSE5MJfhpOPNG6KfBHtryN90GNmbt8jM4u9ixZvkZBg5tzp5d19iw5WsUUUlUr+9LIU8n0tKUhIfm9iOaPu5Pdp0ey9LZh1mwfghf9fxDt6/dJ7W4c+0pAVFPSU5X4mZujqddEWRvqBw6ZFp3Qv3DGDy9BxXq+iK3tsS3gjf7N1wkPil/37JyZT35aXIXxk/bg0Kh5pF/OAdO3GX4581RqzX07lybZ88iWPrHKb6b0oWxn68G4NPhfsRGJuLkakexMh6UqerD/SsBdBrSjMsnHtK0fRVG9tSylNHhCfw4cBWbLk5kSt8/CHoc9kbXCpCaoiQxIY3unzfmz9XnOXI4u9bHIo/jcqLPNy1ZPm3/G4/jn8LT6wHUal1FF5QV8nbV1Ut5CC685BfiKQe4AyJyVuAtJALO2DnZMuv4RKJfxtJ+SAsOrTz1XsaYkSmck14An7ykuGQcXO2p0aIyVjaWXNh9hQu7r/DFnH48v/1Cz5cyJSEVB1d7+k7qRkJ0Il9VH0NqonZBxVjwZ+9sl6cQh09RVyJCwgF9cRAHJw2KIP0+e4/vwsmNf+V7Tcbw5NpzPvq00XsLymwdbajTrgZntl6gdf+mKBUqvlv2BQH3gnQCWh8iBEFg5MqveHT5CUdWnzbaLj1NiYOrPb7VimMmN6NEZR9dUFaxYVlsnWw5u/0S6WlK/G8G0GVEOxzdHEiILsBiiglafKBM2Yc5KhNM+H+KR1f8sbSxpOeYTphJlPcUKQraD2nJl3P68fmvfegyoh2DfulNt+87kJKQyvVjdwh+9JJGXevy6Y9dMbeQ4+rlQp9Jn3A4c0KSiIoMGuQ6p5oKKHlz892cOLfzCoNrjmdan0VEhsTSuFPNd9a3CQXDvQtP2L38NN2HtUJmJmPZqfHcvvCUL5v/woj282jRrTbFy+lPziwyVQp3bL7Mkt+OkZaqZMnC47wKjePzz1bQqW8DXvhH8NlHs4mNSqJ0eW+D57azt6JidR80Gg0yyQ9fnSY+tPL7Gf+o6YQl7uV+zHCuhz9BI77+6raDqx0dBjVj9eQ/adGzAXv/OMGILr/TvuxY4mPyT/OtW78Uv87owfARG3kaEJF93x6/ok2ziiRnTpC/HLKG27eCeBkUzfyNXzJn7WDqt66MnaM1jq52iKLI9C/WMHv4Brb/cRpbe0smDFipdy5rO0vUqgyun37w2teZE5PH/UnLjjWoWrfkGx1v52jN3SsBvAqKzr/xv4yIoCi8fLOfUWcPJxIybUFkgoxq2NKizkQc6YEzHSjPboqaadP6Og5tjSpdxU9d5hAZEkOnYX7vbZwJUYnYO9vqUgvzQsmqxRm3aThf1yzhJfYAACAASURBVBqr23Z220Xqd6qt1+705vP8dn4qT28E0K/kMF1AZghyczkdv/ajy4h27F54yGi7mb99g5vjFCCb+ZMLh/E217e36DvpE26fuc+zW4H5Xo8xJEQn4uCWr/r3G6PTsDbUalUVe2c7Lh+4QYnKPtw8eVfvfWMMZWqVonG3evm2ex9WCqIoEvwwBEVq3unDPuW88SjhzsLLM+j6XXuqNK3At0sG4+Bqz6O//bF1tKHNoI907R//7U/5eqXf+Xj/Z5GVvvgu/nvHMDFlJpjwgeHIqlN4l/Zk6G8D2LvoCKFPtQX5apWasX7TMbc0x87JNpfkcFRoDC8ehFC4WCEmbh+J/81Alo/agCJFuwrqjAVyjqKmvN5x5tzCEiu9bdIUHzFDIvVtzFRa72DtsUmxyWyfe4AGHWrQc7gfO34/lmfuu1RQQO/nUPrik/6dlJ3aJ1hJxp+ZHqVndCsVyZCmMxlJzRSMvWyzxi+9Dr3UOskrVZKmKFpnp9BJ5eyl4iJCpgCJKElTlEqkS8+pkaRGGjPdjXwSwos7gay7OpW4yEQWfrNGl17pfyeIpacm0MZhoLYPpYqgq9rnqXW9UhzfeU3bSeaYXgVG0q3mFF3fv3y1jmVHf0BISUeelUqVWcsS+CSMlAQFB7df5YvvWrNi7FZ2P5lDswbDSE5fD2jTHUWaEJtuT1DsZErYFM2+Tr30s0wzc0uJBYFGQ9221bh7yR//x+F82/pX5h8ajfn8Y5zep63PybCTPA+ZH+Wu46OxsbUkKjIRlSqD0YPXEB8QhW189nM0ZV4/Xj2PpIggZ/WSU5ilaa9t0ShJapBKhYePK+kKFXFxabpx3rz8nJtZPmlZz5tKRdiDFJ7cDNR7HvTENaSiLZKJoJlEFEKWlP38jmw+hem7vqdtWALbFh0n6GmEXqquNHVTkMjto87g4971OLPvpnHz+KyxSOqSRMmzpvdeKIAkvszcgCGzaEzoJ7fR9P2LT6jSpDxBj17mqqk1Fyzo1qQRbq8uERue/X2t2aoK5pbmuHm7IpPJuHr4Fp2Gtca3ajH8b+qLarwL3Dn7ALcirtRuU41bp+4blYIXBIHE6CTCX0Tp1Vo9vRFA808bc27HJV09WHxUIjM+XUiLPo3zZEDqd6xFlaYVObLqFMGPQg22AahQvwz2to7UsU/lgbwXMfHOyGWJuKoiMfN31j0PPUZ35NrR2zoz6rfCeypzsrG35s65B9y/8JikuGSIg+U/bMj3ODO5GZ4lC+NVqjAeJdzzbOvk7sjwJYMJefKKtRO3UrhYIao2q4itk81bM3E786k9BHAr4srhVadYMXoj36/6isv7r7Nn4WG6jGjH5um72PjznzTsXIf6H9fi8oHrPLsVSI8xnUzS+P8DMDFlJpjwAeKlfxhLR66jQadatOzbBM9ShXXS2qp0VZ4eMBFBUTToVIdN03bqAjIAO8EeV/YjkGUOm4Gj4zqKmEUgEwoQbL0hrh6/S68fOtDjuzbIXqeuzIS3xuE1Zzmx6TzDP5qut3330pMAHE1cy87gJQC0/ayR9v+f1s+338TYFKLCDD+D9o42NGpZgUd3Qins5QRAukLF40dqsgKybFQnTvl6z55f7/qMmNeHdTO1KXgx4Ql81XQ6n430o3Id4wySvYM1A3svpd8nixnUeylBAblV6natPItLIXs+6lyDvTuMixyEB8cQF5l/qtBX0z5h/fVpVGlQpgBXVjCkJir4vtUM9q87z2ej2zN/73d8v6g/HsXc8jzOuZA9Go2G8JDcaacfKq4cuknd9jWo/3FNLhuYcB5ff45eYzvp3itFy3pRopIP66fs4Napu5hnsr/7lxyj1WdN38sYA+4GITOTMWBqL4YvGWy0XZEyntg62hDxIrda3qlNf9F1RDu9bc9uBbJp2k66jGhHy77a2jlHNwcqNiiL34BmDP1tAOmpSpaPWm80ILOwssCrlAdxEQncv/AIJzM7GjsU4usWRWhsJaeE4KJjhMrV8SU+MvHdBGSASqnS3f83gUcJd8rUzP19/nrBQPwGNOfuXw/1therUCRPtrKWX1Wm7B7NX3/+zbaZWmsPW0cbzC3NcfFwQiapBx44vRdOhR0pXaMkTXs04KddP2BhZY5v1RK6+2VuIad4JZ83vr68cPPkXRBFGnSuzY3jd3Ev6sacUz/RbnBL3TVe3HsVZw8n/AY0Q6VUY2VrlU+vJujBxJSZYIIJr4MMdQY75uynZqsqDJ7Z97WUrNJSFHz+ax9Wj9+st70SroTyM9FYIUPN2o1jubq2FZf2XTPeWUFqfqQy2BImSG5uxoLTEwl5Gsb1Y3cY/PMnrP5pp64gW0/AQSrDLWWtpOcxJN8N+mxVlmCG9DgJU6bHUkhhRExCTx5fZsDTR8pmScU9pGyWVHRD+iLXGGYkdDAiGa53vzOkm/UNbkXAvYgLQroCjUbU3deUMK0AxCceg9keuhxNairuHlp/p5mDlkNSJishZVokn6uNpYwSZT21zEoWw5fZ96WDN/EoWRhlVJyOwYmNSMBM0E+T0iIRc3Kk8kjvT9YzkZ7dpt2AJgzvMI/Ax+G6+5OWkMq3fZczcW4vxny+hr4DGrJt1Xl+Xf4ZZoC9ozVXTj0g8sStbN2WrEmYhME6sfE8JzaeZ9KaIaze8hXbFx7l8IYLxq0OpAIzkmcs65npNLg5w1vNID4qx7VLBWGcsn215JESwQXJNUufTSFTuOTR9UCmDdbWuVVtWIahsz/F3skGlUqNu6cz43ovITxeOz6ZTKD3921Y+/sJRHtrhFQj4868HlFqGyEV4MAws2VMsMPQNYtKKSOdt1jIK1Uco2ftQIU7qdEBFJepcJc56M6ZGJPMweUnGTKrL/43AyhTsxSrJ2xB1IjEhsXr6rREBJ7eCKB8/bI8+tvf4DW8DYIfhnBywzla9G1stE3o0zBObDhL6Zqlcu17disQRzd7+kzsxpZfdusyClISUlk3aRsVG5Sl/9SexEcm8PJpGHfOPeT4+nP5qu4pFUpeZVqYbJ+9j/5Te7JmwhbCDQSGjbrVY/W4zbm2FwQaUUMUsagBdxwwFywIfhiKT4UiRn3b8oKDqz1dR7RHZibjaQ7LgODHoSQaSFEeMLUXL5+F6aTkc+Lq4VsE3AlCo9Hg4uGEc2EnPv2xGy8eBFOteSUOrTihq+U+tOIkL5+G4VjIgaiQaBxc7XD2cOKPkWt197xpjwZ0+qYtY1r+jJO7I2GSNOh3gSwREBt7azoO8yM+KpEjq07SdUR74iMT2LfkKIdXnqRRFy1jlhCVgLuPm15NuQl54AOtKTMFZSaY8IHjxom7PPrbny/nfkaRMl66dEZjWHZrDta2VhxecZJXYjwvsUaDA1aEUwZLigquZCWLVa9Qkf2BBlTY3hH+DPmD4CevGNFMK1+dkphGvwmdWDd193s7pwn6SIxOwqd8EZ3JqIWVBWPWD2PB1ytJjEnGTG7GztA/sHe2497FJ0SEZCv2pSoTeRoXjiLDCQuzBEo5ueJo6cLA8R/z+7htunbpGakEpESg1MCzsOeUqOKDSqnW1XhY2VhQ1NaMJNUq1GIWmyBiJYynpFV2UJIf5HIZhYu4EPgw93cgOUFBucpF2HB0FO6eTnz2dQtuXn7G1C9Wk5qUtzJeTkwbtBK5uRl7Axdog7I3RHJ8Kr+fmMCfi46xZpbxep+3xZ1L/ty5pA02/D6tT/NONYh4GQe2WmayfDUfbl8JIDX53dWOvm/EahJ4rGmIKvpH3bZHmklYcgNHWbZReNDDUNb/tAOPEu6c2pz9WWXkkIY/tfkCg2d+qh+UvSOc3nKB3hO64lu9JIWKuhEVkntiLIoiF/ZcpXTNUnT82o/9fxzT23/jxF1iwuL5Yk4/VozeqBdwPbj0JJdC4+siQ52Rp0Jf2dq+LL05m8kdZ+k8MguCJDGF+1iQwk+AC4Espbj4nOBHLylVrfhrB2VmcjM+m9KDNRO28Mmoj3Pt3zHHsEjNnEF/oDGivti0RwNcPZ0JvBdEVGgM7Ya0pHSNkiREJ+Lo5sD8wUsJy/T7+vTHrlRqWJ6p3efx6nk4VrZWlKxSjFfPIyhSxkvHJJ7dfomAu0F8MupjzC3kXDt6m3vnH+nOKZPJ0BhREX4dpCalsW3mXopXLMonozqyatxmOnzZEqdCDsRHJfLkegA1W1Xh4PKTfDqhi1ax0YT/LExBmQkm/AeQmpTGwq9X0v2HjphbykmJz6w7ErQvf0HQ1kEJgpadaiXrTqgYzzP6oGYAAEmkkUpPaotq5JlKi3M//4O5p35CrVKTlqzA0saSwytP6plZSlEQs1Xp6vOexUfpM74LolKFYGZG2LNwUuJTqNOyElcO39JnDKRdS9gIMSN71V5vDV7KbNnZZv+dNRmTrvAbYzqk241I/OtJ6ytzs5VGpfSl7IGU6ZDU/Ehr3bL60a/n06PBDI/PCLI+q3WTttK6fzOada/L0+vPmbhtJL8PW8WRNWcAOLr2NG0GfkSGOoPNv+zWsZcKTQrX4+SkZmwHrAEVCYofqFEoksp1fVk6bjtoNMRkJHIv3gZFxgbAlR+n7+fo6QWUcy2Ota0lpCtRp6vxMnMhw+4woWnHUYnOWArhlLaxxEbuZNwgXKNh9JIBNOpQnS8aTWXc8s8JeRKmNcoGPXZ1/uqBHNp0kT2rzhEVns04ifGSNEMpA5sllW+EuVSlKtg67xA7n87lt+HruXDwlnZHihHRBcmzlPVZfuL7HQBHopZTokoxJg1cpW0gfe4lbJsmOju1UCZlKaXPada9kktr77T9+fWoQ5OO1Rn3+VrINEyXmclo2rYKy6fuRch6thKzmTu9mrGs+2PE60oPku+/UcbLwDtC+vwbY6cFmUCQWoOKcXrblUwmUNOeajJ9Ri4tWUHgvWD9PgRB10bUiIiiiDKnP5t0fG/BmqWnKVGkKJg7aAmt+zdl8/RdRtv639TKxX+9YCDLvl+vN3F/cT+Y0KevcPF0zuUb9j7RY3RH9iw8ROD9YKYfHMc4v+nEhhtPkZfiEQIp7CLLO0TBUl4wlCb2snxtAgyhz8Ru7F5wCDO5mXHW1QAMiaHIzeV4lSqMk7sDZev4UqlROW6dvs/m6bvoPaELSTHJFK/sQ3xUoq6eT61U8+Savy7139xSjtxCTtCDEHqP78LtM/fZu+gIapWagLtBKBUq2gxqTtDD7BRSb18Pvln0ObHh8Wz9dQ+hT19RtKwXIU/yXlDNCy8ehLBp6p8MmtGbP+fsp1mvhuxddARlmhJ7FzsUKQqS4lJwcLUnMcZQVoIJOph8ykwwwYS3hSZDw/ZZe7GysUQuydXXaETI9JNCFHEspF1FfoWDLiDTwpoUZhPCUEqgrT+599cjPvX5CpncDDdvZ0SNyJDZfek5phPbZ+97q/HuilyNUqFkUpc5ett3zD1Ig4416TX6Y/78/eh/ylvmv4i0ZAX7lmjZ0AadavPoij+nN5/X7Z//xQrmf7mK48otepOGZ4o4UjO2oQ3IAMxJ18wjKK0HgoBuMumfmIYiYxNZIbNC2YUrN9PwavCAipk1XnIL7YStqI0HRTPLykR14TzHbWVjwdb7s7hw4CYH1pxj7bVpKFLT6VLkm1xt5RZySpTz5PvOC7QbjAT7r4tNsw9waP05Nt2ZRfcyI0lNfP1JJsCwj6az6MQEJizux4xvNuZ/wBugYZvKtOxWm9H99dUfW3euweEdV7Xvh/8QMrBF3yQQwDxze/5IiEnCubAjcRHZAbq5pYH043eEI6tO89X8/tw+fS/Pdhd2X+HKwRuM2TqMpMr2xCk8ADXKkLsUS7HCysYSp0L2/0hQZmFlwYBpPTmz9aJOBGXn/IN0/rZtgRiXdDENBfXJ+TkpGIrGazPXD995rfH4DWjG/QuPefU8nP5Te7J7wduxy817N6TbyA5813CinkmzKIps+cVwtoZWEMRD9++k2GQWDVtNx2F+PLrij7Wdfu1W6NNXuVImwwIjSYhOQpmuwtLGgkJFXJlx5EfG+U1Hbm6mF8C9DuKjErly8AYlqhTDrYgroFW6tLSxwN3HjcdX/PGtXkJbk2ZC3jAFZSaYYMK7gCI1HfLwxQm8F0zRsl5cfGxoRb8sKTm2KNJUgIrgx2EgapjcaTZLrs2i3eCWpGWuFroXdWNM62k8k6qX6dWUSFfNtZNze2db+hQfTaQkHQ5AMJdz6eBNLGws+P3sZK4eva3bp8nQIMgEkuJTObHpPMnxqfpKcdJ0EIkBryBZ+de9bKUmzdI+lIbliEUJYyGtizO0gm5MeU4wxralGVGzM2TG/Y7qXAzh0r5rBN4L5uvfB7Fv8VGe38nyvMvg/oXHbH6+iMXfrePA8lMoVFaAU44e5KSm2qBIU4IgoBE1KDKKkoPDJDGxPaLzQ+RyM/qOakNiTBJ9R7UBcwvMLeScP3iTZ7ez/fZymnHL5TIWHhnDsc0XWfbjDtCInNtzjYiQWP06vMx7tjvsD+YOXZPNoEkhCdAEiZE01pnBZlq2AqaYnltpND4yGf87Qay4MIUBVcehlrBWeqynlIWzzpy4Za6+B9wOokPR4ewLXKA9nx5DK61ZlDI3kjZSA2wHbVAiSFiIboObUKtpecb3XARyifpiahpehe04dv0ZouI1AsoCMBTS+rKCuBpk3U9jRuBSiBoRW+KIIxKtD1kWQrGXJYJQWF/F0cB4rx6+RYNOtTm47ASuXs58/FVrrh65lfNE+Q+8gEiITmTWZ4sAqNK0Ai37NWXf4iMG0/dUSjVDhi/iWehysr9joWgch9J/ai8815/l+Z0XHF19Ot+6sbdBj9Ed2b3gENEvswPApzcC6DZSW8+1beZekuNzf6ea925EsQpFSE5J4t6iQJQ5SrwE4pCjVQwuKCo2KIuljSU3T96lbG1fwgMiciluvi5ObT7PnbMP9Dzg8sOu3w7lCryUCiU75x0AoFHXutRtX4Mrh24aPN7e2Y6GXeqwcOgKPaZwXOtpmFtZMG3/WL6tO574qDfzFLtx4i5DfxvAuR2XaNm3CSc3/cX2WfsYPLMPG6bsoO3gFqag7D8MU1BmggkfAPpP7cnz2y+4sNu44luBIUKGWoMlL0lDRDphFjiJawFEV4fVHqv376U3ZxNpoDg8L7x4EEKhom65grIslK9dir1LjnJy0wXkFnI9IZNV9+Zqpf8dbbhy9DYPLr/7OpD/rwgLiGDRsFV8PNSPouW8Obv9EgAjm/6Eb40SLL02kwPLT2EjSyEmI+ekOB1LWSyuHk70G92eDbMPYibkTnOSCc+wUIl08xlGcmwyG6bv0W7PTMkbNLEzUSExJBjxFBu7YjBJcSnagCwT/reDDbYFbUz3197rr3knCga5XMZ3LWcwbM6nrL09k36VxrxxX+EhMTTqUJ0LBwxP6N4EPYZ+ROmK3ozvrVXRlP6ql6tRnOCn4e/sXP8kfM2cwfFrQmMmADWQya5jL/5ISZlLgY5/6R9G+XqlGTS9FwnRSexdfJR4qZDKe0T1FpVZNGwVHb5sReNu9Xh++wXhgZFEhUQTH5VIjZ4VubSvNPqLHkVQ2TXj7P7zLP52NbX8qtGiT2NObnpzE+e8YOdkiyiKegEZaFMoJ3eaTbWPKjHv7BRGNZuiC8zM5GZ0G9meuIgE1k3S1pRaypOAOMA5swcNPt4bad2yC3aJNtw+cz/fsbh5u1C/U21Wjd2ETCajRd/GLP1u3VtfoyZD89rCF0qFEqXCuI+Yq6czTbrXp2brqiwbuT5X3VjjT+pRr0NNHl99xov72e+sl8+038NRzX7SC8h8yhehUBEXbpzIO5Cyc7JlxuEJrPlxK5oMDXJzOcrM30xVuorTWy7w6Y/dCMqsHTYhD5jSF00wwYS8kByXQmxY3Dvpy9zKHKVCiS8i9/kSBbPR/vhfxYkZeOCqf0A+q8WVG5VHqVCRWMBVyywWaXiDiWx58QeWNhasHLeZvZnpI6JKTamqxej8tR9Dqv6ARq1GKVk9n7BlBHt+P8zBZScQBIF2Q1pSskIRDq44qb/KLmW2pPUtWQya1INMqiaXkZtpyfn3m8IYgyZqCr5i/K7qXPKCKl3F7gUHad67EW0GNuPomtMAPLvxHFEU0SiVlBBtieULUlgFuAGJONl8T+Oi3oQFRNBpcDPWT95OISGcNP5CQ5PM3hXYyafz4rQzZoJcj/3J8mDbOfcAH3/Zkk2zMov2pbV6GRk4ONmw+de9uho+vbo9A/fY3MKcslWL8PialskVpCxp5qKEjYMVPywZRNTLOG2wl1XDl5om6Uj77Py45gseXwtg4ORuyM21z5EmQ4PMTEbt5uW4ekw7gdJTzJSyNVnMXw6W47dv1jD30FiudKrBz70WaptKJwfSmkRryWq91ONMocLBxQZXT3ucXe2p3aQsozvNy26b+axbWpvT7ONqLB2fKchioB4Scnjh5VdLZoRBM+aVp5dGmqWKKv1eSjvR+1wzMBcs6F27PFefzuFJQAT165bBOtKXqKCYXN8LY9+7kxvfT0CTHy7svkKjrnXZ8/thZDIZJar44F7UjYZd6nDnzH3O7LtEalqbXMeFv3Rhap852AmO3L/wmO9XfMmlfddITUozcBYt3H3ciH4Zq6uJKggsrCz4dvHnWNoYVqKNCIri2NozWNlYMmr1UH7uNhef8kXo+LUf+xYf0UtzLqeyRE13UqiFpW0hihZ9Qj+/aiz7bgNDZvXl6Y2APM2vATp+7cfGzDrmdkNacGzNmffKEL4N9v9xjBJVimFpbWFwjIdXnuTM1gtG6+lyBokdvmyFjb11vkFZcnwKR1afokztUhxdc5o2n7dg+aj1uv1Prj17Z3YG/y9gCsr+NzB6zTCCn7xk+6y9//ZQTPgfwq7f8jeULCg06gxkZjKcBDtqiS8JoCsqzHFBiTfZvjQFhgxePXv91fYv5vTD3FLO8Q3n6DqinS4oA5h3ajKbf9ltMLe+SGlPZnyqnbCKosihlacoXb0E0/b8wIx+i/OcoJjwejiz9QK1Wlflq/n9ObjsBKFPXxEflYi5pTn1/WrRr5on2w5N4GV4BlZmqVR3K4xv2VJEBkcT9PAlrfs25qsqRVmxYStX76wkPd0ca1kY5W20AZkxJMQkITMTsLK1RJGin1rkXaow7j6u3Dz9QD9IyQOiKNJhcAtdUGYI8w6OYdOcQxQpXZjlFyYztvsi4qO1Cw0WVhZsfDCHtNR0BEEgLjIRN09nNszYy44FR3D3csSvXxNunX3A9N2jmNr7d5zcHNBoNHQf2Y7hjaegzqM2slBRVwZN7YEgk/Hw2nMKFXHGzduZxJgUVMqCB+xV65Vi0Oi2JMankhyXgtxMxi9frTHYtutXLdix8P0pq/4TSI5Jo1m1mlgEX6eSe0liNPHaoOwDx/PbL2jcrR6grb18fvsFz2+/4PKB6/Sd9AmRkVHcu7cFBfX0jrPiBDbYA6BIUXDuz8t8/LWf0flG7TbVqFC/LCkJKQUyJQYYvXYYFRuUxbu0Z57t7Jxs+Xhoa37qMofPf+1DRFAUS0euy1UDbC5YUB0LVOIjKlRXUaxkZU78rvXC3DZzLx2+bGlUMRHA1cuFxJgk0tOUOLk74uzhxPM7Lwp0Lf8GRFFkyberQRCMBo6vI3CyevxmLKyM2LTkwJHVpxkyux8t+jRm+8w9BT6HCf8dmIKy18TBFSdIjE7UmRrm9NAwwYT3jbrta6BMU3LrtOG0ECtbK92qqZVgQ4Vchr2vh8jgWHzKeYMgy1W7sSXoD64evc3aH7ciakS6//Ax7Ye0xM7JFkVqOuPb/4qtow3th7SkbO1SPMmckAY9CmX9lB0G1dmsbC1zbQ958hInd0cGTevJouFrgRx1JJKVc40Bvy+9eh9jq/oS6PUtGcu7YNM+NFw/foe7fz3Cb2Az2n/REoCB03pwce81tv98FQEoktk2JOglITdecnb7RQBKVi3G6c3nyUhXUcvMDCxVgBuCYGmQdZEyXvsXH2HQxC7cPHkPF09nXL2ckckEXD2dCXr4EtQZ2ffbiCJlFuvSr+xINj75jSWjNqJIVerXHmo0jFjYn3sXnnBxnzbFMSU2mUkrB+PkZkd6qhJ7ZxsSY5OZ1G0eYYGZptISVjbcP5X1k7cDMLrlVKbtG0tSXDIWluaEv4hixv7RjPH7JfucWcGkIFC4mBu/n/uJGQOWkhiTTEaGhk5ffMQ38/tTolIRTu34m40ztKI6Qqb3npObPV/O6EXhoi7M+GYDbbvXpnK90tjYWTKi+VTUOQM56eTQzIyK9XyJD4sj+kWE4TbGFD6z7qv0OyLdL2WkjQTMRhXzsj4TwQCjqD1RrkMSY5MpVbUYCDJSEhXYOuT9LiuIWp+x7/y7hjEBoy0zdtNucAtS3B+x/8wMlJoRgBILplOMaGRCtkWEZ6nCHF931mA/5euWpnKTCqyZsIX+U3tibWeVbzBQ/+NaXNhzheotKuu27QhfhZWNJeYWcpLjU4gMieHFg2DW/rgVexd7mnavz6apf5KeU7kyB8wFC+q2qKMnoFG4mBthAXmnvfsNbMb+JVp7gE++78CWGR9+sCFdSPGtXoKgh6Gv5SMqRXqaMt97K0VYQAQv7ge/cU2aCZjSF/+X8OjvpwB8vXAg9k52zOq/6F8ekQn/3+BZsjBpRtgimZkMj5KFc9UJvA0igqKwdbKlaFlPQv31GbOHl5/SuEtd2g36SLdt+eiNnPvzst4YUhJSadWvCU+uPSclIQ1rOyvkFnLUqtwT94v7rjNt72gmdc5WbZx/dgobp+3E1dMFF08nYsMKJtdsQsGgVCg5sPQ4AGt/3KqrVcgPAXeCCmYubgBxEQmsHLeZ4hWL4n/7BTGv4hFFkWIVitD3x84sPD+FGZ8tIfxFVL59RYXE8PfhWzTvUZ8j687h4GpHo461qNmiEoEPQqnapDxLx2Z7NB1cc45D67WeVmJ6OhXrYtRA2wAAIABJREFU+zLr4FjW3ZvL4FrjCHkcZvRcj688o7vHECA7CJh7+icOJq5DkZJOclwKijQlKQmpWNtZIQgCTm4O1G1TjZU/agO7Rd9t0PU3cGoPFp2ZSOCDl1w9/ZBg/1dMXvsViyfvwcragvk7v6WQpxNtfbTy+mI+zFqR0h7UalmZ9dM//MltfrCytdSJNChS0nH1yik889+DJkPDweUnsLG35tdRpbn6fBpp8SkknA/CXK3v2efq5UJCjsl3ySrFaNG3Cc9uBbI2UyHx8MpTtB3cIk+1QntnOyo1Ls/KMRt5eOkpjbvV5cbJu8w+PpkeHoN17Vw8nOgyoj09x3RmfJvpBNwNMtpnTgiCoJdGWbScN1a2lghGWCUbe2tkZjKS41Oo0qQCz2+/yDfV8UOC3FzOqFVD2TZrL+d2XPpHznlw2fE898vMZJStpV0AfRc+af+zMAVl/1tYP3l7gSlnE0x4l9i76IjRfa36NeHUuygMF/TVFO9feESHr1qzbNQGvWbTey3gj2sz6eo2CDsnG+xd7QkPzL0yamVryeIR60GQkZqYyrF1Z9kTvYYuroNQq/V/OK4cvkmjzrV07Fe7IS2IC4/n8v7rtOzXFCsbrRKdMT8vQyv4xlbQjTFixvA6vjnvZEX+H6gvy4mCBGT67EpGrm2CWtJHHteQnpLBk6v+em0+6lmPrb/upWHn2nT7ti1LRq7XO0bvM5D8PaXnQhaenYxHMTcqNyrHi4ehnNp2iRotKiHIBAqXdIfj2XUbmkzWTpDJsLazRq3MYECF74nKFKbRGFPglDKnmZ/xqGZT2Bu7lnsXnjB74FJ+3vU9o1tN143VxsGGJZens2L0Jm0XEuXQddP2cGTdX9T2q0KHAY0oXr4I37b4heiIRGQyGYnfteKbBpPQxGZOzjMZPD0vv8yxunk707ZvQ1ZN2IaYoSmQz530OdX5emUY3p/1WefE63wvctbZ6SDxqhMyJ/ZJ8ak4uNpnHiYiM8JyGzx/Qb47/8L3KwupSWkcmXuGT77vQHJ8Kp4tmrN2or4MfXxkAr41ShD86CWKFAV9JnYjKiSGdRO36rE1USHROLo5YGVrpfPXqtW6KjFhcZSo7EPxSj4oUhS6NMiE6EQOLj9B6/7NcolaxIbHs3/JUep3rPVaAZkhnNl6kYWXftGKhny+TDe2LPgNbM6xtWcxk5vRqFtd/hix9q3O909DrVIzufNsYt7hIujbYuD03kS/jMHJ3ZHLB96P+JEJ7w8fZqj4H0BKQipxEabVehM+HDi5O+Jd2pNntwLfab8+5bxp3b8Zm6btNLhfZiajafd6JMenEvY8Itd+z1KFcxm77lpwiMB7wXw1v7+2D5kMuVyGd2kPPhnZgYC72e17j+3Mz93nA/Dg0hMadKz1ri7NhA8Q+/44ToV6pflz/kFSEgxI3OeBse1m0nNUB6ztrPh9xHouHbjBkrHbtBYLW4yvZN+/+ARLawtK1yjxxuN+FRDB4VWnSE1MZXSr6Xr7UhNTSYxNpnbrKgaPjQiJ4eCqM4zr8hu9yo0iKnOSN3HNELb+drRAqUounk70/OFj1v3052uJPnzIiI9MxLmwlj3yLFWYMAMLPh8qLK0tcHJ3zLfdzvkHSU9TUsuvql57lahk55GTRNil0H1SByo2LEvwo5ccX3/WYB3i/j+O0WN0R92/+0z8hN7juhBwJ4g1E7aw5ZfdeqbCNg42jF47jGWj1ufqKyo0RueD9Towkxiayy3kLPp7Bttm7WHxN2sYOL2XruwjC5Y2FtRtX4Pe47twcGneDNCHiqiQ6A+KkVKlqzi0/CQlqxb7t4fyYUMmezf/vWOYmLJ3hDptqxMTFmfQk8QEE/4JdP2uvVFDzLdB8OOXrJu8nWU3ZtG31Le67Vkr1QeWHmf0mq8ZtnAgPby+zHV8VEiMtqg8cyU667iRTX9io/8iDq88wbIbs4kIiqJwsUKANs/+uEqrGnfn3EPdam7Y83DSUxVUbFCa++cf685htAbGwP4Cpdu9xqr5O69R+YdX7N8ZjPjWGWNXpBBk2cfGhsdzcOUpABp2roVniUL433pByco+NO5aBxDIUGdgbWfFme2XCHoYSrWPKrF6wlae3gggLDCSyOAoHXvUZVhrHl99RnpCip7/nO45kQmUrl+KAytP8tP2kcwfsowja84U6DqlSI5LYfq+MbS17K1jf6WM07Se85lzYhIDyn2n34ceQ5R93zTJKdjYmHN+81n902eOW6r86FHCnU++a8eKMZtITzFSV1SA50r3uRlhBo31Z8inULtDNPx3Xttynl8UQdTgUcyNa0eyrQSM1bR9KNg0bReDZvRmz8LDhAXkXqyS4szWCwTcCWLW8UmsHLuJI0f/4gneKO6MB8y5+GAt1Qu/okOtUkb7sLA0p1mPBnj7erB24jYe/v2UlWOMG5X/uHUEkcHRXJP4REoRH5mARwl3g5kPhlD9o0o8vPQE0C6yzf9rKqvHb+bmKa2Z9tKR6xg4vbdeHf62mXtx93HD1dOZ4McvC3QeE/KGKIqoVWrkFqbpvVF8wDVlH+ao/oOo36k2rfs3w8rWKv/GJvy/Qb+fulO+bun3fh7f6iUIC4jIlR7yJnDxcGL7y+WMWDqEEUuHcFy1jY+/bI27T6FcppoA5euVxtLGkqfXA1j/dGGu/WqlGms7Kxp1rQOATCYwftO3HE7dhFsRFxacn4ZSoaJf6RG6Y05u+otv6k3g++ZTGN1yql5/B5adoEK9MlRpUv6tr9WEDwedv2lD28+zaxOt7az4++BNgh69Ii1ZgbuPG/cvPqWT62C+aTgZgLK1SpIQnYSVjSXzT09mo//veJZwZ7GkZqtqk/Ls+M14rQ3AL/vGUq5WKY6tP8v5N/QK/KX3AkKevDI6GYoKiSH61buxvciCtZ0VvcZ2onGXOiwfvQml4s3EBj5kZC16GFLq/JChSFGwfNQGOn/bFo/i7vm2D3oYwrA64+gyoi3xXsVQsAYoDRQnPPJnrgTacPucYdl0Vy9nft43BltHG+xd7PEb2JxNU//U7ZfLzXK9u2u3qU6f4kONjmf/kmN0HdGeYhWK6Lblpdxbp31NXYD32/mpbJ+1VxeQZcGQ+ElkcDSPrph8KN8VRI2IzMw0tf+v4r2H0oIgtAEWAmbAKlEUZ77vc/4buLz/OpUblyfjNRzsTfjfh0wmo3z9MrgVceX8rr/fyzma9mhA/Y61sCosR11YxYHpx7ASrN+sM0FGs96NSYxJ4vKB64gaDWe2X+TeX484lLKRwTM+ZdFwrQR3rdZVmbL7ByytLejo2J+0ZAXLb83G0c2e+Ej9dKvPfL9l7eMFRL+MpXCxQqQmpfFDy6ncO/8ku5GooZ1tXzoPa8MXs/tSt10NevsYnjTsmLOfgdN6EhUaS1hARL5slX79SQHqS14DBVFz/H+BN7x/oA3s05IVeJXy0N3D9kNacHDZcZ3QhwDYOlqToVIR8khrpeBVyoMRSz5nzcStfFtvAgBNu9cjOjgSTWaKl62dJQmhUWSkGlY4EzPA3FKOvYsdcwcueaPxV2xQllFrvmbqJ/OMLoxM2jYKMzMZYkaG3nMivWtiTqVPkVzts+BbrQitPmvKjtn7iHlHHof/BEur54smneRL/QOl1ysTMp8twegzpquFkx4nGunvH0SGOoOVYzbxxdx+bJzyJ0lx+l6PGlFDPLEICDjhglqpZkTbcYSb98vVV1xyD67emkwhoZDedlEUcW1dlLp1xpKhkdO1XznsAtJ0SowuHk5sCV6mm6jHRyXi4GKnU1A1BrVKzbLv19NlRDua9miAnZMtSXHJyGQyELRpclEhMdjYW+NRsjA3T9xBFEW6j/qY68fucHHP1Vx9ysxkmFua65QKnQs74Vu9uEG2zsbe+oO3P3H1cqFGy8qc2HDu3x6KDkEPQylRycfgAqoJEnygTNl7DcoEQTADlgCtgFDgmiAI+0VRfPg+z/tv4Orhm1w9fDP/hib8v8PuBYdo2qMBvcd34dCKk3p5/W8LvwHNiI9K4NM+PxGHHyqa4OmpxtPyCs4v1K/vSYbWY8ennDfjNnxDN/dsVa5pvRbQb2I3llyZQeka2tqAA8uO8/uw1bo2Z7ZdpM/EbiwZrl+wHRYYSVvrPmx6vpjexYYSk8UY5JhkqZVqdv52kD/nHWD7y+VUblKee389MjjODT/vZOj8z1gzcRupiR/2j7cJxiEzk9G8ZwP8bwZyYU82S1W/Qw06DW0NAoS/iMKpkINe/csYv1+wd7bhxol7rLg1m62/akUMzv2pv/ghtzTXyuTngdbyXpzI2P5G4/cp782CC9P5rtFEXtwPNtimYoOyNO5Wj89KffN6nRv5+tZsVYXydX1ZOnLd6/X3H0RsfAyxmkiSU9/de/OfhFqlZveCQ7Ts14Q9vx/WbY8Tk3mMDal8D6ixZR0VRCUWmKNR5RbYEAjBwsCULcRewV+bWqNSTQJg1aoImjb9hTMZO3SsycSOM7ly8AaObg5sf7WCjVP/xMb+/9g777Aori4Ov7MFlt4RxIIgdtHYW2zRaKKxG3uLiRqNJvHTGLupamKLKcYau7H33nvDjgWVKoL03nd3vj9WkYVdWBAEDe/z8Dwwc+fO3WXKPfec8zsm2JW1efks1oFarWb7wn1Y2VsSF6m90CY3lmPvYktyfArxUQmZ6ootejTmy+bTdPZ3fP0ZRi8ayrOACMKDIqnVvCrBD0OJi4jXCmv8eGIX7F3s8Dpyq0TPq+q0rkG7gS059e/5fNUbLEqe+Yfj7ObIhV1X6fhJWw6tOlHcQyp5lODwxaL2lDUCHoui6AcgCMK/QFfgrTPKSilFFy9eVKe3XMDYyZgQG4FoiR0J4QG4AraCRYH7lhvLcalSlr/+2UY4vwEaEYHQ0KbEKE4w4csbjBzdj9tn7rNwxN8G9ztl/VjioxJ44hOitcJ8cbcXl/dep23/5siN5QT7hHDn3AOtY0VRJD1bzRUTcwWdR70PQEJMEpHBWZWq9OQbiWrmffIn/Sd3Y3JWoyzL6rcqQ82aGZsZMqt3vian2vlOLxH0pKgUyiq7ITk6hu7P3l9JIb9jev45PxjelkP/nOK9/i04sublirPfrUC+bjlT7+E3jt0GUc3ax3+w/sdtes9v42hpWD6VKFK5nhuPr+ev9mTvCV04v+sKdy/46G2z6NyP/PDxfEL9n+cWZfXiZMmBFNXZJnZqUXP9ZWnfvFsjrBws9QrvFCX5UlnMRubnFPTknGU9j1SKWlTjnRHOlaWxxKkmEbjkDGbKcKpI7V8MJvfz6fGaFQfP/MMxszLF2tGK2PA41KKaB5iQxDZeWN6JdOUeXWmMMRacIprRgO3zHlIwZy2WaJcEUIlKYiRVychonGVrGc6dbkRDSQdsBG2hkbjIeNZ9v5Xu4z5EIpVQ1t2J73rNy3P82Q0y0HjKdOXK5WacPPEJ4bfPl2NsYkSsZTrz/j2FUm1GrboKjIQ0TI3MGfZjX05uOs+j6358OmcA9y89JCE6UW+fxcmJjec4s/USyhIUIeV7M4D3h7bm7/FrqNGsCvYutoVaIqeUokXQV5G8UDoXhF5AR1EUP33+9yCgsSiKOpcLZYJctODNr0NSSikvsHO2ISE6kbS0NBKQoqYaLwKWBPwxIwEZBUtYt3O2ISk2mYiUNFTUzLHfTOGDi4M1VvaWmFqakByfgr93UB4vEIEq9d0KXBRdJpfiXqciPl4vj6/TqoZGrTQ8DjtnGwP71jyXajatio+XL8oMldb2rJhamGDtaEV0aExmTaPc0SOPr2eyWDhPyKy9FHxiq7u/NxXN91Cmoj0SiYSYsLjM/5+lnTmImuLBufYgaLxQ3ucf6Nxvbm2mKUb9PNwxNxQmxrjVceXeJf3GlS7qtKqJ97kHqPQUFpdIJFRt6K43b0bIcj1k/6+616mI763AzD3mVmaYmCuIeBqVrzG+GoKO37I30bMnP576bHORFDGdVCoDZlm2hmPGM43HKI++tec2xX+/SKVSHCvYE+ofhpIMEikPZFc4fIYF4UiQkIQKFSaAgJQUzBAQsskAqFGTKNiiFl2y9ZOMCY8wzmXdvVrDyggSCfcvP8x13CIiKpRIkeY4f3bsnG0wtzbL835LQ0kKzsCLXDsVxkYPcS/vQMSTaDLSNeGNUqmUMq4OhPqGIZaA/2FxIREkqPOxsGBsYoy9iy2iKPLMPzzf6pCxRF4TRfGtlTlu0KCB6HX1aqH0JUgkhfpdFbX/TtdTU+vOEgRhhCAIXoIgeKkpgau/pZTyCqQkpqIwV5CCGjUeZL3lRCpRUFkOQRAwMjEiNSUNfROOtNQ0wp9E8uiGxgjKSFdSvYmHJicgF0RRpE6rGgUal0QqQWGmQCrTTEQrVNNMFh7fDCAiJBKVqEQmN9wIDQuKxM0zd2nf5IQUnvmHY2ppinOlMpSp6IC5lZnWZLeUkktEcDRRWQxqAY0xlZdBBpprNTE2iSr1dKvSmVmZZubW5IV1GSvkxjLKeTgbPPYX6DPIQGM7pKXkHj5pCEYKIyxszV+zQVZ8ZCBD2yADcOTVv8niQaVSkZqUhpmVKZqrXNd8R7MtHRVqFIjYI2L63CDJ+TyTIEEi5AzrFIhEnsfzz+92UJ42cwpK4jEmkfLEY0ES+hf0TC1MsTVwASQNGS8NMgApaenlCfB9mmmQgeY7i34WS9nKTphaFDBPOgsSiQRbJxtsDChVUFKwd7GjQg1to1tAwNLWQu87Li0ljaePQ1FlqHBydcTexQ5pCVcrfd2ICIXyU9gUdfhiMFA+y9/lgJCsDURRXAYsA7AUbMUGQusiHlIppbw+pKlSRvwwiFFf/0WEmFMBzoQBNBDyP81oN6gl/reD8BUCCBCj8GUWIi1fnpf11GA1joINABvWLmHHb/up196T2i2q80mNr4gM1jO5uwFH1VsZ32aW3nwufTRu9w4/7JnE+V1XiQiOonWfZtzzekCX09NIog1377pSuep13rEXCT2tO/9GixDY67OOczuvMHfw7/rbicDzSBojhRF/XpmD15GbZKRl4HX4FrfP6ImYzlYkW2fXuorrvkpIoyGFbAvaxxuEIAjYl7Mj4kkkpKE15xw5ZghLddRP0sk9WHL9Fz6v943W5n6Tu9OiRxO+fnc6FQy5xwKhX//uNOlcn6mdZpMYa1iNtKOntrJv6RGadG7A00ehOfYLMoGK1coxoe13enPO9DFt6tccXXOamye9GfHrIP4evwYX4TWojr5KIXV995SuSWEuIYaX09OIE7OHaKqx5gPqymx0SuJrh4KWnPDFTCJg9KJhLPtmHefSEklkNy8X6jKwpCu1qcxVaqHml8zDVMQhpyd1nz/PsxJjmY6v2JrYuNGAERJ2UYbfqSloe+GSxWTuoyKFSggkY5r2hL6N+tN+cWvGNp6co99IMYY7DERkKKB5xGZwG2u+pLJgr9W2ZrOqfPHHcMa3nIGVUCHPr+GcaEsqS7NtVWLFe9QUsilVJgO+8Mmn71GmogPxUQncOnUX/9tBBnmAyro70bBjXWydbVApVZzcdA6bMtbUGlFNZwkZmVxGgw51uHP2PklxyXn2X9TYqq1xtnHCXngZDWDvYsvcozOY+uHPPAvQX7KgRYNGqJRqfG8F0G5yS+TGcp75hXHy3ws5ioZn5ViO+66U10VRG2VXAQ9BECoBT4G+QP8iPmcppZQYVEoVx9efoVlLV3afDgSyen0yMCYcChCy61LZmWPrzgBQEVtS+I4oPEmnIcacpQyPMg0yE3MFcZHxbF+4j+0L99Hz686s9llMZ7MBevs/s+0SamX+JzKXD9xgRN2JLLv5K4f/OcnHziO4qY4imq2ADRlp4O3dn6RK3zLj+x5smrUrzxfrqHrfsNpnMb8O/dOgl3B6ajqWduYsnaCRRW/dpxkDZ/Riyy97cn0RlfJ6qf9+HUbOG8zoBpMy1dhe4HcrgGqNKvPgymOD+rIto30PtejRmCr13RnTcFK+xrRp9k6eBUSw3v8vutkMybN9t7EfANBpRHviIxOY1nm2zhBa+3J2TPxnDEmxSfwy5I9cw2yVYgahxCECPwycz7KrC7h25CYbf95ZYsQEXgd2Qixx4k2gbuY2KRsoK3mzw9i2L9xHz686ETN3I/foQjJdEcjAlL3UQkIwSaQzIdtRViThiijG5hBvsokz4u/llfjy0w9RI8UJFY6ZuWga1KKaW0hJYidgBEAKj9l2bi7v9X1X5zifIkHF4GxbPYnJEXIJ/1v5OaPrTzIwfByMeUpqNu+fhAM4INd7zMEVmtqFlnYW1G1Tk6ZdGmBsYkzgvSec2HBO57vh44ldSI5P4dyOy1oKpU98QhBFkT7fdOXgyhPERyVgZmVKh2FtsCljzZUD1xk4ozcpCSmEB0UWq1hG9LNYop/Fam2LfBrNCM//6SwxkJVzO68weNbH+HsHsfGnHXjUc2PBme9JS0nn1OYLRTnsEk8JqvetRZEaZaIoKgVB+AI4jEYSf5UoineL8pyllFLSeHjND6WYgaXwOfHiUjTO41hMGEfl5y/I/JI1X0IQBKpjS4boRyremGCOLMuK6s8HprBs4ssiotsX7qNJ5/psDllOn7KfYWppipOrAyGPn5GaopkcWzta5ipckBsB3k/oaNQfhbkCtagiiSqA9gqvv/9w/FMOsTVsBae3XODMtkvcPOmts786rWuyb9nRnC/drKv5WVbCXTyctWpFndp8AYfy9nz2y0BObDzH/Uu6cyh0ecSy/54vD1lBV+dLyqp+EXPzhDcLPl2SwyADOLr2NKMWDNFrlO1L2sDJf8/x75xdeL5bneiwl5MWa0crhn7fJ1MiPy9MzBWsuLsQlVJFVEgMbp4VDTIGo8RY9t/35kehLRWQM3P5RJp8VF/nZCcyOIpJ7b+nQYe6zD/9PZf3X2PtrC052kWI8fjgTCo/AVKC0hcy7/uV1HXzICqkZCXr67tf9BaVNqCIeJYOcZeXITljIjHqJqTRBAWHcZI8wPG50IcuT9ibUJIiLDCCxNgkWrZrgvmx2ySxFQEBUywQBAFBTEa3AJL+54KHqzvbdvzMvQs+bJ2/N+c5iSGJ70DrfVOZ4Jiy/DI8f2UgdH3DibFJBhtkAG4oucdY0vgZsAQuYM0CHLDP61DioxI4s+2lwmqVBu6MnD+Y26fvceXA9cyFi/5TenDn7H3unNUd7XHn7H3SktNo278FNmWsSU1K5di6M0Q8jyB5cVyNplUYOX8I67/fWiI8Zy/IyyB7waafd9B/Wk+e+YXhUd+d4TW++s8Lf4hiyTXKilToI79YCrZiY+G9V+6nemMPAu8Fl/gaF6X8t1CKGfgTRyJmGJOMGyYoBNMC9TVoZm/Wfbc11zblq5Zl4uoxHFx+nIPZVvpkMin7UzaSlpqOscIIBCEzv2DjzzvxbFmd8a1nFWhsWVGJKi6KzqSSPfQwAHeGMXxAdyavH8fQquN0hn6BZpI9fct4/tc6mxKfHqPsnwe/cWH3VZZPWq/dXBBo2bsp7nVdUSlVPHnwlOvHvHGsYI/CXIH/naBMlS9DQhnzpIBqhP8VoywvmnSuj8xIxrksxZzX+/9FUlwScZEJVH6nEhlpSmydrJnZ/Rcu7NYkbg/5rg9RoTHs+/tIrv13H/chfb/tTvSzGMpUdKCH3TAq1ijP08ehKPPwSPmI0UQYfUJqel8gA3v75cyYbo//jvsGhfwOmNaT5t0a8c+0TZk1mtSimstISEL7vna0GUXNmHCk+uRBi4J81pzLdyhjPs6fqkwkRUzETLBCLuhZxMrLKCuB91SfSd24ddI7xwJAmpjCFdxIY3GWrZE40gdPQdsD9gKJRIK9iy3TtnzNuKZTc+z3FyPxZTXakRrgaD+X6pEXdH6vEWIM3gxBRdaaad5UZCwe2cIXB0zriZW9BX99tTqXT6xNsphEAKlkIMeODMpig+QVah3WalGNZl0bUr6qC6H+YRxdc5pH+VRS1YeFjTl9vu1GXEQ8Z7ddyjVksKTiXteVYT/248iaU7jXcWXtrC16Dbtj4ra3Wuijfv0G4oULXoXSl0IhFOp3VeTFo4uDAdN6kRSfxPEN50p0jYtS/lvIBDkemSuBr560rI/PFw6lWmMP4iLjWfDZUp15LEqlig7yPjm2V6juwu+XZvP4hn/eJzJgoiMVpJiKPqSSDLw0QI1ZSFnMiY2I48TGs3oNMoDY8DjMrU1xdi9DqG8WCWY95y9XpSwrJ2/MOVxR5PSWC5zeovFkVGngTquPmxAVEkNSbBJdPn+fjDQlO37bn0Xt8dVkwPNFCZw4FieX9l2j/9Qe1G1TK9OLamppwsR232ldBwpTY61VerVKnecqsrm1Ge0GtWLl5A2Y25ixY5Em3zPw3pM8x5UmphBGE9LTX4T/SomMHMvfS6fwrsKw8NgNP25n+4J9TFw9ht4TujCt8xwiU8JI4ZMcbcNjulOO2djioKOnIsKQazFroWs9xlDWQu1iXgv7WossL72nRmojjLDVuGj0FH7PPP8bdA9tnruLoT/0JTE2meCHL1PtjQUT3MUHBDCAVLoixRcLjlMD/eVT1Go14U8iUabr/pKdMOEJ/5DOrCxbRVIjzyEXdBcZdhBscBFXE851UvkQIy5izSncyWkYbvhxO8NnD2D2oalM7viTQZ/fVDCjRg4hl4Ljfe4BCdFJDJjWk9XT/i3URfmEmERWTFqPmZUpH0/syp2z96lQzQUrB0tOb7mA3+2cdeVKGr43Azi77SJRIdG06NYIYxOjUsdFCeSNN8o6jWiH97kHBN57qfhzZM1JmnVtRO0W1fA+e7/0wivljcTEXMGAaT11Krc1/rAedVrVxMLWjK9bziQ5XhNWsfLeIuKjEviyWc7VUkMYOL0Xa2Zupmazaq809qzUFBTcEruTyCDUlMOEtVQkGGPBioHTevFdz9zr5HQY1gY3T1e+XTs0Frt3AAAgAElEQVRWb1HSF2x6spSUxFSDcs8eevny0Ms38+8bJ+9R1r0MY34bxpE1p/VKmOtCbizXGYZXyqux8acdfPT5+8jkUm6fuU/Q/aeM+W0Y0zrPyWyTPWzKsYId/nkIani2qsGdM/c4suZUvscUTRLp9Mqx/enTmiRVOmNwP6nJafzw8QIWnv2Bph814OCWI0gIyxGkJiUU2Wt8VVdv7EHUs1jCAyNe2zn/q6ydtYXRi4ax/Jt1Ws/5soIVTmIqiSxFjjEmOgQ+suPmWZGoUN1haSaCGc7iWUKYQwYjgGjMZd/hocwAdBtlAK6Y4sAN1FzDDAsUQs58shesnLyBeSdm5TnOokYUxSKb8yXFJfPPtE007dKA9oNbMabRt5kCPG8Ch1efAuDu+YKlJrwtlOTwxZJZ0jof2JezwyZbkrdKqebBlUdUaeBOww/eKaaRQYehrekyukOxnb+UN5uazatxce811n23NcfPF40nM6HtLBwrOtCkc31Aozpo62TN5A4/FvicLbo3pt3Altw5pycES1S//DEEUY0xxjTEgvqspg6zaEIM5QQrnCuVIT01XWdh0qwMmNqTuUP+4Ke+i3Jt51jeHnsXW7pYDsq1XW5jDXkcyp/jVlGndU2q1Hcz6DAbR0s2+P+BfdnSGotFwd4lR/BsVYM/r85BpVRx5cANvW3rtfekaiMPrZBHXYhqscAeUDPkSLmWY7u1dSBPbj7Ld3/Lv1nHgOk9qdvAE3MOAlklzlMwYxOvs37nZ78MYoP/X7zbs0nuDfP7LMgDUaV6+fO8aPabkCP2KqhVao2Xac4AZHJtw1siSLAUbDERDPMmNevakFun9KjMAh6CLfU5TQV68tXIrTRQJmAj6Pa+qUU1d8QoLlObG3yGD5UJN6AYgYm5fgPvbeLiHi+S4pJRq9QE3n2Cs1uZ4h5SKflErS6cn8LmjTbKKtYox6GVJ7QEAgbO6EVSXDKutSqwYvKGzFCl4kCZofpPqWWVUriUq+LMkwdPc21jbmVGv8ndadixLl3HdMDryM18JVyD5mW+M2Y1R9VbkRvL8ajnRmx43KsMPQeCIGAl2OAgOCAVNJOPz38byopseV/ZadGjMcGPQjm27jThTyJzbbvgzPdcP37nlceqzFCy+ZfdNP2oPu0GtcyzfUxYLFM+/Pk/nzxdlKydtZUA7yAWjljKnr8OM/GfMfSf1oM2/VqwIWAJ63z/pM83Xfl09gCmfzQn177G/PYJk9ePM1jZMTuWghWWbAJeGmAy2T3SA4/pz3nKhXsXHzK+5Qw+XziUOVP74yDpiZ3VNIyZgC1d8USeQ3GvKPG9FcCyb9bRoEOd13bO/zIxYbFsm7+XwbN6v1I/F3ZfpeMnbRg5L7ti4kvMBXOqCPYMG9AVmaBf6fAR0YSxhDR+Rs0AklmFP+8RL+a+gHZqywVG/FrARbE3lCsHb/LxN12RyjQ5nxWql8PUUjtXfPRvwzIXT0spJTfe6PDFTiPbo1KqterZKEyNEQQID4rITNovLo5vOFus5y/lzcbEXEFKHmEYXzafisLUmO/3fIuRQs6odyYa3L9EImFv4jqMFEZ4n7vPn1+t5vF1P+zK2vDtunGc+vf8q34EvZhammJpa8HjmwG5tmvcqR4nNp7LtY2lrTk/HpjC1cM3+W3UskIZn6hWsWbmZhp2rMuYRUOJj0pEpVRx5+x9bmcXchAkPL4ZqMmJeYNyWt4klBlKZg9czBe/D6dqA3eqNHhZLPrcjss069qQZt0aMbpB3hL4lvYWLBy1jJObcr+ucqMOlvjQD3PXOqgy0lA9fUBlHVLhhpIYm8TXLWfw2dyBBMbv4cSWsyz4bAkSddEXue01vjNqtZiZV5eemkF0aAwO5fLxefRc91rqiy88k3rEHCTy/KmcZq1J9qbfd+FBkdw44U27QS0zS53oI0aMJwBQYY4ZMVTGArlghN/tQL5oNJnu4z5kwenvMxV6E2OSeHzDn7sXfbh33gePBm4kRCXSqnczzu+8jFJH/mUsZYCqWtsy+IogjlArl7FtnbeH3y/Pzt+HzwOnSo5UbVgZmVzK2e2Xi72siUQqQa16eb1FhURzZf912vRtzrH1Zxj2Q1+e+Dxl1dRNACjMFNRoWoX4qEQu7cvpYS+leCip4YtvtFG29H9rc2xb8e0GAG6c0C2v/TpwrGCPwkxBkAGV7UspRR9xkQlYO1plSvTq4t5Fjbz7/E+XIDeS5SvheHvkKuKjEuhXfpTW9qiQGASJgK2TdY76KIXF6EVD2Tx3V57tXGtVYPX0f/Xut7SzYHvEKmLD47iaS1hbQbl66CZXD99GIpUgkQh0HtmejHQl9y8Znm9WSuGgVqk5uuYUFaq5APCBUV/MrM2Ii4zHsby9wZ7KO2fvU7dNrVcyymSCjJrY065lZSrXdeXv/+XuxTWU5ZPWs2n2TrqN+wCZIEediwx6YWHnYodzJUd2/nYAURRZPW0T1Rp7FPui4v+Wj0SVoWTR6JXFOo7XxY3jd+g1vjNunhUzn+NlKjqQmpSWGeIdLsbzgOako8kZjuUUscyikWifGYGwc/EBdi4+kNlvmYoO1HuvNi26NaLPxK5YOViSFJdM1UbufDT6faRSCYlxyfzxxUrCMvMIdSl9ShHJ22OblpS/SI3cUJga0/fb7uxafABBIjBwek+iQmI4sOJ4seTwtujeiFrvViclMRUrB0uadW3Ihd1X8Wxdk23PSxGs/2GbVk20Ws2rsmziOt7t2aTYc49dPJxJjEnKM2Xgback55S90UaZoXUaXjfdv+yEtb0lc4dklwEvpRTD8b8dSCXPinqNMpnDS1ni/E4wjRRGmFub0d12qM79677byqgFQ/i5/2/56tcQJBIJrrXKM++Tv/Jsu/6HbUzbPJ6v352uc398VAKbf9lFs66NaNGjMdsX7c8UPSk0RDVqpWZ6vH/pEfpN6aHfKNMn6fyGr+SXFO5ffoSTqyN+twMYOX8I5jZmtOzdlIigSG6dvodEonnZGpvImTNI9/PXpbITSXFJhTKeU5vO0W1sx0Lp6wWJsUnI5DKt1fiiomHHuqSnpGNubUaNZlW5e/4BnUa2x8zKVG99p3yhs2aZbmXTrF41gGPrz+JasxxyuSQzDUBXbbK3ie0L9zNgek/e7dkEURQJD4rEo74byyeuIzU5jUCMSWcGcAHYCrxLEkO5yEYaiCYohJyqvmGBERxcdSJHWZSsDJzei8ad6rHnr8MAWBBKAqGAc2YbKSsop9NYKzoqeVbk4p6rBNzVqKL63wnCxcOZQTN6ZXqiXgdVGrjTbmBLbp26mynqsWbGZkbOG8ylvdc4t+MyHvUqERUSje+tAK1jfW8FMmhm7xIxX+0/pQeJsUn8PX4NtVpUK5x7vJRC5Y02ykoqKyatRyZ/vQ+vUt4+op/FUqG6S5H03W5Qy1wLYd46dZdP5w5EIpEYpGSYH/pP7cHpLRcNant53zU+nz8k13HUblmDM9su4lDOjir13fQWoS4MMtKVyI3152KUUvQ8fRyKIBHwuxNIfFQCd88/wMzKlHsXHqJWq5HIJExaM5bhswewcvIGrWPrtfekTb8WDKz4eaGMxdzWnKS4wld6e105ZO/2bIKZlRkHVx7n7vkHgEY629qx6MMmc2PqxrGc3+XF7r+OvJXGlz5EUWT999u0tl05cJ0h3/dh6YS1ZOAAJAPbgQXw3HOVSn/u0pX6BSy1YmKu0FIsrYoVqQwknm4oqY8x23HmFjYGqEC+Co071ePWqXukJqUCEBcRj4uHk1abp49C8fd+Qv32nlw7eltrX8UaLq987Tb64B2S4pJ56OWLs1sZ2vRrQWJsEn999U+Otme3X6Lr2I7s/v0Q36z5AhtHK64evqnltU+OT6ZclbIcW3e62BV6D606wfCfB1C+mgsT/xnDVy2mFVk0TEmn1FP2H0KlVJWIVZFS3myiQ2OwzyWvQxUdo3dfbjiWt+fLJZ/x+EZAru1ObDjLwBm9WDtrS4HOo4+2/VvwSfWvDG6/Z8lhVj9azGD3L3LsG/fnp0hlEtb/sI1fjs54LSHDMWGx2DhaEhP2/GX2CgVPi4JgMZYQLFBigzFP8UDAUjAv7mEVGuu+34q9iy2nNusXcVo9fRPtBrXKsT3xeZ5x9WZV8Lni+8r5KbHhcUilEjqPbM++pUdfqa+syI1lGCmMijx/5tapuyTGJHE5Sz3PF6vn1g6WKDNUJMYWjldRl3GlXbvs5R8RQZFcP3ITUVlaZiL6WSwn/z1Pj6864bVwG8kcAfqAViihCUk0Jl28hZFgnO9zWDlYEhn80pCQCjLqYUuiuI8UtmOFJUaGGmQFXE+o/W51ylctS/32dTi27jQPr/lRtrITz/xzFmq+ecKbtv1b5DDK+n7bHYm0YM/jsu5OdP2iI3fO3KNizarUbVuLmLA4tvy6m5TEVJ3H3Lv4kBrNquJWpyKWduZYl7GiZe+mWNlbcvifk4T4PiMtJZ1dvx/g1qm7BRpXYeJ7M4CDK48RdD+YT2uNL/b8vOKiJIcvlqzZRCmllJJJRroShVnhSwxPWjeWpNhkts7bnWu7nYsP0OjDwi8pkZGmpGoWoYa86D7uQ/6dkzP/zNrRCvd3KvFFo8kAVG/iUaBVv8p1XRk4vRd9J3VDInn5SJTJpIz9Y7iWYVyzWVUCvIOo0bRKvs/zOngqxvKYAcSzg2RWEsMBblOGNPHtqdXofe4B1Rp75Nrm2Pqz1GhWFYWp9gT1x/1TSE1MZd6JWSy7NY9Ja8cy5LucRdTzw4S2s2jdtzlunhVfqZ+s+N0OxK5s0XolBEGg3cCW2Djpltv/ZPYAPv6ma5GOQR9LJ6wlLjKeJp3rU61R5WIZQ0nioZcvTx+FMmJoC+TCZnRP3aRoKmznH0t7CyJ0qNuaC5Y4CA4FMvTyg8JMQYsejdm2YB9Lvl5NtcYeDJzRixY9GusMFW/dp5lOIao/x63KNQIkNxp+UJfNc3dxbucVts7bw4Yft3Ng+TG9BtkLdizaz/gVn3N5/3U2zd7JjkX72fjTdhp0qMOncwbQokdjLuy+WuBxFSbJCSkcXn2K/lN6MHx2f0AjpFKqDFlyKPWUvWU4lLen04h2nN5yAf87uRdQLaXk8+i6HzWaVskU9MhKQWv4VGvkwa4/DlKzWTW6fvFBplc3LTmNEL9w7l3wycxR870ZSPPujTi/80rBP0Q21Go1Xy8fZZBSZOdR73P79D0OLD+WY1/XMR15eFVT/FmZriQ5LoU5h6ex4NO/85TPf0GamEJKDVP+2HGCKq4WrAv6AysbKxJjk7CwMQdBY0QamxjhVteVcpWdychQaoey6AuvyupBy/p71vZ5ednyGboVgilKhmbZIiGV+fjTm2oFDG0qidw84Y1nyxrcPqO/LtPuPw6x+OJPjKgzASOFEVKZBBtHK/qXG4nMSEa5ai6gVvP9nm9ZM3PzK41n1ZSNDJrZO89C6IYiN5KhzCjaaAtRFFk1dROB93R7l/ctOUz4E/0iQ6+D5PgU0lNLvWUAl/dfR2FqzIjOrqw4/Adp6auz7E3DlIsYCZYG9SUzkvHH5dkkxiSBAFb2lsUaxtbrf53Z8useQHNd7vnrMKaWpmSkpuuMOrIra6MlpvGCUL+wXPNF5UYyvWWKAu8GU65q2Xx/D2qVmisHrmt5m9NS0jPz8xq8X4dhP/bL/BxJcclsX7gvX+cobC7u9cKmjDWCINB/Sg8sbMz+c8qQJdVT9lYZZSbmCowURv9pZZkOw1pjZmWKubVhBSdLKdmc236ZoT/00WmUFZSpnX7m2/Xj6OsyQmu7pa05VRtVpmWvJrTt34LpH81h6YS1/LR/cq5GmZHCiJX3FhIWGEGZCg581/PXXKXu5cZyEqITqVijPIH3nuQ6VplcmplfkJ1dvx9k+pbxmX/3dPyEw8rNbAhcQifTAXmGZiSKSdzClrMbxgF2eHk/5GjlYQx/rwkOzvZ4n73PirsL6Tzqfe5e8GHN9H+5fvwOLXo01hlSUxJQoWtSZkf62/Wo58rBG3w6Z0CuRtnKyRvo801Xjqq3AhB47wkhvs9QKlUolSoeX/cDeKVJv0wmpUpDdzzquVGreTWMTYxIS3n1kCBndyeinp565X7y4tHz70AXD6/p3/e6yO3/+18kNTkNn32PqSrG4yf0J5kByGVxODvsY+zH3XC0c0AqlXD/8iO8Dt3Ua4B8vmAI2xbs49i604U+RnfPikzbPB5jEyNERI6vO8vprbpDjSUSCb3+15mHXn5EhWirp+oTbHLzrIhvHqVUsmNsYsSXS0aQkphCWko66akZnPr3fKaACGiu9+Gz++N7M4D01Ax6T/gIuZEmf/jSvmv4XNVf13Ddd1v55Of+nNhwNofQh9eRW3gduZX5d9OPGuDZqga3T2uu7aZdGpAYk/RaRTf87wThfyeIclXKEhseR1RoDC26N+JcIS6+lmRKcvjiW/Wm7jOpG06ujswZtPi1nrdxp3o8vhGQ46FSHNw958OIeYMQBIF33qvN5rm7CmWSUErxoFarSYxNwt7FNqfktz6PSx7elZsnvYmLiGPAtJ5s+HF75vb46ESNBPyhm7w/pDWLzv/Ix63GMmzMnwSbSDBLDscVuxwiBJM3jCP4YQiTO/5Evfdq86fXXE5uOo+P12OS41OQCAIqlRr1c8+eY3l7Lu7xossXHRjz+VxicELECDOCqY6pVqjMkTWnWXZrHgdXHM9h6E3Z+CXrf9BOik9PyUBhZmxQrLyfVE2K6h/ghXBHFSJSFnAr7BdMLvqSnppBT/tPqFDdhXsXH1K7RXXaDWpJuSplubD7ap79G+TlyqtNPv6vAApCSSQNyBpudBEb3i5vg1qlNmjh6X3px3m2kUgKlgTTokdjPps7kIjgKC7vv86/i3fQ47cuRNx/xtEFZwss1iGRSlBlqApdYKeUtwdHwRJ7MY1YfkWWIcMixJqDv71UV6zRtAp9JnVDZqRR8RRFkYy0DGLD43lw+RG3z9yn04h2r2yUpYhJhJGCKRIcsOHvG7+yYsomwgLCeeITgoW1KV/+PRLXWuUBzftMEITMe0NuLOP4hrP5iuoxUsip1aIa5aqWRRAEZEYyMtIy2L5wv95jzG3MCQuKYM0MjUdcJpcxasEQVk3dlGn8Jccns3ravwz7sR+CAP/O2UVEcBQSiYQ2/ZrTsndTgu494c7ZB4T4PtPqX6VUsWLSenqO70z99z3Zu+SI3rDHi3u9+OL34Ty4/Jj01HSqN6lCamJqsSghBj8M4fHNABwr2GPjZE3VhpVzNT5LKXqEFwUGSwKWgq34nk0XkuNTCvRCsrA1p2rDyvT4shMPrjziyOpTPAso2hVtiVTCt+vGEREcxfJv1hXpuQzB2sGSnw5Mxb2uKwHeQWyavZMrB67nGRddSslFYWrMp3MHsuTr1foFZPI5eZdIJOyOW8tHFgP1toksCw8iR5Oa1ub5lms4MhFPwQ6ZkYyuYzrSd1I30tMyGFRpTOY9W7dNLSb+M4bYiLhM40UikSBIBES1SFhgBGe3X4Jmjuw/MhK1+CI/Kx5LetEQS60JbeV6bkxY+blWuOOY34cjk0r4bfRyrTEfVW/l7/+t0RkeUr5qWZITUogKiaF590bsehTDrTs/52jXrs1EDh6eg0wuJSo0hlP/nqfDsDaIapH01HRunrz7+hZ+8vl/TRaTuYkxySwAKiJwCmu+5x1skZQwQZJXoUoDdypUc+HY+twL7RrC8jsL+Kz2+LwbZj1/fTcmrR3L/9rMIjY8jkdiNM+oTxp9MDXxwbP2GSYM/pAVX2/UWZw3L4Z83ydzAmkIppamtB/ckmtHbhP8MERnG4lEQt/J3ZDJZYUu3lNKyUduLMfWyZrqTTyoWLM8TTrV5+qhm6jVahKiE/G9GaBRCqxaFhcPZw6tOqkzz+wFPmIUz2hLBv2Ry/3wcNvE8VM/MbbuNOycbWg74F1SElPYNn+f3miHwsLa0YoeX3UiOT6Zao09mNX9VwZO68mlAzeICY1hwqrR7P37iNZimmMFezqPep9VUzZq9SWVSZFIBJ1eRme3MtRpXZOylZ0I9gnhyJpTOdrYu9jSYVgbTMwV3D59jysHc9bPtHO2offELiybsK5ELL5Ub+zBO+1qY+1gxcrJG9ifvPGaKIoNintcRUWdOg3EQ4e8CqWvsmWFQv2uSpynbMziT1g5eYPBhUCzkhCdyP1LD/FtXYNqjSoT4vusUI0yMyvTHMmaapWaTbN3EBte8JDJao0q43c7qFCUcGIj4lk9fRM9v+rMpjk7eX9wa9w8K/LPtNdX06OUwiU1OY1tC/Yycv5g1n23lYTnCnKvgpFCTkpSKoeVW1g6YQ07Fu2n65iOfPH7cNpLeqMUM3jwtAKptMlyVH3SLbvy97XOuFd2JyNdyYQ2M7VCKxPERNaeOMvprrG4OIjEHPVHLhjlOL9SzODSYRvUZBXMsCTdeAwf/xxP4/r1UCnVxEXEUa+dJwBt+79LRHAkEUFR1G1dg89q/y9Hv59U/5JltxfkMMrMrc345dgM5EZy5Ao5t07fZdXAuWgS47N6NCJ4cPIavrf88b0RwOUD1xn+c392/X6QNTM3M3L+EIOKXhcXpoIpjcQMAhlGMgJ2gNNbZpAB+N0KpFnXhoXSl6mFSb5LP1jaW6IwVxAbHkesGM9TeqBkDADJKfW5dKUDvyb/j0vp/z4Pl0pnbJMpPH0UalD/cqP8vZprNK1CfGQC7Qe3IsT3GS6VnXLUcTIyMQJR8560sDUvlOdIKW8OGWkZhAVGZBaIzmr0W9iY417XFTsXWx56+XFxjxfDfurH31+v0XlfxIpxhNALFaM1fWe4cc+nGcOGzGDcT/3wPveAdbO2kJpceIWkcyM2PI5VUzbSsndTKlQvx5Jrv2DrbMPHk7qSEJXEo+t+9J/ag9jwuMz3VXhQJPFRCbw34F1O/ns+sy6gSqlC3zJKqF8YVRu6o1KqMLM21dkm8ml0ZgRK/faejFowhFsn73Jx70sjICo0hkMrT9B7wkds/kW34JZUJkWlVGFubcbHE7uwdf7eIrtn719+hNxYzqCZvbF1tgHfIjlNiaIE2MI6KXGessbCe4XSV24JnQWhSn03pm/9H+OaTn0phV0APOq58fWykYxtMgWVUoVEIuH3y7PZ9PP2Qo3nNbMypXGnekikUpp3a8iikcv+07l2bwPm1mb0/LozPlcfF1pSrpW9JesD/sL/TiBmlqZI5TKGVhlLghjLVYaiZkC2Iy6zcd1TuvfoRNTTaIZWHZe5J1ZM5A4epPELoACeYc4wGqBAJmhPMlPEJK7QjgyyeyjuUo0vKCfYY6QwwtmtDNWbeGBirsDKwRJrRysadqjD9eN3mD98ic7PtOrBb9x//uL9ddifmdt3RP1DD7thmX9HigncpQUZTEVjmKVjwlAakEDtBjWYteMb+lccldm+/5QeXD5wPd/5DEVCPj1obyOf/TKI1dP/feXaPz2+6kTrPs0Y13Rqvo5b7bOYoVXH4S1G8ozDgPbigxmDWDm3J9Uae7Bm+mZGLhicqRSaF5XfqcR7A95lxbcbDC6v0mt8Z8ICIynj6oCtkzU7Fu3Psbg5fPYA/G4H4nvDn6AHT/Ps892eTbi016tQ36WlFBynSo6kJKS+lnd5hWoudP78fXb/cSjHYsJdMYJQjpD9mi/r8BU1o0Ioznmli4czjT58h9TENO6cvU9kcBQqlZoq9d1wdi/DsXXa3vUGHeriXqciW+fvzbNguyAIjP5tGH+OW5WvMdVpXZMmnesTdD+YI2tOZ97T/SZ3Z9/fR0mIyWlsTdn4Fc8CwnnmF0bdNrW4e9GH3X8cytd588sLQ/CYuO2t95QdOFA4nrJy5d5yT1lhkZ+XSI2mVfC/E5RriF9EcDQpCanUbFbllYynwHvBrJ6xOfOmVKvV/Dr0D4NekPkhKS6Z01suMnLeYDLSlDTsWLdQQn1KKT4SY5NYM3Mzw2cP4ObJuwaHhKSJKTwjCSOgTDavSVxkPB+ZvwxhnHdiFgAmmGHMOVKyGWUmsqP8Negi1/+5Q7/J3bX2+SGSxm+89Dw5kchvBDESN+y12iowxZiLOTKdjNiEPZpcofTUdALvPckhBrL8zgJ+G7lM7+cdXe8btoatRCqX4n32PuY2GmN22/y9Wu3sBQtqimcJpBNKbDEmjMoYYSyY0n5oG6wdX4pmtBvUkqePn5UMg6wUAA7/c5LOI9uzc/GBV+pnx6L9BZLFlys0uYhSANLJPkEVULLi2w0cSv+XO+fu4+Civ+ZgdsysTHm3ZxOiQqLZtsAwpbYX7eTGctoNfFenitzVQzdo0b0xF3bl/Q4zszKl04j2mFmZcmjViTzbl1K0CILAyF8HE+IX9lpSJYIePGXZhHV0/aIjVg6WPL7hz5mtF4EXWbhxgIPWMfEREYhCwQwymVyGYwV7YsJiXynd4umjUEwtmnFwxYnMd+RnvwxCYWrE71+szNHe6/BNMtIy+ORnjUS8iZkxwQ9DOfzPSZITtEuJiKJIXGQ8Lh7OWoaqQ3l7Pvz0PbYt2KtT+v7WqbvcOnWXSrUr8OmcAdy/9JAz2y5xbN0Z2g1qqfMZ9sTnKeu+28qQ7/rwx7hVRe7ZbvRhPYJ9QnLky72NlAp9FAN9vumKIBF01jfKikQiYdSCoez+4yDHN5zV2y4mLJaZ3X8hLCDilcaVnprOlQPXtcJHsioAFSYqpYq1s7ZQs3lVLu+/nvcBpbwR7P7jIJ1HtjNosuYvxvCE+qTzGRBKAL9SW1RiLugWSajRrCoHUjYiSCRMnfYXf/65h6SkjwABgZNYKU+gEOzwux2IUyVHrWPTKUPOyqFVqNaxEQ0Udlrx/IIg4CrG84ivSGMmYIqM5ThxEYVgm+tnUmYoc83TaTeoJd7n7zP/07/5ad+3xEclMrP7rzoTmO0Fy+fmYhrwstxtrrIAACAASURBVF5TtzEduXr4JgBDf+iL97kHeD3/u0TwH/WOZSXofjAffPrqkRVW9paYWpjw0/7JWNhZUL2RpgbaB0Z9ca1VXqeSaMUa5QkP0uTbVMCYCGaTzk9ZWvhgTRifzf2SB5c1111qchom5gqdE06FqTGpyWl0HdORlr2bIpFK+LL5VDqPel+rnUQiwa2ORn1OnzciIy2Dgyt1G1G3T9/LVH3Li6S4ZP76clVmuFspxYsoihxceZxQv7DXdk5lhjIzFLxO65qMXjSM9LQMouOimbLwJ2KTF2VpfQcbQoG8Fx+q1Hfjg8/aER4YoQmrRfP5/B74U7FKczZ8tz3HMdWbVGHIrI+Z/+kSIoJzL9WgUqqQG8lIfa6OX61hZWLCYjM9Qdl5YTS9wKWyE70ndgERjq07zdPHLw2Vrb/uod2glnQa0Q6vw7e4fuw2H41qj52zLSbmCp1GmVQmpd2gllzYdZWlE9bi2bIGI+cNJiYsFhsn3fUIrewtUZgpOLz6JG36NS9yL1n99p5Y2Jj9J4wyKDXKXjve5x6AAepXarWaH/suZMTcgTy48jjXmP/CehjWbFaViau/4LPa41859CYvEmOTSg2yt4zIp9FYO1rl2S5VTOYJTUjnx+dbKpNEUx7wEfp87R8q+iGTSWnUqR4d+zTjmv2f3ExagogEB1Ioh8ZgUmaosLSz0DpWTgQ5c7QCsTURGDVvSA7FQifBEmvxEYF0RwmUwwirPAyyao098lQ5HTCtJ4+u+xEZHMXIunnXQtPFptk76De5B6sfLmb5N+tLlkFWSiaFISAQFxnPo+t+/Dt3F5FPovnj8mws7S1Y/eh3ylR0oKv1EJLjkzG3NqP94FbsXHyANv2aZ14TZoIF7uIVguhPKq2R8xArrlMFG+q0qcmlvZpQ46uHb7D64WJkMhkyIykrp2ykYo1yyIzktO3fgsSYRC7u8WLp/9ZkytFnr0X40ej3SU1K4/2hrdmxaL9BpRkkUkmeYVn6KOwIjv8q5Z/Xv3rVAsK6RCNeF9kNl0YVjLkR0Y+45BpICcEGHzwwrOB5qz7N+WvcKsxtzIkJiyVYjCMYG5RCLRydL9O6Z1VUXtGUdS9Dg47vkJKQgkqpYuPsHZSrWjZPoyw6NJaylZ3wufoYqUxK4P1gHt/wp0xFB4OMjqePn7FmxmaMFEZ0/KQN7w1sidxYTnpqOg8uP+Lo2jNkpGUwcv5gIp5Esnr6ZiRSCcoM3RFa1o5WOFawp/fELnife8CVA9e5feYeJuYKvfVG1/+wjc/mDmDjTztwKG+PIAhaCzENOtTF3fN52GUhWBhLvl79yn2U8uq8tUbZ3Qs+BreNfBLFgyuPiIt4PTlX9y894sc+C4rcICvl7SUhOjFzdV0fISSTzqhsW2WkUAOV6IdU0H37K5UqLuy+SrexH+Bq7owgvHjRvExsTo5PRq1S893Ob5jZ/RcAXFGTxBTS+QHNoyUWM8YyuM947F10G1sKwZSq6E6Y1kVqUhp2ZXM33E5tvkD3cR8a3KcuVk3dxIHlx+k14SPOGxDqVUrxIJMXzitsdINJmb9f3OdFqF8YG37czoeftWNDwF8kxiZhaWuBwsyYuMgEgn2eMvSHfmydt5dKtSvA1cc4i2mksB0jFMgFjbfg5glvkhNSkEgkdB39AXOH/M7jGwG41ixHj687c+vkXVKTYuluPcQglcbyVV1Y+r81nNhwlh5fdyYqJDpHjswLFKbG9BzfGXsXW85uv8z1Y7cL5bsqJf/0+KoTCdGJOcRX3mTUQWl4iiIZXEGKHKlgeHiuKIpkpCuJCYslTozHlzZkMBlECAqB9dv2Mfijs7i6VWDvX4dxKG/HnbP3KVPRgXfeq51n/5XrVcpURlQpVSwevZyuYzrqNZr0kZ76sgg0aJ43VRtVpv/UHoBm8T/4YSiiKOZqGEWFRCOTy1g1ZSMj5w3m6sEbiKKYa5hmbHgcSyesY9DM3phZmmhKZShV2DnboDBXkJKQQoueTVAqVcVejPpN43WGLwqCoADOoKlTIwO2iaI4U1/7t9Yoyw9qtdrguP3COt/jG/6v7XylvH34ez+hUu0K3L/8SG8bTdx/FFBOa7uERATyVuObP3wJP+z9lhGeOVUOAfq4jOCPy7PZEfUPz/zDOb7hLCsXbCKATiixxIQoPDBj8fAVtO3V0vAPlwuRwVG41qzACu8FfFpLt4x50L3gQikB4dmqBsfX6w9pLqX4EASBvt92w+eK/uu/oHQY2oa0lHQ2/LidA8uPcWD5MUATOmhpb8HKuwsxNjVm/fdb2Ra+EhNzBX3LjSAqJAazbMW7K9Ysz7s9mmSWZ7mw24vk+GQCvIM4tVl3Qd3c2Dx3F5/9Oog1Mzazee4uFl/4GQsbc505KY061ePqoZs89PJl5LzBPLjyWG9B3lKKltiIeBJjkop7GIWOIAgYocj3cRlpGcjkMpQZSgJRkcEErf1qOrNr73KCBY3H+IWKdnRoDGXdy+Tat2utCjy8mlM+sIyrQ2bIcUFRZii5e/4Bd88/yPex6anpmFubkZyQYrAQSnpqOisnb9Da1vWLjljaW7Jo5FJ+6reQ+Kj85ZoJgsD4FaMIfhhaopWEi5rXGL6YBrQVRTFREAQ5cE4QhIOiKF7S1fiNMsokEgmt+jTj3PZLuQp5iKJICDGEYYKIFDviqaij6G1uWNpZ4OZZkZsnvQtj6KWUUqg8vPqYNv1a5GqUlcWaJ/xMMpsg0wh7giWPkeQRJgiah39Zdye9+5XpSka9M5HK9dwA+HHvtwya0Zstv+5m4887eJFb8N3uSWz6eYehHy1XlOlKZHIpFWuU58PP2vH10pE89PLly2ZTKVPJkXfa1mLM4uHI5NJXOo9HPTdsnKx11qEppfjpMqYDt07d1SrHUFBqt6xOx2FtNSvdzydL+5cdzdFOrVYTGx5HT4dPaN69EdM3j6ez6QCmb9W9aCGTSane2INPa40nMTaJyKfR+TaKsr+zIoKj2PjTDrqP+5AK1Vx44vMUQSJg62StJewhN5JR+93qXDtyC9AYc51GtGPrvD35On8phUN+as79F7BztsnMLlEh58USYlZEFID2zLn9kNac3nIx176d3RwJvBecY7tEWrxlQfYuOUK/KT1Iy0eZAFsna7qN+5Ck2CQC7j7B6/At1szcklnw3pDw5eyIoshDL98ClZ0qJf+IGgv8heX84mLXa5W/UUZZpdoVaNalISkJKTkkwS1szRk4vRcSqYSZPyzHJ/xr1HQGIIbLJDCZ2gYkoL6g04h21GpRnYdevjkUeEoppbiJjYjHoXzu17NUkFFDTOAhXUilHhKiseQeNbIIWuRGvXae3L+U96T38XXNamZflxG0G9SKr/4eQcfhbXn6KJTYsDjqvVebSe2/N+iceWFmbcqj635M7zKHH/Z8y6QOPzD38HQOpv8LQEpiKksnrmXMomF59KSflr2a4FDeni166seUUrw4uTpiZmmK14XrpJGCBdbIhJyTOkPp8WUnzmy7RNjz1fj9y48TcCco12PO77zCzsUH2Ju0AZlcysKRS3O0afjBOzy67kdseBygUTOs196T60cNCyM0UhiRkZ4zxD02PI71P2xDIpXQtn8LHMrZ5VBaHD5nAFt+3ZOZwxQbEY+5tW5xn1JKyQ+2TtY07lRPr5hMy95NadChLlcP3kCZruTG8TtaYfY1m1fjxgnvzIV1B9KI5jQirbL0Eoo54ZBNtTfAOwgXDyd8bwXkOsYX4iFZiQ2Pw9rBktjXlKaSnfioBM5svYhnqxoGH+PmWZFLe73wuxVIrXer035wKw6tOqG3jpqh7F1y5BV7ePMpRE+ZvSAIWfX1l4miqCUPLQiCFLgGVAb+FEXxsr7OSnxF0awKb363Azmx6Wzm6l9WVBkqpDIpp3ac42mqe6ZBpqEx8bIOpIiGhw9sm7+Xv8evfqMMsu7jPqRKA/fiHkYpr4lL+67Rpm/zXNvYmtrTxNSWZiYPaMpTPAU7pIJhXiQXD2d2LNqfrzEdW3eazmYDOLrmNKG+YbTu05zEuMIL2/l0zkB2/HaAqJAYRjeYxPWjt2kv6U17SW86mQ6gi+UgDq04TlpKwQuxu9aqwI5F+4u11k4p+qnRojIzftvAVdpyjalcpix+YkyB+1Or1Fw8e4WtFy6x6cJlTl+6TIoBAiJLJ6zlI7MB9LQflml4ZaX7lx9So2lVzKw0xtDMrr8w/Kf+VGvsYdC4lOlKFKbGuY772LozbJq9U2u7i4czvjcDiHiiHaqVlpKG3LjgxmsppQD0ntiVyu9UQmFqjJHCiK5jOiKVad4ptVpUo26bWiwauZS4yHjSU9MZOX8wjhU0xpVrrQp88ElbTm95Gbrrgi0OzELG38ATJGzHmoF46Fg8vHfxIZU8K+Y6vsrvVNK5mLh3yREGzuhN2/4tXuHTvxo+Vx/n21stihrlVq/DNylXxbmIRvbf4kVOWWH8AJGiKDbI8pOjXo8oiipRFOuiySVpJAhCLX1jK9FGmVMlR8b9+Rkffa6RBRZFkYt7dBeyTE5IYc3MzaQJKSSl5tSWS1G2IUVu+EQtI13JE5+Qgg++kDC1MOHzhUOZtHYsvf73EV8tHYlrrQo52kllUjzqudF1TMdiGGUpxcHt0/dwKG9HzWZV82xrJCj0CnvoIyE6kTKuDnk31MG677eyeMwKPm8wCRMz/TkHCWIC3mI0t8R4QsSoPA0hj/puepUQ01M193dqchpyYxlV6ruhMDVmS+hyDis388eV2QaNXZAIpQbZc9LEFBLFONQlSIL/yL27BCcuIYOJQHtS+IsgPiBOzGkYGcKhUxc5FuJOKHuJ4Ch3GcM9cld3e4FSqSI+W/2gxp3rM/fIdBRmxsSGxbLs1jy+2/kNNVtUY0LbWYxfNjJXY+sFarUaqVyWr7B7gEYfvMPNEznD7h9d8ytdtCvllTGzMkWQSEhNTsPa0ZLGnepjbm1Gix6Nca/jyuLRy1Gr1Nw+fY9rR2/z15f/0Oebroz981N6ftWJAyuOaamBCoKAp2DPO+zAnSF4soT62CDL8r6SSCS07tMsU4UwN2LC4jKNwKwkxSXzx9iVJMYk8cXvw98Yz3HWjxsVGmOQ8nIpJRNRFGOBU4DeiXqJDl985h/O3iWHeeiVM2lTFwnRicSciEEgFdAuBqqQHMckwyhnGaUSxhe/D8fawZIQvzCePgol4E4QxmbGPPMPx93TFYW5Me8PacWyidrFI1VKFfM++StH3PToRcNITU5j1ZSNr/NjlPKa2PLrHkYvGobPVV+dylLqlCye3ixFo/Oqc1WndU2kUgkbFm8hjjQsMcZcsMj1GF0EeAcRFaLbi/FMjOMhjUhnKqAgkhNE8iOe5Hyhgqa2jZFC/n/2zjq6iayNw88kTZp6qSIVimtxWNzdF118kUUWWRZ2WT7c3R0Wt8Xd3V2LS4GWQil1lyTz/REaWmopFCgwzzmcs7m5c+emm8zMe+/7/n7J7kp8SLBfKNXaVqTln43Zs+gQl/ffYPzeoRzRbuGZxwsGVByeohhIeh+Cv0c0ohoPwgilAhpyoeIgbmIgWYWv/0Dw7JUx4JyoTU1fvNlHemcniiKb9z4lWrNA36blZ/x5Trh42ODvfIFyeekytg3Gpsa8fPSKSR3m6r+nQ9f1x887gI4jW3J07SmWDVnHyG2D+V/9CWmMqlM5jVddM4QqrcqjjlMnKxl+78Ij6nev+VEiBRIS8cz7/V99UOXn5c//GkygcIX8FKtWmAX9VyTpHxsTx5+/TyVYYUeOfC4E3r1GXqyTLBJaCVYp/n4rNi+La2FnsufOqjewTono8GgUypQfbS8fuMHDK0+p160GW2fsSf3DfmVEUUx0P3p05Sl5iufkajLZYhLp4wuqL9oDcaIoBguCYALUAqak1D/TBWUKYwWaOI1eXvTCnqtJ+lRoWgaPM/eTdTg3EhTYiw95yU60NEVnensOK+0RTNIh2fq1eObxgnINSvHirjeCIFCvWw2CXgdj72TLW58Arhy8Sdn6JZIcF69k9KEs65E1p6RV/++c4xvOUKFpaU5vTVbM56NQmiho23kEF6mJSF3gGObidsrigEwwfIO9QLm82GRL6l0jiiIvsEjgoQYiNQnAk2BxC9YfPPy7FMzB+L1Dmfbrgg+HShabrNa0GtSEX1x64f/uAbW2rBUAjXrVYfm92cRGxzG541wefCCWIv1e4D6h+LMO0KXLRNCFx/Qji/gMY8Hkq83LpUAO7vomJ5YhIqRcO50iscQQFJw0nTCOZvizB3MMC8oGLunJkkGruX7MI8l7kzrMBaDN300B3ap/Wl578ZhamhoUkNlktabur9V57fkmxXqRiJBIzKwMt5+QkEiOeCsflakxPaZ2wNpBl2Y4rvWMZPs/IpCXjEKMq0rAXQAfIuhISdHG4AWwczsuk7t4ThQqRZo7XHIjGYIs9XtUVjcHgj6ow0yIUqXUZ158Td56B5C7hJve4snniS+VW5T7bOdTGCtQx6pTkaH4PviSkvjobqKr39WVyYDNoiimKPee6YKydsN+RqlS4vP4tV6KOCFKlZJWg5pgbGrMiY1nsbKzpPesLqwYtkEvd5pPsMFcnIcvSwE5toTjkg6Rj4yk5/ROvHz0in1Lk36W5Di44gTFqhVBZabCKX92fJ/5sWnqLo5ot3Dr5F1++ac5h1ed1PeXyWX0nN4JKztLJnecy4BFPfDz8ker0bJp6i4evxNhkPh+eXD5CdXaVuTs9ssZYiIJcCvgKUcu9QTibwDuhFMTD3pS7AOJ/VTndukxDy4lze/XoCaWpAXPGpoQwIZE1QTZ3BwZv2com6bu4spBw0yc4wOw5Ni7+DB7Fx/GxFzFumcLMTY1xthESb/y/9MFaOL7RY4flVCciQ/I4olhOF60IS9fLyir27UGJ0YsBJ4DOfXtCubgTNLi/rRQoMDU1IfADzZfZdzEnNRrLy1tzPln/QAsbc2xzW5D7S7VCPYLwTMZkZA+s7sQHqyrrbSwMcPK3jJJnw8pWrlgqpL/8Slj2fNkJehNMEfWnEpVUU2pUhq84yYhkRYyuYzCFQvwd62xxKZQw6sR1fiT9wMRjxyE0pEQNmJt4N62Vqtl1QidmNPvc7vy/K43oQFhyfbNnicbhxI8I6U0d3Vcyr+FUdsG8/j6U9aM2pJh99SPwfvhKxr2rM2JjWcBnViJTVbDDLrTg6mFCfW61aDyzz8xs8cikDbTMwxRFG8DSXdSUiDT1ZSpY9Us/2c9QW+CKVEjaS1cbHQsQ+tP0H9JlSoFGrUGzQc/sOyCDSUFM0oKKlyFtPOQPwcKpRGmliY8ufFc32ZhY87fa/ox7egoClcskCTdUKPW8PTWc24c82DJoNWIokiPqR2Z3m0hhSrk578pOynXqCQ9Z3Tmn7X9GbbxD2KjY/WFrQ8uPyEsMJwcebN9dQlYiS/H4VUnado34+oJj196xfuALB53Akm+xixCDOel6E+wGJxkpykyPJoZJ8cw6eAwRm0bzNSjI5FjhBzvJOPIuIFFgsuSTVZrlnnMYOWI/zLcIDMqPJoW9l1pZNYeURSZsHcoAEbKHzsgAxCTvTUo+dqVZVqNFrdoU+zphpJxCOzChB64chRLIe1A50NkgpyqlYwxkidMiQrAgoXYpqFS+tfqvpzZcoG+ZYfSq+Rf5MiTlZmnx7E7dC0dRrTExPx9LeW1w7cxNjGmaJWCDFnTH+/7PqnPSyajcoufUtz97j65PQ161MTjzH1W/G8DO+bsT1Piuv3wFpz8L/3eaB8iCAKTDgyjcoufPnksiW+X/GXzsH/ZUUIDwhKpKyYklhjikl18K0cYSZVFDWH9+G20HdKMyi1+wtTChJFbBtFjakfsnXQL70appC7G8/Tmc35qWArZux21EjWK0HViO/0YexYdpFD5/LQb/vNHzTGjEEWR01suUK9rDX2b/BPtXuIxMVfR8LdadJ3Yjia/1+XcjsvsnH/gh0nfz0Chjwwl0+2UxXN5/w26T27PjWQKlqMTKGO9fRlgcErTlyZ7nqxkzelAnS7VKFatEJun7cbc2gxLG3NePn5NmXrFqdq6PAsHrCRf6dwIgsDDK08SGfptnrab3MVzUrKWOzO7L2LImn4cWH6MC7uukNXNgdzF3bB3ssPCxgLn/Nk5tPKE7sAlSX12JL5fPG+/oFCF/BQslzdl77KEdWQJUhBlyvc7DNpY3YqnVtSgy2FIfIH+MKtBFEXuEYA/dYmjCXIuYck6iouW+pqBYQ0mArqbgKWtBXMvTEQQBBzFV7xgP1oavBstEHNmYM/7lcDFN6fz4t5L/SLM58DSxhxBEOhbVheUiV9xZTSzYIYXUYRCAjNkJbNw+oq7ZKArepcJcophQ5ziCpGxJzDHCrlgmM1Dcji+lTNypAezx80mTi3HDF8KYJniw4mljTmDlvfB0taCI2tOATpT8/7lhwG6VNv2w1qw9PYMrh/1YNZvi7m0/zqCADNPjmV+v+XsWnAwxfnI5DK6jG3D3hSu4aYWJoT4h6VbxU0ml/Hy0aeLV+Us4szJzefJU8KNM9syLmVa4tvA2t6SjqNbExYYrt+9SgljTDDmCh8ucSnYjc1HmE6DbrfI+4EPLQY2omRtd1YO30igbzAtBjbC4/Q9Hl9LW4MgNjqWrbP20GpwYzZN3UXeUrkwtTDB0s6CwhXzExsdh4OLPYXK5dMf45w/O29e+H/xtMa75x9SuGJ+cuTNhs/j1/g8eoVTvuyf9Fuu2KwseUvl4vCqk7x66qtvN1QZ9nsgs97mM21QplFr8Dhzn1K13blmoK9LZuPFvZeMaTGdkrXd9VLErz3fMLzRJGq2r0zRKoXwOH0fMytTuk5sR0RwBONaz0wyztObz7HNbkPj3nXYPnsfz+95U797TbLmtCc0MJywwHAiQiNpPqABy/5el0TA4JehzfF54ptmgazEt83exYfpPLYNoijy4PKTTxqrZesibNi8DWiZoPUgpvgDOfQtvgTyhkF6CwoNhQiiDo/pQIEPBDuiwqOJCo/GJqs1KlNjckVkoXCpnfjLTxCnVhLhfRfHt6YIgsCe8HWsHvkfVrYWtM7a/ZM+S1oMXtmH68c8eP3sDfD1TUYzA4Uw5RYtCKc9GvKhYh1OPMJMyPjUmfRQrFph/r0zE5WZipioWIxNlPz7zzrCgyOTpBnJEtSVBL4OwvP2i2THvH3qHoVi1bx4u4K/aozmyc3UV/CnHR/F/P4r8Dh9P9n3ve77MKnDXPKUzEX/Bd307Q1M2nFEuyXV71fWnA60HNSYHXP34/P4dbJ9XAs5JWuOmxr2TraE+GeMP5Oflz9VW1dgz6JDGTKeROYka04Huk74hRvHPRJ5kokiBL0JZt3YrWmOIRNk5BDf4Ml01AwAjJCxDzsOYSYkL+hkCHKlEed2XtYvTPwytDnXj95m2MaB/OLc06Axnnl40ahnbUC3+K1UKclTIielahcjNDCcx9c8ObHxLJ1GtyaXuyvndl6mdudqyYqmmVqY0GFkS1aP3PRJdiwpsX32fnpM7cCigau4d+ER+Urn+uigzNrBCjd3lzQDaomvQ6YNygDO77pCr5mdUwzKZHIZbkVdeHrzeYaf29zaDAcXuxRv5IYSGRbF2e1JfeKOrT/DsfVnGLHpT0rULMKVAzcI9guhRrtKBL4OxtzalLM7Luv7q8yMiYuOY8e8/Ti62nP/wiP+WdcfK3tL/F685eTm81RvXZG2/zRDZaZi0cBV+mPlRnK0Ui3BD8HqkZvoNLo1MVGxPEvNADfBrln87lh8u2shZ+aumMDe3fUJjb4IFAfuY8RF3En8UP4GI7Q0/GDwHITiACkkuy0fup7dYWu5ceIO7pULsvSvtVjYmNNx5AQ0ao3e86Z5/4bc+QJKcffOP6J5/wb6177P35LVzQHfZ36f/dyZFaVgTGlRSSjriSEOG6wx+soBGcD9C49YMWwjKnMVwX4hmFub8ceS34gIiSI2JplgSqvb2y1RswgPrzzl+V1vXj315a2XP4+u6eptlSoldjlseHLjGYuuT8PjzH0UKgV5irsR4h/KvL7/cufMA0L8Q5l2bBRbZuxNMSBLyJPrnrh9YF+SXK1jyVrulKpTjOiIaIL9Qlj619pUV+Mdc9rjeSt996V63WqwbWbGpP9GhERKD3Q/AD8PbIidky3RkYm/i3Gx6hRryJLDRbDGUjyMFwcpVKEQb8/fIWsG1/if3nqRGu0qMaT2WIOPsXeyxTabDT81KsXFvddo+WcjosKjubTvmv7Zq8XARphbm3Fh9xXcirpwfteVZMcyUhrhUsAJm2xZeO35Jl1zL1u/BEZKoxTHBlDHqbm07zqVmpfF54nvJy0chgWGY6TI1I/+n50vLPSRLjL9/xk/L3/Mrc30RdIJKVKxAJ1Gt2bDxO1cP5qxu2mNe9eh4E/5GNk0ReXKDGHn/AO89nxDoQr5yVcqFw8uP8Exp30SlazTWy7od7riHxYXDlhJ9V8qYu9kS6la7hxadQIHVzvK1tPVFMrkMlSmxqwbp1vRUpmp6DGlPTeO3eHOuQcGSYtLfHusHbOFX/7XnJyFnTnx37k0+8vkMr2vi42jJfW71eDUxnO8DTlMcePa+HEWc5TkJAsKIbGYggwtoAYSm9IKxEEKQgn/TdmJha05DX+rTQe3PnrJ/DWjN+v7HNFuwcHFDhNz1WdXwvpvyk5+alyaAYt6EBURjUKpwNfzzQ8dlIGudsgqjbqqL010VCyx0XH6Gpbw4AjGt5nFKzGEl5gRiyNK3uBMBNk+UPBs0qcuRasUxL1qISyymJM1pwMKYyOMlEaIWpGA10GoY9VY2prjWsiZ6MgYTMxVDFjQg9CgcIxVSnbOP8DRtacMmqudky2qVDz6AGq0q4SoFVn299pU+yVEYawgNjp99ThKlZLIsKi0O0pIvGPHnP0ojBV43U+8gvRK0gAAIABJREFUK+uUNyvpFai1FnRXkukTejK4xugMm2M8Po9fs3bMlnQd8+bFW0Y2m8Lvc7vy5MYzzu+6ws9/NOTczveL4YdWniB/GV1pyfGN5/TKkx8SGhDG8MaG+WB+SOGKBYgMjcQuh41OfEQUCX6bdFf7+tHbdJvUntun71OnczXO77pKZGhySrSpo1FrCPEPxaWgU5L/tz8SUlD2kajMjFPcDnavVgiZXEa1thVTDMoEQaBQ+Xzcv/g4XSo6O+Ye4NTm5NP9CpbLi2NOe05u+vSiaY8zuhXXhEGXodw+fY/bp+9Rs31l6natgeftF+QqlpOIdz/Unwc0oOf0zkzuOJdj68+gUWsQRWjWrz7lG5dmWtfMWYsn8WmIosiGCdtxr1KIPrN/5dDKEzy99TzZvjK5jO6TfsH7gQ9xsWoa9azDyc0X2Dl3HyVrF8PNxIkcqQRELsgJZA5qBuvbBE5hRzCkshqqidOwasR/KXqYxe8oHIz9j3XPFzKo6sjPaub+R6Xh9JvfjddP3rBncfKS4hJfn+tHblOjfSWOrj2tbwsUQ3lMLeL4G4Bo4BGTMRZPYJNA/GP3wkPsXvg+5W768dE8uPyYmyfucvXwDR5qA1DkKIClrTkBt6+SFxvylcjFwGW9+b3MkHTPtWjlgqmKb7hXLYSlrQU75x1I17j+LwPIVzoXvs8NWzTIXyaPQXU2EhIJSW7Hxy6HDZVa/MTaBAto3zp7Fx+mZvvKbJq6i5k9Fid6Lzw4Qp+plcXRmux5sqbL58/c2ow8Jdx4fN2TiJDkAyhBJnBq8wUa9KiFTbYsaOLUzOu7PNm+GyftoOPIlmyctIMu49qwdPDajxKl2jX/IH1m/8rCASu/qrqkRFIyffGEQqlIdnWiVG13Oo9uw1tvf3xSya39qVEparSrROl6xdN13uiI6EQFkAkpU78EZeqWoHn/BhSrVjhd434Ojq0/w7XDtyhSqQD2TjZ6Y8c7Zx/y+Lon7lV06kdxMXHM77ec8W1msn7Ctq85ZYkvwO3T91g8aDUFyuWhw4iWWNiYJ3rf2ERJrxmd2bf0KAeWH+fo2tPcOnlXL0Jw5dBN+i9MvZ7LWrAkF4cwowNGzMGEnjgxlpzY6PuoTI0pWbMoZeoVx8pO95DcoEctTmxMexeviWUn1o3dwor7c1h+bzaFK+RP75/BYI6uO2Pwg67E1+HmiTtY2lqQr1QufdsLIC7BogBAHH+RSvIuoDO1XT9+G1cP3eSWNghvluHps4ibt6fhzWLuEMSTm89589yPur9WT/dcW//VhEOrTiRpl8llNO/fgNJ1iqU7IAO4cfwO+UrnxiKLedqd0S3CXdid1O9TQiK91GxfmYPLjxMX+/XVaWWyjFEJ7DiylU5BKAGmlkn9/Jr3r0+p2u7Y5bBJ8l5yKIwV9JjaAYA2Q5ql2C88KJywoHAeX/fkztn77JqfsghQZGgkVw/dpOBPedk2cy+9ZnbG1CL94ktajZbDq05Qq2OVdB/7PRCfviipL34EGo0GmUyWJJovU78ET248IyI0iv3LjqV4/K2Td3FwseNmMiqOH8ua0ZuRG8lp+08zXAs5kT23I5Fh0Zza/Ok7Z6lhYq6iYvOyHFt3Jons+KYpOynfpDTZcztSvkkZyjUsSVY3BwRBSHKBCX4bmuz2uMT3h1ajZd/So1g7WNHwt1qYWpqye8FBVGYqmvWrz4YJ2whMYKIZF6tGZWpMVFgkc3otZZvfChyc7fDz9k/xHC6CNU5iFDEcQokxcsGGDiNaUqJmUQBErYjX/Zdo1Fo6jWmD9wMfntx4ZpDwQGx0rH6Ho2Lzskw+PILG5h0+/Q+TDApjo3Sn5Uh8eXbM2U/PGZ30NWFazEi6vihHg2FGyVFiBMHUAPIkaM1PMFWJFq8wvdsiVj2cw5UDNxL9VtLi/oVHOOVL7PUmCAK/jv+Fw6tOfNLO7/WjHuQs4qzPtEgJI4URvs/9DH6I9heDeIwGLRZYE0y+dynLZeoVJyYqltun7qU5Rp4SbtTpXC3VHXqJzE/1thVp0qceCwaswLWQE27urtw5cz/FxeovSgZeqMe3nUWp2u4MWt6bl49e433/JfnL5iUuJk5f+gE64TY3d1dC/JP3R/sQ9yoFOb7hLK8935A3wSLSh1zad53qbSuy/99j9Jn9K6c2nccuhw2tBjfh2LrT+utcPNeO3KbnjM5c2nedNaM30/afZqwYtjHdn/vRNU9cCzvTaXRrRFFEEARqdajCiCaT0z3Wt0hm3SDMdEHZvQuPqNmhsl5qWKFUIFfI0cYk/gsqVUrGtJxOgx61Ut2+jQyLSlV++GPRqDWsH7+N+t1qUKtDVXLkzUr23I5snLQjw88VT/4yeWjcqy7XDt8mxD+UrDkdEl0g41dD1XEaXj58xaV911NddZH4cQj2C+G/yTtRGCsYsflP/H0CWdB/hc5MNoE8/jMPL1wLO/Hg4iNkMhmWdhapBmTxyAQZJpgBOnWnUnWKMbDyiGT7bnq9DJWpcbo/w7kdl/FKw9/pYzExV1GjXWXm9ln2WcaXyDhEUeTuuYcUqVSAO2cfYE4QQbyERKbmXlgQDKSt8BZNJHGUTdIeS2miOUlkqAnTfl3AtOOjeXLjGZPazzFonqe2XGD0jr8StfWe1YX9/x775FRcI6WRQXWWuYvn5OmNZwaN6Sm+wZO6wFBARhR+BNKOnpWK4OBih3OBHAYFZaIoIlfIU6y/kcg81O5UlaA3Ibx64ktWNwd8Hr8m5G0oVdtUoHDFAszssYiCP+Xj4ZUnHFt/5mtP97Nx7cht7p57SM32lRm0vA83jnlw7533azzH1p+BdPwNXj19Q5M+dVGqFCz+c3WK/V7ce0npusXJWzIXW2fsofuUDtw8cQeZXEaHka3YMXc/N455JJ7v4VtU+rkcMZEx3Dp5N30fNgHxz9nxnNtxGT+vtO/3Ep+PTJe+eOOYBwpjBSVqFsXR1Z4Q/9AkF/cGPWqRzc2BCk3LIDeSJ5GA/5IcWH6c1aM3Maf30s8unHHzxB3+qjmGoDfBVPq5HD2mdKDz2DZJ+u2afxCfJ75YO1hRr2t1GvWsjbGJMpkRJX404mLimPjLbP79Z70uIPsAM2szwgLDAdBqtWjUGsytzdJ1jmEb/2BO75SDmzWjN+vl59PD7LPjOb/rctodP4Iy9Uuwd/FhfeqvRObm3I7LVGlZHoDcWGNFFwROAnEInMSKX8ltoEiJOVao2JOk3YR9mKMTC7ly8CbdCv1B9jxZmXtxokHjjtj0JxPazk7UZmVn+cnF9aYWJvzUqBR3zz1Ms+9PjUtxaf+NNPuJoshzrIBhvH8scCCGaQRYRvL4mqfB4iJPbz5n3u//4vXg8yygSGQc+UrnRhOnplAFnR9X/e416Ti6NfcvPGJm90V4P3zF4dUn023B8C0SHRnDkbWn0ag12GSz5pmHVyKvQlNL0yRZR9YOVh8Oo+e15xtunrjD3iVH0typ3jZrLzXbVyb4bSg75u6ncMUCPLnxjDtn72OkkPPbtI6Ub1Ja3//qoZtkzWlPofL5M9Qy6umt51/1efpLIqUvpoP9y47Sa2ZnIkOj2D57X5L3I8N08sduRZyZ2+ffLz6/EjWKYJvdhqPrdMXmhqweZhTxq6Nnt1/C3skWe+eUxRRyFnamxi+VkSvkPPPw4u75h1jbW1K4YgEu77+eKfLCJb488cp1ehLI48s/kNp95uFFjXaVEgkkpIVMLuP5nZQrevYtOcKAhT0MHg+gSsufiAqPYv14w2ohA8RgXqFFhhZXVJgLKdffyI3kFKlUQPLx+4bQarV4P/TBKX8OXj56TSkxC77iWILRYo2MrGRBJhi25qgQlGQV7+HNUjR0BcCIZWTlURIbgH7lhrLVL/ki/IRky+1IiH8oVw/dTNS+b+kRyjUoyYU9H1/j9fPAhqwZtSlJCntyyGQyg3asoohAmyh9M54SHDzUn35Nm7JhwvaPmK1EZkEURR4TSCCOaDEmm20oV09f50aC0o6MVrH+1oiNjqWdcy+0okjdLtXoNLo1q0dtolm/+jjnz46flz+xMXHsmLMfp3zZ+XvV78zvtxy5kZwa7Suz6I9ViUptyjYoidJYwc55B3jz4m2yKuLxbJiwje6T27Nk8Br+HbKOFgMbkbOwC2ZWZuxecAi3oi60/LMRW99ZW2zNIIuLH5HMLImf6XbK4tmz8BAPrzxJ9kv84q43r574EhUR81UCi5xFXchXJrf+tbGJMlnfB0EQcK9SKNFqS0ah1WjZNmtvkm3x/GXy0HGUTrnu5ok7rBzxH/P7Lufued2qavkmpSlWvTA9Z3Tif+sHUKdztQyfm8S3i9dDH1wLvU8D613qb2p2MLwY2NC0xLcvA9I1r54zOjOpnWFpYw/EAG7RgTcc5jV7uUgZrok+aMXkr8I//9GQvYskxcVvjQP/HqdZ33qALn02u2BLIcGe7IKtwQFZPHmELLizBQfq4UBd3NlB7hR82W6dvMukg8Mo+a5mMjl6z+pC0JukmRMvH72ifreaaUrlp4Rtdhtio2INqgl2K+qS6uJIQuQYIZCcyM1VTNQxnN1+ieiIH2MF/XvlPoF4MYtwNhLJKp4GrGXulqT16T86arUGrUbLgeXHsbAxxzZbFvKVysWhVSfZPG03Di72FCqfjwrNyrBrwUE8b3th52SLla0FxqaJM5J2zj1A3lK56D2rC71ndkn1vMFvQ9k6Yw99ZnfBzMqUbbP2cmTtKRRKI+r8Wg1jUyUBr4MpXr1Iuj5P4YoFyF0sZzr/ChJfi0wblPk88eXSvutJ2h1d7Xnm4cWu+Qc5stowv5iMZsec/SwcsFL/uv/CHnQZlzSN0Dl/dgYu7YljTvsvNjcHFzscXOwwUhiRxdGaZn3r02FES/37B5Yfx+veS1wKOOH98BUPLj3+YnOTyPw4FXTh5fMAXZ3Zu38qU6U+hdHOyZZ9kevpMaUDv03rSPVfKpGvVC6W3p7B+L3/MP3k6FRNMEG3em/vZEvRSgUBnUFnarQa3IQbxzwIfZdWmRqB4ht8MEOLK7rLmxkwliBK84Dk5cmt7CykVKtvEHWcmvO7rlC9bQU02jjCxVDCxVA8xECuinBLDCFUTPqdadSzNuP3/kP+0rkRte8fSG0Fa9yFLLgLNtgIKaclTWo3h2Prz9JsQANWPZpLmXrFMTJ678nXbVJ7gnxDkvVjCvQN5tkdL0zMPy4oq9amAgeWHzeob/nGpQ1KXQQwFlSY4gNMA+J31l4iYwh5yfFRc5XIPGhEDUHkBYolaDUjnAG8JXlbEgldWuHQDQNQmZvQenATfXuLgY3JlsuRY+vP4FbUhVzurqwetSlJ6p/3Qx9m9liMg6s9GLA27/vcjxXDNtJmSDPqdK7GjWMe7Jx/AHNrM9SxavKVypWs0IrcSI61g1WymwOl6xajZG339H/47xwpfTEDcHS1Z/qJ0QxrMBGvBz6ZRr5698JDyZr4eT3woXfJv/XpYjZZrandqSpbpu+hVB13Gveqy8T2czJ0BfLMtouc2XYRgKA3wURFRCdxf7+w+yrGpsZsmyVtf0skxtHFjuObEqfxPbnxnG6T23Nm20UGL+/D2JYzKFQhH41716VmezVWdhYsG7KOI6tPMvfiJJr1a5Dqd0ur1XJ07WkmHRyG0kRJXIya36Z25PcyQ3hy83mivmN3/Y1MLmN4o9QVoTSihsu8JYIqQH7gP2ABMB9wBIoTyCu9yhTofo9thjTj9NaL6f0zSWQSrh+9TZbqTlw1siVCXRktl4FlgDkQRyiDcBcfY5UgfbVGu8qMaTEdjVqTNJXXANRqDS8fvUIQBLI4WlOnS3V6TOlAVEQMNtms8bz1glHNpqZ4/IPLj7HLYUPQG8OVHOORyWVo4pLWgibpJ5OhMlely1y2NLbcYAdhnAXMUPCc4lggF76pxwSJZNAQhyaZ4FqkAJF8mztlnyMD6UNee77h8TVPNkzcgW32LHSd2A6F0oi1YzbrfcdyubugUCnIkS87Pk+SBkyPrj7lnzrjDK7VCgsMZ8X/NlCyljtthjRj05SdHF1zilqdqrL8n/U4uNjRZVxbVo34T39Mva7V9aUqO+ftT+Shu3rkpk/8K3x/ZOb0xW/qavvmxVv+qjkG32eZIxiL5+GVJym+l/Cmb+dkS+5iOVEYG+F524t7Fx8S8+59QRBQqhQpGmV/LHN6LU0SlAW8DmLbrL0IgkD7ES0ICwxnz8LDkongj8q7VC+VmTF22azRxKkR5O9X/i/sucrfK3+naqvy/JqvPyEBYVw5eJNmfevzv6ZTmHN+gj4I65KvH0e0W+g1ozOLB6WsODW1y3ymdpmvf917Vhfq/lqDJwNWADp11bkXJrB/2VGD6tnu4E8E6wGXdy2dgLHAGGAh4IOWrGh5hRzdZ2vcuy7rxm4lLCjtHTiJzEmgGMLZMzWIVvcGpgLz0AVkAApimIUnDSiR4BitRmuQHUNKVGhahp7TOrHkrzWMaJx4saBkzaJUb1cp1ePjYtSYWRkm1/8hd889oFi1wmnWpGXL7ZhuQRGFoKQsOVCLcYhEoBCkHbLvBQXGGHODD58uFGzEnvQr4X5VBBlaEZ23WMI05RTS0z+Vw6tPUqVlOSJDozi+8SwRwRGJFAoPrTpJVjcHoiNSXuD5GBuB60dvo9Voafe/n9k2ay8xkbE4uNhhYq5CZZb4/9mhlSe5tO86WRytP1nZVeLrkmnTF0vWck/WmDmzBWTp4dHVp0xsP4eYqFgCXgXy3+Sd+nzu9sNbMHLr4EQBVEasBImimKzKHoB5FjOyuTlS79catBnS9JPPJfFtk6eEG6d3JE09PLv9Ek2sOvHirjcRIZH8Pq8bxqZKrh25zayz45IEX3cvPKRRrzos85hp8Lm3zNhDlVbl9a//t2EAB1ccN1hgJAgn3gdk8fwOvAbuADJcncP4dcwvdBrdmk6jW3P33AMpIPvGeYlIdFy8aEwESSXw5cSRshhSeln3bCHDNv7BgErDk03TvXz0OmqTGDRiyrtZ2XI58Noz/eqjoLOMKVqlYJr93KsW4s7ZBx91DiNBgUKQ1Hq/JwRBwI1ILE3+BAKAOOQsw5FjmKUigiQBL+6+pOvE9kSFR1OwXN5kJeO7jG3Lz380zPBz3zxxh6uHbtL2n+YcWqnzNnx0zTOJloA6To2/TyCPr3tKtZ8G8l2mLwqCMA1oDMQCT4FfRVEMFgQhJ3AfiNfsvSiKYi9Dx3V0tad2x6qEBoZ9kgfDt8SJjWd5dM1TL8n957JeRIZFpepv8amEBYYzu9dSchdz5e3L5OttsuZ0oMeUDiz5a43kX/GdE/AqiAplcnPtwLUk75mYq3Bzd6XBb7Vo2qcuvp5vGNtqBh1GtKTjKJ2XSjzqGDWNzNozdP0Atr1dwZ7Fhzm06gSvn6b8IOr/MgBLWwvmnJ+AibmKiJBIds47kI7ZJ3cpU6F7UN+OpfIRVt4BrBm9OR1jSmR2tAhA/K6uFbogPKFhcxxK3oKB8vip0X9BdwRBoKFp+yTviaLIQ4IIoADterxFprTDLvYZbkLS86pj1Rh/hE9fPIakQWVzc+DAN7yAKZHxOAiW9BlclAnjWqIGcqDAWrD52tP6JATZ+4VrUft5ds20Wi1Dao9l0bWpbJmxm+q/VOLExrOJ+szuueSzic49uuaZxEBa4tPJrIlhn5q+eAQYKoqiWhCEKehcJ4e8e++pKIrFP2bQ8OAIfF/4/VDGxz5PfBPlI1/cew1/n+QDpYwkLiaOB5dTTr8M8Q/l6e3neu8qgILl8tJhREtm917G22SMhXMXy4nX/ZeS5P43Rmx0bJL0WSs7C/5Z0xdjU2Pm9FlGh+Et2TJ9N13GtkFlZkx4cIQ+BRfAyEiOXKF7SJ7Ufg622bPQ+q+m/LXid/x9ApiYioLiuNYzMFLI6TiqFWvHbEnX3K3wJoBQwDJB63JsLSOxizpNtlhzjKQV4e+OrGgIlu1GrW0KdAFGAqPR7ZhFoGIAuVF88nkcnO1o3LsuLRy74iMGEIgMFRpcsUApGPOCQHwYiUg1CAdoRRjTMBdPYi8k3qlzcLXnzLZLHz0XmUyGIAgpquZZO1ilKr0t8WMiN5JjbmxJfiFtQ3WJxDy58YwJv8xiyJp+3Dh2B697L3l667n+/Y+pTZWQSI5PCspEUUyoI30RaJlS3/QQERL5wxcnpqVg96WICo9mw4TtVGpelic3nuP73I+Xj19z7+IjwgLCkvSXG8kZsrYfa0Zt4uyOz2P0K5HBvFtVNLM0ITI0Stek0aVfzTk7jrVjNnN03RkATmzQrRAeXHGcDiNbUbhCfkY1n67P7Z+wfxhbpu/WDx3wKohFA1cB0HN6J9Y9W4ilnQWT2s1JUhcT/51vMbARNxN45xhCYSy4SjMi6QoUQBA2Yyaexz3UAbkgN0j5SuLbw5EsRCgXEWz8iKCQmshxwogmmGKPMeHkwhhTIbH5uV0OG5r0qWtwauzUoyOxdrBibr9lnPB7RjBjEKkMvOItf+IuhuGPpS4gA2AR4IuWPHjwHFfRi9xCFqzsLKnduSov7np/UtB09dBNqrQqz6nN55N9v0LTMp8U9El8nzT5vS6HV5382tP4dEStTqlBFBOppybiM9Sandx0HnWsGrnCiEa967Cw/wpp4fkb5UcR+ugKJIyk3ARBuAGEAsNFUTyT3EGCIPwG/Aag4uOKnyU+PxWalsXM2gzflX6EBYanaOKrUWsYWHmEXplI4tshLDAcp3zZErXZZstC57FtqdetJgDW9pb0Kv4X3g9fManDvER9Byzszp1zD1JcUFgyeA1LBq9h/fNFjN01hCG1x3L9mEeiPkZGchTGCsytTQ2SwI/HwtSCBYPaceX6dY7uW4CdaI2pkC3tAyW+aQRBIHeMBU0HWLNkyiDMUGKhF6hIPjUr0DeY36Z14sW9l4nS4zuObEXj3nXYMmNPooUFc2szfnMfhI8YQBDjgHghjxxEso7HNETU37t2A85AbwC0dMRHvoXGre9SKLsbpzadT7dH34fcPf+QHlM78viaZyIBATMrU6q0Ko9NNuuPrlmT+H6xy2H7UYITEu+JX2h2ypcNhbFCCsq+Yb7ZoEwQhKNA1mTeGiaK4q53fYYBamD9u/deAy6iKAYIglAK2CkIQmFRFJNIXomiuBRYCmAp2Hyb2qw/AAmV8tJCCsi+TWq0q8S+pUcTtQW9CWFAxWEE+gaTzc2RhdemIDOSQwLxmKW3phMbHcuLey9ZO3Zrmudpn7M3//ksxbWwc5KgrGjVQuQtmYtt/itpaNqe2GjD1EhbDW7CoVUneevtj4vglPYBEt8V1uZZyK7Iqq/JTW11XKvRsnjQagYv70PH3L/r29sMaUbXAgOYfW48XSe04623P2ZWphxefRKAAORAxQ9GUxBNdmx5QQiPgUvA+EQ9YjUtWb9xJSWEjKuPXjNqE7U7VaXBb7WIjYpFYawgPCicszsu4/P4dYadR+L7ISXBry+J3EiGTda06zsT1oolh6WN+TtjZysEWWK9umDfYNSaz/soaWlrQWRY1Gc9h8SPSZpBmSiKtVJ7XxCEzkAjoKb4LsldFMUYIObdf18TBOEpkA9IXcdXQkIiXTQf0IBsbo78N3kHgb7p9z1KiFar1ckMJ8Dz9gvyls7Npb3XCH4bgrGJki2+yzBSGulSADQazu64zNQuCw0+T6fRrQkPCk8kDhJPzsLOHN94lmy5HRmz8y+G1puQ5niWthao49TJ1jdK/Bh43ffBpUAOnt/1Nqj/8Q1naTf0Z/3rhVencO3wLfy8/WnnotOksnOyxT/BrpYKNfCGD9cojQgmDzZg8Qc+kY5oNB8+UApoM9h9JiYqlr1LjmTomBLfN5o4Ddb2lgS//Xg7iE/F0s6SP5b8ZlDf1LIOlSoFgkyg/wKd8mp8faWlrQVvvf2Z1NHwReSPIS4mLu1OEpmW7zZ9URCEeuiEPaqKohiZoN0eCBRFUSMIQi4gLyDJx0h8dkwtTXFwtjX44exb5+nN51jbW2WIv52ljQWhfsH6ejIAI6Vcb1YbFR5N33JDGbfnH87vvML6iduZemQEh1efSlfefrO+9RlcY3SSdp0oSBOGNZzE+D1DsbK1MGg8l4I5ePqB6bTEj4XH6XvU717LsN+9AA1/q0VUZBQdRrWgdodqnN1+iWVD1iXq5v9BmmFOzHnLIKJYQ7zioxGryU4EMsGaHdsn0af3GC49uQaUSnDkVWwJg2/ND0riu2LH3P10GNmSxX+ufr+j/IUJ8g1mZNOUjdU/FddCzrQb9nPi+9FnqC+ztLXAys7yk/wOJb4umTUo+1SfsvmABXBEEISbgiAsftdeBbgtCMItYCvQSxTFzy8l+INg72yHTJZpLea+Ku2G/UzHka1wdLX/2lP5Itw+dY+VwzdmSMqoylyVKEe++i+VKFOvBMWrv/cL9Lz9gmC/UKLCo4iNVmNiruLmCcPTstr972csbMyZfHg4ZeqWQGVqTLbcjnQZ24bld2czv/8KrB2tsHe2NXjcx9c8KVQ+v+EfVOK7w98nEGMTw7y1oqIiGTtnPWXKz+DPmdf5qVRv5v69NM3jlIIKd4KwoQFmdMWS5uRhHTneyd6vHPEfe04upWyRuWTJ8i/ggZXlYuz5G9cM9EqTkPgYQgPC2LvoMM361f/aU/nmWTN6MzXap24SLyHxMXyq+mKeFNq3AckrQUh8EmZWpgxZ3ZdL+68jCLBt1r5MkSueWTi27jRWdpa8efH2a08lcyMkDurtnWwJfB2MYKSTDxfVcfyx+DfuXXjIv/+sT3TclYM3yV8mN9OPjeD8rvRlJO+cfxBjCxNy5LZj4oH/6dsPrDjG+LazGLq2PyoLVbIiICkRExUrqStK8PyuFwXK5ErV4gPgf+NX4xO7CmJ1aYiBRBNEc8qJIoKQ+hfJQjCjJABh6G6f7+tjHlx6TI+2YY/XAAAgAElEQVQig2j1czkunDqLr2IvFSoV4+YOKSCTyBx4PfChTpdqqVoqZHYEuVz/3wmzOlIkAz3L4lHHqtO8VkhkbjLrTlnGJrpLfHYiQiJZN24rWo2Wxr3rYmKukjxpEvDMw+trT+GbpFzDEpzeelH/usvYNgT7hSAzkrMvcgP7lx/j4aVHqMxNWTVyE7uDVyHIBP6uPS5d54kMjWTlUF2QN573nmWNetZm1NbBbJ25h9Wj0meHkT13Vt48l4LwH53j68/y+7yueN33wUghZ83TBawdu4Vts/bq+8SKMTx8lI3EdWEqIulDALOxS0Gx0RBEUSQ8OJxDK08A0KRDTR5eST1AlJD4klRqXhbXQs7YZMtCwCspeSm9tPm7KRGhUdTrWgPPBD5lEt8W321NmcTX4eYJnYfT7dP3vvJMJL4HBLmcXMXc2PfvCb3qVbthLXh87RkDKo0ia047+s75FRNzFcWqFiIyLIom1l3Y4b8CuZE8jdHTJn+ZPPSa2YURTSZzw8DdsXgUSiMa9arNxok7PnkeEt82Wq2W1SM30W1ye4pUzM+B5cf4dfwvhAWGc3LTeSztLGgzpiFd/0wqC67FmVgMu0sbGcn5995ssjha0a3wQN54+3GfYEJxRYsSU9GTwoKKfGVyc3Td6Yz+mBISH41T/uzsWniI4ZsGMrDyiK89nY/CoN2xT6B+1xr8+W9vRFEkOiKGa4dvMabldA5rNhPiH8bdcw/YMm03eUq6fdZ5fIiRwggrOwsCXgd90fN+r0hB2UfS8LdaGCmM2LXg4NeeioTEd0mBMrnxOHtf//qXIU3xvO2Fha0ZlVuU4/Tm8wxrNBmAnEWcGfHfH2RxtGJat0UMXtGHEU2mfPS5c+TNxqitg+ng1odgv5B0HWvvZEvbf5qxY+4BwoIM9zST+H4JDQhj2V9rGfhvL5b9vQ6PM/ep0vIn6nfX+ex5P3hJXPB+oDsJc15VrMCOtIVlbLJas+nVMq4cusnB5cdY+3QB+eya4hu6CnABIJpofHL0YO/iwxn/ASUkPoFzOy5Tv3tNwgLDKVatcCKfvu8BQQYlaxZl+vHRHz1GzsLODG8ymUt7r2HnZEvn0a04rNmMVqOllWM3ACxszHEukD2DZm0YLQY2pEDZvEzuODdDhL0kMieZPigzsTBBLpdELSQkPhcatTbRjlebv5rQq8xQph0aRsPuNQh4GcDd8w8BeH7Hm25FBrHkxlSmdJ6Pc/6PvzFZ2pgzbvcQhtafkO6AzEhhRNt/mrF40BpJnlgiEdGRMexfepSyDUpwfteVJGbmeYnkAV2IYhhgjpK5OHMfpZC6f1I2N0dWPZrLnD7L9AFXre5VCNa6Ex+Q6VDx/GUbrjMLO+Hj0yElJDIa74ev2DRlF+o4DZ3HtP7ugjJRC9ePeTCp/Zy0OxuA/8sAZnRfzNoxW/FLYLlSomZRLh+4kSHnMJTjG8/h7xMoBWQZQGZOX8zU0U6Vlj/h8/g1m6bu+tpTkZD4bgkLDqdkzSIIMgFRKxIXq6bxbzX5o8pI1o7dyujtg6nXtQaCXI4gExBkAo+vezJm+1/M77cCtRhLtBiZrsLx3MVzssFrMYIgI+ojTDjrdKnGrgWHpIBMIllunbxLoQrJK3LaCpaUJZg89MSN9pTFA9c0AjKAt97+RIVHc3rzeYpXL8KYHX+j1sYSHp408NKSk2gD0yElJL4kIf6hRIZGpvvhPkQM56oYwVnRhouinKdi4DcrFoIge//PAPw+8MDM4mhFTGTM55hZirz19ufY+jNf9JzfM1ptxvzLaDJtUCaTyajdqRolahT92lORkPiuqd6mAvP6rwLASGmEuZUpd88/xN8nkLvnH9LWuQ9Nf69L0coF9ccIMhkzey9ixcHjXBBzcYmqXEZNgBhm0DknHxhOeHAEN47dZt3zhRQol9fg+QqCQI682fC6/zJdn1PixyIyNBJre8tk31MISnIKduQW7FAJpgaNp1ZrOLvjEqsezWPq0ZFsnraLnvmGYMxZIPHDqTErccD8Uz+ChMRnQRRFxHQ8UcaKMdzBjmD2Es0SwvmPFwzjGT9efZMgCOQs7MyLe9L9RyLjybTpi0oTJZf2XefczstfeyoSEt81olZEGxeHqNHQZ04XDq8+ydnt7393WkHLoBpjWXZrKh1y9QXApUAO1hw6jR9rAV0KYxwiD+hEOTEMI0GR6jmjI2PomPt3AE5sOse8CxMBqC1rleZ8m/Spy/EN0oqhROrsW3KUloMas3L4xgwbc3rXhTjnz072vNm4e/4hgiCQR4zmId2IYgRggYL55OAWSiFLhp1XQiKj8bz9Aveqhbh9Km3BsBeEEsViEq7ja6nBWxaTi290t+wjEWQC4cGf7gsq8XXJrOmLmTYo+3lAAwqVz8/eJVKxtITE5+Sttz+OOe3xfvCK0nWL0bvUkCR9osKjCfINwTZ7FgJeBREZHkUobsQHZDoEohjBS3qSE7sUz+ecPzsRoe9vah6n79Ot8B+0H96SI9otXNx3jRGNJyd7bIWmZYiNjuXpzecf+WklfhTCgsLxfuhD8epF9Iq1GYH3w1d4P3ylf+1sYc/AoXWZPul3QsKiyIEKMykgk8jknNx0nj+W9DQoKItBAByTtGswAxKLLC26PpWIFIIW5wI5Er0WBAGZXJZhXquCAIXK52Po+gGsH78Vr/s+GTJuQlwKOuH9wPBxWw5qTDY3BxYMWIlWk0kjgR+MzFxTlmmDsk1Td6EyM/7a05CQ+O7xevCKHHkcuXbnNrPnrKV2n0q8vOZLlVblWT50A836NSAmMgYHFzuWe8wkKjwKtUaDytyaqCSih5ao0zhfzQ5VuH0qcYG5130fJrWfw6T2cxi+aSC7Q9fSMdfvhPiHAmBsoqTFn43we+HPgeXHM+yzfzES1i58BjNTieQ5uvY0fWb/mqFBWULsnWxpO7Q568dtxTHcDEfB7LOcR0LicyCKIlZ2lvrrbEpkRY4f29GSMJMhDhN8AKtEfSOCIxlcY3SSMZzyZWfklkF0Gt1a3ybIBEwtTDi7/RIeZ+4nOSa9aLVawoMjyJE3K3+v7kvfskOT75jwGpzOa3OD7jVZNeI/g+fk+8yPYlUL41bEhaeSt5lEGmTaoEyj1hARIm0RS0h8bt74vmHPs8f4MJBbswtgb7eXFi3kyOVyZp4eS1hgOPlK5cLniS/dCw/EyEiOWq3BvVIOTp2JAd4vniiZTw5MUj2fvZNtqqpf49vMouPIVsy/NInfig0iJjKWXjM7s378Nvx9JMNTCcNRGCtQmRkjN5Jn2Gp8PHY5bGgzpBlLBq0hNlpSRJP49oiLNkwoyRZr7FmAP9FoaAU8xZTh5CdpmrogE6jVsQrGJkpEUXe9l8llvPZ8Q/8Kw4iOiE7cXxBoP6IFPzUujZmVKbN7LvnozyOTCTjnz0Gofxi7Fhz46HFSHl9GdGQMkekQpzq7/RLnd12RdskyGdJOmYSERKbkrjoM77cr4Z0wwVv/ASxfupwrSyaQRXi/CvrfS93NUv3u4baBey6ePe6Kt297RFwwZgVOeGCSipKdVtQiU4F79cIcXn0yxX5rx26hdueqDN80kE1Td3Fh99VvIyBLadVV2h37KhSonYfef84lKM4UBaG4IcNa+HQBjmLVClOuYUmW/rVWCsgkvln8vP1Rx6WV26ALnIqIdgSxBl+WY4aAE9bIE4jkmFqaUrlFOUBnnRJvcxL0JiTVBRFRFFk3disAjXrVocu4tvg+8+PgivRnRGi1Iud3XWZiu4yRxE9ILndXGvepy9YZe9I/Lykgy1Rk5vTFTKu+KCEhkTpuRV0wUujWVZQqJc361SeLY9rS3h8Sps4GHyjFxYmdeEXiVdToiMQSwI071adYpAx3ZlCAfpTjCW4fBGQFyuVli++/NOpTm7tiABfIQuc/z9Gt/wJCxXC0opYAMYAA0R/tB4FLWGAEE9vNoWqr8lw9dCvdn0vixyZKjGTpgZe8CN1CKBsJYD8eFCRE/HijcZWpMe2G/UxWNwcpIJP4pqnetiLulQsl8qhMDUEQsBGsKSTY4yrYIRfer+kXLJeXbhN/4cn1ZwyqNoonN57h7xOIv09gunao9y4+zKoR/xEdEU3z/g1S7BchRnBHDOK6GMtD0R+1+HmtUcytzajduRqL/liFz+PXn/VcEj820k6ZhMQ3SPbcWek5rROntpznwPLjFK1cgAJl8xITFcuBf48ZPI4gCBgbJ7dkFI7RB6paDy4/ZuzuIYxsMgWA2KhYosKisU/BILf/gu4U/CkfuxYc5OAdD3yFZYhibqJeAWjwpxFyIJK/ENBiyjIKilFYv6vLsXeyoe+8biwcsBJtZlvWknbEMj2eRBGhng0o37XIiWEqnjSgRDrHss1uQ/W2FbB2sGLnvAPfxq6thEQquBZ2ZkzL6ekKmoxNlMn6m4UFRfD6mV+G1UypY9UgQP3uNZPcz4LFCDxwJYYZgCmBvCKYrpQSBZQmStyKuqJUKQ1fMDGgvqxhz9psmrwj0y/ClKhRBD8vf3ye+H7tqWR6MtsjRTxSUCYh8Q3y6qkva8dt5emNZwBcO3KbexceERUencaRiXEt5ES0xg9PbgHF9O1ZLKfgGppYtGBq5wXsjVz/viFBzCaTyRiyth8yuYAgCNhmy8KTG8/pXepvtKKWC1ghkpuEB8fgAiwGBEQgggY8oCnlRBFBENj/7zG2zdxLWNDH72xI/LjEYA586FNmhJoskMDY2dTChLpdq2ORxVxvhhsZGkVkaCR2TrYYKYwI9A3i2PqzBL0J/mLzl5D4nCiMFYjatOXsi1UrTJM+9fDzeksWR2t8nrxmw4TtNPytFlb2llw5cIOmv9dn0Z+rMmxur5/5Uax6EVwLOnF2+yXCAt/fAzzREMMC3id6ZSeMeXjxG9VyOpCzsDN7I9YhCALz+v7L7oWHPmkuDi52mFqYEPw2dTGUr41FFnM6jGhFXGwcj68/48YxD64fvf21p5Upyczpi1JQJiHxjXL33INEr9MVkL1bBcxf2g31xTie8idB5ENQ5CR3vtcId++jEhI/0Gq1WuISrBQaKY3IUzIXMgEsbCzIkScbUzrN1c0lIgb/lwG6UyGixeKDCVwBGgFCgjaBSFqTpcARWv7anJvH72TegEzaEcv0mBFGIG9ILOUdhYUqlFqtGuBa2Jm4mDhiomI5tu50ot0vMytTTC1NCXgVKNWDSHyXHF51gq6T2hHqH8rmabuTvF+iRhF+alya0IAwJneYQ1ysrvbMvWohfh3flqNrTxP0JoQa7Soxq+eSDN1FenrzOY+vefL8jhe22bIkCspicSRp5U1ewlASF6tm+5x9LBq4inb/+5l+87vTb353/tdgAlcO3kz7xMlc1ys0LcPeJUc+7QN9AcKCwhneeBKt/27K1UO3qNyiHMWrF+bMtks8vu75tacnYSBSUCYh8YNiZWdJ7hJuHFp1kkKCDRrxDSsfDWX9sB2cuJe8D8vLx75UaFqG87uuYGKuosUfDRG1InKFDHVcXCL/pnjkghwT8TExaID4+gUFkFTBythYQ57iuVg7ZkuyaTISEoaSC0sC6U4ES4FsQABWxn/Q5ecqPLvjxdF1p1M8NiIkUlL/lfiueXHvJf8OWUep2u70mtmZqPBo5HIZb168xd7ZjrDAcBYNXJXkuNun7iXyNtsxd/9nmd/l/dcp17Ak1dpW5Ml1T87uuAyAgrfo0jQSLuh5Y0YcJNj52zBxOxsmbmfMjr8Zt/sfOuT6Xb9QaChKlRK3oi7sWfRt+OVGhUezbuxW+szuwry+y1EYK6jVuyKW+VRc2XgHmSDJSMQj7ZRJSEhkKpr2rcemKbv0r+WCHCO1EkeXxMbPKlNjBJnuBmhqqeLh1aeYmKuIDItiSqd579pNGb5pYIrnyg94/L+9+46vsrz/P/66TtbJJJOwAmHvpchUwDqKpYpbbKXWWlFbW/v92eGorW21jtpqa11oEVdBcFIcLBmiIIRNGIFACCskJITsea7fHwkxMQkr4z5J3s/HIw/Ofd33ue7PORc5J5/7uu7r4noKeAjohC8LMKyhlOv4JlErxb90HuvmuDHG1FuXyJnwM/6MsCXs5QcUEIybAnoUB7Fm9ganQxPxGusXb2H94m+GucV2iyE3K++spn1vCtkZOSyctRyAydMvZeQVw1n76UbiseTzECX8hYrvjlyC+QXdCAVX7e+NP17zFPf9525mp77Ee88soGOPWCI6hvPs9JcpLS7l0O40bvj1lXz66lJysmqOzLj6F5N475mPG305jaZUXlbOnk0pDJzYmzeWrWPxM8UY3+FERR4lNjeVmDKtpQhKykTEi7hcLgKCAsg8/M2QrY7dY+ncqwNRnSOqykLCg/lv6ktsqxwqWZhbxH2v3g1A9tETZ3y+UBPMKFvOEX5LMZaOuPHgQyJXUR5wFdZ68CuZz0CPSwmZNBo/409fTl5kOPX6edJyFdsiCskjiFD8TcDpnyD1Oro/w+kQavl4xhJu+u0UOvXqwLpPN3Lj9zsxZ8HtHE4to//gIEL2BlJyvP6p/f9++4vMeeJDZu36F8cOZXJkbzovb3q6xjE/feIWLnPdUKMsJi6a1B0Hm+Q1NaVPX11KZu9AjjMPCIcySMu6mvLI+wnP3oGf9T9tHeIMJWUibYw72M3Pnv1xrWEnr+9+jhm/fYMpP7+CBS8tYf/2A+Rl55O0PpkHr3jslHXaM7js5GN86EJUjbKR1nL59CI+eu5Tgsy37zuTxtS5d0eOHczUsFBpNay1bCeLLEZRzIUE8Dnt7Ub6EKmLO63MO099xMgrhnP9fVfy0fMLidmTTwzQy9OJfjeOYMHLi/GUleNy1T1E79DuI7WSrurm57zJ1b+4gg+fq1h0OrpzZIudZdVay86kQKDmEjUZWT8jZNSjFK897kxgXkITfYiI15j0k4uZ+7f5HEz65v4vX39fSkvLmPf0/9j6xU6eWPh7Duw6xNO3vQCnn6DrnHXu1ZFwd6QSsiZ2yx+ux+Vy4ePrw2u/n+10OCKNYj9ZpPEolrEAFDOZQ3xIGM/RkbqX6pCWa+2nG1n76cYaZfED49i3NRWA8nIPLp9zS8YfvuoJrrz7cr7zw4tY+/EG1i/azJG9RxscsxMsFktd74OL6Lgo/AtDcblcFOQW4u/2Y//2ltcb2FDempTprj+RNia6c2SNhAwq1oUpqexB2fn1bm6Ou5O3nn+f9B5ubrn7NTbZE+TY/EaNIygsiGt/NZn3n/24UeutYlzf/LRxpUWlxHaL4eMZ3j+LmMiZOkZYVUJ2kocppKHhWW3F5/9dxcSp44jtFtOgejYvT+TRm57hl6MfpDCviEcXPEBxQcscVeAyLkLYA9Scxj+Apylcf5RH//cAL238GyOvGE7v83rwk8dudibQNsAYE2eMWWaM2WGMSTTG3Huq49VTJtKGVMwm1Y3zLxtS4+ZuqJgG/KR8m88L7x+kiBeomCmxjFzuYZjdT6gJqVWvqWfIyKlM+8P1vP6Hd1rUTdQt1TtPfXT6g0REWhiPx8OMX7/BXc/8mJKCYvzdZ5+Qx3aL4Xt3XEp5WTnGZXC5XDxy7d/Y+sWOJoi4eQwgiC1cR2nQzeQXdCKQOcRziKyUcH4Yfzdd+3chdcdBojpFEtEh/PQVtiLNPHyxDLjPWrvBGBMKrDfGLLbWbq/rYCVlIm1IYIib/BMFHNmbXqPc5XJxPC2b8y4bwobFW0imhCL+RUVCBuBLMc+yj+8zpI56z+SeMoCwqFDOv3wovc/rTsLCTU27DpnWEhNp1aI4wQnWYBldVebif8RSDNS+eCStU2lJGc//cib9Rvbm4qnj+MljN+PxWPZtTeXLD9ZSVlrGqMnnccGk4axftJnNyxOrZpccMn4A5102hLf+PK9qLbbWwN8EMD4sgol3FPD2008QRiQ+5pvk6+QEJgPH9WXXuj1OhemY5krKrLVHgCOVj3ONMTuAzoCSMpG27sSxHP5xx0tMvf9qZj08p6r8hfVPsu3LnVXrz5QSRu3Z6tyU0K7Oeq3HUv2+epfLxYhJwwiLDKF9t2j8/CuSu7zsfNYv3sKy2asa82WJSBsUTxT5PEiO73gKyi7EbRYRbdfrfrI2yFPuYfvqXWxfvauqrP/oPtz22M24XIbtq5N45bdv0n1wV6Y//SM85R5OZORwy8PX88uxD7WqhAwqFr3uOSyeZbO/JMLUHtoZ1SmSvON5jJsyksdv+acDEbYa0caYhGrbM6y1M+o60BgTDwwHvq6vMiVlIm1MWUkZAUHfTBsdGOKm59B4/nj1U5RVfjG5yQayqTl7Uw5ujsO3ZlA8qfpsZ9MeuYFtX+xgx5okVsz9qtV94YmI84wxDCKK4rIEClhOsA3D3yghkwo71iSxY00Stz/+Q4ZdPJDkTSnsXLuHnWv38LNnbyN+UBwLZixm6MQBXHzzOBbNWs6ejfucDrtRdO3fmdXzE2rdP37S/718J0dTM1jy1opmjsw7NGJP2TFr7YjTHWSMCQHeA35lrc2p7zjdAS/Sxkz74w0seHFh1fYlP7wIa22N9Wl6EUwQPwFOlmUQxE/oRRB18XhsjaTM18+X9Yu3cGhPmhIyLxUeE8aUeyYREq7FRKVlCzCBRJgYrVEmdbIeDx/861Nu+PWVdO7VAYAXfvUac5/6iOCwIPKO5zPzwdlM/d3V9U6p39LMe/p/xA+K42fP3saIy4fW2BcaEcKuhD089/NXWffZJocidM7Je8oa4+dMGGP8qEjI3rbWvn+qY9VTJtLG+Pi6OLQnrWr7hvuuIjvjW7M0GTcjbDF7uYlCggkknx4E42/qXoDX4/EQ3j6saltLBHm//mP60G9kb44fPcHKeaudDkdEpEnsWpfMFT+9hP3bDzLumpFkp+ew+I0V7Ph6N/l/nscf3v01qz5Yy6Zl2+g7shc71iQ5HfI5GXbxIMZcNYKi/GJyj+cRFhXKoteXM/nOy0hYtBmouLXghw9fx5wnPnQ42rbBVFyt/g+ww1r7j9Mdr6RMpA2JiA0n80jNhSM79GjPq/e/XetYfxNAP05eeXafst6ykjJcPj489vGDLJu9it0bWscQkNZs9fwENi7dRlF+kdOhiIg0mS8/XIuPrwtPuYdVH6xlyPgBTP/bNMrLyjmcfJRHpz5DTmYufS7oxcczljgd7jnxC/Bj1OTzePH/ZmGM4f9euYvnfvYK9744nfnVRsbc9LspfP72F2Snn3AwWuc14+yL44BpwFZjzMluyQettZ/UdbCSMpE2xFpba8rg0uIyAkNOnXSdielD7uPFDU9hrWXF3K8aXJ80PSVkItIWrF+8hWt/NZlVH6xly8rtbFlZe/K7/OzGXYuzuQSGuLn2V5OrJuqy1vKPn74IQPLmFApyCjHGcMkPLyInM5ek9XudDNdxzTklvrV2FdS5knedlJSJtCGXThvPwteW1Sg7sOsw1947mfiBcWdUx9pNW5i7YCNFRQF07Wy5e9pkIiIjKSks4W8/fp7kzSlNELmIiMi5yT9RQElRKX3O71FvUlJaUoa1tpkja7grfnoJq97/mv3bD9Yod7lcDBrXD+MytIuewNpPN5L45U6HopQzoaRMpA0JjQipMWzB5XLRa1g8j079B0nrTn/1bF/eUdYdG0Wx53nAxZadx/jqi9u4qld3vjvtYiVkIiLilRbO/JzLfzyxVfUUuYMCiImLrpWQAVxz7/d495kFLfYeuabUjMMXz4qSMpE2ws/fl9Li0hplnspPphVzz2yihy22lGJ+Xa0kmqySP3OIf1QtxikiIuJtsjNyaN81BmNMrR4x0wJnp7rwmpEMHj+Ad56sPWnHxJvGknEwUwlZPbw1KWsxc39e8sOL+MGD19JzWHyL/OURcdoPHrqOrz/eUKu8uLCEv3760BnVUUrtRShhOKkZObzz5EcNjFBERKTpfDxjMbc//oMaZb5+vkyefmmLGdrXtX8X7nz6R5SVlvPi/80iKy27xv5b/nA9nXp20Ky6LVCLScqGfWcQn7yyhO6DunL/m78kIND/9E8SEQCCQgMpyC1k94bawzZe+e2btdYxqY8/aXWUrsaVkVPV6yYiIuKN9m1NZe0nG/npk7dw42+uYvh3BnHfq3eTlJBc50VLbzL++tHc/vgPGXRhP2Y9PIc1C9bXOia8fTsWzlxGaGSIAxG2DM29TtnZaDHDFzcs2cp1/+9KZj08h4k3jVNvmchZKCoorneGxcnTL8VT7uH7d13OgpcW1X6uLeAQBfgCcfhQyAOU8CfAH0ihHQ/TkcgmjV9ERKQxbFm5naSEZDr0iKXviB48c+fLlBSVOB3WKd34m6s4sPMw/3mg9vI1AEPGD2D0leeTcSCTwBA3H/37s2aOsGXx1mvILSYpWzZ7Ffu27OfHf5nK0rdXUlRQ7HRIIi3GrX+6iWWzV9W5r33XGFw+Ln7y2M21krIUm00qIyjh50A2gTxOT1aRyWS6DulP7u4kOhWG4jItptNdRETauKKCYlK2pZKyLdXpUE4pJDyYHz1yI8tmr2LH17vrPW7U989nxm/ebMbIpCm0qL+kUhIP8J8H3mbZnC+dDkWkRSkvK+dg0pE69736wNsYY3hy2nM1yktsEQcYRAmPA12AQRTyFgeJZDDtuPf68cQVheJjWsy1HRERkRbB18+X2x6dyhuPzD1lQjb4ov6kbj/QjJG1bN48fLFFJWUicm5yj+cR2TG8zn3t46IAWL94c43ydPIo5vZvHW0oYgwm2ENhnmZbFGkJTk5kMP760U6HIiJnaOr9VzPv6f+Rd5pFrS+8dhSL31jZTFG1Dt6alOkSt0grFxEbTkh4MOmpx+re3yECT7mHxz/7fY3y5V99xW8fSaO0bFCN8qj2JTz9wZ9Y9qZmdhJpKJfLRURsOzKPHG+yc4y7ZiQej+Ximy9i39ZULrt1IlhLwsLNbFm5vcnOKyLnrlqLC2wAACAASURBVKy0jLCoENJTj9U5kVZIeDA3P3gtKYkHNNFWK6GkTKSVm3znpbz79//Vu3/Xuj3ED4rjN5f8qUa5x3oIoIhSxlMxqQfAEcrTV/Lxv8NI3pTSZDGLtAUul4ufP/cTCnIK2b56F6vnJzTJeVbM/Yo7/34riV/tJDDEjV+AHyWFJXQf0lVJmYiXmvvUfJ5a8gfi+nXms5mf89rvZ+PycdG1fxe69uvEsO8MZtbDc8jJzHU61BbHW3NYJWUirVz20RN06N6efVvrvqH53hfu4IErHqtV7jIuhljYwfcpoD8uighjDwMIIyAoAJePRj+LNERQWCABbn98fH3YszGlSc/18n2vVz1OWl97aQwR8S4ej4eHJv+V+/7zMz59dSnh7dvxi+duZ9UHX3Ng12G+eO/rWotgy+mdvKfMGykpE2nlDicfJTwmrM5989JeJWHRJhIWbqpzf5AJ4nyg3B7AYHCZiqnvs9NPaB0UkQbKy87n6dtfcDoMEfFCxhhi49uz6v01TPvDDfj6+/L8vTNrLRbdULf+6SZysnL54J+fNGq9cvZ0qVuklbtg0jC2rNxR577QqFBe/+Pc09bhY3yqpr338fWh36jeZBzMbNQ4RaRtcfm4GDi2L75+uj4s8m03/W4Kt/1lKj6+Pjx3z6s8M/2lRk/IoGIisKK8okav15tpog8RcURYVCiXThvPd35wERkHjnFw12Hm/m0+Ho+H8rJyuvTuwM5TTLdb3dgpFzDiu8NY/Ppy0valN3HkItKa/eiRG9m7ZT/TbxpLUV4Rc/82/7QzzYm0FfOfX0j8oO3sXLsHT3nTjbd7/9mPm6xub9Rqhy8aYx4B7gAyKosetNZ+UrnvAeB2oBz4pbV2YUPOJSLnplOPWI4dyuTZu16mS++O9BnRk1e2/p32XaP54t3VLHnri9PW4XK5uOOpW9i8PJF//eyVZohaRFo7d3AACZ9tYuW81UR1jOCKn15CTFwUL9z7mtOhiTiuILeQ7auTnA5DmlFjDF98xlo7rPLnZEI2AJgKDAQmAS8YY3wa4VwicpY2rUjktd/P4UjyUdZ9tom3H32PgOAAPOWWJ761YHR9OvaMZd/WVNYsWN/E0YpIW9AuOozOvTtSkFux3mHmkeO8948F+Lv9T/NMEWksI68Yzh1PTaPn0HinQ2lWbW344hRgjrW2GNhnjNkDjAS0sJFIc7MVPV3V1zGJ7RrDD7rdXffh1lJALuWUE0o4Pr4+XPLDi/hs5ufNFbGItHI/fnQqPQZ3Y8xVI7jl99eTuHoXRfnFLHhpkdOhibQZ1lqsx0NRftu7p8wbNUZSdo8x5kdAAnCftfY40BlYU+2Yg5VltRhjpgPTAdwENUI4IlJdaUkpxmWg2odQeVk5V98zCb8AX97687vkZOUBUGgL2UoZ+UzB0o5O7Rdz65UDWT0/od7Fp0VEztas38/hg2cX0LV/F/559wwyDmZx/GjjT2Ig0hJ9/87LiO4SRVBYYJMO51332SbWfVb37MvS/E6blBljlgAd6tj1EPAi8BfAVv77d+AngKnj+DoXU7DWzgBmAISZSC24INLIjh3Momv/zjXWKbut7728uOGpqu0XfjULgG2UksN8IACAg+k/4Jn/3MBoPLjOYgTyLQ9fj8vHhTEVHwUFuYWcyMjBx8+H9P0ZHEw6wtH9GaepRURaqxPHcjhxLIfUnYedDkXE6/QcFs/Kd9cw7uqRTofS6rToiT6stZeeSUXGmFeABZWbB4G4aru7APrkFXFAwsJNTLhxbI2k7Mi+o1wdcStv7XsBP38/AIpsIQVM4GRCVsFQwM85xp9pT/QZn9Nay4fPfUpOZi4AQaGBhEWHUl5aTvuu0Zx/+VACQ9y898yC09QkIiLStsx8cDbdh3Tl5V+/4XQorZK3JmUNmujDGNOx2uY1wLbKx/OBqcaYAGNMd6A3sLYh5xKRc1NUUEJQWGCd+2Y9PJvhVw4kZ3A4SVE9KTepwGNAcbWjzn6OnrKSMgJD3FXbBbmFpO1LJ+NgJolf7eLYoSzcwQGnqEFERKRtyj2ex5YV2yktLnU6FGlGDb2n7CljzDAqhiamAHcCWGsTjTFzge1AGfBza215A88lIuegIKeAwBA37mB3rZt5F7+5kifnLOJ46VyouqczhYrE7M+AJb7rPKJSI87qnO8/+zE/eOg6SopK2L/9IAeTjpB15Dg+vj5Mvf9qNi7dytuPvtfwFyciItJGDBjThzFXXUBedj7vPPmh0+G0SC16+OKpWGunnWLfY1T8ZSciDlvw0mJ+9uyP+ccdL9UozySbE6X3Qo1JduKpuJYyh57dv2D61CF89WIO+ScKzvh8pSVlvP7Hd3AHBdClbyf6j+7NlJ9NIicrl8emPkvu8bxGeFUiIiJth7/bH+vxUF5a5nQoLVqrTMpEpGVIS0knYdFmJtw4lhVzv6oqL6IcDz1qHe9LOP35JzNnvMg/73yF7991+TldlSsqKGbPxn0kb0rBGMO+ralekZBNuWcS5WUeFs1aTklRidPhiIiInNamZdvYtGzb6Q+UFklJmUgbsXLeai6dNp7Lb53IoteXAxBDMHt5gxKerHakxc0a2tMZP18/0valc3hPGnc8NY33n/2YzMNZZ31ua63XrHNmjKFTzw5ExIazd3MK21cn1djfpU8nLrnlIrCVa7blFLBlxXaS1u91KGIRERFpLOopExHHLXlzJRdeM7KqxyzABNLZJnCQv1LKPUAubh6hJ/kYE1b1vC/eW8OWFdu5dNp4ImLbsXHpVqyFvVv20yE+hrSUDLLTTzj3ws6CtZYX/29WvfvdwQH4BfiReTiLRbOWY4zh/MuHcskt43nrL++Sm+V8T5+IiHi3oLAgbv3TjRQXljDzwf86HY5UarX3lIlIy7Pqg7Xc8vD1XPPL75F5OIvOm1LYtXslh1iED9CVUPwrE7LIThHc9fdbKcgt5NihLBa/vpySolJ6ndcdl8vFd2+7mP2JB7jy7u/yyatLSfxyp7MvrhHs2biPPRv3ERMXza1/vonsoydY/OZKtqzYzo2/mcKBnYcoLiypMQxURESkuu/eNpHFb6zgklvGOx2KfIuSMhHxGm/95V3CokKJ7BjB0IsHMfnOy0j8chd7t+znyN6jVcc9deu/iewQTruoUGK6RvOnD39LUFgQX364lsDQQEqLSuh7QS82L09k59e7HXxFjS/jwDFeuPc1QiNDuPKuy/EL8OPrBeu56ueTKCooVlImIiL16tgjloFj+rL8nS+dDkVaCGOtdTqGKmEm0o4ylzgdhkibY4yh9/k96Dm0G3H9OrNhyVZStqXi4+tD/okC8rLzAegzoic9h8Xz6atLHY5YRETEexljCAoLPKuZi73BEvvuemvtCKfjaCrR0SPslCkJjVLXzJmmUd8r9ZSJCNZakhKSSUpIBmDMVSOYPP0yYrvFcGhPGr7+PliPxeXjYs7jHzgcbcs0/vrR9BrenY2fb2Pj0q1OhyMiIk3IWtviErK2QsMXRaTFWD0/gQFj+mKMYevK7WxZud3pkFq0G39zFeOuHkloZCjxA7vi4+NDwqJNToclIiIiXkJJmYjU6T8PvI2vny9lWqSywZa+vYq0femUl3u448lp3PDrKzl2OIuUbalOhyYiItKmqKdMRFocJWSNI/NwFivfXQOAy+Vi7FUj6D+ql5IyERGRZuTNU+K7nA5ARKQtyTxynICgAPqN7O10KCIiIuIl1FMmItKM/Px9sR5LWHQokR3CyUrLdjokERGRNkM9ZSIibVyXPp34wUPXkXs8DyxKyERERJrRyeGLjfHT2JSUiYg0g+6Du/Krl6aTeTiL4PBgXv71G06HJCIiIl5CwxdFpNWJ6hhBnwt60mNIN1w+FdeefP18eefJDynILXQkpn1bU3nmzpcJiwplz8Z9lBaXOhKHiIhIW+atwxeVlIlIqzBkwgAuvGYU+TkFZB4+TtK6PSR8tonSkooZJC+9ZTztYsIcS8oADu0+wqHdRxw7v4iISFvnrUmZhi+KSKuQtC6Z/BMFpGw7wIKXFpG0fi+lJWX4+PrQc1g8A8b2pbys3OkwRURERGpRT5mINDu/AD+iOkVQXlpOxsHMRqmzqKCY1//4DuddOoS7/nErhXlFAJSXlpOyLZXZj39AxoFjjXIuERERaXm8eZ0yJWUi0mSMMXQbGMegcX2JiYtm+HcGkbBoM6XFpWQePk5wuyCiOkey5M2VjbaQ8oYlW9iyYjvlZeVYaxulThEREWkdlJSJSKsXEOhP35G96D+qN4GhgViPJSXxAGsWrOfYoSz6fPYQGQcz+fTVpVXP8fXz5ZJbLuKyH01g5bzV7Fq3p8FxlJWWNbgOERERkeaipExEGsV3b7uY6M6RbF+dxPwXFlYNH6zugUmP8eB/72Xwhf156sf/BioSqIWvLcPlcjH+htFMvGksy2avImn93uZ+CSIiItKKefPwRU30ISIN1nNYPEFhgbz96HtsXLq1zoSsXXQYg8f3Z9OyRLoP7sqMLX+vsd/j8bD8na/472Pvc/7lQ5srdBEREWlDmmvxaGPMTGNMujFm25nEpZ4yEWmQqE4R/O71X7D6fwn87o1fENkhHB8/n5oHWSjKLyLjUBZH96Uz+/H3Sd68v876xl0zkpXzVjdD5CJSXVBYEJEdwzm467DTobQaLh8XEe3bERIeTN6JAgrziijKL8JT7qWX6kWkMc0C/g28cSYHKykTkXPiF+DH9+64hLFTRlJcVML+xAN88d4aUnccoqSo5Jzr7dSrA5/N/LwRIxWRkyI7hNOxZwc69+pA+67R+Ph+cwEl/0Q+k6dfxuezV2GMwRjDsUOZ7N28n31bUykqKHYwcu8SGhlCRGw4kR3CiehQ8W9wu6Bax5WXleMp93DhNaP4+JUluIMDCAxx13jfgTonJTLGAFBWUlaVzFX8W0xRfnGtssK8Ii1KL3IazTl80Vq70hgTf6bHKykTkXMy7Q/Xs3Leavzd/oydcgGfz17V4DqjOkWSeTirEaITaZt8fH2I7RZDp56xdOwZS0RseI39WWnZHElOY+sXO0hPPVZr7b7R3x/Bm3+aV7Ud3TmSHkPjmXLPJNzBbgBKikpI2XaA5M0ppKe2nmUmAtz+RHaKqJFohUWH4nLVvNPDWktuVh5ZadkcT8smKSGZ42nZ9S5M3z4umj4jevLJK0vOKS5fP9+qZO6bf920iwkjNj6mqtwd7Mbf7Vf1vJNJXX0Jn8fjqUjwqiV1dSV+OZm5WuOxEbhcLjzeejNTG9OIzRBtjEmotj3DWjvjXCtTUiYiZyUoLIjr/m8yE24cS79RvVn32SYemvx4o9Q9/obRLJ/zVaPUJdJauYMC6Ngzlk49O9CxZyxBoYFAxR/a5WXlHN2fweE9aax6fy3Hj2Y36FzHDmVx7FAWaz/ZUFUWEOhPt4FxnHfpENp3ja764z89NYPkzfvZn3iA4sJz7y1vTi4fF+ddOoQhEwYw4YYxvPmneWSlZXMw6QhbV+4gJzPX8aU1ykrLyMsuIy87v1HrdblcBAT54w5210j4AkPcRHYIxx3sJjg8iAsmDW+UWXHbspi4KHoP78HXn2wgO/0Emz7fRkriAafDkoY7Zq0d0ViVKSkTkTMyZPwARkwaRlFeEUvf+qLG1fTGEtkhosF/RIq0Bu2iw6p6uzp0b4+ff0UPiLWWovwijuxN53ByGhuXbq23h6apFBeWkJSQTFJCco3ymLhoeg7txnmXDiYgMACoWNR939ZU9m3Z32gLxTeGXsO7M+aqEfj6+bJ+8WZee2g2A0b3YclbK50Ordl4PB4K8yp6xo4frfsYvwA/xl51QZN83rclgy7sh4+vD7MenkNEbDhDJw5gwk1jATh2MJNNn2/j0J40h6NsO7y1w1JJmUgLd9Pvrq4xZKUp9BnRk7zj+fzttueb7Ab1rv06c2DnoSapW8TbuFwuYuKiKhOvDkR1iqjqcQI4cSyHI8lHSUrYyxfvrqG0xPvX3ss4cIyMA8dYs2B9VZk7KID4QXFccMVwYrpEVZWnpaSzd/N+UhIPNNt9UDFdophw4xjaxbRjz8Z9vPPkRw26/1XkXBw/ms3yd74ZERITF82wiwdy2a0TATi0+wibliWScaD1DA32Jt48Jb6SMpEWzt/t1+RXMa/91WTC27dr0hnDxl0zko/+/VmT1S/S3Pzd/nTo3r6qxyssMrRqKJzH4yHjQCZHko+y9pMNZB4+7vgwuaZQVFDMzrV72Ln2m+Fvxhhiu8XQY2g3Lpg0DL8APzBQmFvIvq2p7N28n8wjxxvl/EGhgVx03Si69O3MsYOZLHlzJdkZOY1St0hjyDhwjMVvrKja7tSzAxdMGkb7rtEApGxLZdOyRLLTTzgVopwjY8xsYCIV954dBP5orf1PfccrKRMRrxAUFtTsw7BEGiokPLiqt6tjj/b4u/2r9pUUlZC2L53DyUdJ/HIXucfzHIzUe1hrSUtJJy0lna8+WldVHhjipvvgroyZcgFRnSIqD4bDyWns3byf1B0Hz6jH0MfXh/MvH8rgi/pTkFPAqve/ZuGs5U30akQa1+HkNA4nfzOUMX5gHBNuHENEbDjWY9mzcR9bVmzX50kDNOPsizefzfFKykTEcf1H9WbHmiSnwxCpxRhDVKeIqkk1YuKiaszGl5edz5Hko6RsS2X1/AQNh2uAwrwitq9OYvvqbz4LjDF07BFLj6HdGH3l+fj6V/zZkn+igH1b9rN3S2rVfah9zu/B6CtH4PJxsX7RZmY++N9W2fsobUtK4oGqSUFcLhc9h8Xz3dsmEhweTHlZOUnrktn6xQ4K84ocjrRl0PBFEZFTuOCK4cz+6/tOhyFnyRhDbHwMvYbF021gHDFx0WQeygJz+ud6u8gOEcQPimPDki1kHj7OkeQ0Nn2+jYwDmZrWuhlZa6t6Dla9/3VVeVBYED2GdGX89aMJj23HkAkD+Oi5T5n9+Adaq0taLY/Hw+4Ne9m9YS9Q0Svc94KeXPXzSQSGuCkpKmHH6iQSv0rSBaIWSEmZiDjK5ePC5eNqERMZtGV+AX7ED4yj57B4YuNjKgotHNl7lORNKXz98QYm3f4dOsS355XfveVssI1g5PfOw7iMZp3zUgU5BWxbtZNtq3YC8PTnj7Dy3TUORyXSvMrLymv0LvsF+DFgTB+uv+/7+Lv9KcwrYtsXO9i1LpmyUn3HnuSt19WUlImIo867ZDAblmxxOgypJqpjBN2HdKP74K70H92b/YkHKS0uZd+2VDYs2dKqFgwWEWktSotL2bw8kc3LEwFwB7sZdGE/pj5wNb5+vuRl57NleSJ7Nqa06R5/b33pSspExFGDxw9g1sNznA6jTXIHBdBtYBzdB3cltltMVXnm4Sz2btnPxy8vZtT3zuP1P77jYJQiInIuivKLSFi4iYSFm4CKiYmGTBhQde9ldvoJNi1LZH/iAd1/6QWUlImIYwIC/SktLtWXQRMzxtChe3t6DOlGtwFdKqYgp2K68v2JB9T7JdKESopLGTCmL09//ojToVQxLsOuhGQKTzPjrY+vj4aWtyJ52fl89dG6qllPw9u3Y+jEgUy4YQx+Ab7MeeJDaJzVKLyWJvoQEanD6CtHsPp/CU6H0aqEhAfTfXBXegzpRnhsO7AVkyUc2XuUfVtTWfvpRk2EINKMstNPcFOnO5wOo4ahEwdy8dRxp03KysvKcQcFeFVC2RIFtwvyynu6stNPsGLuV6wAJtw4lsgO4a0+KQMlZSIitfQa3p0Vc79yOowWKyI2nOGXDKLP+T0pzK+YDjnveD57t+xnxbzVWmxUROrl6+97RhPZvIkmu2moQRf243s/vcTpMMTLKSkTEUeERoaQm5XrdBgthsvlotvALgwc169qYd3jadm4Q9yUl3t4/Q+670tERORUNHxRRORbLrputKawPoXAEDf9RvWm38he+Af6Yz22aoHizMNZVceNu2Yk7aJCHYxURESk5VBSJiJSTccesaTtS3c6DK8R2y2GAWP70rVfZzBQlFfEjjW7ef/Zjyku1CKgIiIirZmSMhFpdjFx0WQcaLuz/fn6+dJzWDwDxvahXXQY1lrS92eQ+OUuls/5UrNRioiINBH1lImIVBp//WiWvrXS6TCaTWhkCAPG9KH3eT3w9felrLSM5I0pLH3rC3IydV+diIhIc2i195QZY94B+lZuhgPZ1tphxph4YAewq3LfGmvtXQ05l4i0Hp16xmJtxTplrXFoXlzfTgwY25eOPWIByMnMZcfqJOY88aFXTossIiIizmpQUmatvenkY2PM34Hq8y8nW2uHNaR+EWmdtq9OYsKNYwgMceMf6H9Wzy3KL6Yov4jCvKKKx3lFFFaWFVWWndznaYbLYQGB/vQZ0ZP+o3sTFBaEtZaDuw6zeXkiC19b1uTnFxERkTPXKnvKTjLGGOBG4DsNrcvlcuHr3/ZGVZYWl+o+Emkzlr79xTk9zxhDQKA/7hA37uAAAkPcuIPdBIa4aRcdWrkdUFVmXOaM6y0tLq1K5ir+rZb4VSsLCPSn/+g+dB/cFZePi5LCEnat28OCl5dQkFNwTq9LREREml6rHb5YzUXAUWvt7mpl3Y0xG4Ec4PfW2jr/CjPGTAemA7gJ4obfXIUxYD1tJ0EJiw4jKSFZi+iKnIa1lqKCYooKihu9bj9/X9zBbtwhbgKDA6oSv4jYdhXllUng9356CU/+6N98+eFaPOVe+skuIiIiLcppkzJjzBKgQx27HrLWflT5+GZgdrV9R4Cu1tpMY8z5wIfGmIHW2pxvV2KtnQHMAAgzkdbXz4fZf/2gWYYdeYtOPTvQf3Rvp8MQadNKS8ooLckj93jeKY+78JpRJG9OaZ6gREREpFF5a4px2qTMWnvpqfYbY3yBa4Hzqz2nGCiufLzeGJMM9AESGhStiIiIiIjIOfLWpMzVCHVcCuy01h48WWCMiTHG+FQ+7gH0BvY2wrlERERERERalca4p2wqNYcuAowH/myMKQPKgbustVmNcC4REREREZGz1qon+rDW/riOsveA9xpat4iIiIiISGPx1qSsMYYvioiIiIiIyDlqewuCiYiIiIhIm9Oqhy+KiIiIiIi0BN6alGn4ooiIiIiIiIPUUyYiIiIiIm2Ct/aUKSkTEREREZFWT/eUiYiIiIiIOMxbkzLdUyYiIiIiIuIg9ZSJiIiIiEirp+GLIiIiIiIiDvPWpEzDF0VERERERByknjIREREREWkTvLWnTEmZiIiIiIi0et58T5mGL4qIiIiIiDhIPWUiIiIiItImeGtPmZIyERERERFp9TR8UUREREREROpkrLVOx1AlzETafz38ArP/+gEeb01jm0BQWBBT77+a0uJSp0ORFmjohIFNfo7gdkEEtwsiPfVYk5/L27WLCeNERo7TYVTx8fchKMRNblZ+k9QfGhlCblbeGR3r8nVhPRbr8Z7vlXPl5/YjunMkR5KPOh1Ks2oXHcaJY97z//tMtYsO5cSx3AbVERYdSk4D62gpXD4ufHx99HdHM3H5uNj+1S5efeBtp0Op14Qbx7Jvy35m7vjnemvtCKfjaSo+PiNsUFBCo9SVl2ca9b3S8EUvUJBTwMwH/+t0GNJCvck8p0MQERERaRG8td9HwxdFREREREQcpJ4yERERERFp9bx5og8lZSIiIiIi0iZ4a1Km4YsiIiIiIiIOUk+ZiIiIiIi0et48fFE9ZSIiIiIi0iZ4PI3zcyaMMZOMMbuMMXuMMfef6liv7Cmb9sgNeMq9NI0VEREREWlF4gfEkZSQ7HQYrYoxxgd4HrgMOAisM8bMt9Zur+t4r0vK3n70PadDEBERERGRVqgZhy+OBPZYa/cCGGPmAFMA70/Kcjl+bIl9d7/TcTgkGjjmdBBSi9rFe6ltvJPaxTupXbyT2sV7tdW26eZ0AE1r/UIw0Y1UmdsYk1Bte4a1dka17c7AgWrbB4FR9VXmVUmZtTbG6RicYoxJsNaOcDoOqUnt4r3UNt5J7eKd1C7eSe3ivdQ2rZO1dlIzns7UFUJ9B2uiDxERERERkcZ1EIirtt0FOFzfwUrKREREREREGtc6oLcxprsxxh+YCsyv72CvGr7Yxs04/SHiALWL91LbeCe1i3dSu3gntYv3UttIg1hry4wx9wALAR9gprU2sb7jjbX1Dm0UERERERGRJqbhiyIiIiIiIg5SUiYiIiIiIuIgJWXNzBhzgzEm0RjjMcaMqFYeb4wpNMZsqvx5qdq+840xW40xe4wx/zLG1DXFpjRQfW1Tue+Byvd/lzHmu9XK1TbNyBjziDHmULXfk+9V21dnG0nzMMZMqnzv9xhj7nc6nrbOGJNS+dm06eQ6OsaYSGPMYmPM7sp/I5yOs7Uzxsw0xqQbY7ZVK6u3HfQ51jzqaRd9v4ijlJQ1v23AtcDKOvYlW2uHVf7cVa38RWA60LvypznXWGhL6mwbY8wAKmbMGUjFe/+CMcancrfapvk9U+335BM4bRtJE6t8r58HrgAGADdXtok46+LK35OTF5nuB5Zaa3sDSyu3pWnNovb3Qp3toM+xZjWLur+v9f0ijlFS1systTustbvO9HhjTEcgzFq72lbMyvIGcHWTBdiGnaJtpgBzrLXF1tp9wB5gpNrGq9TZRg7H1JaMBPZYa/daa0uAOVS0iXiXKcDrlY9fR59XTc5auxLI+lZxfe2gz7FmUk+71EftIs1CSZl36W6M2WiMWWGMuaiyrDMVi8+ddLCyTJpPZ+BAte2TbaC2ccY9xpgtlcNPTg77qa+NpHno/fc+FlhkjFlvjJleWRZrrT0CUPlve8eia9vqawf9HjlP3y/iGK1T1gSMMUuADnXsesha+1E9TzsCdLXWZhpjzgc+NMYMBOq6R0nrGJyjc2yb+tpAbdMETtVGVAwX/QsV7/NfgL8DP0Ft4TS9/95nnLX2sDGmPbDYGLPT6YDkFupMDgAAAetJREFUtPR75Cx9v4ijlJQ1AWvtpefwnGKguPLxemNMMtCHiisyXaod2gU43BhxtkXn0jZUtEFcte2TbaC2aQJn2kbGmFeABZWb9bWRNA+9/17GWnu48t90Y8wHVAy3OmqM6WitPVI5/Drd0SDbrvraQb9HDrLWHj35WN8v4gQNX/QSxpiYkzeOGmN6UDFpxN7KoQ25xpjRlTP7/Qior0dHmsZ8YKoxJsAY052Ktlmrtml+lX/AnHQNFZOzQD1t1NzxtWHrgN7GmO7GGH8qboqf73BMbZYxJtgYE3ryMXA5Fb8r84FbKw+7FX1eOaW+dtDnmIP0/SJOU09ZMzPGXAM8B8QAHxtjNllrvwuMB/5sjCkDyoG7rLUnb0K9m4qZggKBTyt/pJHV1zbW2kRjzFxgO1AG/NxaW175NLVN83rKGDOMiqEjKcCdAKdpI2li1toyY8w9wELAB5hprU10OKy2LBb4oHKFDl/gv9baz4wx64C5xpjbgVTgBgdjbBOMMbOBiUC0MeYg8EfgCepoB32ONZ962mWivl/ESaZi0jgRERERERFxgoYvioiIiIiIOEhJmYiIiIiIiIOUlImIiIiIiDhISZmIiIiIiIiDlJSJiIiIiIg4SEmZiIiIiIiIg5SUiYiIiIiIOOj/A2qmIzSLZ5EjAAAAAElFTkSuQmCC\n", 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" + ], + "text/plain": [ + " geometry providentia nes obs \\\n", + "FID \n", + "90 POINT (99.04540 19.93245) 0.065191 0.065191 NaN \n", + "\n", + " absolute_difference relative_difference \n", + "FID \n", + "90 3.706749e-09 0.000006 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "gdf_diff.loc[gdf_diff['relative_difference'] == gdf_diff['relative_difference'].max()]" ] @@ -1768,9 +1870,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "11388 0.000532 0.000532 3.343764e-06 \n", + "12161 0.001565 0.001565 3.318575e-06 \n", + "13187 0.001954 0.001954 -5.429364e-06 \n", + "... ... ... ... \n", + "41394 0.013110 0.013110 -1.945477e-06 \n", + "42998 0.013245 0.013245 -2.473612e-06 \n", + "43058 0.017365 0.017365 4.490074e-06 \n", + "43569 0.014070 0.014070 2.019532e-06 \n", + "43757 0.011703 0.011703 -3.059476e-06 \n", + "\n", + "[954 rows x 6 columns]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "gdf_eval['nes-prov'] = (gdf_eval['nes'] - gdf_eval['providentia'])*100 / gdf_eval['nes']\n", "gdf_eval" @@ -1898,9 +2811,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "31.754816180063894" + ] + }, + "execution_count": 46, + "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()" @@ -1908,9 +2832,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "-46.530489827009234" + ] + }, + "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'].min()" @@ -1925,18 +2860,64 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.scatter(gdf_eval['nes'], gdf_eval['model'])" ] diff --git a/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb b/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb index 59ad844..825ab97 100644 --- a/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb +++ b/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb @@ -205,7 +205,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -237,7 +237,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -351,28 +351,28 @@ " ...\n", " \n", " \n", - " 9995\n", - " POLYGON ((11.30000 51.00000, 11.40000 51.00000...\n", + " 4995\n", + " POLYGON ((11.30000 46.00000, 11.40000 46.00000...\n", " \n", " \n", - " 9996\n", - " POLYGON ((11.40000 51.00000, 11.50000 51.00000...\n", + " 4996\n", + " POLYGON ((11.40000 46.00000, 11.50000 46.00000...\n", " \n", " \n", - " 9997\n", - " POLYGON ((11.50000 51.00000, 11.60000 51.00000...\n", + " 4997\n", + " POLYGON ((11.50000 46.00000, 11.60000 46.00000...\n", " \n", " \n", - " 9998\n", - " POLYGON ((11.60000 51.00000, 11.70000 51.00000...\n", + " 4998\n", + " POLYGON ((11.60000 46.00000, 11.70000 46.00000...\n", " \n", " \n", - " 9999\n", - " POLYGON ((11.70000 51.00000, 11.80000 51.00000...\n", + " 4999\n", + " POLYGON ((11.70000 46.00000, 11.80000 46.00000...\n", " \n", " \n", "\n", - "

10000 rows × 1 columns

\n", + "

5000 rows × 1 columns

\n", "" ], "text/plain": [ @@ -384,13 +384,13 @@ "3 POLYGON ((2.10000 41.10000, 2.20000 41.10000, ...\n", "4 POLYGON ((2.20000 41.10000, 2.30000 41.10000, ...\n", "... ...\n", - "9995 POLYGON ((11.30000 51.00000, 11.40000 51.00000...\n", - "9996 POLYGON ((11.40000 51.00000, 11.50000 51.00000...\n", - "9997 POLYGON ((11.50000 51.00000, 11.60000 51.00000...\n", - "9998 POLYGON ((11.60000 51.00000, 11.70000 51.00000...\n", - "9999 POLYGON ((11.70000 51.00000, 11.80000 51.00000...\n", + "4995 POLYGON ((11.30000 46.00000, 11.40000 46.00000...\n", + "4996 POLYGON ((11.40000 46.00000, 11.50000 46.00000...\n", + "4997 POLYGON ((11.50000 46.00000, 11.60000 46.00000...\n", + "4998 POLYGON ((11.60000 46.00000, 11.70000 46.00000...\n", + "4999 POLYGON ((11.70000 46.00000, 11.80000 46.00000...\n", "\n", - "[10000 rows x 1 columns]" + "[5000 rows x 1 columns]" ] }, "execution_count": 10, @@ -461,28 +461,28 @@ " ...\n", " \n", " \n", - " 9995\n", - " POLYGON ((11.30000 51.00000, 11.40000 51.00000...\n", + " 4995\n", + " POLYGON ((11.30000 46.00000, 11.40000 46.00000...\n", " \n", " \n", - " 9996\n", - " POLYGON ((11.40000 51.00000, 11.50000 51.00000...\n", + " 4996\n", + " POLYGON ((11.40000 46.00000, 11.50000 46.00000...\n", " \n", " \n", - " 9997\n", - " POLYGON ((11.50000 51.00000, 11.60000 51.00000...\n", + " 4997\n", + " POLYGON ((11.50000 46.00000, 11.60000 46.00000...\n", " \n", " \n", - " 9998\n", - " POLYGON ((11.60000 51.00000, 11.70000 51.00000...\n", + " 4998\n", + " POLYGON ((11.60000 46.00000, 11.70000 46.00000...\n", " \n", " \n", - " 9999\n", - " POLYGON ((11.70000 51.00000, 11.80000 51.00000...\n", + " 4999\n", + " POLYGON ((11.70000 46.00000, 11.80000 46.00000...\n", " \n", " \n", "\n", - "

10000 rows × 1 columns

\n", + "

5000 rows × 1 columns

\n", "" ], "text/plain": [ @@ -494,13 +494,13 @@ "3 POLYGON ((2.10000 41.10000, 2.20000 41.10000, ...\n", "4 POLYGON ((2.20000 41.10000, 2.30000 41.10000, ...\n", "... ...\n", - "9995 POLYGON ((11.30000 51.00000, 11.40000 51.00000...\n", - "9996 POLYGON ((11.40000 51.00000, 11.50000 51.00000...\n", - "9997 POLYGON ((11.50000 51.00000, 11.60000 51.00000...\n", - "9998 POLYGON ((11.60000 51.00000, 11.70000 51.00000...\n", - "9999 POLYGON ((11.70000 51.00000, 11.80000 51.00000...\n", + "4995 POLYGON ((11.30000 46.00000, 11.40000 46.00000...\n", + "4996 POLYGON ((11.40000 46.00000, 11.50000 46.00000...\n", + "4997 POLYGON ((11.50000 46.00000, 11.60000 46.00000...\n", + "4998 POLYGON ((11.60000 46.00000, 11.70000 46.00000...\n", + "4999 POLYGON ((11.70000 46.00000, 11.80000 46.00000...\n", "\n", - "[10000 rows x 1 columns]" + "[5000 rows x 1 columns]" ] }, "execution_count": 11, @@ -520,7 +520,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -529,7 +529,7 @@ }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -552,7 +552,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -829,7 +829,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -861,7 +861,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 18, @@ -1141,7 +1141,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 22, @@ -1173,7 +1173,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -1451,7 +1451,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 27, @@ -1483,7 +1483,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 28, diff --git a/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb b/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb index 98c4665..39cdc4f 100644 --- a/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb +++ b/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb @@ -78,16 +78,7 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2814: UserWarning: Shapefile does not exist. It will be created now.\n", - " warnings.warn(msg)\n" - ] - } - ], + "outputs": [], "source": [ "# Method can be centroid, nearest and intersection\n", "grid.spatial_join(shapefile_path, method='centroid')" @@ -243,469 +234,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable lmp was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable IM was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable JM was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable LM was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable IHRST was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable I_PAR_STA was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable J_PAR_STA was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NPHS was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NCLOD was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NHEAT was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NPREC was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NRDLW was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NRDSW was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NSRFC was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable AVGMAXLEN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MDRMINout was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MDRMAXout was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MDIMINout was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MDIMAXout was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable IDAT was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DXH was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SG1 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SG2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DSG1 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DSG2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SGML1 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SGML2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SLDPTH was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ISLTYP was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable IVGTYP was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NCFRCV was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NCFRST was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable FIS was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable GLAT was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable GLON was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PD was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable VLAT was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable VLON was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ACPREC was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CUPREC was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MIXHT was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PBLH was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable RLWTOA was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable RSWIN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable U10 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable USTAR was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable V10 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable RMOL was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable T2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable relative_humidity_2m was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable T was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable U was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable V was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SH2O was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SMC was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable STC was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable AERO_ACPREC was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable AERO_CUPREC was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable AERO_DEPDRY was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable AERO_OPT_R was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DRE_SW_TOA was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DRE_SW_SFC was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DRE_LW_TOA was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DRE_LW_SFC was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ENG_SW_SFC was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ADRYDEP was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable WETDEP was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PH_NO2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HSUM was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable POLR was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_optical_depth_dim was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_optical_depth was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable satellite_AOD_dim was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable satellite_AOD was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_loading_dim was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_loading was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable clear_sky_AOD_dim was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable clear_sky_AOD was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable layer_thickness was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable mid_layer_pressure was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable interface_pressure was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable relative_humidity was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable mid_layer_height was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable mid_layer_height_agl was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable air_density was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable dry_pm10_mass was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable dry_pm2p5_mass was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable QC was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable QR was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable QS was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable QG was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_001 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_002 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_003 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_004 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_005 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_006 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_007 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_dust_008 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_001 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_002 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_003 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_004 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_005 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_006 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_007 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_ssa_008 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_om_001 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_om_002 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_om_003 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_om_004 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_om_005 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_om_006 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_bc_001 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_bc_002 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_so4_001 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_no3_001 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_no3_002 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_no3_003 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_nh4_001 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_unsp_001 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_unsp_002 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_unsp_003 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_unsp_004 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aero_unsp_005 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NO2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NO was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable O3 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NO3 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable N2O5 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HNO3 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HONO was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PNA was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable H2O2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NTR was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ROOH was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable FORM was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ALD2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ALDX was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PAR was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CO was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MEPX was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MEOH was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable FACD was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PAN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PACD was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable AACD was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable PANX was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable OLE was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ETH was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable IOLE was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TOL was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CRES was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable OPEN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MGLY was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable XYL was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ISOP was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ISPD was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TERP was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SO2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SULF was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ETOH was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ETHA was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CL2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HOCL was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable FMCL was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HCL was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable BENZENE was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SESQ was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable NH3 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable DMS was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SOAP_I was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SOAP_T was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SOAP_F was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SOAP_A was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable O was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable O1D was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable OH was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HO2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable XO2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable XO2N was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable MEO2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable HCO3 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable C2O3 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CXO3 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ROR was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TO2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TOLRO2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CRO was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable XYLRO2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable ISOPRXN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TRPRXN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SULRXN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CL was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable CLO was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TOLNRXN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable TOLHRXN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable XYLNRXN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable XYLHRXN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable BENZRO2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable BNZNRXN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable BNZHRXN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable SESQRXN was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_dim was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_1 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_3 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_4 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_5 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_6 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_7 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_8 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_SALT_total was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_OM_total was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_BC_total was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_SO4_total was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_NO3_total was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_NH4_total was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_1 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_2 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_3 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_4 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2353: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_5 was not loaded. It will not be written.\n", - " warnings.warn(msg)\n" - ] - } - ], + "outputs": [], "source": [ "grid.to_netcdf('grid_with_tz.nc')" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -714,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -723,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -732,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -755,11 +293,12 @@ " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']]]],\n", " dtype=object),\n", " 'dimensions': ('rlat', 'rlon', 'strlen'),\n", + " 'dtype': dtype('O'),\n", " 'grid_mapping': 'rotated_pole',\n", " 'coordinates': 'lat lon'}" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -777,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -786,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -795,7 +334,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -804,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -943,7 +482,7 @@ "[95121 rows x 3 columns]" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -961,7 +500,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -997,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1031,7 +570,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1067,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1089,7 +628,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1102,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1214,7 +753,7 @@ "[15000 rows x 2 columns]" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1232,7 +771,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { diff --git a/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb b/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb index 6585bf6..c55d60e 100644 --- a/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb +++ b/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb @@ -36,26 +36,7 @@ "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/rotated_nes.py:166: UserWarning: There is no variable called rotated_pole, projection has not been defined.\n", - " warnings.warn(msg)\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Original path: /gpfs/scratch/bsc32/bsc32538/HERMESv3/OUT_Complete_single/GFAS_p13h/HERMESv3_GR_GFAS_d01_2022050100.nc\n", "# Rotated grid from HERMES\n", @@ -64,9 +45,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "There is no variable called rotated_pole, projection has not been defined.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnessy_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_netcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mnessy_1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/esarchive/scratch/avilanova/software/NES/nes/load_nes.py\u001b[0m in \u001b[0;36mopen_netcdf\u001b[0;34m(path, comm, xarray, info, parallel_method, avoid_first_hours, avoid_last_hours, first_level, last_level, balanced)\u001b[0m\n\u001b[1;32m 68\u001b[0m nessy = RotatedNes(comm=comm, dataset=dataset, xarray=xarray, info=info, parallel_method=parallel_method,\n\u001b[1;32m 69\u001b[0m \u001b[0mavoid_first_hours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mavoid_first_hours\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mavoid_last_hours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mavoid_last_hours\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m first_level=first_level, last_level=last_level, create_nes=False, balanced=balanced,)\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0m__is_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mparallel_method\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Y'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/esarchive/scratch/avilanova/software/NES/nes/nc_projections/rotated_nes.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, comm, path, info, dataset, xarray, parallel_method, avoid_first_hours, avoid_last_hours, first_level, last_level, create_nes, balanced, times, **kwargs)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0mavoid_first_hours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mavoid_first_hours\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mavoid_last_hours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mavoid_last_hours\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0mfirst_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfirst_level\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlast_level\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_nes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_nes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m times=times, **kwargs)\n\u001b[0m\u001b[1;32m 75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcreate_nes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, comm, path, info, dataset, xarray, parallel_method, avoid_first_hours, avoid_last_hours, first_level, last_level, create_nes, balanced, times, **kwargs)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0;31m# Get projection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_projection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;31m# Complete dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/esarchive/scratch/avilanova/software/NES/nes/nc_projections/rotated_nes.py\u001b[0m in \u001b[0;36m_get_projection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'There is no variable called rotated_pole, projection has not been defined.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'dtype'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprojection_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mRuntimeError\u001b[0m: There is no variable called rotated_pole, projection has not been defined." + ] + } + ], "source": [ "nessy_1 = open_netcdf(path=path_1, info=True)\n", "nessy_1" @@ -81,312 +78,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': masked_array(\n", - " data=[[[16.27358055114746, 16.335533142089844, 16.46847152709961,\n", - " 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False],\n", - " [False, False, False, False]],\n", - " \n", - " [[False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " ...,\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False]],\n", - " \n", - " ...,\n", - " \n", - " [[False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " ...,\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False]],\n", - " \n", - " [[False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " ...,\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False]],\n", - " \n", - " [[False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " ...,\n", - " [False, False, False, 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"output_type": "stream", - "text": [ - "Rank 000: Creating bounds_file_1.nc\n", - "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! 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Variable cell_area was not loaded. It will not be written.\n", - " warnings.warn(msg)\n" - ] - } - ], + "outputs": [], "source": [ "nessy_1.to_netcdf('bounds_file_1.nc', info=True)" ] @@ -524,20 +119,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nessy_2 = open_netcdf('bounds_file_1.nc', info=True)\n", "nessy_2" @@ -552,310 +136,18 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': masked_array(\n", - " data=[[[16.27358055114746, 16.335533142089844, 16.46847152709961,\n", - " 16.40639305114746],\n", - " [16.335533142089844, 16.397274017333984, 16.530336380004883,\n", - " 16.46847152709961],\n", - " [16.397274017333984, 16.45880126953125, 16.59198760986328,\n", - " 16.530336380004883],\n", - 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False],\n", - " [False, False, False, False],\n", - " ...,\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False]],\n", - " \n", - " [[False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " ...,\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False]]],\n", - " fill_value=1e+20)}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nessy_2.lon_bnds" ] @@ -876,20 +168,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Original path: /gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc\n", "# Rotated grid from MONARCH\n", @@ -907,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -923,154 +204,27 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rank 000: Loading O3_all var (1/1)\n", - "Rank 000: Loaded O3_all var ((1, 24, 271, 351))\n" - ] - } - ], + "outputs": [], "source": [ "nessy_3.load()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': array([[[16.2203979 , 16.30306824, 16.48028979, 16.39739715],\n", - " [16.30306855, 16.3853609 , 16.56280424, 16.48029011],\n", - " [16.38536121, 16.46727425, 16.64493885, 16.56280455],\n", - " 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- "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nessy_3.lon_bnds" ] @@ -1084,24 +238,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rank 000: Creating bounds_file_3.nc\n", - "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n", - "Rank 000: Writing O3_all var (1/1)\n", - "Rank 000: Var O3_all created (1/1)\n", - "Rank 000: Filling O3_all)\n", - "Rank 000: Var O3_all data (1/1)\n", - "Rank 000: Var O3_all completed (1/1)\n" - ] - } - ], + "outputs": [], "source": [ "nessy_3.to_netcdf('bounds_file_3.nc', info=True)" ] @@ -1115,20 +254,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - 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False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " ...,\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False]],\n", - " \n", - " ...,\n", - " \n", - " [[False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " ...,\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False]],\n", - " \n", - " [[False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " ...,\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False]],\n", - " \n", - " [[False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " ...,\n", - " [False, False, False, False],\n", - " [False, False, False, False],\n", - " [False, False, False, False]]],\n", - " fill_value=1e+20)}" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nessy_4.lon_bnds" ] diff --git a/tutorials/5.Geospatial/5.5.Calculate_Geometry_Cell_Area.ipynb b/tutorials/5.Geospatial/5.5.Calculate_Geometry_Cell_Area.ipynb index a1bf2e7..db7d983 100644 --- a/tutorials/5.Geospatial/5.5.Calculate_Geometry_Cell_Area.ipynb +++ b/tutorials/5.Geospatial/5.5.Calculate_Geometry_Cell_Area.ipynb @@ -163,27 +163,27 @@ "data": { "text/plain": [ "\n", - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", " ...\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]\n", "Length: 150, dtype: geometry" ] }, diff --git a/tutorials/6.Others/6.1.Add_Time_Bounds.ipynb b/tutorials/6.Others/6.1.Add_Time_Bounds.ipynb index 7723801..c891654 100644 --- a/tutorials/6.Others/6.1.Add_Time_Bounds.ipynb +++ b/tutorials/6.Others/6.1.Add_Time_Bounds.ipynb @@ -41,7 +41,15 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The history saving thread hit an unexpected error (OperationalError('database is locked')).History will not be written to the database.\n" + ] + } + ], "source": [ "array = np.array([[datetime.datetime(year=2020, month=2, day=20), \n", " datetime.datetime(year=2020, month=2, day=15)]])\n", diff --git a/tutorials/6.Others/6.2.Selecting.ipynb b/tutorials/6.Others/6.2.Selecting.ipynb index 7118c4d..7610fb8 100644 --- a/tutorials/6.Others/6.2.Selecting.ipynb +++ b/tutorials/6.Others/6.2.Selecting.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -44,17 +44,17 @@ "output_type": "stream", "text": [ "2022-11-15 12:00:00 37 2022-11-17 00:00:00\n", - "CPU times: user 244 ms, sys: 1.35 s, total: 1.59 s\n", - "Wall time: 3.33 s\n" + "CPU times: user 312 ms, sys: 1.53 s, total: 1.84 s\n", + "Wall time: 6.43 s\n" ] }, { "data": { "text/plain": [ - "'\\nCPU times: user 1.17 s, sys: 6.79 s, total: 7.96 s\\nWall time: 31.4 s\\n'" + "'\\nCPU times: user 244 ms, sys: 1.35 s, total: 1.59 s\\nWall time: 3.33 s\\n'" ] }, - "execution_count": 13, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -88,17 +88,17 @@ "output_type": "stream", "text": [ "2022-11-16 00:00:00 9 2022-11-16 08:00:00\n", - "CPU times: user 70.8 ms, sys: 363 ms, total: 433 ms\n", - "Wall time: 1.57 s\n" + "CPU times: user 67.2 ms, sys: 347 ms, total: 414 ms\n", + "Wall time: 415 ms\n" ] }, { "data": { "text/plain": [ - "'\\nCPU times: user 295 ms, sys: 1.47 s, total: 1.76 s\\nWall time: 6.77 s\\n'" + "'\\nCPU times: user 70.8 ms, sys: 363 ms, total: 433 ms\\nWall time: 1.57 s\\n'" ] }, - "execution_count": 12, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -134,17 +134,17 @@ "output_type": "stream", "text": [ "2022-11-15 12:00:00 12 2022-11-15 23:00:00\n", - "CPU times: user 95.1 ms, sys: 490 ms, total: 585 ms\n", - "Wall time: 1.47 s\n" + "CPU times: user 89.8 ms, sys: 439 ms, total: 529 ms\n", + "Wall time: 530 ms\n" ] }, { "data": { "text/plain": [ - "'\\nCPU times: user 274 ms, sys: 1.44 s, total: 1.71 s\\nWall time: 7.53 s\\n'" + "'\\nCPU times: user 95.1 ms, sys: 490 ms, total: 585 ms\\nWall time: 1.47 s\\n'" ] }, - "execution_count": 11, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -175,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -183,17 +183,17 @@ "output_type": "stream", "text": [ "1\n", - "CPU times: user 14.4 ms, sys: 70 ms, total: 84.4 ms\n", - "Wall time: 5.55 s\n" + "CPU times: user 17.3 ms, sys: 51.1 ms, total: 68.4 ms\n", + "Wall time: 94.3 ms\n" ] }, { "data": { "text/plain": [ - "'\\nCPU times: user 77.3 ms, sys: 248 ms, total: 325 ms\\nWall time: 17.1 s\\n'" + "'\\nCPU times: user 14.4 ms, sys: 70 ms, total: 84.4 ms\\nWall time: 5.55 s\\n'" ] }, - "execution_count": 10, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -221,24 +221,24 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 4.11 ms, sys: 22 ms, total: 26.2 ms\n", - "Wall time: 4.2 s\n" + "CPU times: user 5.03 ms, sys: 13 ms, total: 18 ms\n", + "Wall time: 112 ms\n" ] }, { "data": { "text/plain": [ - "'\\nCPU times: user 13.9 ms, sys: 74.9 ms, total: 88.8 ms\\nWall time: 16.3 s\\n'" + "'\\nCPU times: user 4.11 ms, sys: 22 ms, total: 26.2 ms\\nWall time: 4.2 s\\n'" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -264,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ diff --git a/tutorials/6.Others/6.3.Plot.ipynb b/tutorials/6.Others/6.3.Plot.ipynb index c9b3ec9..81f1f9d 100644 --- a/tutorials/6.Others/6.3.Plot.ipynb +++ b/tutorials/6.Others/6.3.Plot.ipynb @@ -62,7 +62,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -71,7 +71,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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\n", 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", + "image/png": 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\n", 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" ] @@ -154,22 +154,22 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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\n", 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" ] @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -293,7 +293,7 @@ "[17061 rows x 1 columns]" ] }, - "execution_count": 16, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -304,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -416,7 +416,7 @@ "[17061 rows x 2 columns]" ] }, - "execution_count": 17, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -428,12 +428,12 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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\n", "text/plain": [ "
" ] @@ -468,7 +468,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8 (default, Aug 12 2021, 07:06:15) \n[GCC 8.4.1 20200928 (Red Hat 8.4.1-1)]" + "version": "3.7.4" }, "vscode": { "interpreter": { -- GitLab From 7ba6d2514a92d0c423487194a491d55d4e9d7bfe Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Thu, 6 Apr 2023 16:16:42 +0200 Subject: [PATCH 11/21] Add clean output script --- tests/clean_output.sh | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 tests/clean_output.sh diff --git a/tests/clean_output.sh b/tests/clean_output.sh new file mode 100644 index 0000000..bac741a --- /dev/null +++ b/tests/clean_output.sh @@ -0,0 +1,25 @@ +#!/bin/bash + +rm 1.1.* +rm 1.2.* +rm 1.3.* + +rm 2.1.* +rm 2.2.* +rm 2.3.* +rm 2.4.* + +rm 3.1.* +rm 3.2.* +rm 3.3.* + +rm 4.1.* +rm 4.2.* +rm 4.3.* + +rm slurm* +rm log* +rm Times* +rm CONS* +rm NN* +rm -rf regular_shp \ No newline at end of file -- GitLab From 048c89c3f8958a6863c13f4dd9de6154875b9818 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Thu, 6 Apr 2023 16:31:58 +0200 Subject: [PATCH 12/21] Fix indents --- nes/nc_projections/default_nes.py | 26 ++++++++++---------- nes/nc_projections/lcc_nes.py | 12 ++++----- nes/nc_projections/mercator_nes.py | 12 ++++----- nes/nc_projections/points_nes.py | 14 ++++++----- nes/nc_projections/points_nes_ghost.py | 12 ++++----- nes/nc_projections/points_nes_providentia.py | 16 ++++++------ nes/nc_projections/rotated_nes.py | 12 ++++----- 7 files changed, 53 insertions(+), 51 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 00ca9e3..1969ce0 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -2416,8 +2416,8 @@ class Nes(object): # Split strings into chars (S1) if len(var_dict['data'].shape) == 2: data_aux = np.chararray(shape=(var_dict['data'].shape[0], - var_dict['data'].shape[1], - self.strlen)) + var_dict['data'].shape[1], + self.strlen)) for lat_n in range(var_dict['data'].shape[0]): for lon_n in range(var_dict['data'].shape[1]): chars = [v for v in var_dict['data'][lat_n][lon_n]] @@ -2427,10 +2427,10 @@ class Nes(object): elif len(var_dict['data'].shape) == 4: data_aux = np.chararray(shape=(var_dict['data'].shape[0], - var_dict['data'].shape[1], - var_dict['data'].shape[2], - var_dict['data'].shape[3], - self.strlen)) + var_dict['data'].shape[1], + var_dict['data'].shape[2], + var_dict['data'].shape[3], + self.strlen)) for time_n in range(var_dict['data'].shape[0]): for lev_n in range(var_dict['data'].shape[1]): for lat_n in range(var_dict['data'].shape[2]): @@ -2572,7 +2572,7 @@ class Nes(object): raise NotImplementedError("SHAPE APPEND ERROR: {0}".format(att_value.shape)) if self.info: print("Rank {0:03d}: Var {1} data ({2}/{3})".format( - self.rank, var_name, i + 1, len(self.variables))) + self.rank, var_name, i + 1, len(self.variables))) else: raise ValueError("Cannot append None Data for {0}".format(var_name)) else: @@ -2916,17 +2916,17 @@ class Nes(object): geometry = [] for i in range(aux_b_lons.shape[0]): geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), - (aux_b_lons[i, 1], aux_b_lats[i, 1]), - (aux_b_lons[i, 2], aux_b_lats[i, 2]), - (aux_b_lons[i, 3], aux_b_lats[i, 3]), - (aux_b_lons[i, 0], aux_b_lats[i, 0])])) + (aux_b_lons[i, 1], aux_b_lats[i, 1]), + (aux_b_lons[i, 2], aux_b_lats[i, 2]), + (aux_b_lons[i, 3], aux_b_lats[i, 3]), + (aux_b_lons[i, 0], aux_b_lats[i, 0])])) fids = np.arange(len(self._lat['data']) * len(self._lon['data'])) fids = fids.reshape((len(self._lat['data']), len(self._lon['data']))) fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), - geometry=geometry, - crs="EPSG:4326") + geometry=geometry, + crs="EPSG:4326") self.shapefile = gdf else: diff --git a/nes/nc_projections/lcc_nes.py b/nes/nc_projections/lcc_nes.py index 1fda0b1..cf48cad 100644 --- a/nes/nc_projections/lcc_nes.py +++ b/nes/nc_projections/lcc_nes.py @@ -483,10 +483,10 @@ class LCCNes(Nes): geometry = [] for i in range(aux_b_lons.shape[0]): geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), - (aux_b_lons[i, 1], aux_b_lats[i, 1]), - (aux_b_lons[i, 2], aux_b_lats[i, 2]), - (aux_b_lons[i, 3], aux_b_lats[i, 3]), - (aux_b_lons[i, 0], aux_b_lats[i, 0])])) + (aux_b_lons[i, 1], aux_b_lats[i, 1]), + (aux_b_lons[i, 2], aux_b_lats[i, 2]), + (aux_b_lons[i, 3], aux_b_lats[i, 3]), + (aux_b_lons[i, 0], aux_b_lats[i, 0])])) # Create dataframe cointaining all polygons fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1]) @@ -494,8 +494,8 @@ class LCCNes(Nes): fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), - geometry=geometry, - crs="EPSG:4326") + geometry=geometry, + crs="EPSG:4326") self.shapefile = gdf else: diff --git a/nes/nc_projections/mercator_nes.py b/nes/nc_projections/mercator_nes.py index 10135f4..3b74b39 100644 --- a/nes/nc_projections/mercator_nes.py +++ b/nes/nc_projections/mercator_nes.py @@ -463,10 +463,10 @@ class MercatorNes(Nes): geometry = [] for i in range(aux_b_lons.shape[0]): geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), - (aux_b_lons[i, 1], aux_b_lats[i, 1]), - (aux_b_lons[i, 2], aux_b_lats[i, 2]), - (aux_b_lons[i, 3], aux_b_lats[i, 3]), - (aux_b_lons[i, 0], aux_b_lats[i, 0])])) + (aux_b_lons[i, 1], aux_b_lats[i, 1]), + (aux_b_lons[i, 2], aux_b_lats[i, 2]), + (aux_b_lons[i, 3], aux_b_lats[i, 3]), + (aux_b_lons[i, 0], aux_b_lats[i, 0])])) # Create dataframe cointaining all polygons fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1]) @@ -474,8 +474,8 @@ class MercatorNes(Nes): fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), - geometry=geometry, - crs="EPSG:4326") + geometry=geometry, + crs="EPSG:4326") self.shapefile = gdf else: diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index ba0f234..5c61bf1 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -342,7 +342,7 @@ class PointsNes(Nes): msg = "WARNING!!! " msg += "Different data types for variable {0}. ".format(var_name) msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, - var_dict['data'].dtype) + var_dict['data'].dtype) warnings.warn(msg) sys.stderr.flush() try: @@ -399,7 +399,7 @@ class PointsNes(Nes): if self.info: print('Rank {0:03d}: Writing {1} var ({2}/{3})'.format(self.rank, var_name, i + 1, - len(self.variables))) + len(self.variables))) if not chunking: var = netcdf.createVariable(var_name, var_dtype, var_dims, zlib=self.zip_lvl > 0, complevel=self.zip_lvl) @@ -474,7 +474,7 @@ class PointsNes(Nes): self._set_var_crs(var) if self.info: print('Rank {0:03d}: Var {1} completed ({2}/{3})'.format(self.rank, var_name, i + 1, - len(self.variables))) + len(self.variables))) return None @@ -525,15 +525,17 @@ class PointsNes(Nes): # dimensions = (time, station) axis = 0 else: - raise NotImplementedError("The points NetCDF must only have surface values (without levels).") + msg = "The points NetCDF must only have surface values (without levels)." + raise NotImplementedError(msg) else: raise NotImplementedError( "Parallel method '{meth}' is not implemented. Use one of these: {accept}".format( meth=self.parallel_method, accept=['X', 'T'])) data_list[var_name]['data'] = np.concatenate(data_aux, axis=axis) except Exception as e: - print("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format(var_name)) - sys.stderr.write("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format(var_name)) + msg = "**ERROR** an error has occurred while gathering the '{0}' variable.\n".format(var_name) + print(msg) + sys.stderr.write(msg) print(e) sys.stderr.write(str(e)) # print(e, file=sys.stderr) diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py index 1d64337..f0b3423 100644 --- a/nes/nc_projections/points_nes_ghost.py +++ b/nes/nc_projections/points_nes_ghost.py @@ -337,7 +337,7 @@ class PointsNesGHOST(PointsNes): msg = "WARNING!!! " msg += "Different data types for variable {0}. ".format(var_name) msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, - var_dict['data'].dtype) + var_dict['data'].dtype) warnings.warn(msg) sys.stderr.flush() try: @@ -394,7 +394,7 @@ class PointsNesGHOST(PointsNes): if self.info: print("Rank {0:03d}: Writing {1} var ({2}/{3})".format(self.rank, var_name, i + 1, - len(self.variables))) + len(self.variables))) if not chunking: var = netcdf.createVariable(var_name, var_dtype, var_dims, @@ -447,7 +447,7 @@ class PointsNesGHOST(PointsNes): else: try: var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max']] = att_value + self.write_axis_limits['t_min']:self.write_axis_limits['t_max']] = att_value except IndexError: raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], @@ -461,8 +461,8 @@ class PointsNesGHOST(PointsNes): elif len(att_value.shape) == 3: try: var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - :] = att_value + self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + :] = att_value except IndexError: raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], @@ -486,7 +486,7 @@ class PointsNesGHOST(PointsNes): self._set_var_crs(var) if self.info: print("Rank {0:03d}: Var {1} completed ({2}/{3})".format(self.rank, var_name, i + 1, - len(self.variables))) + len(self.variables))) return None diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py index 56c27e6..3dc5cfa 100644 --- a/nes/nc_projections/points_nes_providentia.py +++ b/nes/nc_projections/points_nes_providentia.py @@ -291,10 +291,10 @@ class PointsNesProvidentia(PointsNes): values['data'] = values['data'][self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] elif coordinate_len == 2: values['data'] = values['data'][self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], - self.read_axis_limits['t_min']:self.read_axis_limits['t_max']] + self.read_axis_limits['t_min']:self.read_axis_limits['t_max']] elif coordinate_len == 3: values['data'] = values['data'][self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], - self.read_axis_limits['t_min']:self.read_axis_limits['t_max'], :] + self.read_axis_limits['t_min']:self.read_axis_limits['t_max'], :] else: raise NotImplementedError("The coordinate has wrong dimensions: {dim}".format( dim=values['data'].shape)) @@ -370,7 +370,7 @@ class PointsNesProvidentia(PointsNes): msg = "WARNING!!! " msg += "Different data types for variable {0}. ".format(var_name) msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, - var_dict['data'].dtype) + var_dict['data'].dtype) warnings.warn(msg) sys.stderr.flush() try: @@ -427,7 +427,7 @@ class PointsNesProvidentia(PointsNes): if self.info: print("Rank {0:03d}: Writing {1} var ({2}/{3})".format(self.rank, var_name, i + 1, - len(self.variables))) + len(self.variables))) if not chunking: var = netcdf.createVariable(var_name, var_dtype, var_dims, @@ -480,7 +480,7 @@ class PointsNesProvidentia(PointsNes): else: try: var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max']] = att_value + self.write_axis_limits['t_min']:self.write_axis_limits['t_max']] = att_value except IndexError: raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], @@ -494,8 +494,8 @@ class PointsNesProvidentia(PointsNes): elif len(att_value.shape) == 3: try: var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - :] = att_value + self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + :] = att_value except IndexError: raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], @@ -518,7 +518,7 @@ class PointsNesProvidentia(PointsNes): self._set_var_crs(var) if self.info: print("Rank {0:03d}: Var {1} completed ({2}/{3})".format(self.rank, var_name, i + 1, - len(self.variables))) + len(self.variables))) return None diff --git a/nes/nc_projections/rotated_nes.py b/nes/nc_projections/rotated_nes.py index 41afd48..3cd6f99 100644 --- a/nes/nc_projections/rotated_nes.py +++ b/nes/nc_projections/rotated_nes.py @@ -542,10 +542,10 @@ class RotatedNes(Nes): geometry = [] for i in range(aux_b_lons.shape[0]): geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), - (aux_b_lons[i, 1], aux_b_lats[i, 1]), - (aux_b_lons[i, 2], aux_b_lats[i, 2]), - (aux_b_lons[i, 3], aux_b_lats[i, 3]), - (aux_b_lons[i, 0], aux_b_lats[i, 0])])) + (aux_b_lons[i, 1], aux_b_lats[i, 1]), + (aux_b_lons[i, 2], aux_b_lats[i, 2]), + (aux_b_lons[i, 3], aux_b_lats[i, 3]), + (aux_b_lons[i, 0], aux_b_lats[i, 0])])) # Create dataframe cointaining all polygons fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1]) @@ -553,8 +553,8 @@ class RotatedNes(Nes): fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), - geometry=geometry, - crs="EPSG:4326") + geometry=geometry, + crs="EPSG:4326") self.shapefile = gdf else: -- GitLab From 3bb98fc6ac2e836ada560100b0fb8edf6d62a7c0 Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 11 Apr 2023 12:55:36 +0200 Subject: [PATCH 13/21] Refactoring of sting data methods --- nes/nc_projections/default_nes.py | 114 ++++++++----------- nes/nc_projections/points_nes.py | 40 +++---- nes/nc_projections/points_nes_ghost.py | 40 +++---- nes/nc_projections/points_nes_providentia.py | 35 ++---- 4 files changed, 90 insertions(+), 139 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 1969ce0..3a73e60 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -6,7 +6,7 @@ import warnings import numpy as np import pandas as pd from xarray import open_dataset -from netCDF4 import Dataset, num2date, date2num +from netCDF4 import Dataset, num2date, date2num, stringtochar from mpi4py import MPI from cfunits import Units from shapely.geos import TopologicalError @@ -194,8 +194,7 @@ class Nes(object): self.global_attrs = self.__get_global_attributes(create_nes) # Set string length - # 75 is the standard value used in GHOST data - self.strlen = 75 + self.strlen = None else: @@ -317,20 +316,20 @@ class Nes(object): return new - def _get_strlen(self, strlen=75): + def _get_strlen(self): """ Get the strlen - Parameters - ---------- - strlen : int - Max length of the string + Returns + ------- + int + Max length of the string data """ if 'strlen' in self.netcdf.dimensions: strlen = self.netcdf.dimensions['strlen'].size else: - strlen = strlen + return None return strlen @@ -338,16 +337,15 @@ class Nes(object): """ Set the strlen + 75 is the standard value used in GHOST data + Parameters ---------- strlen : int Max length of the string """ - if strlen is not None: - self.strlen = strlen - else: - raise ValueError('String length cannot be set as None.') + self.strlen = strlen return None @@ -1856,7 +1854,7 @@ class Nes(object): def _get_lazy_variables(self): """ - Get all the variables information. + Get all the variables' information. Returns ------- @@ -1881,6 +1879,8 @@ class Nes(object): variables[var_name]['data'] = None variables[var_name]['dimensions'] = var_info.dimensions variables[var_name]['dtype'] = var_info.dtype + if variables[var_name]['dtype'] == np.object: + variables[var_name]['dtype'] = str # Avoid some attributes for attrname in var_info.ncattrs(): @@ -2015,6 +2015,9 @@ class Nes(object): # Data type changes when joining characters in read_variable (S1 to S+strlen) if 'strlen' in self.variables[var_name]['dimensions']: self.variables[var_name]['dtype'] = self.variables[var_name]['data'].dtype + if self.strlen is None: + self.set_strlen() + else: if self.master: print("Data for {0} was previously loaded. Skipping variable.".format(var_name)) @@ -2240,7 +2243,8 @@ class Nes(object): netcdf.createDimension('lat', len(self._lat['data'])) # Create string length dimension - netcdf.createDimension('strlen', self.strlen) + if self.strlen is not None: + netcdf.createDimension('strlen', self.strlen) return None @@ -2352,6 +2356,25 @@ class Nes(object): print("Rank {0:03d}: Cell measures done".format(self.rank)) return None + def str2char(self, data): + + if self.strlen is None: + msg = 'String data could not be converted into chars while writing.' + msg += " Please, set the maximum string length (set_strlen) before writing." + raise RuntimeError(msg) + + # Get final shape by adding strlen at the end + data_new_shape = data.shape + (self.strlen, ) + + # nD (2D, 3D, 4D) data as 1D string array + data = data.flatten() + + # Split strings into chars (S1) + data_aux = stringtochar(np.array([v.encode('ascii', 'ignore') for v in data]).astype('S' + str(self.strlen))) + data_aux = data_aux.reshape(data_new_shape) + + return data_aux + def _create_variables(self, netcdf, chunking=False): """ Create the netCDF file variables. @@ -2391,13 +2414,10 @@ class Nes(object): raise TypeError("It was not possible to cast the data to the input dtype.") else: var_dtype = var_dict['data'].dtype + if var_dtype is np.object: + raise TypeError("Data dtype is np.object. Define dtype explicitly as dictionary key 'dtype'") if var_dict['data'] is not None: - - # Transform objects into strings - if var_dtype == np.dtype(object): - var_dict['data'] = var_dict['data'].astype(str) - var_dtype = var_dict['data'].dtype # Ensure data is of type numpy array (to create NES) if not isinstance(var_dict['data'], (np.ndarray, np.generic)): @@ -2407,49 +2427,10 @@ class Nes(object): raise AttributeError("Data for variable {0} must be a numpy array.".format(var_name)) # Convert list of strings to chars for parallelization - if np.issubdtype(var_dict['data'].dtype, np.character): - try: - # Add strlen as a dimension if needed - if 'strlen' not in var_dims: - var_dims += ('strlen',) - - # Split strings into chars (S1) - if len(var_dict['data'].shape) == 2: - data_aux = np.chararray(shape=(var_dict['data'].shape[0], - var_dict['data'].shape[1], - self.strlen)) - for lat_n in range(var_dict['data'].shape[0]): - for lon_n in range(var_dict['data'].shape[1]): - chars = [v for v in var_dict['data'][lat_n][lon_n]] - data = chars + ['']*(self.strlen-len(chars)) - for char_n, char in enumerate(data): - data_aux[lat_n, lon_n, char_n] = char.encode('ascii', 'ignore') - - elif len(var_dict['data'].shape) == 4: - data_aux = np.chararray(shape=(var_dict['data'].shape[0], - var_dict['data'].shape[1], - var_dict['data'].shape[2], - var_dict['data'].shape[3], - self.strlen)) - for time_n in range(var_dict['data'].shape[0]): - for lev_n in range(var_dict['data'].shape[1]): - for lat_n in range(var_dict['data'].shape[2]): - for lon_n in range(var_dict['data'].shape[3]): - chars = [v for v in var_dict['data'][time_n][lev_n][lat_n][lon_n]] - data = chars + ['']*(self.strlen-len(chars)) - for char_n, char in enumerate(data): - data_aux[time_n, lev_n, lat_n, lon_n, char_n] = char.encode('ascii', - 'ignore') - - var_dict['data'] = data_aux - var_dtype = 'S1' - - # TODO: Detect exception - except Exception as e: - print('String data of {0} could not be converted into chars while writing'.format(var_name)) - print(e) - # TODO document that exception - pass + if np.issubdtype(var_dtype, np.character): + var_dict['data_aux'] = self.str2char(var_dict['data']) + var_dims += ('strlen',) + var_dtype = 'S1' if self.info: print("Rank {0:03d}: Writing {1} var ({2}/{3})".format( @@ -2483,6 +2464,8 @@ class Nes(object): if att_value is not None: if self.info: print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) + if 'data_aux' in var_dict.keys(): + att_value = var_dict['data_aux'] if isinstance(att_value, int) and att_value == 0: var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], self.write_axis_limits['z_min']:self.write_axis_limits['z_max'], @@ -2517,9 +2500,12 @@ class Nes(object): print("Rank {0:03d}: Var {1} data ({2}/{3})".format( self.rank, var_name, i + 1, len(self.variables))) - elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype']: + elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype', 'data_aux']: var.setncattr(att_name, att_value) + if 'data_aux' in var_dict.keys(): + del var_dict['data_aux'] + self._set_var_crs(var) if self.info: print("Rank {0:03d}: Var {1} completed ({2}/{3})".format( diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index 5c61bf1..2c2d66d 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -156,7 +156,7 @@ class PointsNes(Nes): netcdf.createDimension('station', len(self._lon['data'])) # Create string length dimension - if hasattr(self, 'strlen'): + if self.strlen is not None: netcdf.createDimension('strlen', self.strlen) return None @@ -341,8 +341,7 @@ class PointsNes(Nes): if (var_dict['data'] is not None) and (var_dtype != var_dict['data'].dtype): msg = "WARNING!!! " msg += "Different data types for variable {0}. ".format(var_name) - msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, - var_dict['data'].dtype) + msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, var_dict['data'].dtype) warnings.warn(msg) sys.stderr.flush() try: @@ -351,11 +350,8 @@ class PointsNes(Nes): raise e("It was not possible to cast the data to the input dtype.") else: var_dtype = var_dict['data'].dtype - - # Transform objects into strings (e.g. for ESDAC iwahashi landform in GHOST) - if var_dtype == np.dtype(object): - var_dict['data'] = var_dict['data'].astype(str) - var_dtype = var_dict['data'].dtype + if var_dtype is np.object: + raise TypeError("Data dtype is np.object. Define dtype explicitly as dictionary key 'dtype'") # Get dimensions when reading datasets if 'dimensions' in var_dict.keys(): @@ -379,23 +375,10 @@ class PointsNes(Nes): raise AttributeError("Data for variable {0} must be a numpy array.".format(var_name)) # Convert list of strings to chars for parallelization - if np.issubdtype(var_dict['data'].dtype, np.character): - try: - # Add strlen as a dimension if needed - if 'strlen' not in var_dims: - var_dims += ('strlen',) - - # Split strings into chars (S1) - var_dict['data'] = stringtochar(np.array([v.encode('ascii', 'ignore') - for v in var_dict['data']]).astype('S' + str(self.strlen))) - var_dtype = 'S1' - - # TODO: Detect exception - except Exception as e: - print('String data of {0} could not be converted into chars while writing'.format(var_name)) - print('Error:' + e) - # TODO document that exception - pass + if np.issubdtype(var_dtype, np.character): + var_dict['data_aux'] = self.str2char(var_dict['data']) + var_dims += ('strlen',) + var_dtype = 'S1' if self.info: print('Rank {0:03d}: Writing {1} var ({2}/{3})'.format(self.rank, var_name, i + 1, @@ -428,6 +411,8 @@ class PointsNes(Nes): if att_name == 'data': if self.info: print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) + if 'data_aux' in var_dict.keys(): + att_value = var_dict['data_aux'] if len(att_value.shape) == 1: try: var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value @@ -468,8 +453,11 @@ class PointsNes(Nes): if self.info: print('Rank {0:03d}: Var {1} data ({2}/{3})'.format(self.rank, var_name, i + 1, len(self.variables))) - elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype']: + elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype', 'data_aux']: var.setncattr(att_name, att_value) + + if 'data_aux' in var_dict.keys(): + del var_dict['data_aux'] self._set_var_crs(var) if self.info: diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py index f0b3423..8dc0fed 100644 --- a/nes/nc_projections/points_nes_ghost.py +++ b/nes/nc_projections/points_nes_ghost.py @@ -336,8 +336,7 @@ class PointsNesGHOST(PointsNes): if (var_dict['data'] is not None) and (var_dtype != var_dict['data'].dtype): msg = "WARNING!!! " msg += "Different data types for variable {0}. ".format(var_name) - msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, - var_dict['data'].dtype) + msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, var_dict['data'].dtype) warnings.warn(msg) sys.stderr.flush() try: @@ -346,11 +345,8 @@ class PointsNesGHOST(PointsNes): raise e("It was not possible to cast the data to the input dtype.") else: var_dtype = var_dict['data'].dtype - - # Transform objects into strings (e.g. for ESDAC iwahashi landform in GHOST) - if var_dtype == np.dtype(object): - var_dict['data'] = var_dict['data'].astype(str) - var_dtype = var_dict['data'].dtype + if var_dtype is np.object: + raise TypeError("Data dtype is np.object. Define dtype explicitly as dictionary key 'dtype'") # Get dimensions when reading datasets if 'dimensions' in var_dict.keys(): @@ -362,7 +358,7 @@ class PointsNesGHOST(PointsNes): var_dims = self._var_dim else: # For data that is dependent on time and station (e.g. PM10) - var_dims = self._var_dim + ('time',) + var_dims = self._var_dim + ('time',) if var_dict['data'] is not None: @@ -374,23 +370,10 @@ class PointsNesGHOST(PointsNes): raise AttributeError("Data for variable {0} must be a numpy array.".format(var_name)) # Convert list of strings to chars for parallelization - if np.issubdtype(var_dict['data'].dtype, np.character): - try: - # Add strlen as a dimension if needed - if 'strlen' not in var_dims: - var_dims += ('strlen',) - - # Split strings into chars (S1) - var_dict['data'] = stringtochar(np.array([v.encode('ascii', 'ignore') - for v in var_dict['data']]).astype('S' + str(self.strlen))) - var_dtype = 'S1' - - # TODO: Detect exception - except Exception as e: - print('String data of {0} could not be converted into chars while writing'.format(var_name)) - print('Error:' + e) - # TODO document that exception - pass + if np.issubdtype(var_dtype, np.character): + var_dict['data_aux'] = self.str2char(var_dict['data']) + var_dims += ('strlen',) + var_dtype = 'S1' if self.info: print("Rank {0:03d}: Writing {1} var ({2}/{3})".format(self.rank, var_name, i + 1, @@ -421,6 +404,8 @@ class PointsNesGHOST(PointsNes): if att_name == 'data': if self.info: print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) + if 'data_aux' in var_dict.keys(): + att_value = var_dict['data_aux'] if len(att_value.shape) == 1: try: var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value @@ -480,8 +465,11 @@ class PointsNesGHOST(PointsNes): print("Rank {0:03d}: Var {1} data ({2}/{3})".format(self.rank, var_name, i + 1, len(self.variables))) - elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype']: + elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype', 'data_aux']: var.setncattr(att_name, att_value) + + if 'data_aux' in var_dict.keys(): + del var_dict['data_aux'] self._set_var_crs(var) if self.info: diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py index 3dc5cfa..458084f 100644 --- a/nes/nc_projections/points_nes_providentia.py +++ b/nes/nc_projections/points_nes_providentia.py @@ -379,11 +379,8 @@ class PointsNesProvidentia(PointsNes): raise e("It was not possible to cast the data to the input dtype.") else: var_dtype = var_dict['data'].dtype - - # Transform objects into strings (e.g. for ESDAC iwahashi landform in GHOST) - if var_dtype == np.dtype(object): - var_dict['data'] = var_dict['data'].astype(str) - var_dtype = var_dict['data'].dtype + if var_dtype is np.object: + raise TypeError("Data dtype is np.object. Define dtype explicitly as dictionary key 'dtype'") # Get dimensions when reading datasets if 'dimensions' in var_dict.keys(): @@ -395,7 +392,7 @@ class PointsNesProvidentia(PointsNes): var_dims = self._var_dim else: # For data that is dependent on time and station (e.g. PM10) - var_dims = self._var_dim + ('time',) + var_dims = self._var_dim + ('time',) if var_dict['data'] is not None: @@ -407,23 +404,10 @@ class PointsNesProvidentia(PointsNes): raise AttributeError("Data for variable {0} must be a numpy array.".format(var_name)) # Convert list of strings to chars for parallelization - if np.issubdtype(var_dict['data'].dtype, np.character): - try: - # Add strlen as a dimension if needed - if 'strlen' not in var_dims: - var_dims += ('strlen',) - - # Split strings into chars (S1) - var_dict['data'] = stringtochar(np.array([v.encode('ascii', 'ignore') - for v in var_dict['data']]).astype('S' + str(self.strlen))) - var_dtype = 'S1' - - # TODO: Detect exception - except Exception as e: - print('String data of {0} could not be converted into chars while writing'.format(var_name)) - print('Error:' + e) - # TODO document that exception - pass + if np.issubdtype(var_dtype, np.character): + var_dict['data_aux'] = self.str2char(var_dict['data']) + var_dims += ('strlen',) + var_dtype = 'S1' if self.info: print("Rank {0:03d}: Writing {1} var ({2}/{3})".format(self.rank, var_name, i + 1, @@ -454,6 +438,8 @@ class PointsNesProvidentia(PointsNes): if att_name == 'data': if self.info: print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) + if 'data_aux' in var_dict.keys(): + att_value = var_dict['data_aux'] if len(att_value.shape) == 1: try: var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value @@ -514,6 +500,9 @@ class PointsNesProvidentia(PointsNes): len(self.variables))) elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype']: var.setncattr(att_name, att_value) + + if 'data_aux' in var_dict.keys(): + del var_dict['data_aux'] self._set_var_crs(var) if self.info: -- GitLab From c60e8de1e9723bcb8f2f72074b1130573be5a200 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Tue, 11 Apr 2023 13:00:48 +0200 Subject: [PATCH 14/21] Document exception in nan filling --- nes/nc_projections/default_nes.py | 2 +- nes/nc_projections/points_nes.py | 2 +- nes/nc_projections/points_nes_ghost.py | 2 +- nes/nc_projections/points_nes_providentia.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 1969ce0..0142346 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -1973,7 +1973,7 @@ class Nes(object): try: # This operation is done because sometimes the missing value is lost during the calculation data = data.filled(np.nan) - except: + except TypeError: msg = 'Variable {0} data missing values cannot be converted to np.nan.'.format(var_name) warnings.warn(msg) sys.stderr.flush() diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index 5c61bf1..07ed91c 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -313,7 +313,7 @@ class PointsNes(Nes): try: # This operation is done because sometimes the missing value is lost during the calculation data = data.filled(np.nan) - except: + except TypeError: msg = 'Variable {0} data missing values cannot be converted to np.nan.'.format(var_name) warnings.warn(msg) sys.stderr.flush() diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py index f0b3423..2523bd3 100644 --- a/nes/nc_projections/points_nes_ghost.py +++ b/nes/nc_projections/points_nes_ghost.py @@ -308,7 +308,7 @@ class PointsNesGHOST(PointsNes): try: # This operation is done because sometimes the missing value is lost during the calculation data = data.filled(np.nan) - except: + except TypeError: msg = 'Variable {0} data missing values cannot be converted to np.nan.'.format(var_name) warnings.warn(msg) sys.stderr.flush() diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py index 3dc5cfa..392337a 100644 --- a/nes/nc_projections/points_nes_providentia.py +++ b/nes/nc_projections/points_nes_providentia.py @@ -341,7 +341,7 @@ class PointsNesProvidentia(PointsNes): try: # This operation is done because sometimes the missing value is lost during the calculation data = data.filled(np.nan) - except: + except TypeError: msg = 'Variable {0} data missing values cannot be converted to np.nan.'.format(var_name) warnings.warn(msg) sys.stderr.flush() -- GitLab From 77a3752e2cdbb4bda446f8ba7783719fa8ab3713 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Tue, 11 Apr 2023 13:15:20 +0200 Subject: [PATCH 15/21] Fix bug in read variable for Providentia --- nes/nc_projections/points_nes_providentia.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py index 77778e9..bd5718f 100644 --- a/nes/nc_projections/points_nes_providentia.py +++ b/nes/nc_projections/points_nes_providentia.py @@ -498,7 +498,7 @@ class PointsNesProvidentia(PointsNes): if self.info: print("Rank {0:03d}: Var {1} data ({2}/{3})".format(self.rank, var_name, i + 1, len(self.variables))) - elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype']: + elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype', 'data_aux']: var.setncattr(att_name, att_value) if 'data_aux' in var_dict.keys(): -- GitLab From eeeaa1928b8362451dc0c7d5049b901e04b8442d Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 11 Apr 2023 15:09:47 +0200 Subject: [PATCH 16/21] Delete variables inside try --- nes/nc_projections/default_nes.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index da7bc54..c794d98 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -355,9 +355,9 @@ class Nes(object): """ self.close() - self.free_vars(list(self.variables.keys())) - del self.variables try: + self.free_vars(list(self.variables.keys())) + del self.variables del self.time del self._time del self.time_bnds -- GitLab From 9d2fdec8310d4a6cd2bfbee2f3691d7d8cfc60cb Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Tue, 11 Apr 2023 16:56:06 +0200 Subject: [PATCH 17/21] Refactor filled nan and changed paths in bounds test and tutorials --- nes/nc_projections/default_nes.py | 70 +- nes/nc_projections/points_nes.py | 12 +- nes/nc_projections/points_nes_ghost.py | 12 +- nes/nc_projections/points_nes_providentia.py | 12 +- tests/2.3-test_bounds.py | 6 +- .../1.1.Read_Write_Regular.ipynb | 4 +- .../1.2.Read_Write_Rotated.ipynb | 4 +- .../1.3.Read_Write_Points.ipynb | 878 ++++++-- .../1.Introduction/1.4.Read_Write_LCC.ipynb | 4 +- .../1.5.Read_Write_Mercator.ipynb | 4 +- .../1.6.Read_Write_Providentia.ipynb | 1902 +++-------------- tutorials/2.Creation/2.3.Create-Points.ipynb | 16 +- .../2.4.Create_Points_Port_Barcelona.ipynb | 15 +- .../2.Creation/2.5.Create_Points_CSIC.ipynb | 15 +- .../5.3.Add_Coordinates_Bounds.ipynb | 797 ++++++- 15 files changed, 1835 insertions(+), 1916 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index da7bc54..7e71950 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -163,6 +163,9 @@ class Nes(object): self.netcdf = None self.dataset = None + # Set string length + self.strlen = None + # Initialize variables self.variables = {} @@ -193,9 +196,6 @@ class Nes(object): # Set NetCDF attributes self.global_attrs = self.__get_global_attributes(create_nes) - # Set string length - self.strlen = None - else: if dataset is not None: @@ -213,6 +213,9 @@ class Nes(object): self.dataset = None self.netcdf = self.__open_netcdf4() + # Get string length + self.strlen = self._get_strlen() + # Lazy variables self.variables = self._get_lazy_variables() @@ -251,9 +254,6 @@ class Nes(object): # Set NetCDF attributes self.global_attrs = self.__get_global_attributes() - # Get string length - self.strlen = self._get_strlen() - # Writing options self.zip_lvl = 0 @@ -1673,11 +1673,11 @@ class Nes(object): if self.master: if not create_nes: if 'lat_bnds' in self.netcdf.variables.keys(): - lat_bnds = {'data': self.netcdf.variables['lat_bnds'][:]} + lat_bnds = {'data': self._unmask_array(self.netcdf.variables['lat_bnds'][:])} else: lat_bnds = None if 'lon_bnds' in self.netcdf.variables.keys(): - lon_bnds = {'data': self.netcdf.variables['lon_bnds'][:]} + lon_bnds = {'data': self._unmask_array(self.netcdf.variables['lon_bnds'][:])} else: lon_bnds = None else: @@ -1879,7 +1879,9 @@ class Nes(object): variables[var_name]['data'] = None variables[var_name]['dimensions'] = var_info.dimensions variables[var_name]['dtype'] = var_info.dtype - if variables[var_name]['dtype'] == np.object: + if variables[var_name]['dtype'] in [str, np.object]: + if self.strlen is None: + self.set_strlen() variables[var_name]['dtype'] = str # Avoid some attributes @@ -1968,16 +1970,8 @@ class Nes(object): raise NotImplementedError('Error with {0}. Only can be read netCDF with 4 dimensions or less'.format( var_name)) - # Missing to nan - if np.ma.is_masked(data): - try: - # This operation is done because sometimes the missing value is lost during the calculation - data = data.filled(np.nan) - except TypeError: - msg = 'Variable {0} data missing values cannot be converted to np.nan.'.format(var_name) - warnings.warn(msg) - sys.stderr.flush() - pass + # Unmask array + data = self._unmask_array(data) return data @@ -2014,10 +2008,11 @@ class Nes(object): self.variables[var_name]['data'] = self._read_variable(var_name) # Data type changes when joining characters in read_variable (S1 to S+strlen) if 'strlen' in self.variables[var_name]['dimensions']: - self.variables[var_name]['dtype'] = self.variables[var_name]['data'].dtype if self.strlen is None: self.set_strlen() - + self.variables[var_name]['dtype'] = str + self.variables[var_name]['dimensions'] = tuple([x for x in self.variables[var_name]['dimensions'] + if x != "strlen"]) else: if self.master: print("Data for {0} was previously loaded. Skipping variable.".format(var_name)) @@ -2030,7 +2025,40 @@ class Nes(object): return None + @staticmethod + def _unmask_array(data): + """ + Missing to nan. This operation is done because sometimes the missing value is lost during the calculation. + + Parameters + ---------- + data : np.array + Masked array to unmask. + + Returns + ------- + np.array + Unmasked array. + """ + + if isinstance(data, np.ma.MaskedArray): + try: + data = data.filled(np.nan) + except TypeError: + msg = 'Data missing values cannot be converted to np.nan.' + warnings.warn(msg) + sys.stderr.flush() + + return data + def to_dtype(self, data_type='float32'): + """ Cast variables data into selected data type. + + Parameters + ---------- + data_type : str + Data type, by default 'float32' + """ for var_name, var_info in self.variables.items(): if var_info['data'] is not None: diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index 1be7ecb..40bec4d 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -308,16 +308,8 @@ class PointsNes(Nes): raise NotImplementedError("Error with {0}. Only can be read netCDF with 2 dimensions or less".format( var_name)) - # Missing to nan - if np.ma.is_masked(data): - try: - # This operation is done because sometimes the missing value is lost during the calculation - data = data.filled(np.nan) - except TypeError: - msg = 'Variable {0} data missing values cannot be converted to np.nan.'.format(var_name) - warnings.warn(msg) - sys.stderr.flush() - pass + # Unmask array + data = self._unmask_array(data) return data diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py index 3c6ed32..ad2e80f 100644 --- a/nes/nc_projections/points_nes_ghost.py +++ b/nes/nc_projections/points_nes_ghost.py @@ -303,16 +303,8 @@ class PointsNesGHOST(PointsNes): raise NotImplementedError('Error with {0}. Only can be read netCDF with 3 dimensions or less'.format( var_name)) - # Missing to nan - if np.ma.is_masked(data): - try: - # This operation is done because sometimes the missing value is lost during the calculation - data = data.filled(np.nan) - except TypeError: - msg = 'Variable {0} data missing values cannot be converted to np.nan.'.format(var_name) - warnings.warn(msg) - sys.stderr.flush() - pass + # Unmask array + data = self._unmask_array(data) return data diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py index bd5718f..6326ea1 100644 --- a/nes/nc_projections/points_nes_providentia.py +++ b/nes/nc_projections/points_nes_providentia.py @@ -336,16 +336,8 @@ class PointsNesProvidentia(PointsNes): raise NotImplementedError('Error with {0}. Only can be read netCDF with 3 dimensions or less'.format( var_name)) - # Missing to nan - if np.ma.is_masked(data): - try: - # This operation is done because sometimes the missing value is lost during the calculation - data = data.filled(np.nan) - except TypeError: - msg = 'Variable {0} data missing values cannot be converted to np.nan.'.format(var_name) - warnings.warn(msg) - sys.stderr.flush() - pass + # Unmask array + data = self._unmask_array(data) return data diff --git a/tests/2.3-test_bounds.py b/tests/2.3-test_bounds.py index 3896a6f..c2322cf 100644 --- a/tests/2.3-test_bounds.py +++ b/tests/2.3-test_bounds.py @@ -26,9 +26,9 @@ if rank == 0: # READ st_time = timeit.default_timer() -# Original path: /gpfs/scratch/bsc32/bsc32538/HERMESv3/OUT_Complete_single/GFAS_p13h/HERMESv3_GR_GFAS_d01_2022050100.nc -# Rotated grid from HERMES -path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/HERMESv3_GR_GFAS_d01_2022050100.nc' +# Original path: /esarchive/exp/snes/a5s1/regional/3hourly/od550du/od550du-000_2021070612.nc +# Rotated grid for dust regional +path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/od550du-000_2021070612.nc' nessy_1 = open_netcdf(path=path_1, parallel_method=parallel_method, info=True) comm.Barrier() diff --git a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb index afe6277..a9f2812 100644 --- a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb +++ b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -483,7 +483,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, diff --git a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb index 6a7a05d..7a126e6 100644 --- a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb +++ b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -408,7 +408,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, diff --git a/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb b/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb index ec7fe84..7e72919 100644 --- a/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb +++ b/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb @@ -46,10 +46,36 @@ "execution_count": 3, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "station_start_date |S1\n", + "station_zone |S1\n", + "street_type |S1\n", + "country_code |S1\n", + "ccaa |S1\n", + "station_name |S1\n", + "station_area |S1\n", + "city |S1\n", + "pm10 float32\n", + "station_emep |S1\n", + "station_type |S1\n", + "country |S1\n", + "altitude float32\n", + "station_code |S1\n", + "longitude float32\n", + "station_end_date |S1\n", + "station_rural_back |S1\n", + "time float32\n", + "latitude float32\n", + "station_ozone_classification |S1\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -324,8 +350,8 @@ " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'station_zone': {'data': array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nature', 'nature', 'nature', 'nature', 'nature', 'nature',\n", @@ -338,8 +364,8 @@ " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'],\n", " dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'street_type': {'data': array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'unknown', 'unknown', 'unknown', 'unknown', 'unknown',\n", @@ -352,8 +378,8 @@ " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'country_code': {'data': array(['CH', 'CH', 'CH', 'CH', 'CY', 'DK', 'DK', 'DK', 'EE', 'EE', 'ES',\n", " 'ES', 'ES', 'ES', 'ES', 'ES', 'ES', 'ES', 'ES', 'ES', 'ES', 'ES',\n", @@ -363,8 +389,8 @@ " 'CZ', 'DE', 'LV', 'FI', 'IT', 'CZ', 'RU', 'NL', 'ME', 'GR', 'LT',\n", " 'DE', 'DE', 'IT', 'FI', 'DE', 'IE', 'IE', 'DE', 'DE', 'CZ', 'EG',\n", " 'FI', 'CH', 'NL', 'AT', 'IE', 'NL', 'IT'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'ccaa': {'data': array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'castilla-la mancha', 'galicia', 'islas baleares',\n", @@ -378,8 +404,8 @@ " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'station_name': {'data': array(['payerne', 'tnikon', 'chaumont', 'rigi-seebodenalp',\n", " 'emep - ayia marina', 'tange', 'anholt', 'risoe', 'lahemaa',\n", @@ -400,8 +426,8 @@ " 'malin head', 'westerland', 'schmcke', 'churanov', 'marsa matruh',\n", " 'pallas (matorova)', 'jungfraujoch', 'de zilk', 'vorhegg',\n", " 'carnsore point', 'kollumerwaard', 'lamezia terme'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'station_area': {'data': array(['rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural',\n", " 'rural', 'rural', 'rural', 'rural', 'rural', 'rural', 'rural',\n", @@ -414,8 +440,8 @@ " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'city': {'data': array(['nan', 'nan', 'nan', 'nan', 'ayia marina', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'san pablo de los montes', 'noia', 'mahn', 'vznar',\n", @@ -428,8 +454,8 @@ " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'pm10': {'data': array([[17.9 , 15.4 , 16.2 , ..., nan, 14.83, nan],\n", " [22.9 , 16.8 , 20.5 , ..., nan, 24.8 , nan],\n", @@ -452,8 +478,8 @@ " 'yes', 'yes', 'yes', 'yes', 'yes', 'yes', 'yes', 'yes', 'yes',\n", " 'yes', 'yes', 'yes', 'yes', 'no', 'yes', 'yes', 'yes', 'yes',\n", " 'yes', 'yes', 'no'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'station_type': {'data': array(['background', 'background', 'background', 'background',\n", " 'background', 'background', 'background', 'background',\n", @@ -471,8 +497,8 @@ " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'country': {'data': array(['switzerland', 'switzerland', 'switzerland', 'switzerland',\n", " 'cyprus', 'denmark', 'denmark', 'denmark', 'estonia', 'estonia',\n", @@ -491,8 +517,8 @@ " 'Finland', 'Germany', 'Ireland', 'Ireland', 'Germany', 'Germany',\n", " 'Czech Republic', 'Egypt', 'Finland', 'Switzerland', 'Netherlands',\n", " 'Austria', 'Ireland', 'Netherlands', 'Italy'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'altitude': {'data': masked_array(data=[4.890e+02, 5.380e+02, 1.136e+03, 1.031e+03, 5.320e+02,\n", " 8.000e+00, 1.000e+01, 3.000e+00, 3.200e+01, 6.000e+00,\n", @@ -533,8 +559,8 @@ " 'IE0006R', 'DE0001R', 'DE0008R', 'CZ0005R', 'EG0003U', 'FI0036R',\n", " 'CH0001G', 'NL0091R', 'AT0005R', 'IE0008R', 'NL0009R', 'IT0016R'],\n", " dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'station_end_date': {'data': array(['nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", @@ -546,8 +572,8 @@ " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'station_rural_back': {'data': array(['nan', 'nan', 'nan', 'nan', 'regional', 'nan', 'nan', 'regional',\n", " 'nan', 'nan', 'remote', 'remote', 'remote', 'remote', 'remote',\n", @@ -560,8 +586,8 @@ " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'station_ozone_classification': {'data': array(['rural', 'rural', 'rural background', 'rural background',\n", " 'rural background', 'rural', 'nan', 'nan', 'rural', 'rural',\n", @@ -580,8 +606,8 @@ " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan', 'nan',\n", " 'nan', 'nan'], dtype=object),\n", - " 'dimensions': ('station', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''}}" ] }, @@ -636,6 +662,42 @@ "execution_count": 12, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_start_date. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_zone. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable street_type. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable country_code. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable ccaa. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_area. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable city. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_emep. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_type. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable country. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_end_date. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_rural_back. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_ozone_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -749,10 +811,37 @@ "execution_count": 13, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time float64\n", + "station float64\n", + "lat float64\n", + "lon float64\n", + "station_start_date |S1\n", + "station_zone |S1\n", + "street_type |S1\n", + "country_code |S1\n", + "ccaa |S1\n", + "station_name |S1\n", + "station_area |S1\n", + "city |S1\n", + "pm10 float32\n", + "station_emep |S1\n", + "station_type |S1\n", + "country |S1\n", + "altitude float32\n", + "station_code |S1\n", + "station_end_date |S1\n", + "station_rural_back |S1\n", + "station_ozone_classification |S1\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -797,10 +886,194 @@ "execution_count": 15, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ASTER_v3_altitude float32\n", + "EDGAR_v4.3.2_annual_average_BC_emissions float32\n", + "EDGAR_v4.3.2_annual_average_CO_emissions float32\n", + "EDGAR_v4.3.2_annual_average_NH3_emissions float32\n", + "EDGAR_v4.3.2_annual_average_NMVOC_emissions float32\n", + "EDGAR_v4.3.2_annual_average_NOx_emissions float32\n", + "EDGAR_v4.3.2_annual_average_OC_emissions float32\n", + "EDGAR_v4.3.2_annual_average_PM10_emissions float32\n", + "EDGAR_v4.3.2_annual_average_SO2_emissions float32\n", + "EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions float32\n", + "EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions float32\n", + "ESDAC_Iwahashi_landform_classification \n", + "ESDAC_Meybeck_landform_classification \n", + "ESDAC_modal_Iwahashi_landform_classification_25km \n", + "ESDAC_modal_Iwahashi_landform_classification_5km \n", + "ESDAC_modal_Meybeck_landform_classification_25km \n", + "ESDAC_modal_Meybeck_landform_classification_5km \n", + "ETOPO1_altitude float32\n", + "ETOPO1_max_altitude_difference_5km float32\n", + "GHOST_version \n", + "GHSL_average_built_up_area_density_25km float32\n", + "GHSL_average_built_up_area_density_5km float32\n", + "GHSL_average_population_density_25km float32\n", + "GHSL_average_population_density_5km float32\n", + "GHSL_built_up_area_density float32\n", + "GHSL_max_built_up_area_density_25km float32\n", + "GHSL_max_built_up_area_density_5km float32\n", + "GHSL_max_population_density_25km float32\n", + "GHSL_max_population_density_5km float32\n", + "GHSL_modal_settlement_model_classification_25km \n", + "GHSL_modal_settlement_model_classification_5km \n", + "GHSL_population_density float32\n", + "GHSL_settlement_model_classification \n", + "GPW_average_population_density_25km float32\n", + "GPW_average_population_density_5km float32\n", + "GPW_max_population_density_25km float32\n", + "GPW_max_population_density_5km float32\n", + "GPW_population_density float32\n", + "GSFC_coastline_proximity float32\n", + "Joly-Peuch_classification_code float32\n", + "Koppen-Geiger_classification \n", + "Koppen-Geiger_modal_classification_25km \n", + "Koppen-Geiger_modal_classification_5km \n", + "MODIS_MCD12C1_v6_IGBP_land_use \n", + "MODIS_MCD12C1_v6_LAI \n", + "MODIS_MCD12C1_v6_UMD_land_use \n", + "MODIS_MCD12C1_v6_modal_IGBP_land_use_25km \n", + "MODIS_MCD12C1_v6_modal_IGBP_land_use_5km \n", + "MODIS_MCD12C1_v6_modal_LAI_25km \n", + "MODIS_MCD12C1_v6_modal_LAI_5km \n", + "MODIS_MCD12C1_v6_modal_UMD_land_use_25km \n", + "MODIS_MCD12C1_v6_modal_UMD_land_use_5km \n", + "NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km float32\n", + "NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km float32\n", + "NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km float32\n", + "NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km float32\n", + "NOAA-DMSP-OLS_v4_nighttime_stable_lights float32\n", + "OMI_level3_column_annual_average_NO2 float32\n", + "OMI_level3_column_cloud_screened_annual_average_NO2 float32\n", + "OMI_level3_tropospheric_column_annual_average_NO2 float32\n", + "OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 float32\n", + "UMBC_anthrome_classification \n", + "UMBC_modal_anthrome_classification_25km \n", + "UMBC_modal_anthrome_classification_5km \n", + "WMO_region \n", + "WWF_TEOW_biogeographical_realm \n", + "WWF_TEOW_biome \n", + "WWF_TEOW_terrestrial_ecoregion \n", + "administrative_country_division_1 \n", + "administrative_country_division_2 \n", + "altitude float32\n", + "annual_native_max_gap_percent uint8\n", + "annual_native_representativity_percent uint8\n", + "area_classification \n", + "associated_networks \n", + "city \n", + "climatology \n", + "contact_email_address \n", + "contact_institution \n", + "contact_name \n", + "country \n", + "daily_native_max_gap_percent uint8\n", + "daily_native_representativity_percent uint8\n", + "daily_passing_vehicles float32\n", + "data_level \n", + "data_licence \n", + "day_night_code uint8\n", + "daytime_traffic_speed float32\n", + "derived_uncertainty_per_measurement float32\n", + "distance_to_building float32\n", + "distance_to_junction float32\n", + "distance_to_kerb float32\n", + "distance_to_source float32\n", + "ellipsoid \n", + "flag uint8\n", + "horizontal_datum \n", + "land_use \n", + "latitude float64\n", + "longitude float64\n", + "main_emission_source \n", + "measurement_altitude float32\n", + "measurement_methodology \n", + "measurement_scale \n", + "measuring_instrument_calibration_scale \n", + "measuring_instrument_documented_absorption_cross_section \n", + "measuring_instrument_documented_accuracy \n", + "measuring_instrument_documented_flow_rate \n", + "measuring_instrument_documented_lower_limit_of_detection float32\n", + "measuring_instrument_documented_measurement_resolution float32\n", + "measuring_instrument_documented_precision \n", + "measuring_instrument_documented_span_drift \n", + "measuring_instrument_documented_uncertainty \n", + "measuring_instrument_documented_upper_limit_of_detection float32\n", + "measuring_instrument_documented_zero_drift \n", + "measuring_instrument_documented_zonal_drift \n", + "measuring_instrument_further_details \n", + "measuring_instrument_inlet_information \n", + "measuring_instrument_manual_name \n", + "measuring_instrument_name \n", + "measuring_instrument_process_details \n", + "measuring_instrument_reported_absorption_cross_section \n", + "measuring_instrument_reported_accuracy \n", + "measuring_instrument_reported_flow_rate \n", + "measuring_instrument_reported_lower_limit_of_detection float32\n", + "measuring_instrument_reported_measurement_resolution float32\n", + "measuring_instrument_reported_precision \n", + "measuring_instrument_reported_span_drift \n", + "measuring_instrument_reported_uncertainty \n", + "measuring_instrument_reported_units \n", + "measuring_instrument_reported_upper_limit_of_detection float32\n", + "measuring_instrument_reported_zero_drift \n", + "measuring_instrument_reported_zonal_drift \n", + "measuring_instrument_sampling_type \n", + "monthly_native_max_gap_percent uint8\n", + "monthly_native_representativity_percent uint8\n", + "network \n", + "network_maintenance_details \n", + "network_miscellaneous_details \n", + "network_provided_volume_standard_pressure float64\n", + "network_provided_volume_standard_temperature float64\n", + "network_qa_details \n", + "network_sampling_details \n", + "network_uncertainty_details \n", + "population float32\n", + "primary_sampling_further_details \n", + "primary_sampling_instrument_documented_flow_rate \n", + "primary_sampling_instrument_manual_name \n", + "primary_sampling_instrument_name \n", + "primary_sampling_instrument_reported_flow_rate \n", + "primary_sampling_process_details \n", + "primary_sampling_type \n", + "principal_investigator_email_address \n", + "principal_investigator_institution \n", + "principal_investigator_name \n", + "process_warnings \n", + "projection \n", + "qa uint8\n", + "reported_uncertainty_per_measurement float32\n", + "representative_radius float32\n", + "retrieval_algorithm \n", + "sample_preparation_further_details \n", + "sample_preparation_process_details \n", + "sample_preparation_techniques \n", + "sample_preparation_types \n", + "sampling_height float32\n", + "sconcso4 float32\n", + "season_code uint8\n", + "station_classification \n", + "station_name \n", + "station_reference \n", + "station_timezone \n", + "street_type \n", + "street_width float32\n", + "terrain \n", + "time uint32\n", + "vertical_datum \n", + "weekday_weekend_code uint8\n", + "sconcso4_prefiltered_defaultqa float32\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -1343,32 +1616,26 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ESDAC_Iwahashi_landform_classification. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ESDAC_Meybeck_landform_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_Iwahashi_landform_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_Meybeck_landform_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable GHOST_version. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_25km. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_5km. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable GHSL_settlement_model_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable GHOST_version. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -1519,93 +1786,80 @@ "Rank 000: Filling GHSL_max_population_density_25km)\n", "Rank 000: Var GHSL_max_population_density_25km data (28/173)\n", "Rank 000: Var GHSL_max_population_density_25km completed (28/173)\n", - "Rank 000: Writing GHSL_max_population_density_5km var (29/173)\n", - "Rank 000: Var GHSL_max_population_density_5km created (29/173)\n", - "Rank 000: Filling GHSL_max_population_density_5km)\n", - "Rank 000: Var GHSL_max_population_density_5km data (29/173)\n", - "Rank 000: Var GHSL_max_population_density_5km completed (29/173)\n", - "Rank 000: Writing GHSL_modal_settlement_model_classification_25km var (30/173)\n", - "Rank 000: Var GHSL_modal_settlement_model_classification_25km created (30/173)\n", - "Rank 000: Filling GHSL_modal_settlement_model_classification_25km)\n", - "Rank 000: Var GHSL_modal_settlement_model_classification_25km data (30/173)\n", - "Rank 000: Var GHSL_modal_settlement_model_classification_25km completed (30/173)\n", - "Rank 000: Writing GHSL_modal_settlement_model_classification_5km var (31/173)\n", - "Rank 000: Var GHSL_modal_settlement_model_classification_5km created (31/173)\n", - "Rank 000: Filling GHSL_modal_settlement_model_classification_5km)\n", - "Rank 000: Var GHSL_modal_settlement_model_classification_5km data (31/173)\n", - "Rank 000: Var GHSL_modal_settlement_model_classification_5km completed (31/173)\n", - "Rank 000: Writing GHSL_population_density var (32/173)\n", - "Rank 000: Var GHSL_population_density created (32/173)\n", - "Rank 000: Filling GHSL_population_density)\n", - "Rank 000: Var GHSL_population_density data (32/173)\n", - "Rank 000: Var GHSL_population_density completed (32/173)\n" + "Rank 000: Writing GHSL_max_population_density_5km var (29/173)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable GHSL_settlement_model_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_IGBP_land_use. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_LAI. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_UMD_land_use. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_IGBP_land_use. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_LAI. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_UMD_land_use. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable UMBC_anthrome_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable UMBC_anthrome_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable WMO_region. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biogeographical_realm. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biome. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WMO_region. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_terrestrial_ecoregion. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biogeographical_realm. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable administrative_country_division_1. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biome. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable administrative_country_division_2. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_terrestrial_ecoregion. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable administrative_country_division_1. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable associated_networks. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable administrative_country_division_2. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable city. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable climatology. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable associated_networks. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable contact_email_address. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable city. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable contact_institution. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable climatology. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable contact_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable contact_email_address. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable country. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable contact_institution. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable data_level. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable contact_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable data_licence. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable country. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable data_level. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable data_licence. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -1613,6 +1867,25 @@ "name": "stdout", "output_type": "stream", "text": [ + "Rank 000: Var GHSL_max_population_density_5km created (29/173)\n", + "Rank 000: Filling GHSL_max_population_density_5km)\n", + "Rank 000: Var GHSL_max_population_density_5km data (29/173)\n", + "Rank 000: Var GHSL_max_population_density_5km completed (29/173)\n", + "Rank 000: Writing GHSL_modal_settlement_model_classification_25km var (30/173)\n", + "Rank 000: Var GHSL_modal_settlement_model_classification_25km created (30/173)\n", + "Rank 000: Filling GHSL_modal_settlement_model_classification_25km)\n", + "Rank 000: Var GHSL_modal_settlement_model_classification_25km data (30/173)\n", + "Rank 000: Var GHSL_modal_settlement_model_classification_25km completed (30/173)\n", + "Rank 000: Writing GHSL_modal_settlement_model_classification_5km var (31/173)\n", + "Rank 000: Var GHSL_modal_settlement_model_classification_5km created (31/173)\n", + "Rank 000: Filling GHSL_modal_settlement_model_classification_5km)\n", + "Rank 000: Var GHSL_modal_settlement_model_classification_5km data (31/173)\n", + "Rank 000: Var GHSL_modal_settlement_model_classification_5km completed (31/173)\n", + "Rank 000: Writing GHSL_population_density var (32/173)\n", + "Rank 000: Var GHSL_population_density created (32/173)\n", + "Rank 000: Filling GHSL_population_density)\n", + "Rank 000: Var GHSL_population_density data (32/173)\n", + "Rank 000: Var GHSL_population_density completed (32/173)\n", "Rank 000: Writing GHSL_settlement_model_classification var (33/173)\n", "Rank 000: Var GHSL_settlement_model_classification created (33/173)\n", "Rank 000: Filling GHSL_settlement_model_classification)\n", @@ -1893,58 +2166,55 @@ "Rank 000: Filling daytime_traffic_speed)\n", "Rank 000: Var daytime_traffic_speed data (88/173)\n", "Rank 000: Var daytime_traffic_speed completed (88/173)\n", - "Rank 000: Writing derived_uncertainty_per_measurement var (89/173)\n" + "Rank 000: Writing derived_uncertainty_per_measurement var (89/173)\n", + "Rank 000: Var derived_uncertainty_per_measurement created (89/173)\n", + "Rank 000: Filling derived_uncertainty_per_measurement)\n", + "Rank 000: Var derived_uncertainty_per_measurement data (89/173)\n", + "Rank 000: Var derived_uncertainty_per_measurement completed (89/173)\n", + "Rank 000: Writing distance_to_building var (90/173)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable ellipsoid. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable horizontal_datum. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable land_use. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ellipsoid. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable main_emission_source. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable horizontal_datum. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measurement_methodology. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable land_use. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measurement_scale. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable main_emission_source. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_calibration_scale. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measurement_methodology. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_absorption_cross_section. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measurement_scale. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_accuracy. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_calibration_scale. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_absorption_cross_section. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_precision. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_accuracy. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_span_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_uncertainty. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_precision. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zero_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_span_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zonal_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_uncertainty. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_further_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zero_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_inlet_information. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zonal_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_manual_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_further_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_inlet_information. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_process_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_manual_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_absorption_cross_section. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_accuracy. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -1952,11 +2222,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Rank 000: Var derived_uncertainty_per_measurement created (89/173)\n", - "Rank 000: Filling derived_uncertainty_per_measurement)\n", - "Rank 000: Var derived_uncertainty_per_measurement data (89/173)\n", - "Rank 000: Var derived_uncertainty_per_measurement completed (89/173)\n", - "Rank 000: Writing distance_to_building var (90/173)\n", "Rank 000: Var distance_to_building created (90/173)\n", "Rank 000: Filling distance_to_building)\n", "Rank 000: Var distance_to_building data (90/173)\n", @@ -2088,107 +2353,86 @@ "Rank 000: Var measuring_instrument_manual_name completed (115/173)\n", "Rank 000: Writing measuring_instrument_name var (116/173)\n", "Rank 000: Var measuring_instrument_name created (116/173)\n", - "Rank 000: Filling measuring_instrument_name)\n", - "Rank 000: Var measuring_instrument_name data (116/173)\n", - "Rank 000: Var measuring_instrument_name completed (116/173)\n", - "Rank 000: Writing measuring_instrument_process_details var (117/173)\n", - "Rank 000: Var measuring_instrument_process_details created (117/173)\n", - "Rank 000: Filling measuring_instrument_process_details)\n", - "Rank 000: Var measuring_instrument_process_details data (117/173)\n", - "Rank 000: Var measuring_instrument_process_details completed (117/173)\n", - "Rank 000: Writing measuring_instrument_reported_absorption_cross_section var (118/173)\n", - "Rank 000: Var measuring_instrument_reported_absorption_cross_section created (118/173)\n", - "Rank 000: Filling measuring_instrument_reported_absorption_cross_section)\n", - "Rank 000: Var measuring_instrument_reported_absorption_cross_section data (118/173)\n", - "Rank 000: Var measuring_instrument_reported_absorption_cross_section completed (118/173)\n", - "Rank 000: Writing measuring_instrument_reported_accuracy var (119/173)\n", - "Rank 000: Var measuring_instrument_reported_accuracy created (119/173)\n", - "Rank 000: Filling measuring_instrument_reported_accuracy)\n", - "Rank 000: Var measuring_instrument_reported_accuracy data (119/173)\n", - "Rank 000: Var measuring_instrument_reported_accuracy completed (119/173)\n", - "Rank 000: Writing measuring_instrument_reported_flow_rate var (120/173)\n", - "Rank 000: Var measuring_instrument_reported_flow_rate created (120/173)\n", - "Rank 000: Filling measuring_instrument_reported_flow_rate)\n", - "Rank 000: Var measuring_instrument_reported_flow_rate data (120/173)\n", - "Rank 000: Var measuring_instrument_reported_flow_rate completed (120/173)\n", - "Rank 000: Writing measuring_instrument_reported_lower_limit_of_detection var (121/173)\n", - "Rank 000: Var measuring_instrument_reported_lower_limit_of_detection created (121/173)\n", - "Rank 000: Filling measuring_instrument_reported_lower_limit_of_detection)\n" + "Rank 000: Filling measuring_instrument_name)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! 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Different data types for variable measuring_instrument_reported_uncertainty. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_accuracy. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_units. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zero_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_precision. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zonal_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_span_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable measuring_instrument_sampling_type. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_uncertainty. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable network. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_units. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable network_maintenance_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zero_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable network_miscellaneous_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zonal_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable network_qa_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_sampling_type. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable network_sampling_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable network_uncertainty_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_maintenance_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable primary_sampling_further_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_miscellaneous_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_qa_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_manual_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_sampling_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_uncertainty_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_further_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable primary_sampling_process_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable primary_sampling_type. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_manual_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable principal_investigator_email_address. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable principal_investigator_institution. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable principal_investigator_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_process_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable process_warnings. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_type. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable projection. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable principal_investigator_email_address. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable retrieval_algorithm. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable principal_investigator_institution. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable sample_preparation_further_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable principal_investigator_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable sample_preparation_process_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable process_warnings. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable sample_preparation_techniques. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable projection. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable sample_preparation_types. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable retrieval_algorithm. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable station_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_further_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_process_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_techniques. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable station_timezone. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_types. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable street_type. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -2196,6 +2440,31 @@ "name": "stdout", "output_type": "stream", "text": [ + "Rank 000: Var measuring_instrument_name data (116/173)\n", + "Rank 000: Var measuring_instrument_name completed (116/173)\n", + "Rank 000: Writing measuring_instrument_process_details var (117/173)\n", + "Rank 000: Var measuring_instrument_process_details created (117/173)\n", + "Rank 000: Filling measuring_instrument_process_details)\n", + "Rank 000: Var measuring_instrument_process_details data (117/173)\n", + "Rank 000: Var measuring_instrument_process_details completed (117/173)\n", + "Rank 000: Writing measuring_instrument_reported_absorption_cross_section var (118/173)\n", + "Rank 000: Var measuring_instrument_reported_absorption_cross_section created (118/173)\n", + "Rank 000: Filling measuring_instrument_reported_absorption_cross_section)\n", + "Rank 000: Var measuring_instrument_reported_absorption_cross_section data (118/173)\n", + "Rank 000: Var measuring_instrument_reported_absorption_cross_section completed (118/173)\n", + "Rank 000: Writing measuring_instrument_reported_accuracy var (119/173)\n", + "Rank 000: Var measuring_instrument_reported_accuracy created (119/173)\n", + "Rank 000: Filling measuring_instrument_reported_accuracy)\n", + "Rank 000: Var measuring_instrument_reported_accuracy data (119/173)\n", + "Rank 000: Var measuring_instrument_reported_accuracy completed (119/173)\n", + "Rank 000: Writing measuring_instrument_reported_flow_rate var (120/173)\n", + "Rank 000: Var measuring_instrument_reported_flow_rate created (120/173)\n", + "Rank 000: Filling measuring_instrument_reported_flow_rate)\n", + "Rank 000: Var measuring_instrument_reported_flow_rate data (120/173)\n", + "Rank 000: Var measuring_instrument_reported_flow_rate completed (120/173)\n", + "Rank 000: Writing measuring_instrument_reported_lower_limit_of_detection var (121/173)\n", + "Rank 000: Var measuring_instrument_reported_lower_limit_of_detection created (121/173)\n", + "Rank 000: Filling measuring_instrument_reported_lower_limit_of_detection)\n", "Rank 000: Var measuring_instrument_reported_lower_limit_of_detection data (121/173)\n", "Rank 000: Var measuring_instrument_reported_lower_limit_of_detection completed (121/173)\n", "Rank 000: Writing measuring_instrument_reported_measurement_resolution var (122/173)\n", @@ -2417,26 +2686,20 @@ "Rank 000: Var station_name created (165/173)\n", "Rank 000: Filling station_name)\n", "Rank 000: Var station_name data (165/173)\n", - "Rank 000: Var station_name completed (165/173)\n", - "Rank 000: Writing station_reference var (166/173)\n", - "Rank 000: Var station_reference created (166/173)\n", - "Rank 000: Filling station_reference)\n", - "Rank 000: Var station_reference data (166/173)\n", - "Rank 000: Var station_reference completed (166/173)\n", - "Rank 000: Writing station_timezone var (167/173)\n", - "Rank 000: Var station_timezone created (167/173)\n", - "Rank 000: Filling station_timezone)\n", - "Rank 000: Var station_timezone data (167/173)\n", - "Rank 000: Var station_timezone completed (167/173)\n" + "Rank 000: Var station_name completed (165/173)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable terrain. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_timezone. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:341: UserWarning: WARNING!!! Different data types for variable vertical_datum. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable street_type. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable terrain. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable vertical_datum. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -2444,6 +2707,16 @@ "name": "stdout", "output_type": "stream", "text": [ + "Rank 000: Writing station_reference var (166/173)\n", + "Rank 000: Var station_reference created (166/173)\n", + "Rank 000: Filling station_reference)\n", + "Rank 000: Var station_reference data (166/173)\n", + "Rank 000: Var station_reference completed (166/173)\n", + "Rank 000: Writing station_timezone var (167/173)\n", + "Rank 000: Var station_timezone created (167/173)\n", + "Rank 000: Filling station_timezone)\n", + "Rank 000: Var station_timezone data (167/173)\n", + "Rank 000: Var station_timezone completed (167/173)\n", "Rank 000: Writing street_type var (168/173)\n", "Rank 000: Var street_type created (168/173)\n", "Rank 000: Filling street_type)\n", @@ -2490,13 +2763,192 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "time float64\n", + "station float64\n", + "latitude float64\n", + "longitude float64\n", + "ASTER_v3_altitude float32\n", + "EDGAR_v4.3.2_annual_average_BC_emissions float32\n", + "EDGAR_v4.3.2_annual_average_CO_emissions float32\n", + "EDGAR_v4.3.2_annual_average_NH3_emissions float32\n", + "EDGAR_v4.3.2_annual_average_NMVOC_emissions float32\n", + "EDGAR_v4.3.2_annual_average_NOx_emissions float32\n", + "EDGAR_v4.3.2_annual_average_OC_emissions float32\n", + "EDGAR_v4.3.2_annual_average_PM10_emissions float32\n", + "EDGAR_v4.3.2_annual_average_SO2_emissions float32\n", + "EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions float32\n", + "EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions float32\n", + "ESDAC_Iwahashi_landform_classification |S1\n", + "ESDAC_Meybeck_landform_classification |S1\n", + "ESDAC_modal_Iwahashi_landform_classification_25km |S1\n", + "ESDAC_modal_Iwahashi_landform_classification_5km |S1\n", + "ESDAC_modal_Meybeck_landform_classification_25km |S1\n", + "ESDAC_modal_Meybeck_landform_classification_5km |S1\n", + "ETOPO1_altitude float32\n", + "ETOPO1_max_altitude_difference_5km float32\n", + "GHOST_version |S1\n", + "GHSL_average_built_up_area_density_25km float32\n", + "GHSL_average_built_up_area_density_5km float32\n", + "GHSL_average_population_density_25km float32\n", + "GHSL_average_population_density_5km float32\n", + "GHSL_built_up_area_density float32\n", + "GHSL_max_built_up_area_density_25km float32\n", + "GHSL_max_built_up_area_density_5km float32\n", + "GHSL_max_population_density_25km float32\n", + "GHSL_max_population_density_5km float32\n", + "GHSL_modal_settlement_model_classification_25km |S1\n", + "GHSL_modal_settlement_model_classification_5km |S1\n", + "GHSL_population_density float32\n", + "GHSL_settlement_model_classification |S1\n", + "GPW_average_population_density_25km float32\n", + "GPW_average_population_density_5km float32\n", + "GPW_max_population_density_25km float32\n", + "GPW_max_population_density_5km float32\n", + "GPW_population_density float32\n", + "GSFC_coastline_proximity float32\n", + "Joly-Peuch_classification_code float32\n", + "Koppen-Geiger_classification |S1\n", + "Koppen-Geiger_modal_classification_25km |S1\n", + "Koppen-Geiger_modal_classification_5km |S1\n", + "MODIS_MCD12C1_v6_IGBP_land_use |S1\n", + "MODIS_MCD12C1_v6_LAI |S1\n", + "MODIS_MCD12C1_v6_UMD_land_use |S1\n", + "MODIS_MCD12C1_v6_modal_IGBP_land_use_25km |S1\n", + "MODIS_MCD12C1_v6_modal_IGBP_land_use_5km |S1\n", + "MODIS_MCD12C1_v6_modal_LAI_25km |S1\n", + "MODIS_MCD12C1_v6_modal_LAI_5km |S1\n", + "MODIS_MCD12C1_v6_modal_UMD_land_use_25km |S1\n", + "MODIS_MCD12C1_v6_modal_UMD_land_use_5km |S1\n", + "NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km float32\n", + "NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km float32\n", + "NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km float32\n", + "NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km float32\n", + "NOAA-DMSP-OLS_v4_nighttime_stable_lights float32\n", + "OMI_level3_column_annual_average_NO2 float32\n", + "OMI_level3_column_cloud_screened_annual_average_NO2 float32\n", + "OMI_level3_tropospheric_column_annual_average_NO2 float32\n", + "OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 float32\n", + "UMBC_anthrome_classification |S1\n", + "UMBC_modal_anthrome_classification_25km |S1\n", + "UMBC_modal_anthrome_classification_5km |S1\n", + "WMO_region |S1\n", + "WWF_TEOW_biogeographical_realm |S1\n", + "WWF_TEOW_biome |S1\n", + "WWF_TEOW_terrestrial_ecoregion |S1\n", + "administrative_country_division_1 |S1\n", + "administrative_country_division_2 |S1\n", + "altitude float32\n", + "annual_native_max_gap_percent uint8\n", + "annual_native_representativity_percent uint8\n", + "area_classification |S1\n", + "associated_networks |S1\n", + "city |S1\n", + "climatology |S1\n", + "contact_email_address |S1\n", + "contact_institution |S1\n", + "contact_name |S1\n", + "country |S1\n", + "daily_native_max_gap_percent uint8\n", + "daily_native_representativity_percent uint8\n", + "daily_passing_vehicles float32\n", + "data_level |S1\n", + "data_licence |S1\n", + "day_night_code uint8\n", + "daytime_traffic_speed float32\n", + "derived_uncertainty_per_measurement float32\n", + "distance_to_building float32\n", + "distance_to_junction float32\n", + "distance_to_kerb float32\n", + "distance_to_source float32\n", + "ellipsoid |S1\n", + "horizontal_datum |S1\n", + "land_use |S1\n", + "main_emission_source |S1\n", + "measurement_altitude float32\n", + "measurement_methodology |S1\n", + "measurement_scale |S1\n", + "measuring_instrument_calibration_scale |S1\n", + "measuring_instrument_documented_absorption_cross_section |S1\n", + "measuring_instrument_documented_accuracy |S1\n", + "measuring_instrument_documented_flow_rate |S1\n", + "measuring_instrument_documented_lower_limit_of_detection float32\n", + "measuring_instrument_documented_measurement_resolution float32\n", + "measuring_instrument_documented_precision |S1\n", + "measuring_instrument_documented_span_drift |S1\n", + "measuring_instrument_documented_uncertainty |S1\n", + "measuring_instrument_documented_upper_limit_of_detection float32\n", + "measuring_instrument_documented_zero_drift |S1\n", + "measuring_instrument_documented_zonal_drift |S1\n", + "measuring_instrument_further_details |S1\n", + "measuring_instrument_inlet_information |S1\n", + "measuring_instrument_manual_name |S1\n", + "measuring_instrument_name |S1\n", + "measuring_instrument_process_details |S1\n", + "measuring_instrument_reported_absorption_cross_section |S1\n", + "measuring_instrument_reported_accuracy |S1\n", + "measuring_instrument_reported_flow_rate |S1\n", + "measuring_instrument_reported_lower_limit_of_detection float32\n", + "measuring_instrument_reported_measurement_resolution float32\n", + "measuring_instrument_reported_precision |S1\n", + "measuring_instrument_reported_span_drift |S1\n", + "measuring_instrument_reported_uncertainty |S1\n", + "measuring_instrument_reported_units |S1\n", + "measuring_instrument_reported_upper_limit_of_detection float32\n", + "measuring_instrument_reported_zero_drift |S1\n", + "measuring_instrument_reported_zonal_drift |S1\n", + "measuring_instrument_sampling_type |S1\n", + "monthly_native_max_gap_percent uint8\n", + "monthly_native_representativity_percent uint8\n", + "network |S1\n", + "network_maintenance_details |S1\n", + "network_miscellaneous_details |S1\n", + "network_provided_volume_standard_pressure float64\n", + "network_provided_volume_standard_temperature float64\n", + "network_qa_details |S1\n", + "network_sampling_details |S1\n", + "network_uncertainty_details |S1\n", + "population float32\n", + "primary_sampling_further_details |S1\n", + "primary_sampling_instrument_documented_flow_rate |S1\n", + "primary_sampling_instrument_manual_name |S1\n", + "primary_sampling_instrument_name |S1\n", + "primary_sampling_instrument_reported_flow_rate |S1\n", + "primary_sampling_process_details |S1\n", + "primary_sampling_type |S1\n", + "principal_investigator_email_address |S1\n", + "principal_investigator_institution |S1\n", + "principal_investigator_name |S1\n", + "process_warnings |S1\n", + "projection |S1\n", + "reported_uncertainty_per_measurement float32\n", + "representative_radius float32\n", + "retrieval_algorithm |S1\n", + "sample_preparation_further_details |S1\n", + "sample_preparation_process_details |S1\n", + "sample_preparation_techniques |S1\n", + "sample_preparation_types |S1\n", + "sampling_height float32\n", + "sconcso4 float32\n", + "season_code uint8\n", + "station_classification |S1\n", + "station_name |S1\n", + "station_reference |S1\n", + "station_timezone |S1\n", + "street_type |S1\n", + "street_width float32\n", + "terrain |S1\n", + "vertical_datum |S1\n", + "weekday_weekend_code uint8\n", + "sconcso4_prefiltered_defaultqa float32\n", + "flag int64\n", + "qa int64\n", "Rank 000: Loading station var (1/174)\n", "Rank 000: Loaded station var ((3,))\n", "Rank 000: Loading ASTER_v3_altitude var (2/174)\n", @@ -2850,10 +3302,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2873,16 +3325,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -2894,7 +3346,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2958,7 +3410,7 @@ " 'description': 'Measured value of surface sulphate for the stated temporal resolution. Prefiltered by default QA.'}}" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb index 25321c1..79af033 100644 --- a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb +++ b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -665,7 +665,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, diff --git a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb index 1e4815c..9b00850 100644 --- a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb +++ b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -436,7 +436,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, diff --git a/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb b/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb index 9049f84..ba935b0 100644 --- a/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb +++ b/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb @@ -49,7 +49,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -64,741 +64,24 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[datetime.datetime(2018, 4, 1, 0, 0),\n", - " datetime.datetime(2018, 4, 1, 1, 0),\n", - " datetime.datetime(2018, 4, 1, 2, 0),\n", - " datetime.datetime(2018, 4, 1, 3, 0),\n", - " datetime.datetime(2018, 4, 1, 4, 0),\n", - " datetime.datetime(2018, 4, 1, 5, 0),\n", - " datetime.datetime(2018, 4, 1, 6, 0),\n", - " datetime.datetime(2018, 4, 1, 7, 0),\n", - " datetime.datetime(2018, 4, 1, 8, 0),\n", - " datetime.datetime(2018, 4, 1, 9, 0),\n", - " datetime.datetime(2018, 4, 1, 10, 0),\n", - " datetime.datetime(2018, 4, 1, 11, 0),\n", - " datetime.datetime(2018, 4, 1, 12, 0),\n", - " datetime.datetime(2018, 4, 1, 13, 0),\n", - " datetime.datetime(2018, 4, 1, 14, 0),\n", - " datetime.datetime(2018, 4, 1, 15, 0),\n", - " datetime.datetime(2018, 4, 1, 16, 0),\n", - " datetime.datetime(2018, 4, 1, 17, 0),\n", - 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"execution_count": 4, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "obs_nes.time" + "obs_nes.time[0], len(obs_nes.time), obs_nes.time[-1]" ] }, { @@ -1357,67 +640,67 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable ESDAC_Iwahashi_landform_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_Iwahashi_landform_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable ESDAC_Meybeck_landform_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_Meybeck_landform_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! 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Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_IGBP_land_use. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_IGBP_land_use. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_LAI. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_LAI. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_UMD_land_use. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_UMD_land_use. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable UMBC_anthrome_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable UMBC_anthrome_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable WMO_region. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WMO_region. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biogeographical_realm. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biogeographical_realm. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biome. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biome. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_terrestrial_ecoregion. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_terrestrial_ecoregion. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable administrative_country_division_1. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable administrative_country_division_1. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable administrative_country_division_2. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable administrative_country_division_2. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -1430,349 +713,408 @@ "Rank 000: Dimensions done\n", "Rank 000: Writing ASTER_v3_altitude var (1/175)\n", "Rank 000: Var ASTER_v3_altitude created (1/175)\n", + "Rank 000: Filling ASTER_v3_altitude)\n", "Rank 000: Var ASTER_v3_altitude data (1/175)\n", "Rank 000: Var ASTER_v3_altitude completed (1/175)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_BC_emissions var (2/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_BC_emissions created (2/175)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_BC_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_BC_emissions data (2/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_BC_emissions completed (2/175)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_CO_emissions var (3/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_CO_emissions created (3/175)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_CO_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_CO_emissions data (3/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_CO_emissions completed (3/175)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_NH3_emissions var (4/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NH3_emissions created (4/175)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_NH3_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NH3_emissions data (4/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NH3_emissions completed (4/175)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_NMVOC_emissions var (5/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NMVOC_emissions created (5/175)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_NMVOC_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NMVOC_emissions data (5/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NMVOC_emissions completed (5/175)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_NOx_emissions var (6/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NOx_emissions created (6/175)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_NOx_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NOx_emissions data (6/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_NOx_emissions completed (6/175)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_OC_emissions var (7/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_OC_emissions created (7/175)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_OC_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_OC_emissions data (7/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_OC_emissions completed (7/175)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_PM10_emissions var (8/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_PM10_emissions created (8/175)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_PM10_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_PM10_emissions data (8/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_PM10_emissions completed (8/175)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_SO2_emissions var (9/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_SO2_emissions created (9/175)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_SO2_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_SO2_emissions data (9/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_SO2_emissions completed (9/175)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions var (10/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions created (10/175)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions data (10/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions completed (10/175)\n", "Rank 000: Writing EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions var (11/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions created (11/175)\n", + "Rank 000: Filling EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions data (11/175)\n", "Rank 000: Var EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions completed (11/175)\n", "Rank 000: Writing ESDAC_Iwahashi_landform_classification var (12/175)\n", "Rank 000: Var ESDAC_Iwahashi_landform_classification created (12/175)\n", + "Rank 000: Filling ESDAC_Iwahashi_landform_classification)\n", "Rank 000: Var ESDAC_Iwahashi_landform_classification data (12/175)\n", "Rank 000: Var ESDAC_Iwahashi_landform_classification completed (12/175)\n", "Rank 000: Writing ESDAC_Meybeck_landform_classification var (13/175)\n", "Rank 000: Var ESDAC_Meybeck_landform_classification created (13/175)\n", + "Rank 000: Filling ESDAC_Meybeck_landform_classification)\n", "Rank 000: Var ESDAC_Meybeck_landform_classification data (13/175)\n", "Rank 000: Var ESDAC_Meybeck_landform_classification completed (13/175)\n", "Rank 000: Writing ESDAC_modal_Iwahashi_landform_classification_25km var (14/175)\n", "Rank 000: Var ESDAC_modal_Iwahashi_landform_classification_25km created (14/175)\n", + "Rank 000: Filling ESDAC_modal_Iwahashi_landform_classification_25km)\n", "Rank 000: Var ESDAC_modal_Iwahashi_landform_classification_25km data (14/175)\n", "Rank 000: Var ESDAC_modal_Iwahashi_landform_classification_25km completed (14/175)\n", "Rank 000: Writing ESDAC_modal_Iwahashi_landform_classification_5km var (15/175)\n", "Rank 000: Var ESDAC_modal_Iwahashi_landform_classification_5km created (15/175)\n", + "Rank 000: Filling ESDAC_modal_Iwahashi_landform_classification_5km)\n", "Rank 000: Var ESDAC_modal_Iwahashi_landform_classification_5km data (15/175)\n", "Rank 000: Var ESDAC_modal_Iwahashi_landform_classification_5km completed (15/175)\n", "Rank 000: Writing ESDAC_modal_Meybeck_landform_classification_25km var (16/175)\n", "Rank 000: Var ESDAC_modal_Meybeck_landform_classification_25km created (16/175)\n", + "Rank 000: Filling ESDAC_modal_Meybeck_landform_classification_25km)\n", "Rank 000: Var ESDAC_modal_Meybeck_landform_classification_25km data (16/175)\n", "Rank 000: Var ESDAC_modal_Meybeck_landform_classification_25km completed (16/175)\n", "Rank 000: Writing ESDAC_modal_Meybeck_landform_classification_5km var (17/175)\n", "Rank 000: Var ESDAC_modal_Meybeck_landform_classification_5km created (17/175)\n", + "Rank 000: Filling ESDAC_modal_Meybeck_landform_classification_5km)\n", "Rank 000: Var ESDAC_modal_Meybeck_landform_classification_5km data (17/175)\n", "Rank 000: Var ESDAC_modal_Meybeck_landform_classification_5km completed (17/175)\n", "Rank 000: Writing ETOPO1_altitude var (18/175)\n", "Rank 000: Var ETOPO1_altitude created (18/175)\n", + "Rank 000: Filling ETOPO1_altitude)\n", "Rank 000: Var ETOPO1_altitude data (18/175)\n", "Rank 000: Var ETOPO1_altitude completed (18/175)\n", "Rank 000: Writing ETOPO1_max_altitude_difference_5km var (19/175)\n", "Rank 000: Var ETOPO1_max_altitude_difference_5km created (19/175)\n", + "Rank 000: Filling ETOPO1_max_altitude_difference_5km)\n", "Rank 000: Var ETOPO1_max_altitude_difference_5km data (19/175)\n", "Rank 000: Var ETOPO1_max_altitude_difference_5km completed (19/175)\n", "Rank 000: Writing GHOST_version var (20/175)\n", "Rank 000: Var GHOST_version created (20/175)\n", + "Rank 000: Filling GHOST_version)\n", "Rank 000: Var GHOST_version data (20/175)\n", "Rank 000: Var GHOST_version completed (20/175)\n", "Rank 000: Writing GHSL_average_built_up_area_density_25km var (21/175)\n", "Rank 000: Var GHSL_average_built_up_area_density_25km created (21/175)\n", + "Rank 000: Filling GHSL_average_built_up_area_density_25km)\n", "Rank 000: Var GHSL_average_built_up_area_density_25km data (21/175)\n", "Rank 000: Var GHSL_average_built_up_area_density_25km completed (21/175)\n", "Rank 000: Writing GHSL_average_built_up_area_density_5km var (22/175)\n", "Rank 000: Var GHSL_average_built_up_area_density_5km created (22/175)\n", + "Rank 000: Filling GHSL_average_built_up_area_density_5km)\n", "Rank 000: Var GHSL_average_built_up_area_density_5km data (22/175)\n", "Rank 000: Var GHSL_average_built_up_area_density_5km completed (22/175)\n", "Rank 000: Writing GHSL_average_population_density_25km var (23/175)\n", "Rank 000: Var GHSL_average_population_density_25km created (23/175)\n", + "Rank 000: Filling GHSL_average_population_density_25km)\n", "Rank 000: Var GHSL_average_population_density_25km data (23/175)\n", "Rank 000: Var GHSL_average_population_density_25km completed (23/175)\n", "Rank 000: Writing GHSL_average_population_density_5km var (24/175)\n", "Rank 000: Var GHSL_average_population_density_5km created (24/175)\n", + "Rank 000: Filling GHSL_average_population_density_5km)\n", "Rank 000: Var GHSL_average_population_density_5km data (24/175)\n", "Rank 000: Var GHSL_average_population_density_5km completed (24/175)\n", "Rank 000: Writing GHSL_built_up_area_density var (25/175)\n", "Rank 000: Var GHSL_built_up_area_density created (25/175)\n", + "Rank 000: Filling GHSL_built_up_area_density)\n", "Rank 000: Var GHSL_built_up_area_density data (25/175)\n", "Rank 000: Var GHSL_built_up_area_density completed (25/175)\n", "Rank 000: Writing GHSL_max_built_up_area_density_25km var (26/175)\n", "Rank 000: Var GHSL_max_built_up_area_density_25km created (26/175)\n", + "Rank 000: Filling GHSL_max_built_up_area_density_25km)\n", "Rank 000: Var GHSL_max_built_up_area_density_25km data (26/175)\n", "Rank 000: Var GHSL_max_built_up_area_density_25km completed (26/175)\n", "Rank 000: Writing GHSL_max_built_up_area_density_5km var (27/175)\n", "Rank 000: Var GHSL_max_built_up_area_density_5km created (27/175)\n", + "Rank 000: Filling GHSL_max_built_up_area_density_5km)\n", "Rank 000: Var GHSL_max_built_up_area_density_5km data (27/175)\n", "Rank 000: Var GHSL_max_built_up_area_density_5km completed (27/175)\n", "Rank 000: Writing GHSL_max_population_density_25km var (28/175)\n", "Rank 000: Var GHSL_max_population_density_25km created (28/175)\n", + "Rank 000: Filling GHSL_max_population_density_25km)\n", "Rank 000: Var GHSL_max_population_density_25km data (28/175)\n", "Rank 000: Var GHSL_max_population_density_25km completed (28/175)\n", "Rank 000: Writing GHSL_max_population_density_5km var (29/175)\n", "Rank 000: Var GHSL_max_population_density_5km created (29/175)\n", + "Rank 000: Filling GHSL_max_population_density_5km)\n", "Rank 000: Var GHSL_max_population_density_5km data (29/175)\n", "Rank 000: Var GHSL_max_population_density_5km completed (29/175)\n", "Rank 000: Writing GHSL_modal_settlement_model_classification_25km var (30/175)\n", "Rank 000: Var GHSL_modal_settlement_model_classification_25km created (30/175)\n", + "Rank 000: Filling GHSL_modal_settlement_model_classification_25km)\n", "Rank 000: Var GHSL_modal_settlement_model_classification_25km data (30/175)\n", "Rank 000: Var GHSL_modal_settlement_model_classification_25km completed (30/175)\n", "Rank 000: Writing GHSL_modal_settlement_model_classification_5km var (31/175)\n", "Rank 000: Var GHSL_modal_settlement_model_classification_5km created (31/175)\n", + "Rank 000: Filling GHSL_modal_settlement_model_classification_5km)\n", "Rank 000: Var GHSL_modal_settlement_model_classification_5km data (31/175)\n", "Rank 000: Var GHSL_modal_settlement_model_classification_5km completed (31/175)\n", "Rank 000: Writing GHSL_population_density var (32/175)\n", "Rank 000: Var GHSL_population_density created (32/175)\n", + "Rank 000: Filling GHSL_population_density)\n", "Rank 000: Var GHSL_population_density data (32/175)\n", "Rank 000: Var GHSL_population_density completed (32/175)\n", "Rank 000: Writing GHSL_settlement_model_classification var (33/175)\n", "Rank 000: Var GHSL_settlement_model_classification created (33/175)\n", + "Rank 000: Filling GHSL_settlement_model_classification)\n", "Rank 000: Var GHSL_settlement_model_classification data (33/175)\n", "Rank 000: Var GHSL_settlement_model_classification completed (33/175)\n", "Rank 000: Writing GPW_average_population_density_25km var (34/175)\n", "Rank 000: Var GPW_average_population_density_25km created (34/175)\n", + "Rank 000: Filling GPW_average_population_density_25km)\n", "Rank 000: Var GPW_average_population_density_25km data (34/175)\n", "Rank 000: Var GPW_average_population_density_25km completed (34/175)\n", "Rank 000: Writing GPW_average_population_density_5km var (35/175)\n", "Rank 000: Var GPW_average_population_density_5km created (35/175)\n", + "Rank 000: Filling GPW_average_population_density_5km)\n", "Rank 000: Var GPW_average_population_density_5km data (35/175)\n", "Rank 000: Var GPW_average_population_density_5km completed (35/175)\n", "Rank 000: Writing GPW_max_population_density_25km var (36/175)\n", "Rank 000: Var GPW_max_population_density_25km created (36/175)\n", + "Rank 000: Filling GPW_max_population_density_25km)\n", "Rank 000: Var GPW_max_population_density_25km data (36/175)\n", "Rank 000: Var GPW_max_population_density_25km completed (36/175)\n", "Rank 000: Writing GPW_max_population_density_5km var (37/175)\n", "Rank 000: Var GPW_max_population_density_5km created (37/175)\n", + "Rank 000: Filling GPW_max_population_density_5km)\n", "Rank 000: Var GPW_max_population_density_5km data (37/175)\n", "Rank 000: Var GPW_max_population_density_5km completed (37/175)\n", "Rank 000: Writing GPW_population_density var (38/175)\n", "Rank 000: Var GPW_population_density created (38/175)\n", + "Rank 000: Filling GPW_population_density)\n", "Rank 000: Var GPW_population_density data (38/175)\n", "Rank 000: Var GPW_population_density completed (38/175)\n", "Rank 000: Writing GSFC_coastline_proximity var (39/175)\n", "Rank 000: Var GSFC_coastline_proximity created (39/175)\n", + "Rank 000: Filling GSFC_coastline_proximity)\n", "Rank 000: Var GSFC_coastline_proximity data (39/175)\n", "Rank 000: Var GSFC_coastline_proximity completed (39/175)\n", "Rank 000: Writing Joly-Peuch_classification_code var (40/175)\n", "Rank 000: Var Joly-Peuch_classification_code created (40/175)\n", + "Rank 000: Filling Joly-Peuch_classification_code)\n", "Rank 000: Var Joly-Peuch_classification_code data (40/175)\n", "Rank 000: Var Joly-Peuch_classification_code completed (40/175)\n", "Rank 000: Writing Koppen-Geiger_classification var (41/175)\n", "Rank 000: Var Koppen-Geiger_classification created (41/175)\n", + "Rank 000: Filling Koppen-Geiger_classification)\n", "Rank 000: Var Koppen-Geiger_classification data (41/175)\n", "Rank 000: Var Koppen-Geiger_classification completed (41/175)\n", "Rank 000: Writing Koppen-Geiger_modal_classification_25km var (42/175)\n", "Rank 000: Var Koppen-Geiger_modal_classification_25km created (42/175)\n", + "Rank 000: Filling Koppen-Geiger_modal_classification_25km)\n", "Rank 000: Var Koppen-Geiger_modal_classification_25km data (42/175)\n", "Rank 000: Var Koppen-Geiger_modal_classification_25km completed (42/175)\n", "Rank 000: Writing Koppen-Geiger_modal_classification_5km var (43/175)\n", "Rank 000: Var Koppen-Geiger_modal_classification_5km created (43/175)\n", + "Rank 000: Filling Koppen-Geiger_modal_classification_5km)\n", "Rank 000: Var Koppen-Geiger_modal_classification_5km data (43/175)\n", "Rank 000: Var Koppen-Geiger_modal_classification_5km completed (43/175)\n", "Rank 000: Writing MODIS_MCD12C1_v6_IGBP_land_use var (44/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_IGBP_land_use created (44/175)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_IGBP_land_use)\n", "Rank 000: Var MODIS_MCD12C1_v6_IGBP_land_use data (44/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_IGBP_land_use completed (44/175)\n", "Rank 000: Writing MODIS_MCD12C1_v6_LAI var (45/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_LAI created (45/175)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_LAI)\n", "Rank 000: Var MODIS_MCD12C1_v6_LAI data (45/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_LAI completed (45/175)\n", "Rank 000: Writing MODIS_MCD12C1_v6_UMD_land_use var (46/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_UMD_land_use created (46/175)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_UMD_land_use)\n", "Rank 000: Var MODIS_MCD12C1_v6_UMD_land_use data (46/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_UMD_land_use completed (46/175)\n", "Rank 000: Writing MODIS_MCD12C1_v6_modal_IGBP_land_use_25km var (47/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_IGBP_land_use_25km created (47/175)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_modal_IGBP_land_use_25km)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_IGBP_land_use_25km data (47/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_IGBP_land_use_25km completed (47/175)\n", "Rank 000: Writing MODIS_MCD12C1_v6_modal_IGBP_land_use_5km var (48/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_IGBP_land_use_5km created (48/175)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_modal_IGBP_land_use_5km)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_IGBP_land_use_5km data (48/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_IGBP_land_use_5km completed (48/175)\n", "Rank 000: Writing MODIS_MCD12C1_v6_modal_LAI_25km var (49/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_LAI_25km created (49/175)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_modal_LAI_25km)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_LAI_25km data (49/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_LAI_25km completed (49/175)\n", "Rank 000: Writing MODIS_MCD12C1_v6_modal_LAI_5km var (50/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_LAI_5km created (50/175)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_modal_LAI_5km)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_LAI_5km data (50/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_LAI_5km completed (50/175)\n", "Rank 000: Writing MODIS_MCD12C1_v6_modal_UMD_land_use_25km var (51/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_UMD_land_use_25km created (51/175)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_modal_UMD_land_use_25km)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_UMD_land_use_25km data (51/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_UMD_land_use_25km completed (51/175)\n", "Rank 000: Writing MODIS_MCD12C1_v6_modal_UMD_land_use_5km var (52/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_UMD_land_use_5km created (52/175)\n", + "Rank 000: Filling MODIS_MCD12C1_v6_modal_UMD_land_use_5km)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_UMD_land_use_5km data (52/175)\n", "Rank 000: Var MODIS_MCD12C1_v6_modal_UMD_land_use_5km completed (52/175)\n", "Rank 000: Writing NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km var (53/175)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km created (53/175)\n", + "Rank 000: Filling NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km data (53/175)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km completed (53/175)\n", "Rank 000: Writing NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km var (54/175)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km created (54/175)\n", + "Rank 000: Filling NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km data (54/175)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km completed (54/175)\n", "Rank 000: Writing NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km var (55/175)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km created (55/175)\n", + "Rank 000: Filling NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km data (55/175)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km completed (55/175)\n", "Rank 000: Writing NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km var (56/175)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km created (56/175)\n", + "Rank 000: Filling NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km data (56/175)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km completed (56/175)\n", "Rank 000: Writing NOAA-DMSP-OLS_v4_nighttime_stable_lights var (57/175)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_nighttime_stable_lights created (57/175)\n", + "Rank 000: Filling NOAA-DMSP-OLS_v4_nighttime_stable_lights)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_nighttime_stable_lights data (57/175)\n", "Rank 000: Var NOAA-DMSP-OLS_v4_nighttime_stable_lights completed (57/175)\n", "Rank 000: Writing OMI_level3_column_annual_average_NO2 var (58/175)\n", "Rank 000: Var OMI_level3_column_annual_average_NO2 created (58/175)\n", + "Rank 000: Filling OMI_level3_column_annual_average_NO2)\n", "Rank 000: Var OMI_level3_column_annual_average_NO2 data (58/175)\n", "Rank 000: Var OMI_level3_column_annual_average_NO2 completed (58/175)\n", "Rank 000: Writing OMI_level3_column_cloud_screened_annual_average_NO2 var (59/175)\n", "Rank 000: Var OMI_level3_column_cloud_screened_annual_average_NO2 created (59/175)\n", + "Rank 000: Filling OMI_level3_column_cloud_screened_annual_average_NO2)\n", "Rank 000: Var OMI_level3_column_cloud_screened_annual_average_NO2 data (59/175)\n", "Rank 000: Var OMI_level3_column_cloud_screened_annual_average_NO2 completed (59/175)\n", "Rank 000: Writing OMI_level3_tropospheric_column_annual_average_NO2 var (60/175)\n", "Rank 000: Var OMI_level3_tropospheric_column_annual_average_NO2 created (60/175)\n", + "Rank 000: Filling OMI_level3_tropospheric_column_annual_average_NO2)\n", "Rank 000: Var OMI_level3_tropospheric_column_annual_average_NO2 data (60/175)\n", "Rank 000: Var OMI_level3_tropospheric_column_annual_average_NO2 completed (60/175)\n", "Rank 000: Writing OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 var (61/175)\n", "Rank 000: Var OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 created (61/175)\n", + "Rank 000: Filling OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2)\n", "Rank 000: Var OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 data (61/175)\n", "Rank 000: Var OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 completed (61/175)\n", "Rank 000: Writing UMBC_anthrome_classification var (62/175)\n", "Rank 000: Var UMBC_anthrome_classification created (62/175)\n", + "Rank 000: Filling UMBC_anthrome_classification)\n", "Rank 000: Var UMBC_anthrome_classification data (62/175)\n", "Rank 000: Var UMBC_anthrome_classification completed (62/175)\n", "Rank 000: Writing UMBC_modal_anthrome_classification_25km var (63/175)\n", "Rank 000: Var UMBC_modal_anthrome_classification_25km created (63/175)\n", + "Rank 000: Filling UMBC_modal_anthrome_classification_25km)\n", "Rank 000: Var UMBC_modal_anthrome_classification_25km data (63/175)\n", "Rank 000: Var UMBC_modal_anthrome_classification_25km completed (63/175)\n", "Rank 000: Writing UMBC_modal_anthrome_classification_5km var (64/175)\n", "Rank 000: Var UMBC_modal_anthrome_classification_5km created (64/175)\n", + "Rank 000: Filling UMBC_modal_anthrome_classification_5km)\n", "Rank 000: Var UMBC_modal_anthrome_classification_5km data (64/175)\n", "Rank 000: Var UMBC_modal_anthrome_classification_5km completed (64/175)\n", "Rank 000: Writing WMO_region var (65/175)\n", "Rank 000: Var WMO_region created (65/175)\n", + "Rank 000: Filling WMO_region)\n", "Rank 000: Var WMO_region data (65/175)\n", "Rank 000: Var WMO_region completed (65/175)\n", "Rank 000: Writing WWF_TEOW_biogeographical_realm var (66/175)\n", "Rank 000: Var WWF_TEOW_biogeographical_realm created (66/175)\n", + "Rank 000: Filling WWF_TEOW_biogeographical_realm)\n", "Rank 000: Var WWF_TEOW_biogeographical_realm data (66/175)\n", "Rank 000: Var WWF_TEOW_biogeographical_realm completed (66/175)\n", "Rank 000: Writing WWF_TEOW_biome var (67/175)\n", "Rank 000: Var WWF_TEOW_biome created (67/175)\n", + "Rank 000: Filling WWF_TEOW_biome)\n", "Rank 000: Var WWF_TEOW_biome data (67/175)\n", "Rank 000: Var WWF_TEOW_biome completed (67/175)\n", "Rank 000: Writing WWF_TEOW_terrestrial_ecoregion var (68/175)\n", "Rank 000: Var WWF_TEOW_terrestrial_ecoregion created (68/175)\n", + "Rank 000: Filling WWF_TEOW_terrestrial_ecoregion)\n", "Rank 000: Var WWF_TEOW_terrestrial_ecoregion data (68/175)\n", "Rank 000: Var WWF_TEOW_terrestrial_ecoregion completed (68/175)\n", "Rank 000: Writing administrative_country_division_1 var (69/175)\n", "Rank 000: Var administrative_country_division_1 created (69/175)\n", + "Rank 000: Filling administrative_country_division_1)\n", "Rank 000: Var administrative_country_division_1 data (69/175)\n", "Rank 000: Var administrative_country_division_1 completed (69/175)\n", "Rank 000: Writing administrative_country_division_2 var (70/175)\n", "Rank 000: Var administrative_country_division_2 created (70/175)\n", + "Rank 000: Filling administrative_country_division_2)\n", "Rank 000: Var administrative_country_division_2 data (70/175)\n", "Rank 000: Var administrative_country_division_2 completed (70/175)\n", "Rank 000: Writing altitude var (71/175)\n", "Rank 000: Var altitude created (71/175)\n", + "Rank 000: Filling altitude)\n", "Rank 000: Var altitude data (71/175)\n", "Rank 000: Var altitude completed (71/175)\n", "Rank 000: Writing annual_native_max_gap_percent var (72/175)\n", - "Rank 000: Var annual_native_max_gap_percent created (72/175)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Rank 000: Var annual_native_max_gap_percent created (72/175)\n", + "Rank 000: Filling annual_native_max_gap_percent)\n", "Rank 000: Var annual_native_max_gap_percent data (72/175)\n", "Rank 000: Var annual_native_max_gap_percent completed (72/175)\n", "Rank 000: Writing annual_native_representativity_percent var (73/175)\n", "Rank 000: Var annual_native_representativity_percent created (73/175)\n", - "Rank 000: Var annual_native_representativity_percent data (73/175)\n", - "Rank 000: Var annual_native_representativity_percent completed (73/175)\n", - "Rank 000: Writing area_classification var (74/175)\n", - "Rank 000: Var area_classification created (74/175)\n" + "Rank 000: Filling annual_native_representativity_percent)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable associated_networks. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable associated_networks. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable city. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable city. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable climatology. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable climatology. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable contact_email_address. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable contact_email_address. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable contact_institution. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable contact_institution. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable contact_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable contact_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable country. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable country. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable data_level. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable data_level. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable data_licence. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable data_licence. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable ellipsoid. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ellipsoid. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable horizontal_datum. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable horizontal_datum. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable land_use. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable land_use. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable main_emission_source. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! 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Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -1780,145 +1122,181 @@ "name": "stdout", "output_type": "stream", "text": [ + "Rank 000: Var annual_native_representativity_percent data (73/175)\n", + "Rank 000: Var annual_native_representativity_percent completed (73/175)\n", + "Rank 000: Writing area_classification var (74/175)\n", + "Rank 000: Var area_classification created (74/175)\n", + "Rank 000: Filling area_classification)\n", "Rank 000: Var area_classification data (74/175)\n", "Rank 000: Var area_classification completed (74/175)\n", "Rank 000: Writing associated_networks var (75/175)\n", "Rank 000: Var associated_networks created (75/175)\n", + "Rank 000: Filling associated_networks)\n", "Rank 000: Var associated_networks data (75/175)\n", "Rank 000: Var associated_networks completed (75/175)\n", "Rank 000: Writing city var (76/175)\n", "Rank 000: Var city created (76/175)\n", + "Rank 000: Filling city)\n", "Rank 000: Var city data (76/175)\n", "Rank 000: Var city 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Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_sampling_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable network_uncertainty_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_uncertainty_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_further_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_further_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_manual_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_manual_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_process_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_process_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable primary_sampling_type. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_type. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable principal_investigator_email_address. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable principal_investigator_email_address. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable principal_investigator_institution. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable principal_investigator_institution. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable principal_investigator_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable principal_investigator_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable process_warnings. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable process_warnings. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable projection. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable projection. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -2038,201 +1422,253 @@ "name": "stdout", "output_type": "stream", "text": [ - "Rank 000: Var measuring_instrument_documented_span_drift data (112/175)\n", "Rank 000: Var measuring_instrument_documented_span_drift completed (112/175)\n", "Rank 000: Writing measuring_instrument_documented_uncertainty var (113/175)\n", "Rank 000: Var measuring_instrument_documented_uncertainty created (113/175)\n", + "Rank 000: Filling measuring_instrument_documented_uncertainty)\n", "Rank 000: Var measuring_instrument_documented_uncertainty data (113/175)\n", "Rank 000: Var measuring_instrument_documented_uncertainty completed (113/175)\n", "Rank 000: Writing measuring_instrument_documented_upper_limit_of_detection var (114/175)\n", "Rank 000: Var measuring_instrument_documented_upper_limit_of_detection created (114/175)\n", + "Rank 000: Filling measuring_instrument_documented_upper_limit_of_detection)\n", "Rank 000: Var 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"Rank 000: Var measuring_instrument_reported_lower_limit_of_detection created (125/175)\n", + "Rank 000: Filling measuring_instrument_reported_lower_limit_of_detection)\n", "Rank 000: Var measuring_instrument_reported_lower_limit_of_detection data (125/175)\n", "Rank 000: Var measuring_instrument_reported_lower_limit_of_detection completed (125/175)\n", "Rank 000: Writing measuring_instrument_reported_measurement_resolution var (126/175)\n", "Rank 000: Var measuring_instrument_reported_measurement_resolution created (126/175)\n", + "Rank 000: Filling measuring_instrument_reported_measurement_resolution)\n", "Rank 000: Var measuring_instrument_reported_measurement_resolution data (126/175)\n", "Rank 000: Var measuring_instrument_reported_measurement_resolution completed (126/175)\n", "Rank 000: Writing measuring_instrument_reported_precision var (127/175)\n", "Rank 000: Var measuring_instrument_reported_precision created (127/175)\n", + "Rank 000: Filling 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"Rank 000: Var monthly_native_max_gap_percent created (135/175)\n", + "Rank 000: Filling monthly_native_max_gap_percent)\n", "Rank 000: Var monthly_native_max_gap_percent data (135/175)\n", "Rank 000: Var monthly_native_max_gap_percent completed (135/175)\n", "Rank 000: Writing monthly_native_representativity_percent var (136/175)\n", "Rank 000: Var monthly_native_representativity_percent created (136/175)\n", + "Rank 000: Filling monthly_native_representativity_percent)\n", "Rank 000: Var monthly_native_representativity_percent data (136/175)\n", "Rank 000: Var monthly_native_representativity_percent completed (136/175)\n", "Rank 000: Writing network var (137/175)\n", "Rank 000: Var network created (137/175)\n", + "Rank 000: Filling network)\n", "Rank 000: Var network data (137/175)\n", "Rank 000: Var network completed (137/175)\n", "Rank 000: Writing network_maintenance_details var (138/175)\n", "Rank 000: Var network_maintenance_details created (138/175)\n", + "Rank 000: Filling 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(153/175)\n", "Rank 000: Var principal_investigator_email_address created (153/175)\n", + "Rank 000: Filling principal_investigator_email_address)\n", "Rank 000: Var principal_investigator_email_address data (153/175)\n", "Rank 000: Var principal_investigator_email_address completed (153/175)\n", "Rank 000: Writing principal_investigator_institution var (154/175)\n", "Rank 000: Var principal_investigator_institution created (154/175)\n", + "Rank 000: Filling principal_investigator_institution)\n", "Rank 000: Var principal_investigator_institution data (154/175)\n", "Rank 000: Var principal_investigator_institution completed (154/175)\n", "Rank 000: Writing principal_investigator_name var (155/175)\n", "Rank 000: Var principal_investigator_name created (155/175)\n", + "Rank 000: Filling principal_investigator_name)\n", "Rank 000: Var principal_investigator_name data (155/175)\n", "Rank 000: Var principal_investigator_name completed (155/175)\n", "Rank 000: Writing process_warnings var (156/175)\n", "Rank 000: Var process_warnings created (156/175)\n", + "Rank 000: Filling process_warnings)\n", "Rank 000: Var process_warnings data (156/175)\n", "Rank 000: Var process_warnings completed (156/175)\n", "Rank 000: Writing projection var (157/175)\n", "Rank 000: Var projection created (157/175)\n", + "Rank 000: Filling projection)\n", "Rank 000: Var projection data (157/175)\n", "Rank 000: Var projection completed (157/175)\n", "Rank 000: Writing reported_uncertainty_per_measurement var (158/175)\n", - "Rank 000: Var reported_uncertainty_per_measurement created (158/175)\n" + "Rank 000: Var reported_uncertainty_per_measurement created (158/175)\n", + "Rank 000: Filling reported_uncertainty_per_measurement)\n", + "Rank 000: Var reported_uncertainty_per_measurement data (158/175)\n", + "Rank 000: Var reported_uncertainty_per_measurement completed (158/175)\n", + "Rank 000: Writing representative_radius var (159/175)\n", + "Rank 000: Var representative_radius created (159/175)\n", + "Rank 000: Filling representative_radius)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable sample_preparation_further_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_further_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable sample_preparation_process_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_process_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable sample_preparation_techniques. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_techniques. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_types. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -2240,42 +1676,45 @@ "name": "stdout", "output_type": "stream", "text": [ - "Rank 000: Var reported_uncertainty_per_measurement data (158/175)\n", - "Rank 000: Var reported_uncertainty_per_measurement completed (158/175)\n", - "Rank 000: Writing representative_radius var (159/175)\n", - "Rank 000: Var representative_radius created (159/175)\n", "Rank 000: Var representative_radius data (159/175)\n", "Rank 000: Var representative_radius completed (159/175)\n", "Rank 000: Writing sample_preparation_further_details var (160/175)\n", "Rank 000: Var sample_preparation_further_details created (160/175)\n", + "Rank 000: Filling sample_preparation_further_details)\n", "Rank 000: Var sample_preparation_further_details data (160/175)\n", "Rank 000: Var sample_preparation_further_details completed (160/175)\n", "Rank 000: Writing sample_preparation_process_details var (161/175)\n", "Rank 000: Var sample_preparation_process_details created (161/175)\n", + "Rank 000: Filling sample_preparation_process_details)\n", "Rank 000: Var sample_preparation_process_details data (161/175)\n", "Rank 000: Var sample_preparation_process_details completed (161/175)\n", - "Rank 000: Writing sample_preparation_techniques var (162/175)" + "Rank 000: Writing sample_preparation_techniques var (162/175)\n", + "Rank 000: Var sample_preparation_techniques created (162/175)\n", + "Rank 000: Filling sample_preparation_techniques)\n", + "Rank 000: Var sample_preparation_techniques data (162/175)\n", + "Rank 000: Var sample_preparation_techniques completed (162/175)\n", + "Rank 000: Writing sample_preparation_types var (163/175)\n", + "Rank 000: Var sample_preparation_types created (163/175)\n", + "Rank 000: Filling sample_preparation_types)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable sample_preparation_types. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable station_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable station_timezone. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_timezone. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable street_type. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable street_type. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable terrain. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable terrain. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:343: UserWarning: WARNING!!! Different data types for variable vertical_datum. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable vertical_datum. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -2283,60 +1722,66 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Rank 000: Var sample_preparation_techniques created (162/175)\n", - "Rank 000: Var sample_preparation_techniques data (162/175)\n", - "Rank 000: Var sample_preparation_techniques completed (162/175)\n", - "Rank 000: Writing sample_preparation_types var (163/175)\n", - "Rank 000: Var sample_preparation_types created (163/175)\n", "Rank 000: Var sample_preparation_types data (163/175)\n", "Rank 000: Var sample_preparation_types completed (163/175)\n", "Rank 000: Writing sampling_height var (164/175)\n", "Rank 000: Var sampling_height created (164/175)\n", + "Rank 000: Filling sampling_height)\n", "Rank 000: Var sampling_height data (164/175)\n", "Rank 000: Var sampling_height completed (164/175)\n", "Rank 000: Writing sconco3 var (165/175)\n", "Rank 000: Var sconco3 created (165/175)\n", + "Rank 000: Filling sconco3)\n", "Rank 000: Var sconco3 data (165/175)\n", "Rank 000: Var sconco3 completed (165/175)\n", "Rank 000: Writing season_code var (166/175)\n", "Rank 000: Var season_code created (166/175)\n", + "Rank 000: Filling season_code)\n", "Rank 000: Var season_code data (166/175)\n", "Rank 000: Var season_code completed (166/175)\n", "Rank 000: Writing station_classification var (167/175)\n", "Rank 000: Var station_classification created (167/175)\n", + "Rank 000: Filling station_classification)\n", "Rank 000: Var station_classification data (167/175)\n", "Rank 000: Var station_classification completed (167/175)\n", "Rank 000: Writing station_name var (168/175)\n", "Rank 000: Var station_name created (168/175)\n", + "Rank 000: Filling station_name)\n", "Rank 000: Var station_name data (168/175)\n", "Rank 000: Var station_name completed (168/175)\n", "Rank 000: Writing station_reference var (169/175)\n", "Rank 000: Var station_reference created (169/175)\n", + "Rank 000: Filling station_reference)\n", "Rank 000: Var station_reference data (169/175)\n", "Rank 000: Var station_reference completed (169/175)\n", "Rank 000: Writing station_timezone var (170/175)\n", "Rank 000: Var station_timezone created (170/175)\n", + "Rank 000: Filling station_timezone)\n", "Rank 000: Var station_timezone data (170/175)\n", "Rank 000: Var station_timezone completed (170/175)\n", "Rank 000: Writing street_type var (171/175)\n", "Rank 000: Var street_type created (171/175)\n", + "Rank 000: Filling street_type)\n", "Rank 000: Var street_type data (171/175)\n", "Rank 000: Var street_type completed (171/175)\n", "Rank 000: Writing street_width var (172/175)\n", "Rank 000: Var street_width created (172/175)\n", + "Rank 000: Filling street_width)\n", "Rank 000: Var street_width data (172/175)\n", "Rank 000: Var street_width completed (172/175)\n", "Rank 000: Writing terrain var (173/175)\n", "Rank 000: Var terrain created (173/175)\n", + "Rank 000: Filling terrain)\n", "Rank 000: Var terrain data (173/175)\n", "Rank 000: Var terrain completed (173/175)\n", "Rank 000: Writing vertical_datum var (174/175)\n", "Rank 000: Var vertical_datum created (174/175)\n", + "Rank 000: Filling vertical_datum)\n", "Rank 000: Var vertical_datum data (174/175)\n", "Rank 000: Var vertical_datum completed (174/175)\n", "Rank 000: Writing weekday_weekend_code var (175/175)\n", "Rank 000: Var weekday_weekend_code created (175/175)\n", + "Rank 000: Filling weekday_weekend_code)\n", "Rank 000: Var weekday_weekend_code data (175/175)\n", "Rank 000: Var weekday_weekend_code completed (175/175)\n" ] @@ -2381,7 +1826,7 @@ { "data": { "text/plain": [ - 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" datetime.datetime(2018, 4, 28, 8, 0),\n", - " datetime.datetime(2018, 4, 28, 9, 0),\n", - " datetime.datetime(2018, 4, 28, 10, 0),\n", - " datetime.datetime(2018, 4, 28, 11, 0),\n", - " datetime.datetime(2018, 4, 28, 12, 0),\n", - " datetime.datetime(2018, 4, 28, 13, 0),\n", - " datetime.datetime(2018, 4, 28, 14, 0),\n", - " datetime.datetime(2018, 4, 28, 15, 0),\n", - " datetime.datetime(2018, 4, 28, 16, 0),\n", - " datetime.datetime(2018, 4, 28, 17, 0),\n", - " datetime.datetime(2018, 4, 28, 18, 0),\n", - " datetime.datetime(2018, 4, 28, 19, 0),\n", - " datetime.datetime(2018, 4, 28, 20, 0),\n", - " datetime.datetime(2018, 4, 28, 21, 0),\n", - " datetime.datetime(2018, 4, 28, 22, 0),\n", - " datetime.datetime(2018, 4, 28, 23, 0),\n", - " datetime.datetime(2018, 4, 29, 0, 0),\n", - " datetime.datetime(2018, 4, 29, 1, 0),\n", - " datetime.datetime(2018, 4, 29, 2, 0),\n", - " datetime.datetime(2018, 4, 29, 3, 0),\n", - " datetime.datetime(2018, 4, 29, 4, 0),\n", - " datetime.datetime(2018, 4, 29, 5, 0),\n", - " datetime.datetime(2018, 4, 29, 6, 0),\n", - " datetime.datetime(2018, 4, 29, 7, 0),\n", - " datetime.datetime(2018, 4, 29, 8, 0),\n", - " datetime.datetime(2018, 4, 29, 9, 0),\n", - " datetime.datetime(2018, 4, 29, 10, 0),\n", - " datetime.datetime(2018, 4, 29, 11, 0),\n", - " datetime.datetime(2018, 4, 29, 12, 0),\n", - " datetime.datetime(2018, 4, 29, 13, 0),\n", - " datetime.datetime(2018, 4, 29, 14, 0),\n", - " datetime.datetime(2018, 4, 29, 15, 0),\n", - " datetime.datetime(2018, 4, 29, 16, 0),\n", - " datetime.datetime(2018, 4, 29, 17, 0),\n", - " datetime.datetime(2018, 4, 29, 18, 0),\n", - " datetime.datetime(2018, 4, 29, 19, 0),\n", - " datetime.datetime(2018, 4, 29, 20, 0),\n", - " datetime.datetime(2018, 4, 29, 21, 0),\n", - " datetime.datetime(2018, 4, 29, 22, 0),\n", - " datetime.datetime(2018, 4, 29, 23, 0),\n", - " datetime.datetime(2018, 4, 30, 0, 0),\n", - " datetime.datetime(2018, 4, 30, 1, 0),\n", - " datetime.datetime(2018, 4, 30, 2, 0),\n", - " datetime.datetime(2018, 4, 30, 3, 0),\n", - " datetime.datetime(2018, 4, 30, 4, 0),\n", - " datetime.datetime(2018, 4, 30, 5, 0),\n", - " datetime.datetime(2018, 4, 30, 6, 0),\n", - " datetime.datetime(2018, 4, 30, 7, 0),\n", - " datetime.datetime(2018, 4, 30, 8, 0),\n", - " datetime.datetime(2018, 4, 30, 9, 0),\n", - " datetime.datetime(2018, 4, 30, 10, 0),\n", - " datetime.datetime(2018, 4, 30, 11, 0),\n", - " datetime.datetime(2018, 4, 30, 12, 0),\n", - " datetime.datetime(2018, 4, 30, 13, 0),\n", - " datetime.datetime(2018, 4, 30, 14, 0),\n", - " datetime.datetime(2018, 4, 30, 15, 0),\n", - " datetime.datetime(2018, 4, 30, 16, 0),\n", - " datetime.datetime(2018, 4, 30, 17, 0),\n", - " datetime.datetime(2018, 4, 30, 18, 0),\n", - " datetime.datetime(2018, 4, 30, 19, 0),\n", - " datetime.datetime(2018, 4, 30, 20, 0),\n", - " datetime.datetime(2018, 4, 30, 21, 0),\n", - " datetime.datetime(2018, 4, 30, 22, 0),\n", - " datetime.datetime(2018, 4, 30, 23, 0)]" + "(datetime.datetime(2018, 4, 1, 0, 0),\n", + " 720,\n", + " datetime.datetime(2018, 4, 30, 23, 0))" ] }, - "execution_count": 12, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "exp_interp_nes.time" + "exp_interp_nes.time[0], len(exp_interp_nes.time), exp_interp_nes.time[-1]" ] }, { @@ -3348,7 +2076,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_providentia.py:376: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_providentia.py:374: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -3361,10 +2089,12 @@ "Rank 000: Dimensions done\n", "Rank 000: Writing sconco3 var (1/2)\n", "Rank 000: Var sconco3 created (1/2)\n", + "Rank 000: Filling sconco3)\n", "Rank 000: Var sconco3 data (1/2)\n", "Rank 000: Var sconco3 completed (1/2)\n", "Rank 000: Writing station_reference var (2/2)\n", "Rank 000: Var station_reference created (2/2)\n", + "Rank 000: Filling station_reference)\n", "Rank 000: Var station_reference data (2/2)\n", "Rank 000: Var station_reference completed (2/2)\n" ] diff --git a/tutorials/2.Creation/2.3.Create-Points.ipynb b/tutorials/2.Creation/2.3.Create-Points.ipynb index 05b8580..5b57a9a 100644 --- a/tutorials/2.Creation/2.3.Create-Points.ipynb +++ b/tutorials/2.Creation/2.3.Create-Points.ipynb @@ -221,7 +221,8 @@ "metadata": {}, "outputs": [], "source": [ - "points_grid.variables = metadata" + "points_grid.variables = metadata\n", + "points_grid.set_strlen(75)" ] }, { @@ -233,9 +234,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -791,7 +792,8 @@ "metadata": {}, "outputs": [], "source": [ - "points_grid.variables = metadata" + "points_grid.variables = metadata\n", + "points_grid.set_strlen(75)" ] }, { @@ -803,11 +805,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable pm10. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable pm10. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, diff --git a/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb b/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb index 53c4db4..a0be25a 100644 --- a/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb +++ b/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb @@ -389,7 +389,8 @@ "metadata": {}, "outputs": [], "source": [ - "points_grid.variables = metadata" + "points_grid.variables = metadata\n", + "points_grid.set_strlen(75)" ] }, { @@ -401,7 +402,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -617,6 +618,14 @@ "execution_count": 14, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -696,6 +705,7 @@ " # Read metadata\n", " metadata = {'station_name': {'data': current.columns[2:4].to_numpy(),\n", " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'altitude': {'data': altitude,\n", " 'dimensions': ('station',),\n", @@ -713,6 +723,7 @@ " \n", " # Assign metadata\n", " points_grid.variables = metadata\n", + " points_grid.set_strlen(75)\n", " \n", " # Making directory\n", " netcdf_path = 'port_barcelona/port-barcelona/hourly/sconcno2/'\n", diff --git a/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb b/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb index a764a7b..7b40b37 100644 --- a/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb +++ b/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb @@ -355,7 +355,8 @@ "metadata": {}, "outputs": [], "source": [ - "points_grid.variables = metadata" + "points_grid.variables = metadata\n", + "points_grid.set_strlen(75)" ] }, { @@ -367,7 +368,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:346: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -572,6 +573,14 @@ "execution_count": 14, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -603,6 +612,7 @@ " # Read metadata\n", " metadata = {'station_name': {'data': current.columns[0:2].to_numpy(),\n", " 'dimensions': ('station',),\n", + " 'dtype': str,\n", " 'standard_name': ''},\n", " 'altitude': {'data': altitude,\n", " 'dimensions': ('station',),\n", @@ -620,6 +630,7 @@ " \n", " # Assign metadata\n", " points_grid.variables = metadata\n", + " points_grid.set_strlen(75)\n", " \n", " # Making directory\n", " netcdf_path = 'csic/csic/monthly/sconcnh3/'\n", diff --git a/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb b/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb index c55d60e..1883f08 100644 --- a/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb +++ b/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb @@ -38,9 +38,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Original path: /gpfs/scratch/bsc32/bsc32538/HERMESv3/OUT_Complete_single/GFAS_p13h/HERMESv3_GR_GFAS_d01_2022050100.nc\n", - "# Rotated grid from HERMES\n", - "path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/HERMESv3_GR_GFAS_d01_2022050100.nc'" + "# Original path: /esarchive/exp/snes/a5s1/regional/3hourly/od550du/od550du-000_2021070612.nc\n", + "# Rotated grid for dust regional\n", + "path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/od550du-000_2021070612.nc'" ] }, { @@ -49,19 +49,69 @@ "metadata": {}, "outputs": [ { - "ename": "RuntimeError", - "evalue": "There is no variable called rotated_pole, projection has not been defined.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnessy_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen_netcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpath_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mnessy_1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/esarchive/scratch/avilanova/software/NES/nes/load_nes.py\u001b[0m in \u001b[0;36mopen_netcdf\u001b[0;34m(path, comm, xarray, info, parallel_method, avoid_first_hours, avoid_last_hours, first_level, last_level, balanced)\u001b[0m\n\u001b[1;32m 68\u001b[0m nessy = RotatedNes(comm=comm, dataset=dataset, xarray=xarray, info=info, parallel_method=parallel_method,\n\u001b[1;32m 69\u001b[0m \u001b[0mavoid_first_hours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mavoid_first_hours\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mavoid_last_hours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mavoid_last_hours\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m first_level=first_level, last_level=last_level, create_nes=False, balanced=balanced,)\n\u001b[0m\u001b[1;32m 71\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0m__is_points\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mparallel_method\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'Y'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/esarchive/scratch/avilanova/software/NES/nes/nc_projections/rotated_nes.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, comm, path, info, dataset, xarray, parallel_method, avoid_first_hours, avoid_last_hours, first_level, last_level, create_nes, balanced, times, **kwargs)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0mavoid_first_hours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mavoid_first_hours\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mavoid_last_hours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mavoid_last_hours\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0mfirst_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfirst_level\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlast_level\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_nes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_nes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m times=times, **kwargs)\n\u001b[0m\u001b[1;32m 75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcreate_nes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, comm, path, info, dataset, xarray, parallel_method, avoid_first_hours, avoid_last_hours, first_level, last_level, create_nes, balanced, times, **kwargs)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0;31m# Get projection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_projection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;31m# Complete dimensions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/esarchive/scratch/avilanova/software/NES/nes/nc_projections/rotated_nes.py\u001b[0m in \u001b[0;36m_get_projection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 173\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'There is no variable called rotated_pole, projection has not been defined.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'dtype'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprojection_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: There is no variable called rotated_pole, projection has not been defined." + "name": "stdout", + "output_type": "stream", + "text": [ + "{'data': array([[[-10.09304333, -10.05597973, -9.96679783, -10.00389481],\n", + " [-10.05597973, -10.01897526, -9.92975521, -9.96679783],\n", + " [-10.01897335, -9.98202324, -9.89277172, -9.92975426],\n", + " ...,\n", + " [ -9.98202515, -10.01897526, -9.92975521, -9.89277172],\n", + " [-10.01897526, -10.05597973, -9.96679783, -9.92975521],\n", + " [-10.05597973, -10.09304333, -10.00389481, -9.96679783]],\n", + "\n", + " [[-10.00389481, -9.96679783, -9.87760735, -9.91474056],\n", + " [ -9.96679783, -9.92975521, -9.8405323 , -9.87760735],\n", + " [ -9.92975426, -9.89277172, -9.80351067, -9.8405304 ],\n", + " ...,\n", + 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[[-61.65505981, -61.5632019 , -61.67092896, -61.76269531],\n", + " [-61.5632019 , -61.47122192, -61.57907104, -61.67092896],\n", + " [-61.47122192, -61.37908936, -61.48706055, -61.57907104],\n", + " ...,\n", + " [101.37908936, 101.47122192, 101.57904053, 101.48703003],\n", + " [101.47122192, 101.5632019 , 101.67092896, 101.57904053],\n", + " [101.5632019 , 101.65505981, 101.76269531, 101.67092896]]])}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nessy_1.lon_bnds" ] @@ -103,9 +271,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank 000: Creating bounds_file_1.nc\n", + "Rank 000: NetCDF ready to write\n", + "Rank 000: Dimensions done\n", + "Rank 000: Writing od550du var (1/1)\n", + "Rank 000: Var od550du created (1/1)\n", + "Rank 000: Var od550du completed (1/1)\n" + ] + } + ], "source": [ "nessy_1.to_netcdf('bounds_file_1.nc', info=True)" ] @@ -119,9 +300,75 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'data': array([[[-10.09304333, -10.05597973, -9.96679783, -10.00389481],\n", + " [-10.05597973, -10.01897526, -9.92975521, -9.96679783],\n", + " [-10.01897335, -9.98202324, -9.89277172, -9.92975426],\n", + " ...,\n", + " [ -9.98202515, -10.01897526, -9.92975521, -9.89277172],\n", + " [-10.01897526, -10.05597973, -9.96679783, -9.92975521],\n", + " [-10.05597973, -10.09304333, -10.00389481, -9.96679783]],\n", + "\n", + " [[-10.00389481, -9.96679783, -9.87760735, -9.91474056],\n", + " [ -9.96679783, -9.92975521, -9.8405323 , -9.87760735],\n", + " [ -9.92975426, -9.89277172, -9.80351067, -9.8405304 ],\n", + " ...,\n", + " [ -9.89277172, -9.92975521, -9.8405323 , -9.80351257],\n", + " [ -9.92975521, -9.96679783, 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[[-61.65505981, -61.5632019 , -61.67092896, -61.76269531],\n", + " [-61.5632019 , -61.47122192, -61.57907104, -61.67092896],\n", + " [-61.47122192, -61.37908936, -61.48706055, -61.57907104],\n", + " ...,\n", + " [101.37908936, 101.47122192, 101.57904053, 101.48703003],\n", + " [101.47122192, 101.5632019 , 101.67092896, 101.57904053],\n", + " [101.5632019 , 101.65505981, 101.76269531, 101.67092896]]])}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nessy_2.lon_bnds" ] @@ -168,9 +533,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "None\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Original path: /gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc\n", "# Rotated grid from MONARCH\n", @@ -188,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -204,27 +587,154 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank 000: Loading O3_all var (1/1)\n", + "Rank 000: Loaded O3_all var ((1, 24, 271, 351))\n" + ] + } + ], "source": [ "nessy_3.load()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([[[16.2203979 , 16.30306824, 16.48028979, 16.39739715],\n", + " [16.30306855, 16.3853609 , 16.56280424, 16.48029011],\n", + " [16.38536121, 16.46727425, 16.64493885, 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...,\n", + " [ 86.96125282, 87.14410443, 87.46910897, 87.28743481],\n", + " [ 87.14410165, 87.32583174, 87.64966558, 87.46910621],\n", + " [ 87.32583243, 87.50645709, 87.82912696, 87.64966627]],\n", + " \n", + " [[-67.82912819, -67.64966751, -67.97544812, -68.1537229 ],\n", + " [-67.64966682, -67.46910745, -67.79608108, -67.97544744],\n", + " [-67.46910676, -67.28743258, -67.6156063 , -67.79608039],\n", + " ...,\n", + " [ 87.28743606, 87.46911022, 87.79608382, 87.61560976],\n", + " [ 87.46910745, 87.64966682, 87.97544744, 87.79608108],\n", + " [ 87.64966751, 87.82912819, 88.1537229 , 87.97544812]],\n", + " \n", + " [[-68.15372103, -67.97544625, -68.30317799, -68.48024479],\n", + " [-67.97544557, -67.7960792 , -68.12502637, -68.30317732],\n", + " [-67.79607851, -67.61560442, -67.94577447, -68.12502569],\n", + " ...,\n", + " [ 87.61560787, 87.79608195, 88.1250291 , 87.9457779 ],\n", + " [ 87.7960792 , 87.97544557, 88.30317732, 88.12502637],\n", + " [ 87.97544625, 88.15372103, 88.48024479, 88.30317799]]])}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nessy_3.lon_bnds" ] @@ -238,9 +748,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank 000: Creating bounds_file_3.nc\n", + "Rank 000: NetCDF ready to write\n", + "Rank 000: Dimensions done\n", + "Rank 000: Writing O3_all var (1/1)\n", + "Rank 000: Var O3_all created (1/1)\n", + "Rank 000: Filling O3_all)\n", + "Rank 000: Var O3_all data (1/1)\n", + "Rank 000: Var O3_all completed (1/1)\n" + ] + } + ], "source": [ "nessy_3.to_netcdf('bounds_file_3.nc', info=True)" ] @@ -254,9 +779,75 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'data': array([[[16.2203979 , 16.30306824, 16.48028979, 16.39739715],\n", + " [16.30306855, 16.3853609 , 16.56280424, 16.48029011],\n", + " [16.38536121, 16.46727425, 16.64493885, 16.56280455],\n", + " ...,\n", + " [16.46727269, 16.38535964, 16.56280298, 16.64493728],\n", + " [16.3853609 , 16.30306855, 16.48029011, 16.56280424],\n", + " [16.30306824, 16.2203979 , 16.39739715, 16.48028979]],\n", + "\n", + " [[16.39739783, 16.48029047, 16.65746762, 16.57435251],\n", + " [16.48029079, 16.56280491, 16.74020402, 16.65746794],\n", + " [16.56280523, 16.64493952, 16.82256006, 16.74020434],\n", + " ...,\n", + " [16.64493796, 16.56280366, 16.74020276, 16.82255849],\n", + " [16.56280491, 16.48029079, 16.65746794, 16.74020402],\n", + " [16.48029047, 16.39739783, 16.57435251, 16.65746762]],\n", + "\n", + " [[16.57435149, 16.65746661, 16.83459876, 16.751261 ],\n", + " [16.65746692, 16.74020301, 16.91755729, 16.83459908],\n", + " [16.74020332, 16.82255904, 17.00013494, 16.91755761],\n", + " ...,\n", + " [16.82255748, 16.74020175, 16.91755603, 17.00013337],\n", + " [16.74020301, 16.65746692, 16.83459908, 16.91755729],\n", + " [16.65746661, 16.57435149, 16.751261 , 16.83459876]],\n", + "\n", + " ...,\n", + "\n", + " [[58.19210948, 58.34380497, 58.44964444, 58.29776032],\n", + " [58.34380555, 58.49539321, 58.6014247 , 58.44964502],\n", + " [58.49539378, 58.64687141, 58.75309835, 58.60142528],\n", + " ...,\n", + " [58.64686852, 58.49539089, 58.60142239, 58.75309546],\n", + " [58.49539321, 58.34380555, 58.44964502, 58.6014247 ],\n", + " [58.34380497, 58.19210948, 58.29776032, 58.44964444]],\n", + "\n", + " [[58.29776072, 58.44964485, 58.55466327, 58.40259426],\n", + " [58.44964543, 58.6014251 , 58.7066318 , 58.55466385],\n", + " [58.60142568, 58.75309876, 58.85849715, 58.70663238],\n", + " ...,\n", + " [58.75309587, 58.60142279, 58.70662948, 58.85849425],\n", + " [58.6014251 , 58.44964543, 58.55466385, 58.7066318 ],\n", + " [58.44964485, 58.29776072, 58.40259426, 58.55466327]],\n", + "\n", 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+ "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "nessy_4.lon_bnds" ] -- GitLab From 65693474e379583851c3caa46c9701e85063702f Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Tue, 11 Apr 2023 17:07:30 +0200 Subject: [PATCH 18/21] Add extensions to gitignore --- .gitignore | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 6f3a619..1058f26 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,13 @@ .idea -logs +log* notebooks/.ipynb_checkpoints .ipynb_checkpoints nes/__pycache__ nes/nc_projections/__pycache__ -*.pyc \ No newline at end of file +*.pyc +*.nc +*.cpg +*.dbf +*.prj +*.shp +*.shx -- GitLab From 4362909bdad1e655de3326e79a591ffdf3affd03 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Cortezon Date: Tue, 11 Apr 2023 17:21:36 +0200 Subject: [PATCH 19/21] Test tutorials --- nes/nc_projections/default_nes.py | 2 +- .../1.1.Read_Write_Regular.ipynb | 166 ++- .../1.2.Read_Write_Rotated.ipynb | 167 ++- .../1.3.Read_Write_Points.ipynb | 961 +++++------------- .../1.Introduction/1.4.Read_Write_LCC.ipynb | 60 +- .../1.5.Read_Write_Mercator.ipynb | 4 +- .../1.6.Read_Write_Providentia.ipynb | 284 +++--- tutorials/2.Creation/2.3.Create-Points.ipynb | 10 +- .../2.4.Create_Points_Port_Barcelona.ipynb | 4 +- .../2.Creation/2.5.Create_Points_CSIC.ipynb | 4 +- tutorials/3.Statistics/3.1.Statistics.ipynb | 25 +- .../4.1.Vertical_Interpolation.ipynb | 10 +- .../4.2.Horizontal_Interpolation.ipynb | 8 +- .../4.3.Conservative_Interpolation.ipynb | 151 ++- .../4.4.Providentia_Interpolation.ipynb | 174 ++-- ...4.5.NES_vs_Providentia_Interpolation.ipynb | 48 +- .../5.Geospatial/5.1.Create_Shapefiles.ipynb | 21 +- tutorials/5.Geospatial/5.2.Spatial_Join.ipynb | 19 +- .../5.3.Add_Coordinates_Bounds.ipynb | 180 +--- .../5.4.Calculate_Grid_Cell_Area.ipynb | 227 +++-- .../5.5.Calculate_Geometry_Cell_Area.ipynb | 40 +- tutorials/6.Others/6.1.Add_Time_Bounds.ipynb | 10 +- tutorials/6.Others/6.3.Plot.ipynb | 6 +- .../6.Others/6.4.Write_By_Timestep.ipynb | 10 +- 24 files changed, 992 insertions(+), 1599 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 186237c..9f9d361 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -1713,7 +1713,7 @@ class Nes(object): if not create_nes: if 'cell_area' in self.netcdf.variables.keys(): c_measures['cell_area'] = {} - c_measures['cell_area']['data'] = self.netcdf.variables['cell_area'][:] + c_measures['cell_area']['data'] = self._unmask_array(self.netcdf.variables['cell_area'][:]) c_measures = self.comm.bcast(c_measures, root=0) self.free_vars(['cell_area']) diff --git a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb index a9f2812..9383899 100644 --- a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb +++ b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -324,101 +324,97 @@ { "data": { "text/plain": [ - "{'sconcno2': {'data': masked_array(\n", - " data=[[[[0.00043756, 0.00040022, 0.00038021, ..., 0.0010805 ,\n", - " 0.00098293, 0.00099733],\n", - " [0.00040069, 0.00036983, 0.00034815, ..., 0.00097833,\n", - " 0.00096023, 0.00098129],\n", - " [0.00036784, 0.00034654, 0.00033321, ..., 0.0010072 ,\n", - " 0.00100705, 0.00104882],\n", - " ...,\n", - " [0.00138594, 0.00145622, 0.0014579 , ..., 0.00385434,\n", - " 0.00425893, 0.00490418],\n", - " [0.00122151, 0.00126218, 0.00129748, ..., 0.0043506 ,\n", - " 0.00469467, 0.00532685],\n", - " [0.00115163, 0.00116421, 0.00118752, ..., 0.00483325,\n", - " 0.00507843, 0.00530437]]],\n", + "{'sconcno2': {'data': array([[[[0.00043756, 0.00040022, 0.00038021, ..., 0.0010805 ,\n", + " 0.00098293, 0.00099733],\n", + " [0.00040069, 0.00036983, 0.00034815, ..., 0.00097833,\n", + " 0.00096023, 0.00098129],\n", + " 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0.00089645, 0.00099238],\n", - " [0.00043154, 0.00043886, 0.00041805, ..., 0.00106261,\n", - " 0.0011123 , 0.00114801],\n", - " ...,\n", - " [0.00138145, 0.00129999, 0.00123086, ..., 0.00198987,\n", - " 0.00213361, 0.00256755],\n", - " [0.00135256, 0.00127368, 0.00119948, ..., 0.00217552,\n", - " 0.0025124 , 0.00323614],\n", - " [0.00130033, 0.00126458, 0.00118686, ..., 0.00252028,\n", - " 0.00299277, 0.00333072]]],\n", + " [[[0.00042468, 0.00041533, 0.00038742, ..., 0.00079357,\n", + " 0.00082948, 0.00095498],\n", + " [0.00042654, 0.0004235 , 0.00040626, ..., 0.00087126,\n", + " 0.00089645, 0.00099238],\n", + " [0.00043154, 0.00043886, 0.00041805, ..., 0.00106261,\n", + " 0.0011123 , 0.00114801],\n", + " ...,\n", + " [0.00138145, 0.00129999, 0.00123086, ..., 0.00198987,\n", + " 0.00213361, 0.00256755],\n", + " [0.00135256, 0.00127368, 0.00119948, ..., 0.00217552,\n", + " 0.0025124 , 0.00323614],\n", + " [0.00130033, 0.00126458, 0.00118686, ..., 0.00252028,\n", + " 0.00299277, 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0.00036186, 0.00034779, ..., 0.00095496,\n", + " 0.00093864, 0.00095694],\n", + " [0.00038271, 0.00039669, 0.00037828, ..., 0.00103301,\n", + " 0.00104353, 0.00106545],\n", + " ...,\n", + " [0.000964 , 0.00094082, 0.00089644, ..., 0.0016536 ,\n", + " 0.00172894, 0.00202166],\n", + " [0.0009503 , 0.00091948, 0.00088608, ..., 0.00176943,\n", + " 0.00191166, 0.00231184],\n", + " [0.00093363, 0.0009191 , 0.00089206, ..., 0.00194169,\n", + " 0.00215044, 0.00234321]]]], dtype=float32),\n", " 'dimensions': ('time', 'lev', 'lat', 'lon'),\n", " 'dtype': dtype('float32'),\n", " 'units': 'ppmV',\n", @@ -483,7 +479,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, diff --git a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb index 7a126e6..b761342 100644 --- a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb +++ b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ 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4.61104453e-11, 4.96835975e-11, ...,\n", + " 6.42673414e-11, 6.38328765e-11, 6.38894007e-11]]]],\n", + " dtype=float32),\n", " 'dimensions': ('time', 'lev', 'rlat', 'rlon'),\n", " 'dtype': dtype('float32'),\n", " 'units': 'kg/m3',\n", @@ -408,7 +405,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, diff --git a/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb b/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb index 7e72919..af55f33 100644 --- a/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb +++ b/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb @@ -46,36 +46,10 @@ "execution_count": 3, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "station_start_date |S1\n", - "station_zone |S1\n", - "street_type |S1\n", - "country_code |S1\n", - "ccaa |S1\n", - "station_name |S1\n", - "station_area |S1\n", - "city |S1\n", - "pm10 float32\n", - "station_emep |S1\n", - "station_type |S1\n", - "country |S1\n", - "altitude float32\n", - "station_code |S1\n", - "longitude float32\n", - "station_end_date |S1\n", - "station_rural_back |S1\n", - "time float32\n", - "latitude float32\n", - "station_ozone_classification |S1\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -520,26 +494,21 @@ " 'dimensions': ('station',),\n", " 'dtype': str,\n", " 'standard_name': ''},\n", - " 'altitude': {'data': masked_array(data=[4.890e+02, 5.380e+02, 1.136e+03, 1.031e+03, 5.320e+02,\n", - " 8.000e+00, 1.000e+01, 3.000e+00, 3.200e+01, 6.000e+00,\n", - " 9.170e+02, 6.850e+02, 7.800e+01, 1.230e+03, 1.340e+02,\n", - " 1.360e+03, 2.300e+01, 3.930e+02, 8.850e+02, 9.850e+02,\n", - " 4.700e+02, 5.060e+02, 5.000e+00, 1.260e+02, 2.600e+02,\n", - " 1.250e+02, 1.000e+01, 6.600e+01, 2.090e+02, 2.190e+02,\n", - " 4.390e+02, 2.100e+02, 3.000e+02, 1.800e+02, 1.603e+03,\n", - " 4.000e+00, 1.570e+02, 8.060e+02, 3.800e+02, 1.630e+02,\n", - " 2.500e+01, 1.000e+01, 5.400e+02, 2.008e+03, 3.450e+02,\n", - " 4.740e+02, 7.400e+01, 2.000e+01, 1.210e+02, 7.000e+00,\n", - " 2.800e+01, 8.990e+02, 2.080e+03, 1.170e+02, 1.000e+00,\n", - " 5.350e+02, 1.000e+00, 1.800e+01, 7.000e+00, 3.600e+01,\n", - " 7.370e+02, 1.500e+02, 5.000e+00, 1.450e+03, 2.500e+02,\n", - " 5.000e+00, 1.205e+03, 6.200e+01, 4.800e+01, 4.000e+00,\n", - " 8.600e+01, 5.900e+01, 2.000e+01, 1.200e+01, 9.370e+02,\n", - " 1.118e+03, 3.500e+01, 3.400e+02, 3.578e+03, 4.000e+00,\n", - " 1.020e+03, 9.000e+00, 1.000e+00, 6.000e+00],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", + " 'altitude': {'data': array([4.890e+02, 5.380e+02, 1.136e+03, 1.031e+03, 5.320e+02, 8.000e+00,\n", + " 1.000e+01, 3.000e+00, 3.200e+01, 6.000e+00, 9.170e+02, 6.850e+02,\n", + " 7.800e+01, 1.230e+03, 1.340e+02, 1.360e+03, 2.300e+01, 3.930e+02,\n", + " 8.850e+02, 9.850e+02, 4.700e+02, 5.060e+02, 5.000e+00, 1.260e+02,\n", + " 2.600e+02, 1.250e+02, 1.000e+01, 6.600e+01, 2.090e+02, 2.190e+02,\n", + " 4.390e+02, 2.100e+02, 3.000e+02, 1.800e+02, 1.603e+03, 4.000e+00,\n", + " 1.570e+02, 8.060e+02, 3.800e+02, 1.630e+02, 2.500e+01, 1.000e+01,\n", + " 5.400e+02, 2.008e+03, 3.450e+02, 4.740e+02, 7.400e+01, 2.000e+01,\n", + " 1.210e+02, 7.000e+00, 2.800e+01, 8.990e+02, 2.080e+03, 1.170e+02,\n", + " 1.000e+00, 5.350e+02, 1.000e+00, 1.800e+01, 7.000e+00, 3.600e+01,\n", + " 7.370e+02, 1.500e+02, 5.000e+00, 1.450e+03, 2.500e+02, 5.000e+00,\n", + " 1.205e+03, 6.200e+01, 4.800e+01, 4.000e+00, 8.600e+01, 5.900e+01,\n", + " 2.000e+01, 1.200e+01, 9.370e+02, 1.118e+03, 3.500e+01, 3.400e+02,\n", + " 3.578e+03, 4.000e+00, 1.020e+03, 9.000e+00, 1.000e+00, 6.000e+00],\n", + " dtype=float32),\n", " 'dimensions': ('station',),\n", " 'dtype': dtype('float32'),\n", " 'units': 'meters',\n", @@ -666,35 +635,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_start_date. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_zone. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable street_type. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable country_code. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable ccaa. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_area. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_start_date. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable city. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_zone. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_emep. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_type. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable country. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_end_date. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_rural_back. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_ozone_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable street_type. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -714,7 +659,43 @@ "Rank 000: Var station_zone created (2/17)\n", "Rank 000: Filling station_zone)\n", "Rank 000: Var station_zone data (2/17)\n", - "Rank 000: Var station_zone completed (2/17)\n", + "Rank 000: Var station_zone completed (2/17)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable country_code. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable ccaa. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_area. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable city. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_emep. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_type. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable country. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_end_date. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_rural_back. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_ozone_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Rank 000: Writing street_type var (3/17)\n", "Rank 000: Var street_type created (3/17)\n", "Rank 000: Filling street_type)\n", @@ -811,37 +792,10 @@ "execution_count": 13, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "time float64\n", - "station float64\n", - "lat float64\n", - "lon float64\n", - "station_start_date |S1\n", - "station_zone |S1\n", - "street_type |S1\n", - "country_code |S1\n", - "ccaa |S1\n", - "station_name |S1\n", - "station_area |S1\n", - "city |S1\n", - "pm10 float32\n", - "station_emep |S1\n", - "station_type |S1\n", - "country |S1\n", - "altitude float32\n", - "station_code |S1\n", - "station_end_date |S1\n", - "station_rural_back |S1\n", - "station_ozone_classification |S1\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -886,194 +840,10 @@ "execution_count": 15, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ASTER_v3_altitude float32\n", - "EDGAR_v4.3.2_annual_average_BC_emissions float32\n", - "EDGAR_v4.3.2_annual_average_CO_emissions float32\n", - "EDGAR_v4.3.2_annual_average_NH3_emissions float32\n", - "EDGAR_v4.3.2_annual_average_NMVOC_emissions float32\n", - "EDGAR_v4.3.2_annual_average_NOx_emissions float32\n", - "EDGAR_v4.3.2_annual_average_OC_emissions float32\n", - "EDGAR_v4.3.2_annual_average_PM10_emissions float32\n", - "EDGAR_v4.3.2_annual_average_SO2_emissions float32\n", - "EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions float32\n", - "EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions float32\n", - "ESDAC_Iwahashi_landform_classification \n", - "ESDAC_Meybeck_landform_classification \n", - "ESDAC_modal_Iwahashi_landform_classification_25km \n", - "ESDAC_modal_Iwahashi_landform_classification_5km \n", - "ESDAC_modal_Meybeck_landform_classification_25km \n", - "ESDAC_modal_Meybeck_landform_classification_5km \n", - "ETOPO1_altitude float32\n", - "ETOPO1_max_altitude_difference_5km float32\n", - "GHOST_version \n", - "GHSL_average_built_up_area_density_25km float32\n", - "GHSL_average_built_up_area_density_5km float32\n", - "GHSL_average_population_density_25km float32\n", - "GHSL_average_population_density_5km float32\n", - "GHSL_built_up_area_density float32\n", - "GHSL_max_built_up_area_density_25km float32\n", - "GHSL_max_built_up_area_density_5km float32\n", - "GHSL_max_population_density_25km float32\n", - "GHSL_max_population_density_5km float32\n", - "GHSL_modal_settlement_model_classification_25km \n", - "GHSL_modal_settlement_model_classification_5km \n", - "GHSL_population_density float32\n", - "GHSL_settlement_model_classification \n", - "GPW_average_population_density_25km float32\n", - "GPW_average_population_density_5km float32\n", - "GPW_max_population_density_25km float32\n", - "GPW_max_population_density_5km float32\n", - "GPW_population_density float32\n", - "GSFC_coastline_proximity float32\n", - "Joly-Peuch_classification_code float32\n", - "Koppen-Geiger_classification \n", - "Koppen-Geiger_modal_classification_25km \n", - "Koppen-Geiger_modal_classification_5km \n", - "MODIS_MCD12C1_v6_IGBP_land_use \n", - "MODIS_MCD12C1_v6_LAI \n", - "MODIS_MCD12C1_v6_UMD_land_use \n", - "MODIS_MCD12C1_v6_modal_IGBP_land_use_25km \n", - "MODIS_MCD12C1_v6_modal_IGBP_land_use_5km \n", - "MODIS_MCD12C1_v6_modal_LAI_25km \n", - "MODIS_MCD12C1_v6_modal_LAI_5km \n", - "MODIS_MCD12C1_v6_modal_UMD_land_use_25km \n", - "MODIS_MCD12C1_v6_modal_UMD_land_use_5km \n", - "NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km float32\n", - "NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km float32\n", - "NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km float32\n", - "NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km float32\n", - "NOAA-DMSP-OLS_v4_nighttime_stable_lights float32\n", - "OMI_level3_column_annual_average_NO2 float32\n", - "OMI_level3_column_cloud_screened_annual_average_NO2 float32\n", - "OMI_level3_tropospheric_column_annual_average_NO2 float32\n", - "OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 float32\n", - "UMBC_anthrome_classification \n", - "UMBC_modal_anthrome_classification_25km \n", - "UMBC_modal_anthrome_classification_5km \n", - "WMO_region \n", - "WWF_TEOW_biogeographical_realm \n", - "WWF_TEOW_biome \n", - "WWF_TEOW_terrestrial_ecoregion \n", - "administrative_country_division_1 \n", - "administrative_country_division_2 \n", - "altitude float32\n", - "annual_native_max_gap_percent uint8\n", - "annual_native_representativity_percent uint8\n", - "area_classification \n", - "associated_networks \n", - "city \n", - "climatology \n", - "contact_email_address \n", - "contact_institution \n", - "contact_name \n", - "country \n", - "daily_native_max_gap_percent uint8\n", - "daily_native_representativity_percent uint8\n", - "daily_passing_vehicles float32\n", - "data_level \n", - "data_licence \n", - "day_night_code uint8\n", - "daytime_traffic_speed float32\n", - "derived_uncertainty_per_measurement float32\n", - "distance_to_building float32\n", - "distance_to_junction float32\n", - "distance_to_kerb float32\n", - "distance_to_source float32\n", - "ellipsoid \n", - "flag uint8\n", - "horizontal_datum \n", - "land_use \n", - "latitude float64\n", - "longitude float64\n", - "main_emission_source \n", - "measurement_altitude float32\n", - "measurement_methodology \n", - "measurement_scale \n", - "measuring_instrument_calibration_scale \n", - "measuring_instrument_documented_absorption_cross_section \n", - "measuring_instrument_documented_accuracy \n", - "measuring_instrument_documented_flow_rate \n", - "measuring_instrument_documented_lower_limit_of_detection float32\n", - "measuring_instrument_documented_measurement_resolution float32\n", - "measuring_instrument_documented_precision \n", - "measuring_instrument_documented_span_drift \n", - "measuring_instrument_documented_uncertainty \n", - "measuring_instrument_documented_upper_limit_of_detection float32\n", - "measuring_instrument_documented_zero_drift \n", - "measuring_instrument_documented_zonal_drift \n", - "measuring_instrument_further_details \n", - "measuring_instrument_inlet_information \n", - "measuring_instrument_manual_name \n", - "measuring_instrument_name \n", - "measuring_instrument_process_details \n", - "measuring_instrument_reported_absorption_cross_section \n", - "measuring_instrument_reported_accuracy \n", - "measuring_instrument_reported_flow_rate \n", - "measuring_instrument_reported_lower_limit_of_detection float32\n", - "measuring_instrument_reported_measurement_resolution float32\n", - "measuring_instrument_reported_precision \n", - "measuring_instrument_reported_span_drift \n", - "measuring_instrument_reported_uncertainty \n", - "measuring_instrument_reported_units \n", - "measuring_instrument_reported_upper_limit_of_detection float32\n", - "measuring_instrument_reported_zero_drift \n", - "measuring_instrument_reported_zonal_drift \n", - "measuring_instrument_sampling_type \n", - "monthly_native_max_gap_percent uint8\n", - "monthly_native_representativity_percent uint8\n", - "network \n", - "network_maintenance_details \n", - "network_miscellaneous_details \n", - "network_provided_volume_standard_pressure float64\n", - "network_provided_volume_standard_temperature float64\n", - "network_qa_details \n", - "network_sampling_details \n", - "network_uncertainty_details \n", - "population float32\n", - "primary_sampling_further_details \n", - "primary_sampling_instrument_documented_flow_rate \n", - "primary_sampling_instrument_manual_name \n", - "primary_sampling_instrument_name \n", - "primary_sampling_instrument_reported_flow_rate \n", - "primary_sampling_process_details \n", - "primary_sampling_type \n", - "principal_investigator_email_address \n", - "principal_investigator_institution \n", - "principal_investigator_name \n", - "process_warnings \n", - "projection \n", - "qa uint8\n", - "reported_uncertainty_per_measurement float32\n", - "representative_radius float32\n", - "retrieval_algorithm \n", - "sample_preparation_further_details \n", - "sample_preparation_process_details \n", - "sample_preparation_techniques \n", - "sample_preparation_types \n", - "sampling_height float32\n", - "sconcso4 float32\n", - "season_code uint8\n", - "station_classification \n", - "station_name \n", - "station_reference \n", - "station_timezone \n", - "street_type \n", - "street_width float32\n", - "terrain \n", - "time uint32\n", - "vertical_datum \n", - "weekday_weekend_code uint8\n", - "sconcso4_prefiltered_defaultqa float32\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -1242,11 +1012,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:313: UserWarning: Variable day_night_code data missing values cannot be converted to np.nan.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:313: UserWarning: Variable season_code data missing values cannot be converted to np.nan.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:313: UserWarning: Variable weekday_weekend_code data missing values cannot be converted to np.nan.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2049: UserWarning: Data missing values cannot be converted to np.nan.\n", " warnings.warn(msg)\n" ] }, @@ -1616,26 +1382,30 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_Iwahashi_landform_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ESDAC_Iwahashi_landform_classification. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ESDAC_Meybeck_landform_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_Meybeck_landform_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable GHOST_version. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable GHOST_version. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_25km. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -1786,80 +1556,93 @@ "Rank 000: Filling GHSL_max_population_density_25km)\n", "Rank 000: Var GHSL_max_population_density_25km data (28/173)\n", "Rank 000: Var GHSL_max_population_density_25km completed (28/173)\n", - "Rank 000: Writing GHSL_max_population_density_5km var (29/173)\n" + "Rank 000: Writing GHSL_max_population_density_5km var (29/173)\n", + "Rank 000: Var GHSL_max_population_density_5km created (29/173)\n", + "Rank 000: Filling GHSL_max_population_density_5km)\n", + "Rank 000: Var GHSL_max_population_density_5km data (29/173)\n", + "Rank 000: Var GHSL_max_population_density_5km completed (29/173)\n", + "Rank 000: Writing GHSL_modal_settlement_model_classification_25km var (30/173)\n", + "Rank 000: Var GHSL_modal_settlement_model_classification_25km created (30/173)\n", + "Rank 000: Filling GHSL_modal_settlement_model_classification_25km)\n", + "Rank 000: Var GHSL_modal_settlement_model_classification_25km data (30/173)\n", + "Rank 000: Var GHSL_modal_settlement_model_classification_25km completed (30/173)\n", + "Rank 000: Writing GHSL_modal_settlement_model_classification_5km var (31/173)\n", + "Rank 000: Var GHSL_modal_settlement_model_classification_5km created (31/173)\n", + "Rank 000: Filling GHSL_modal_settlement_model_classification_5km)\n", + "Rank 000: Var GHSL_modal_settlement_model_classification_5km data (31/173)\n", + "Rank 000: Var GHSL_modal_settlement_model_classification_5km completed (31/173)\n", + "Rank 000: Writing GHSL_population_density var (32/173)\n", + "Rank 000: Var GHSL_population_density created (32/173)\n", + "Rank 000: Filling GHSL_population_density)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable GHSL_settlement_model_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable GHSL_settlement_model_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_IGBP_land_use. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_LAI. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_IGBP_land_use. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_UMD_land_use. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_LAI. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_UMD_land_use. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable UMBC_anthrome_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable UMBC_anthrome_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable WMO_region. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biogeographical_realm. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WMO_region. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biome. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biogeographical_realm. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_terrestrial_ecoregion. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biome. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable administrative_country_division_1. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_terrestrial_ecoregion. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable administrative_country_division_2. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable administrative_country_division_1. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable administrative_country_division_2. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable associated_networks. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable city. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable associated_networks. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable climatology. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable city. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable contact_email_address. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable climatology. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable contact_institution. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable contact_email_address. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable contact_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable contact_institution. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable country. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable contact_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable data_level. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable country. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable data_level. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable data_licence. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable data_licence. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -1867,23 +1650,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Rank 000: Var GHSL_max_population_density_5km created (29/173)\n", - "Rank 000: Filling GHSL_max_population_density_5km)\n", - "Rank 000: Var GHSL_max_population_density_5km data (29/173)\n", - "Rank 000: Var GHSL_max_population_density_5km completed (29/173)\n", - "Rank 000: Writing GHSL_modal_settlement_model_classification_25km var (30/173)\n", - "Rank 000: Var GHSL_modal_settlement_model_classification_25km created (30/173)\n", - "Rank 000: Filling GHSL_modal_settlement_model_classification_25km)\n", - "Rank 000: Var GHSL_modal_settlement_model_classification_25km data (30/173)\n", - "Rank 000: Var GHSL_modal_settlement_model_classification_25km completed (30/173)\n", - "Rank 000: Writing GHSL_modal_settlement_model_classification_5km var (31/173)\n", - "Rank 000: Var GHSL_modal_settlement_model_classification_5km created (31/173)\n", - "Rank 000: Filling GHSL_modal_settlement_model_classification_5km)\n", - "Rank 000: Var GHSL_modal_settlement_model_classification_5km data (31/173)\n", - "Rank 000: Var GHSL_modal_settlement_model_classification_5km completed (31/173)\n", - "Rank 000: Writing GHSL_population_density var (32/173)\n", - "Rank 000: Var GHSL_population_density created (32/173)\n", - "Rank 000: Filling GHSL_population_density)\n", "Rank 000: Var GHSL_population_density data (32/173)\n", "Rank 000: Var GHSL_population_density completed (32/173)\n", "Rank 000: Writing GHSL_settlement_model_classification var (33/173)\n", @@ -2163,58 +1929,58 @@ "Rank 000: Var day_night_code completed (87/173)\n", "Rank 000: Writing daytime_traffic_speed var (88/173)\n", "Rank 000: Var daytime_traffic_speed created (88/173)\n", - "Rank 000: Filling daytime_traffic_speed)\n", - "Rank 000: Var daytime_traffic_speed data (88/173)\n", - "Rank 000: Var daytime_traffic_speed completed (88/173)\n", - "Rank 000: Writing derived_uncertainty_per_measurement var (89/173)\n", - "Rank 000: Var derived_uncertainty_per_measurement created (89/173)\n", - "Rank 000: Filling derived_uncertainty_per_measurement)\n", - "Rank 000: Var derived_uncertainty_per_measurement data (89/173)\n", - "Rank 000: Var derived_uncertainty_per_measurement completed (89/173)\n", - "Rank 000: Writing distance_to_building var (90/173)\n" + "Rank 000: Filling daytime_traffic_speed)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ellipsoid. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ellipsoid. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable horizontal_datum. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable land_use. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable main_emission_source. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measurement_methodology. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable horizontal_datum. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measurement_scale. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable land_use. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_calibration_scale. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable main_emission_source. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_absorption_cross_section. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measurement_methodology. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_accuracy. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measurement_scale. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_calibration_scale. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_precision. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_absorption_cross_section. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_span_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_accuracy. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_uncertainty. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zero_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_precision. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zonal_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_span_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_further_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_uncertainty. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_inlet_information. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zero_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_manual_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zonal_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_further_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_process_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_inlet_information. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_absorption_cross_section. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_manual_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_accuracy. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -2222,6 +1988,14 @@ "name": "stdout", "output_type": "stream", "text": [ + "Rank 000: Var daytime_traffic_speed data (88/173)\n", + "Rank 000: Var daytime_traffic_speed completed (88/173)\n", + "Rank 000: Writing derived_uncertainty_per_measurement var (89/173)\n", + "Rank 000: Var derived_uncertainty_per_measurement created (89/173)\n", + "Rank 000: Filling derived_uncertainty_per_measurement)\n", + "Rank 000: Var derived_uncertainty_per_measurement data (89/173)\n", + "Rank 000: Var derived_uncertainty_per_measurement completed (89/173)\n", + "Rank 000: Writing distance_to_building var (90/173)\n", "Rank 000: Var distance_to_building created (90/173)\n", "Rank 000: Filling distance_to_building)\n", "Rank 000: Var distance_to_building data (90/173)\n", @@ -2353,86 +2127,89 @@ "Rank 000: Var measuring_instrument_manual_name completed (115/173)\n", "Rank 000: Writing measuring_instrument_name var (116/173)\n", "Rank 000: Var measuring_instrument_name created (116/173)\n", - "Rank 000: Filling measuring_instrument_name)\n" + "Rank 000: Filling measuring_instrument_name)\n", + "Rank 000: Var measuring_instrument_name data (116/173)\n", + "Rank 000: Var measuring_instrument_name completed (116/173)\n", + "Rank 000: Writing measuring_instrument_process_details var (117/173)\n", + "Rank 000: Var measuring_instrument_process_details created (117/173)\n", + "Rank 000: Filling measuring_instrument_process_details)\n", + "Rank 000: Var measuring_instrument_process_details data (117/173)\n", + "Rank 000: Var measuring_instrument_process_details completed (117/173)\n", + "Rank 000: Writing measuring_instrument_reported_absorption_cross_section var (118/173)\n", + "Rank 000: Var measuring_instrument_reported_absorption_cross_section created (118/173)\n", + "Rank 000: Filling measuring_instrument_reported_absorption_cross_section)\n", + "Rank 000: Var measuring_instrument_reported_absorption_cross_section data (118/173)\n", + "Rank 000: Var measuring_instrument_reported_absorption_cross_section completed (118/173)\n", + "Rank 000: Writing measuring_instrument_reported_accuracy var (119/173)\n", + "Rank 000: Var measuring_instrument_reported_accuracy created (119/173)\n", + "Rank 000: Filling measuring_instrument_reported_accuracy)\n", + "Rank 000: Var measuring_instrument_reported_accuracy data (119/173)\n", + "Rank 000: Var measuring_instrument_reported_accuracy completed (119/173)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_process_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_precision. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_absorption_cross_section. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_span_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_accuracy. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_uncertainty. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_units. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_precision. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zero_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_span_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zonal_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_uncertainty. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_sampling_type. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_units. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable network. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zero_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable network_maintenance_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zonal_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable network_miscellaneous_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_sampling_type. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable network_qa_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable network_sampling_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_maintenance_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable network_uncertainty_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_miscellaneous_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_further_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_qa_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_sampling_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_manual_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_uncertainty_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_further_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_process_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_manual_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_type. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable principal_investigator_email_address. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable principal_investigator_institution. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_process_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable principal_investigator_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_type. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable process_warnings. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable principal_investigator_email_address. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable projection. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable principal_investigator_institution. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable retrieval_algorithm. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable principal_investigator_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable sample_preparation_further_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable process_warnings. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable sample_preparation_process_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable projection. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable sample_preparation_techniques. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable retrieval_algorithm. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_further_details. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_process_details. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_techniques. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_types. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_classification. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable sample_preparation_types. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -2440,23 +2217,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "Rank 000: Var measuring_instrument_name data (116/173)\n", - "Rank 000: Var measuring_instrument_name completed (116/173)\n", - "Rank 000: Writing measuring_instrument_process_details var (117/173)\n", - "Rank 000: Var measuring_instrument_process_details created (117/173)\n", - "Rank 000: Filling measuring_instrument_process_details)\n", - "Rank 000: Var measuring_instrument_process_details data (117/173)\n", - "Rank 000: Var measuring_instrument_process_details completed (117/173)\n", - "Rank 000: Writing measuring_instrument_reported_absorption_cross_section var (118/173)\n", - "Rank 000: Var measuring_instrument_reported_absorption_cross_section created (118/173)\n", - "Rank 000: Filling measuring_instrument_reported_absorption_cross_section)\n", - "Rank 000: Var measuring_instrument_reported_absorption_cross_section data (118/173)\n", - "Rank 000: Var measuring_instrument_reported_absorption_cross_section completed (118/173)\n", - "Rank 000: Writing measuring_instrument_reported_accuracy var (119/173)\n", - "Rank 000: Var measuring_instrument_reported_accuracy created (119/173)\n", - "Rank 000: Filling measuring_instrument_reported_accuracy)\n", - "Rank 000: Var measuring_instrument_reported_accuracy data (119/173)\n", - "Rank 000: Var measuring_instrument_reported_accuracy completed (119/173)\n", "Rank 000: Writing measuring_instrument_reported_flow_rate var (120/173)\n", "Rank 000: Var measuring_instrument_reported_flow_rate created (120/173)\n", "Rank 000: Filling measuring_instrument_reported_flow_rate)\n", @@ -2674,32 +2434,26 @@ "Rank 000: Var sconcso4 completed (162/173)\n", "Rank 000: Writing season_code var (163/173)\n", "Rank 000: Var season_code created (163/173)\n", - "Rank 000: Filling season_code)\n", - "Rank 000: Var season_code data (163/173)\n", - "Rank 000: Var season_code completed (163/173)\n", - "Rank 000: Writing station_classification var (164/173)\n", - "Rank 000: Var station_classification created (164/173)\n", - "Rank 000: Filling station_classification)\n", - "Rank 000: Var station_classification data (164/173)\n", - "Rank 000: Var station_classification completed (164/173)\n", - "Rank 000: Writing station_name var (165/173)\n", - "Rank 000: Var station_name created (165/173)\n", - "Rank 000: Filling station_name)\n", - "Rank 000: Var station_name data (165/173)\n", - "Rank 000: Var station_name completed (165/173)\n" + "Rank 000: Filling season_code)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_timezone. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable station_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable street_type. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable terrain. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable vertical_datum. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable station_timezone. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable street_type. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable terrain. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable vertical_datum. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -2707,6 +2461,18 @@ "name": "stdout", "output_type": "stream", "text": [ + "Rank 000: Var season_code data (163/173)\n", + "Rank 000: Var season_code completed (163/173)\n", + "Rank 000: Writing station_classification var (164/173)\n", + "Rank 000: Var station_classification created (164/173)\n", + "Rank 000: Filling station_classification)\n", + "Rank 000: Var station_classification data (164/173)\n", + "Rank 000: Var station_classification completed (164/173)\n", + "Rank 000: Writing station_name var (165/173)\n", + "Rank 000: Var station_name created (165/173)\n", + "Rank 000: Filling station_name)\n", + "Rank 000: Var station_name data (165/173)\n", + "Rank 000: Var station_name completed (165/173)\n", "Rank 000: Writing station_reference var (166/173)\n", "Rank 000: Var station_reference created (166/173)\n", "Rank 000: Filling station_reference)\n", @@ -2763,192 +2529,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "time float64\n", - "station float64\n", - "latitude float64\n", - "longitude float64\n", - "ASTER_v3_altitude float32\n", - "EDGAR_v4.3.2_annual_average_BC_emissions float32\n", - "EDGAR_v4.3.2_annual_average_CO_emissions float32\n", - "EDGAR_v4.3.2_annual_average_NH3_emissions float32\n", - "EDGAR_v4.3.2_annual_average_NMVOC_emissions float32\n", - "EDGAR_v4.3.2_annual_average_NOx_emissions float32\n", - "EDGAR_v4.3.2_annual_average_OC_emissions float32\n", - "EDGAR_v4.3.2_annual_average_PM10_emissions float32\n", - "EDGAR_v4.3.2_annual_average_SO2_emissions float32\n", - "EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions float32\n", - "EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions float32\n", - "ESDAC_Iwahashi_landform_classification |S1\n", - "ESDAC_Meybeck_landform_classification |S1\n", - "ESDAC_modal_Iwahashi_landform_classification_25km |S1\n", - "ESDAC_modal_Iwahashi_landform_classification_5km |S1\n", - "ESDAC_modal_Meybeck_landform_classification_25km |S1\n", - "ESDAC_modal_Meybeck_landform_classification_5km |S1\n", - "ETOPO1_altitude float32\n", - "ETOPO1_max_altitude_difference_5km float32\n", - "GHOST_version |S1\n", - "GHSL_average_built_up_area_density_25km float32\n", - "GHSL_average_built_up_area_density_5km float32\n", - "GHSL_average_population_density_25km float32\n", - "GHSL_average_population_density_5km float32\n", - "GHSL_built_up_area_density float32\n", - "GHSL_max_built_up_area_density_25km float32\n", - "GHSL_max_built_up_area_density_5km float32\n", - "GHSL_max_population_density_25km float32\n", - "GHSL_max_population_density_5km float32\n", - "GHSL_modal_settlement_model_classification_25km |S1\n", - "GHSL_modal_settlement_model_classification_5km |S1\n", - "GHSL_population_density float32\n", - "GHSL_settlement_model_classification |S1\n", - "GPW_average_population_density_25km float32\n", - "GPW_average_population_density_5km float32\n", - "GPW_max_population_density_25km float32\n", - "GPW_max_population_density_5km float32\n", - "GPW_population_density float32\n", - "GSFC_coastline_proximity float32\n", - "Joly-Peuch_classification_code float32\n", - "Koppen-Geiger_classification |S1\n", - "Koppen-Geiger_modal_classification_25km |S1\n", - "Koppen-Geiger_modal_classification_5km |S1\n", - "MODIS_MCD12C1_v6_IGBP_land_use |S1\n", - "MODIS_MCD12C1_v6_LAI |S1\n", - "MODIS_MCD12C1_v6_UMD_land_use |S1\n", - "MODIS_MCD12C1_v6_modal_IGBP_land_use_25km |S1\n", - "MODIS_MCD12C1_v6_modal_IGBP_land_use_5km |S1\n", - "MODIS_MCD12C1_v6_modal_LAI_25km |S1\n", - "MODIS_MCD12C1_v6_modal_LAI_5km |S1\n", - "MODIS_MCD12C1_v6_modal_UMD_land_use_25km |S1\n", - "MODIS_MCD12C1_v6_modal_UMD_land_use_5km |S1\n", - "NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km float32\n", - "NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km float32\n", - "NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km float32\n", - "NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km float32\n", - "NOAA-DMSP-OLS_v4_nighttime_stable_lights float32\n", - "OMI_level3_column_annual_average_NO2 float32\n", - "OMI_level3_column_cloud_screened_annual_average_NO2 float32\n", - "OMI_level3_tropospheric_column_annual_average_NO2 float32\n", - "OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2 float32\n", - "UMBC_anthrome_classification |S1\n", - "UMBC_modal_anthrome_classification_25km |S1\n", - "UMBC_modal_anthrome_classification_5km |S1\n", - "WMO_region |S1\n", - "WWF_TEOW_biogeographical_realm |S1\n", - "WWF_TEOW_biome |S1\n", - "WWF_TEOW_terrestrial_ecoregion |S1\n", - "administrative_country_division_1 |S1\n", - "administrative_country_division_2 |S1\n", - "altitude float32\n", - "annual_native_max_gap_percent uint8\n", - "annual_native_representativity_percent uint8\n", - "area_classification |S1\n", - "associated_networks |S1\n", - "city |S1\n", - "climatology |S1\n", - "contact_email_address |S1\n", - "contact_institution |S1\n", - "contact_name |S1\n", - "country |S1\n", - "daily_native_max_gap_percent uint8\n", - "daily_native_representativity_percent uint8\n", - "daily_passing_vehicles float32\n", - "data_level |S1\n", - "data_licence |S1\n", - "day_night_code uint8\n", - "daytime_traffic_speed float32\n", - "derived_uncertainty_per_measurement float32\n", - "distance_to_building float32\n", - "distance_to_junction float32\n", - "distance_to_kerb float32\n", - "distance_to_source float32\n", - "ellipsoid |S1\n", - "horizontal_datum |S1\n", - "land_use |S1\n", - "main_emission_source |S1\n", - "measurement_altitude float32\n", - "measurement_methodology |S1\n", - "measurement_scale |S1\n", - "measuring_instrument_calibration_scale |S1\n", - "measuring_instrument_documented_absorption_cross_section |S1\n", - "measuring_instrument_documented_accuracy |S1\n", - "measuring_instrument_documented_flow_rate |S1\n", - "measuring_instrument_documented_lower_limit_of_detection float32\n", - "measuring_instrument_documented_measurement_resolution float32\n", - "measuring_instrument_documented_precision |S1\n", - "measuring_instrument_documented_span_drift |S1\n", - "measuring_instrument_documented_uncertainty |S1\n", - "measuring_instrument_documented_upper_limit_of_detection float32\n", - "measuring_instrument_documented_zero_drift |S1\n", - "measuring_instrument_documented_zonal_drift |S1\n", - "measuring_instrument_further_details |S1\n", - "measuring_instrument_inlet_information |S1\n", - "measuring_instrument_manual_name |S1\n", - "measuring_instrument_name |S1\n", - "measuring_instrument_process_details |S1\n", - "measuring_instrument_reported_absorption_cross_section |S1\n", - "measuring_instrument_reported_accuracy |S1\n", - "measuring_instrument_reported_flow_rate |S1\n", - "measuring_instrument_reported_lower_limit_of_detection float32\n", - "measuring_instrument_reported_measurement_resolution float32\n", - "measuring_instrument_reported_precision |S1\n", - "measuring_instrument_reported_span_drift |S1\n", - "measuring_instrument_reported_uncertainty |S1\n", - "measuring_instrument_reported_units |S1\n", - "measuring_instrument_reported_upper_limit_of_detection float32\n", - "measuring_instrument_reported_zero_drift |S1\n", - "measuring_instrument_reported_zonal_drift |S1\n", - "measuring_instrument_sampling_type |S1\n", - "monthly_native_max_gap_percent uint8\n", - "monthly_native_representativity_percent uint8\n", - "network |S1\n", - "network_maintenance_details |S1\n", - "network_miscellaneous_details |S1\n", - "network_provided_volume_standard_pressure float64\n", - "network_provided_volume_standard_temperature float64\n", - "network_qa_details |S1\n", - "network_sampling_details |S1\n", - "network_uncertainty_details |S1\n", - "population float32\n", - "primary_sampling_further_details |S1\n", - "primary_sampling_instrument_documented_flow_rate |S1\n", - "primary_sampling_instrument_manual_name |S1\n", - "primary_sampling_instrument_name |S1\n", - "primary_sampling_instrument_reported_flow_rate |S1\n", - "primary_sampling_process_details |S1\n", - "primary_sampling_type |S1\n", - "principal_investigator_email_address |S1\n", - "principal_investigator_institution |S1\n", - "principal_investigator_name |S1\n", - "process_warnings |S1\n", - "projection |S1\n", - "reported_uncertainty_per_measurement float32\n", - "representative_radius float32\n", - "retrieval_algorithm |S1\n", - "sample_preparation_further_details |S1\n", - "sample_preparation_process_details |S1\n", - "sample_preparation_techniques |S1\n", - "sample_preparation_types |S1\n", - "sampling_height float32\n", - "sconcso4 float32\n", - "season_code uint8\n", - "station_classification |S1\n", - "station_name |S1\n", - "station_reference |S1\n", - "station_timezone |S1\n", - "street_type |S1\n", - "street_width float32\n", - "terrain |S1\n", - "vertical_datum |S1\n", - "weekday_weekend_code uint8\n", - "sconcso4_prefiltered_defaultqa float32\n", - "flag int64\n", - "qa int64\n", "Rank 000: Loading station var (1/174)\n", "Rank 000: Loaded station var ((3,))\n", "Rank 000: Loading ASTER_v3_altitude var (2/174)\n", @@ -3302,10 +2889,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -3325,16 +2912,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -3346,62 +2933,50 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'sconcso4': {'data': masked_array(\n", - " data=[[ nan, nan, nan, nan, nan,\n", - " nan, nan, 2.31 , 2.31 , 1.12 ,\n", - " 1.12 , nan, nan, nan, nan,\n", - " 1.71 , 1.71 , nan, nan, nan,\n", - " nan, nan, nan, nan, nan,\n", - " nan, nan, 1.38 , 1.2841667, 1.28 ],\n", - " [ nan, nan, nan, 0.74 , 0.74 ,\n", - " nan, nan, nan, nan, 3.41 ,\n", - " 3.41 , nan, nan, nan, nan,\n", - " 0.74 , 0.74 , nan, nan, nan,\n", - " nan, 1.2 , 1.2 , nan, nan,\n", - " nan, nan, 1.76 , 1.76 , nan],\n", - " [ nan, nan, nan, 3.05 , 3.05 ,\n", - " nan, nan, nan, nan, 2.44 ,\n", - " 2.44 , nan, nan, nan, nan,\n", - " 2.24 , 2.24 , nan, nan, nan,\n", - " nan, 1.37 , 1.37 , nan, nan,\n", - " nan, nan, nan, nan, nan]],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", + "{'sconcso4': {'data': array([[ nan, nan, nan, nan, nan, nan,\n", + " nan, 2.31 , 2.31 , 1.12 , 1.12 , nan,\n", + " nan, nan, nan, 1.71 , 1.71 , nan,\n", + " nan, nan, nan, nan, nan, nan,\n", + " nan, nan, nan, 1.38 , 1.2841667, 1.28 ],\n", + " [ nan, nan, nan, 0.74 , 0.74 , nan,\n", + " nan, nan, nan, 3.41 , 3.41 , nan,\n", + " nan, nan, nan, 0.74 , 0.74 , nan,\n", + " nan, nan, nan, 1.2 , 1.2 , nan,\n", + " nan, nan, nan, 1.76 , 1.76 , nan],\n", + " [ nan, nan, nan, 3.05 , 3.05 , nan,\n", + " nan, nan, nan, 2.44 , 2.44 , nan,\n", + " nan, nan, nan, 2.24 , 2.24 , nan,\n", + " nan, nan, nan, 1.37 , 1.37 , nan,\n", + " nan, nan, nan, nan, nan, nan]],\n", + " dtype=float32),\n", " 'dimensions': ('station', 'time'),\n", " 'dtype': dtype('float32'),\n", " 'standard_name': 'sulphate',\n", " 'long_name': 'sulphate',\n", " 'units': 'ug m-3',\n", " 'description': 'Measured value of surface sulphate for the stated temporal resolution.'},\n", - " 'sconcso4_prefiltered_defaultqa': {'data': masked_array(\n", - " data=[[ nan, nan, nan, nan, nan,\n", - " nan, nan, 2.31 , 2.31 , 1.12 ,\n", - " 1.12 , nan, nan, nan, nan,\n", - " 1.71 , 1.71 , nan, nan, nan,\n", - " nan, nan, nan, nan, nan,\n", - " nan, nan, 1.38 , 1.2841667, 1.28 ],\n", - " [ nan, nan, nan, 0.74 , 0.74 ,\n", - " nan, nan, nan, nan, 3.41 ,\n", - " 3.41 , nan, nan, nan, nan,\n", - " 0.74 , 0.74 , nan, nan, nan,\n", - " nan, 1.2 , 1.2 , nan, nan,\n", - " nan, nan, 1.76 , 1.76 , nan],\n", - " [ nan, nan, nan, 3.05 , 3.05 ,\n", - " nan, nan, nan, nan, 2.44 ,\n", - " 2.44 , nan, nan, nan, nan,\n", - " 2.24 , 2.24 , nan, nan, nan,\n", - " nan, 1.37 , 1.37 , nan, nan,\n", - " nan, nan, nan, nan, nan]],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", + " 'sconcso4_prefiltered_defaultqa': {'data': array([[ nan, nan, nan, nan, nan, nan,\n", + " nan, 2.31 , 2.31 , 1.12 , 1.12 , nan,\n", + " nan, nan, nan, 1.71 , 1.71 , nan,\n", + " nan, nan, nan, nan, nan, nan,\n", + " nan, nan, nan, 1.38 , 1.2841667, 1.28 ],\n", + " [ nan, nan, nan, 0.74 , 0.74 , nan,\n", + " nan, nan, nan, 3.41 , 3.41 , nan,\n", + " nan, nan, nan, 0.74 , 0.74 , nan,\n", + " nan, nan, nan, 1.2 , 1.2 , nan,\n", + " nan, nan, nan, 1.76 , 1.76 , nan],\n", + " [ nan, nan, nan, 3.05 , 3.05 , nan,\n", + " nan, nan, nan, 2.44 , 2.44 , nan,\n", + " nan, nan, nan, 2.24 , 2.24 , nan,\n", + " nan, nan, nan, 1.37 , 1.37 , nan,\n", + " nan, nan, nan, nan, nan, nan]],\n", + " dtype=float32),\n", " 'dimensions': ('station', 'time'),\n", " 'dtype': dtype('float32'),\n", " 'standard_name': 'sulphate',\n", @@ -3410,7 +2985,7 @@ " 'description': 'Measured value of surface sulphate for the stated temporal resolution. Prefiltered by default QA.'}}" ] }, - "execution_count": 26, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb index 79af033..d3ff977 100644 --- a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb +++ b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -570,38 +570,34 @@ { "data": { "text/plain": [ - "{'sconco3': {'data': masked_array(\n", - " data=[[[[0.0415576 , 0.04147514, 0.04152902, ..., 0.03796541,\n", - " 0.03792827, 0.0379032 ],\n", - " [0.04146153, 0.04136137, 0.0412977 , ..., 0.0382663 ,\n", - " 0.03824312, 0.03822216],\n", - " [0.04059546, 0.04081807, 0.04091855, ..., 0.03847397,\n", - " 0.03840729, 0.0383645 ],\n", - " ...,\n", - " [0.04466213, 0.04466087, 0.04464992, ..., 0.03963904,\n", - " 0.04006213, 0.04025601],\n", - " [0.04465724, 0.04464634, 0.04463853, ..., 0.0397002 ,\n", - " 0.0398435 , 0.0401062 ],\n", - " [0.04465516, 0.04465496, 0.04463719, ..., 0.03971382,\n", - " 0.03972259, 0.03986653]]],\n", + "{'sconco3': {'data': array([[[[0.0415576 , 0.04147514, 0.04152902, ..., 0.03796541,\n", + " 0.03792827, 0.0379032 ],\n", + " [0.04146153, 0.04136137, 0.0412977 , ..., 0.0382663 ,\n", + " 0.03824312, 0.03822216],\n", + " [0.04059546, 0.04081807, 0.04091855, ..., 0.03847397,\n", + " 0.03840729, 0.0383645 ],\n", + " ...,\n", + " [0.04466213, 0.04466087, 0.04464992, ..., 0.03963904,\n", + " 0.04006213, 0.04025601],\n", + " [0.04465724, 0.04464634, 0.04463853, ..., 0.0397002 ,\n", + " 0.0398435 , 0.0401062 ],\n", + " [0.04465516, 0.04465496, 0.04463719, ..., 0.03971382,\n", + " 0.03972259, 0.03986653]]],\n", " \n", " \n", - " [[[0.04273837, 0.04270947, 0.04267247, ..., 0.04349075,\n", - " 0.04333649, 0.04320888],\n", - " [0.04272898, 0.04268417, 0.04263616, ..., 0.04356325,\n", - " 0.04341621, 0.04326808],\n", - " [0.04264436, 0.04263159, 0.04260145, ..., 0.04372539,\n", - " 0.04355535, 0.04337517],\n", - " ...,\n", - " [0.04538379, 0.04540338, 0.0454199 , ..., 0.0401468 ,\n", - " 0.04034726, 0.04047008],\n", - " [0.04552482, 0.04551599, 0.04550386, ..., 0.03996951,\n", - " 0.04019922, 0.04038962],\n", - " [0.04552515, 0.04551959, 0.04550923, ..., 0.03988018,\n", - " 0.03998153, 0.04024737]]]],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", + " [[[0.04273837, 0.04270947, 0.04267247, ..., 0.04349075,\n", + " 0.04333649, 0.04320888],\n", + " [0.04272898, 0.04268417, 0.04263616, ..., 0.04356325,\n", + " 0.04341621, 0.04326808],\n", + " [0.04264436, 0.04263159, 0.04260145, ..., 0.04372539,\n", + " 0.04355535, 0.04337517],\n", + " ...,\n", + " [0.04538379, 0.04540338, 0.0454199 , ..., 0.0401468 ,\n", + " 0.04034726, 0.04047008],\n", + " [0.04552482, 0.04551599, 0.04550386, ..., 0.03996951,\n", + " 0.04019922, 0.04038962],\n", + " [0.04552515, 0.04551959, 0.04550923, ..., 0.03988018,\n", + " 0.03998153, 0.04024737]]]], dtype=float32),\n", " 'dimensions': ('time', 'lev', 'y', 'x'),\n", " 'dtype': dtype('float32'),\n", " 'units': 'ppm',\n", @@ -665,7 +661,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 15, diff --git a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb index 9b00850..9b1032f 100644 --- a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb +++ b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -436,7 +436,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, diff --git a/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb b/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb index ba935b0..25ab464 100644 --- a/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb +++ b/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb @@ -49,7 +49,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -75,7 +75,7 @@ " datetime.datetime(2018, 4, 30, 23, 0))" ] }, - "execution_count": 19, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -640,67 +640,67 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_Iwahashi_landform_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ESDAC_Iwahashi_landform_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_Meybeck_landform_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ESDAC_Meybeck_landform_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Iwahashi_landform_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ESDAC_modal_Meybeck_landform_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable GHOST_version. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable GHOST_version. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable GHSL_modal_settlement_model_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable GHSL_settlement_model_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable GHSL_settlement_model_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable Koppen-Geiger_modal_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_IGBP_land_use. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_IGBP_land_use. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_LAI. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_LAI. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_UMD_land_use. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_UMD_land_use. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_IGBP_land_use_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_LAI_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable MODIS_MCD12C1_v6_modal_UMD_land_use_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable UMBC_anthrome_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable UMBC_anthrome_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_25km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_25km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_5km. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable UMBC_modal_anthrome_classification_5km. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WMO_region. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable WMO_region. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biogeographical_realm. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biogeographical_realm. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biome. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_biome. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_terrestrial_ecoregion. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable WWF_TEOW_terrestrial_ecoregion. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable administrative_country_division_1. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable administrative_country_division_1. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable administrative_country_division_2. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable administrative_country_division_2. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -1080,41 +1080,47 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable associated_networks. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable associated_networks. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable city. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable city. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable climatology. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable climatology. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable contact_email_address. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable contact_email_address. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable contact_institution. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable contact_institution. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable contact_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable contact_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable country. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable country. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable data_level. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable data_level. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable data_licence. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable data_licence. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable ellipsoid. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable ellipsoid. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable horizontal_datum. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable horizontal_datum. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable land_use. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable land_use. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable main_emission_source. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable main_emission_source. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measurement_methodology. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measurement_methodology. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measurement_scale. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measurement_scale. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_calibration_scale. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_calibration_scale. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_absorption_cross_section. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_absorption_cross_section. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_accuracy. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_precision. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -1283,27 +1289,7 @@ "Rank 000: Var measuring_instrument_calibration_scale created (105/175)\n", "Rank 000: Filling measuring_instrument_calibration_scale)\n", "Rank 000: Var measuring_instrument_calibration_scale data (105/175)\n", - "Rank 000: Var measuring_instrument_calibration_scale completed (105/175)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_accuracy. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_precision. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_span_drift. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Rank 000: Var measuring_instrument_calibration_scale completed (105/175)\n", "Rank 000: Writing measuring_instrument_documented_absorption_cross_section var (106/175)\n", "Rank 000: Var measuring_instrument_documented_absorption_cross_section created (106/175)\n", "Rank 000: Filling measuring_instrument_documented_absorption_cross_section)\n", @@ -1328,93 +1314,86 @@ "Rank 000: Var measuring_instrument_documented_measurement_resolution created (110/175)\n", "Rank 000: Filling measuring_instrument_documented_measurement_resolution)\n", "Rank 000: Var measuring_instrument_documented_measurement_resolution data (110/175)\n", - "Rank 000: Var measuring_instrument_documented_measurement_resolution completed (110/175)\n", - "Rank 000: Writing measuring_instrument_documented_precision var (111/175)\n", - "Rank 000: Var measuring_instrument_documented_precision created (111/175)\n", - "Rank 000: Filling measuring_instrument_documented_precision)\n", - "Rank 000: Var measuring_instrument_documented_precision data (111/175)\n", - "Rank 000: Var measuring_instrument_documented_precision completed (111/175)\n", - "Rank 000: Writing measuring_instrument_documented_span_drift var (112/175)\n", - "Rank 000: Var measuring_instrument_documented_span_drift created (112/175)\n", - "Rank 000: Filling measuring_instrument_documented_span_drift)\n", - "Rank 000: Var measuring_instrument_documented_span_drift data (112/175)\n" + "Rank 000: Var measuring_instrument_documented_measurement_resolution completed (110/175)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_uncertainty. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_span_drift. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_uncertainty. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zero_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zero_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zonal_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_documented_zonal_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_further_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_further_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_inlet_information. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_inlet_information. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_manual_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_manual_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_process_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_process_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_absorption_cross_section. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_absorption_cross_section. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_accuracy. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_accuracy. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_precision. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_precision. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_span_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_span_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_uncertainty. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_uncertainty. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_units. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_units. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zero_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zero_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zonal_drift. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_reported_zonal_drift. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable measuring_instrument_sampling_type. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable measuring_instrument_sampling_type. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable network. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_maintenance_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable network_maintenance_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_miscellaneous_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable network_miscellaneous_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_qa_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable network_qa_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_sampling_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable network_sampling_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable network_uncertainty_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable network_uncertainty_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_further_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_further_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_documented_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_manual_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_manual_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_instrument_reported_flow_rate. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_process_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_process_details. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable primary_sampling_type. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable primary_sampling_type. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable principal_investigator_email_address. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable principal_investigator_email_address. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable principal_investigator_institution. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable principal_investigator_institution. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable principal_investigator_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable principal_investigator_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable process_warnings. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable process_warnings. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable projection. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable projection. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -1422,6 +1401,15 @@ "name": "stdout", "output_type": "stream", "text": [ + "Rank 000: Writing measuring_instrument_documented_precision var (111/175)\n", + "Rank 000: Var measuring_instrument_documented_precision created (111/175)\n", + "Rank 000: Filling measuring_instrument_documented_precision)\n", + "Rank 000: Var measuring_instrument_documented_precision data (111/175)\n", + "Rank 000: Var measuring_instrument_documented_precision completed (111/175)\n", + "Rank 000: Writing measuring_instrument_documented_span_drift var (112/175)\n", + "Rank 000: Var measuring_instrument_documented_span_drift created (112/175)\n", + "Rank 000: Filling measuring_instrument_documented_span_drift)\n", + "Rank 000: Var measuring_instrument_documented_span_drift data (112/175)\n", "Rank 000: Var measuring_instrument_documented_span_drift completed (112/175)\n", "Rank 000: Writing measuring_instrument_documented_uncertainty var (113/175)\n", "Rank 000: Var measuring_instrument_documented_uncertainty created (113/175)\n", @@ -1662,13 +1650,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_further_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable sample_preparation_further_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable sample_preparation_process_details. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable sample_preparation_techniques. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable sample_preparation_types. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_process_details. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable station_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_techniques. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable sample_preparation_types. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable station_timezone. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable street_type. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable terrain. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:332: UserWarning: WARNING!!! Different data types for variable vertical_datum. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -1695,33 +1697,7 @@ "Rank 000: Var sample_preparation_techniques completed (162/175)\n", "Rank 000: Writing sample_preparation_types var (163/175)\n", "Rank 000: Var sample_preparation_types created (163/175)\n", - "Rank 000: Filling sample_preparation_types)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_classification. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable station_timezone. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable street_type. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable terrain. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:340: UserWarning: WARNING!!! Different data types for variable vertical_datum. Input dtype=. Data dtype=object.\n", - " warnings.warn(msg)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Rank 000: Filling sample_preparation_types)\n", "Rank 000: Var sample_preparation_types data (163/175)\n", "Rank 000: Var sample_preparation_types completed (163/175)\n", "Rank 000: Writing sampling_height var (164/175)\n", @@ -1826,7 +1802,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -1841,7 +1817,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1852,7 +1828,7 @@ " datetime.datetime(2018, 4, 30, 23, 0))" ] }, - "execution_count": 20, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -2076,7 +2052,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_providentia.py:374: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_providentia.py:366: UserWarning: WARNING!!! Different data types for variable station_reference. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, diff --git a/tutorials/2.Creation/2.3.Create-Points.ipynb b/tutorials/2.Creation/2.3.Create-Points.ipynb index 5b57a9a..a6fde8f 100644 --- a/tutorials/2.Creation/2.3.Create-Points.ipynb +++ b/tutorials/2.Creation/2.3.Create-Points.ipynb @@ -234,9 +234,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable area_classification. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -805,11 +805,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_code. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable pm10. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable pm10. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, diff --git a/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb b/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb index a0be25a..40a3b58 100644 --- a/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb +++ b/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb @@ -402,7 +402,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -622,7 +622,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, diff --git a/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb b/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb index 7b40b37..2e78c7e 100644 --- a/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb +++ b/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb @@ -368,7 +368,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, @@ -577,7 +577,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:345: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:337: UserWarning: WARNING!!! Different data types for variable station_name. Input dtype=. Data dtype=object.\n", " warnings.warn(msg)\n" ] }, diff --git a/tutorials/3.Statistics/3.1.Statistics.ipynb b/tutorials/3.Statistics/3.1.Statistics.ipynb index 4b136a9..38904e8 100644 --- a/tutorials/3.Statistics/3.1.Statistics.ipynb +++ b/tutorials/3.Statistics/3.1.Statistics.ipynb @@ -32,8 +32,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 653 ms, sys: 68.8 ms, total: 722 ms\n", - "Wall time: 9.96 s\n" + "CPU times: user 566 ms, sys: 71.4 ms, total: 638 ms\n", + "Wall time: 9.81 s\n" ] } ], @@ -52,7 +52,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -148,6 +148,7 @@ " fill_value=999999,\n", " dtype=int32),\n", " 'dimensions': ('lm',),\n", + " 'dtype': dtype('int32'),\n", " 'units': '',\n", " 'long_name': 'layer id'}" ] @@ -187,6 +188,7 @@ " fill_value=1e+20,\n", " dtype=float32),\n", " 'dimensions': ('rlat', 'rlon'),\n", + " 'dtype': dtype('float32'),\n", " 'long_name': 'latitude',\n", " 'units': 'degrees_north',\n", " 'standard_name': 'latitude',\n", @@ -246,6 +248,7 @@ "text/plain": [ "{'O3': {'data': None,\n", " 'dimensions': ('time', 'lm', 'rlat', 'rlon'),\n", + " 'dtype': dtype('float32'),\n", " 'long_name': 'TRACERS_044',\n", " 'units': 'unknown',\n", " 'standard_name': 'TRACERS_044',\n", @@ -282,8 +285,8 @@ "text": [ "Rank 000: Loading O3 var (1/1)\n", "Rank 000: Loaded O3 var ((37, 24, 271, 351))\n", - "CPU times: user 304 ms, sys: 1.58 s, total: 1.88 s\n", - "Wall time: 11.1 s\n" + "CPU times: user 278 ms, sys: 1.56 s, total: 1.84 s\n", + "Wall time: 10.9 s\n" ] } ], @@ -341,8 +344,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 106 ms, sys: 86.4 ms, total: 193 ms\n", - "Wall time: 2.75 s\n" + "CPU times: user 109 ms, sys: 102 ms, total: 210 ms\n", + "Wall time: 2.46 s\n" ] } ], @@ -367,8 +370,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 66.2 ms, sys: 19.3 ms, total: 85.5 ms\n", - "Wall time: 277 ms\n" + "CPU times: user 53.1 ms, sys: 25 ms, total: 78 ms\n", + "Wall time: 83.4 ms\n" ] } ], @@ -425,8 +428,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 17.6 ms, sys: 8.64 ms, total: 26.2 ms\n", - "Wall time: 422 ms\n" + "CPU times: user 19.4 ms, sys: 8.37 ms, total: 27.8 ms\n", + "Wall time: 414 ms\n" ] } ], diff --git a/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb b/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb index 04d031c..fac142f 100644 --- a/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb @@ -50,7 +50,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -182,7 +182,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -250,7 +250,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -315,7 +315,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -399,7 +399,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 22, diff --git a/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb b/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb index 11d12ec..5e18678 100644 --- a/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -195,7 +195,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -331,7 +331,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -536,7 +536,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 19, diff --git a/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb b/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb index 56a70e0..16782bb 100644 --- a/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb @@ -416,8 +416,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 1min 49s, sys: 1.69 s, total: 1min 50s\n", - "Wall time: 1min 51s\n" + "CPU times: user 1min 50s, sys: 2.2 s, total: 1min 52s\n", + "Wall time: 1min 53s\n" ] } ], @@ -488,8 +488,33 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2267: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", - " time_var[:] = date2num(self._time[:], time_var.units, time_var.calendar)\n" + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1882: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " if variables[var_name]['dtype'] in [str, np.object]:\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1890: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " value = getattr(var_info, attrname)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1550: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " units = self.__parse_time_unit(time.units)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1552: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " if not hasattr(time, 'calendar'):\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1555: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " calendar = time.calendar\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1557: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " if 'months since' in time.units:\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1561: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " time_data = time[:]\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1749: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " nc_var['data'] = self.netcdf.variables[dimension_name][:]\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1676: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " lat_bnds = {'data': self._unmask_array(self.netcdf.variables['lat_bnds'][:])}\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1680: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " lon_bnds = {'data': self._unmask_array(self.netcdf.variables['lon_bnds'][:])}\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2129: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " gl_attrs[attrname] = getattr(self.netcdf, attrname)\n" ] }, { @@ -497,8 +522,17 @@ "output_type": "stream", "text": [ "Weight Matrix done!\n", - "CPU times: user 1min 49s, sys: 1.85 s, total: 1min 51s\n", - "Wall time: 1min 52s\n" + "CPU times: user 506 ms, sys: 9.99 ms, total: 516 ms\n", + "Wall time: 708 ms\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1945: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]\n" ] } ], @@ -515,8 +549,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 205 ms, sys: 23 ms, total: 228 ms\n", - "Wall time: 227 ms\n" + "CPU times: user 187 ms, sys: 3.01 ms, total: 190 ms\n", + "Wall time: 189 ms\n" ] } ], @@ -613,69 +647,44 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1888: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1882: DeprecationWarning: `np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " if variables[var_name]['dtype'] in [str, np.object]:\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1890: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " value = getattr(var_info, attrname)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1552: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1550: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " units = self.__parse_time_unit(time.units)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1554: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1552: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " if not hasattr(time, 'calendar'):\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1557: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1555: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " calendar = time.calendar\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1559: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1557: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " if 'months since' in time.units:\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1563: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1561: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", " time_data = time[:]\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1751: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1749: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", " nc_var['data'] = self.netcdf.variables[dimension_name][:]\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1678: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1676: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", - " lat_bnds = {'data': self.netcdf.variables['lat_bnds'][:]}\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1682: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + " lat_bnds = {'data': self._unmask_array(self.netcdf.variables['lat_bnds'][:])}\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1680: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", - " lon_bnds = {'data': self.netcdf.variables['lon_bnds'][:]}\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1718: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + " lon_bnds = {'data': self._unmask_array(self.netcdf.variables['lon_bnds'][:])}\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1716: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", " c_measures['cell_area']['data'] = self.netcdf.variables['cell_area'][:]\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2098: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2129: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", " gl_attrs[attrname] = getattr(self.netcdf, attrname)\n" ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Flux units: m-2.kg.s-1\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:1937: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", - "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", - " self.read_axis_limits['x_min']:self.read_axis_limits['x_max']]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ @@ -689,35 +698,18 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 6min 12s, sys: 2.87 s, total: 6min 15s\n", - "Wall time: 6min 17s\n" - ] - } - ], + "outputs": [], "source": [ "%time interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', flux=True)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "nox_no total flux: 8.805639456704551e-08\n" - ] - } - ], + "outputs": [], "source": [ "for var_aux in interp_nes.variables.keys():\n", " print(\"{0} total flux: {1}\".format(var_aux, interp_nes.variables[var_aux]['data'].sum()))" @@ -725,22 +717,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_data(interp_nes, var_name)" ] diff --git a/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb b/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb index 3bc762f..1e1bfc1 100644 --- a/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb @@ -42,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -229,9 +229,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2998: UserWarning: No vertical level has been specified. The first one will be selected.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:3015: UserWarning: No vertical level has been specified. The first one will be selected.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:3009: UserWarning: No time has been specified. The first one will be selected.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:3026: UserWarning: No time has been specified. The first one will be selected.\n", " warnings.warn(msg)\n" ] } @@ -379,7 +379,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -557,101 +557,97 @@ { "data": { "text/plain": [ - "{'sconco3': {'data': masked_array(\n", - " data=[[[[53.734375, 53.718746, 53.65625 , ..., 56.859375, 56.29687 ,\n", - " 55.890625],\n", - " [53.79687 , 53.71875 , 53.64062 , ..., 56.281246, 55.65625 ,\n", - " 54.98437 ],\n", - " [53.8125 , 53.79687 , 53.671875, ..., 54.75 , 54.718746,\n", - " 54.09375 ],\n", - " ...,\n", - " [49.125 , 49.843746, 50.640625, ..., 41.609375, 41.468746,\n", - " 41.3125 ],\n", - " [48.57812 , 49.46875 , 51.01562 , ..., 41.687496, 41.484375,\n", - " 41.343746],\n", - " [50.921875, 50.95312 , 50.8125 , ..., 42.203125, 41.843746,\n", - " 41.453125]]],\n", + "{'sconco3': {'data': array([[[[53.734375, 53.718746, 53.65625 , ..., 56.859375, 56.29687 ,\n", + " 55.890625],\n", + " [53.79687 , 53.71875 , 53.64062 , ..., 56.281246, 55.65625 ,\n", + " 54.98437 ],\n", + " [53.8125 , 53.79687 , 53.671875, ..., 54.75 , 54.718746,\n", + " 54.09375 ],\n", + " ...,\n", + " [49.125 , 49.843746, 50.640625, ..., 41.609375, 41.468746,\n", + " 41.3125 ],\n", + " [48.57812 , 49.46875 , 51.01562 , ..., 41.687496, 41.484375,\n", + " 41.343746],\n", + " [50.921875, 50.95312 , 50.8125 , ..., 42.203125, 41.843746,\n", + " 41.453125]]],\n", " \n", " \n", - 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" [[[53.015625, 53.39062 , 53.421875, ..., 54.28125 , 54.374996,\n", - " 54.171875],\n", - " [52.968746, 53.296875, 53.562496, ..., 54.17187 , 54.3125 ,\n", - " 54.218746],\n", - " [52.921875, 53.29687 , 53.546875, ..., 54.703125, 54.812496,\n", - " 54.375 ],\n", - " ...,\n", - " [51.59375 , 51.29687 , 50.84375 , ..., 41.171875, 41.124996,\n", - " 41.109375],\n", - " [51.14062 , 50.9375 , 50.749996, ..., 40.968746, 40.90625 ,\n", - " 40.906246],\n", - " [50.390625, 50.406246, 50.703125, ..., 40.65625 , 40.624996,\n", - " 40.59375 ]]],\n", + " [[[53.015625, 53.39062 , 53.421875, ..., 54.28125 , 54.374996,\n", + " 54.171875],\n", + " [52.968746, 53.296875, 53.562496, ..., 54.17187 , 54.3125 ,\n", + " 54.218746],\n", + " [52.921875, 53.29687 , 53.546875, ..., 54.703125, 54.812496,\n", + " 54.375 ],\n", + " ...,\n", + " [51.59375 , 51.29687 , 50.84375 , ..., 41.171875, 41.124996,\n", + " 41.109375],\n", + " [51.14062 , 50.9375 , 50.749996, ..., 40.968746, 40.90625 ,\n", + " 40.906246],\n", + " [50.390625, 50.406246, 50.703125, ..., 40.65625 , 40.624996,\n", + " 40.59375 ]]],\n", " \n", " \n", - " [[[52.749996, 53.0625 , 53.374996, ..., 53.999996, 53.96875 ,\n", - " 53.781246],\n", - " [52.6875 , 52.968746, 53.359375, ..., 53.703125, 53.76562 ,\n", - " 53.65625 ],\n", - " [52.64062 , 52.890625, 53.312496, ..., 54.249996, 54.109375,\n", - " 53.54687 ],\n", - " ...,\n", - " [51.73437 , 51.59375 , 51.14062 , ..., 41.468746, 41.421875,\n", - " 41.42187 ],\n", - " [51.09375 , 50.92187 , 50.796875, ..., 41.078125, 41.04687 ,\n", - " 41.015625],\n", - " [50.374996, 50.359375, 50.60937 , ..., 40.76562 , 40.71875 ,\n", - " 40.718746]]]],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", + " [[[52.749996, 53.0625 , 53.374996, ..., 53.999996, 53.96875 ,\n", + " 53.781246],\n", + " [52.6875 , 52.968746, 53.359375, ..., 53.703125, 53.76562 ,\n", + " 53.65625 ],\n", + " [52.64062 , 52.890625, 53.312496, ..., 54.249996, 54.109375,\n", + " 53.54687 ],\n", + " ...,\n", + " [51.73437 , 51.59375 , 51.14062 , ..., 41.468746, 41.421875,\n", + " 41.42187 ],\n", + " [51.09375 , 50.92187 , 50.796875, ..., 41.078125, 41.04687 ,\n", + " 41.015625],\n", + " [50.374996, 50.359375, 50.60937 , ..., 40.76562 , 40.71875 ,\n", + " 40.718746]]]], dtype=float32),\n", " 'dimensions': ('time', 'lat', 'lon'),\n", " 'dtype': dtype('float32'),\n", " 'coordinates': 'lat lon',\n", @@ -847,9 +843,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2998: UserWarning: No vertical level has been specified. The first one will be selected.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:3015: UserWarning: No vertical level has been specified. The first one will be selected.\n", " warnings.warn(msg)\n", - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:3009: UserWarning: No time has been specified. The first one will be selected.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:3026: UserWarning: No time has been specified. The first one will be selected.\n", " warnings.warn(msg)\n" ] } diff --git a/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb b/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb index f1d7542..d43b5c9 100644 --- a/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb @@ -61,7 +61,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -303,7 +303,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -508,7 +508,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -751,7 +751,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 18, @@ -961,23 +961,19 @@ { "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)" + "array([[-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]], dtype=float32)" ] }, "execution_count": 22, @@ -1876,7 +1872,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 38, @@ -1915,7 +1911,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 39, @@ -1947,7 +1943,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 40, @@ -2866,7 +2862,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 48, @@ -2898,7 +2894,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 49, diff --git a/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb b/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb index 825ab97..1d7eb47 100644 --- a/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb +++ b/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb @@ -54,6 +54,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "The history saving thread hit an unexpected error (OperationalError('database is locked')).History will not be written to the database.\n", "Rank 000: Loading sconcno2 var (1/1)\n", "Rank 000: Loaded sconcno2 var ((25, 1, 115, 165))\n" ] @@ -205,7 +206,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -237,7 +238,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -520,7 +521,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -552,7 +553,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -829,7 +830,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -861,7 +862,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 18, @@ -1141,7 +1142,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 22, @@ -1173,7 +1174,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 23, @@ -1451,7 +1452,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 27, @@ -1483,7 +1484,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 28, diff --git a/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb b/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb index 39cdc4f..ae968df 100644 --- a/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb +++ b/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb @@ -229,14 +229,25 @@ "metadata": {}, "outputs": [], "source": [ - "grid.variables['tz'] = {'data': timezones,}" + "grid.variables['tz'] = {'data': timezones,\n", + " 'dtype': str}\n", + "grid.set_strlen(32)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2436: UserWarning: WARNING!!! Different data types for variable tz. Input dtype=. Data dtype=object.\n", + " warnings.warn(msg)\n" + ] + } + ], "source": [ "grid.to_netcdf('grid_with_tz.nc')" ] @@ -292,8 +303,8 @@ " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']]]],\n", " dtype=object),\n", - " 'dimensions': ('rlat', 'rlon', 'strlen'),\n", - " 'dtype': dtype('O'),\n", + " 'dimensions': ('rlat', 'rlon'),\n", + " 'dtype': str,\n", " 'grid_mapping': 'rotated_pole',\n", " 'coordinates': 'lat lon'}" ] diff --git a/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb b/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb index 1883f08..7371967 100644 --- a/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb +++ b/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb @@ -48,65 +48,10 @@ "execution_count": 3, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'data': array([[[-10.09304333, -10.05597973, -9.96679783, -10.00389481],\n", - " [-10.05597973, -10.01897526, -9.92975521, -9.96679783],\n", - " [-10.01897335, -9.98202324, -9.89277172, -9.92975426],\n", - " ...,\n", - " [ -9.98202515, -10.01897526, -9.92975521, -9.89277172],\n", - " [-10.01897526, -10.05597973, -9.96679783, -9.92975521],\n", - " [-10.05597973, -10.09304333, -10.00389481, -9.96679783]],\n", - "\n", - " [[-10.00389481, -9.96679783, -9.87760735, -9.91474056],\n", - " [ -9.96679783, -9.92975521, -9.8405323 , -9.87760735],\n", - " [ -9.92975426, -9.89277172, -9.80351067, -9.8405304 ],\n", - " ...,\n", - " [ -9.89277172, -9.92975521, -9.8405323 , -9.80351257],\n", - " [ -9.92975521, -9.96679783, -9.87760735, -9.8405323 ],\n", - " [ -9.96679783, -10.00389481, -9.91474056, -9.87760735]],\n", - "\n", - " [[ -9.91473484, -9.87760353, -9.78840923, -9.82557583],\n", - " [ -9.87760353, -9.84052658, -9.751297 , -9.78840923],\n", - " [ -9.84052467, -9.80350685, -9.71424294, -9.75129509],\n", - " ...,\n", - " [ -9.80350685, -9.84052658, -9.751297 , -9.71424389],\n", - " [ -9.84052658, -9.87760353, -9.78840923, -9.751297 ],\n", - 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" [ 50.00321579, 50.05992889, 50.13208008, 50.0753479 ],\n", - " ...,\n", - " [ 50.05992889, 50.00321579, 50.0753479 , 50.13207626],\n", - " [ 50.00321579, 49.9464798 , 50.01860428, 50.0753479 ],\n", - " [ 49.9464798 , 49.88973618, 49.96184158, 50.01860428]]])}\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -303,65 +248,10 @@ "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'data': array([[[-10.09304333, -10.05597973, -9.96679783, -10.00389481],\n", - " [-10.05597973, -10.01897526, -9.92975521, -9.96679783],\n", - " [-10.01897335, -9.98202324, -9.89277172, -9.92975426],\n", - " ...,\n", - " [ -9.98202515, -10.01897526, -9.92975521, -9.89277172],\n", - " [-10.01897526, -10.05597973, -9.96679783, -9.92975521],\n", - " [-10.05597973, -10.09304333, -10.00389481, -9.96679783]],\n", - "\n", - " [[-10.00389481, -9.96679783, -9.87760735, -9.91474056],\n", - " [ -9.96679783, -9.92975521, -9.8405323 , -9.87760735],\n", - 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{ - "name": "stdout", - "output_type": "stream", - "text": [ - "None\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 10, @@ -782,65 +665,10 @@ "execution_count": 16, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'data': array([[[16.2203979 , 16.30306824, 16.48028979, 16.39739715],\n", - " [16.30306855, 16.3853609 , 16.56280424, 16.48029011],\n", - " [16.38536121, 16.46727425, 16.64493885, 16.56280455],\n", - " ...,\n", - " [16.46727269, 16.38535964, 16.56280298, 16.64493728],\n", - " [16.3853609 , 16.30306855, 16.48029011, 16.56280424],\n", - " [16.30306824, 16.2203979 , 16.39739715, 16.48028979]],\n", - "\n", - " [[16.39739783, 16.48029047, 16.65746762, 16.57435251],\n", - " [16.48029079, 16.56280491, 16.74020402, 16.65746794],\n", - " [16.56280523, 16.64493952, 16.82256006, 16.74020434],\n", - " ...,\n", - " [16.64493796, 16.56280366, 16.74020276, 16.82255849],\n", - " [16.56280491, 16.48029079, 16.65746794, 16.74020402],\n", - 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" [58.60142568, 58.75309876, 58.85849715, 58.70663238],\n", - " ...,\n", - " [58.75309587, 58.60142279, 58.70662948, 58.85849425],\n", - " [58.6014251 , 58.44964543, 58.55466385, 58.7066318 ],\n", - " [58.44964485, 58.29776072, 58.40259426, 58.55466327]],\n", - "\n", - " [[58.40259366, 58.55466267, 58.65885172, 58.50660166],\n", - " [58.55466325, 58.7066312 , 58.81100467, 58.6588523 ],\n", - " [58.70663178, 58.85849655, 58.96305787, 58.81100525],\n", - " ...,\n", - " [58.85849365, 58.70662888, 58.81100235, 58.96305497],\n", - " [58.7066312 , 58.55466325, 58.6588523 , 58.81100467],\n", - " [58.55466267, 58.40259366, 58.50660166, 58.65885172]]])}\n" - ] - }, { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 16, diff --git a/tutorials/5.Geospatial/5.4.Calculate_Grid_Cell_Area.ipynb b/tutorials/5.Geospatial/5.4.Calculate_Grid_Cell_Area.ipynb index 17eb7d5..29a73bc 100644 --- a/tutorials/5.Geospatial/5.4.Calculate_Grid_Cell_Area.ipynb +++ b/tutorials/5.Geospatial/5.4.Calculate_Grid_Cell_Area.ipynb @@ -14,7 +14,8 @@ "outputs": [], "source": [ "from nes import *\n", - "import numpy as np" + "import numpy as np\n", + "import xarray as xr" ] }, { @@ -166,7 +167,77 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[15830419.37602491, 15830657.31464759, 15830893.98187641,\n", + " 15831129.37902908, 15831363.50737192, 15831596.36818251,\n", + " 15831827.96271216, 15832058.29223234, 15832287.3580128 ,\n", + " 15832515.16128843],\n", + " [15832888.43944364, 15833125.4618577 , 15833361.21722112,\n", + " 15833595.70682441, 15833828.93193695, 15834060.89385057,\n", + " 15834291.59384502, 15834521.03319032, 15834749.2131148 ,\n", + " 15834976.13488906],\n", + " [15835346.79240354, 15835582.8966068 , 15835817.73809013,\n", + " 15836051.31816116, 15836283.63810613, 15836514.69920246,\n", + " 15836744.50270247, 15836973.04988706, 15837200.34204102,\n", + " 15837426.38040528],\n", + " [15837794.42326163, 15838029.60727211, 15838263.53292125,\n", + " 15838496.20149222, 15838727.61427387, 15838957.77256741,\n", + " 15839186.6776418 , 15839414.33076289, 15839640.73319616,\n", + " 15839865.88622452],\n", + " [15840231.32048458, 15840465.58229638, 15840698.59009752,\n", + " 15840930.34519075, 15841160.84885779, 15841390.10239228,\n", + " 15841618.10708416, 15841844.86421011, 15842070.37502717,\n", + " 15842294.64079978],\n", + " [15842657.47252579, 15842890.8101492 , 15843122.89812364,\n", + " 15843353.73774392, 15843583.33033225, 15843811.67716125,\n", + " 15844038.77951487, 15844264.63868577, 15844489.2559287 ,\n", + " 15844712.63252152],\n", + " [15845072.86781775, 15845305.27923895, 15845536.44539522,\n", + " 15845766.36759449, 15845995.04712501, 15846222.4852872 ,\n", + " 15846448.68336277, 15846673.64263782, 15846897.36439707,\n", + " 15847119.84990175],\n", + " [15847477.49487919, 15847708.97812176, 15847939.22047551,\n", + " 15848168.22324328, 15848395.9877448 , 15848622.51527306,\n", + " 15848847.8071165 , 15849071.86456124, 15849294.68888149,\n", + " 15849516.28136931],\n", + " [15849871.34221948, 15850101.89526329, 15850331.21182007,\n", + " 15850559.293196 , 15850786.14069547, 15851011.75562783,\n", + " 15851236.13929178, 15851459.2929683 , 15851681.21793849,\n", + " 15851901.91547563],\n", + " [15852254.39832998, 15852484.01919013, 15852712.40796391,\n", + " 15852939.56596235, 15853165.49449526, 15853390.19487267,\n", + " 15853613.66838107, 15853835.91632964, 15854056.94000432,\n", + " 15854276.74067481],\n", + " [15854626.65180506, 15854855.33845814, 15855082.79744119,\n", + " 15855309.03006979, 15855534.03765641, 15855757.82150882,\n", + " 15855980.3829386 , 15856201.72323178, 15856421.84367417,\n", + " 15856640.74556485],\n", + " [15856988.09116465, 15857215.84163662, 15857442.36884046,\n", + " 15857667.67411114, 15857891.75877949, 15858114.62415727,\n", + " 15858336.27153077, 15858556.70220796, 15858775.91748785,\n", + " 15858993.91865496],\n", + " [15859338.70502081, 15859565.51727597, 15859791.11070865,\n", + " 15860015.48665859, 15860238.64643876, 15860460.59134635,\n", + " 15860681.3227187 , 15860900.84184616, 15861119.15002387,\n", + " 15861336.24853621],\n", + " [15861678.48197006, 15861904.35401631, 15862129.01168405,\n", + " 15862352.45630977, 15862574.68920346, 15862795.71170322,\n", + " 15863015.52510921, 15863234.13072645, 15863451.52986768,\n", + " 15863667.72381984],\n", + " [15864007.41061145, 15864232.34043731, 15864456.06035349,\n", + " 15864678.57167045, 15864899.87571356, 15865119.97382447,\n", + " 15865338.86731228, 15865556.55748565, 15865773.04563914,\n", + " 15865988.33307546]])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lcc_grid.calculate_grid_area()" ] @@ -290,98 +361,66 @@ { "data": { "text/plain": [ - 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"[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", " ...\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]\n", "Length: 150, dtype: geometry" ] }, diff --git a/tutorials/6.Others/6.1.Add_Time_Bounds.ipynb b/tutorials/6.Others/6.1.Add_Time_Bounds.ipynb index c891654..7723801 100644 --- a/tutorials/6.Others/6.1.Add_Time_Bounds.ipynb +++ b/tutorials/6.Others/6.1.Add_Time_Bounds.ipynb @@ -41,15 +41,7 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The history saving thread hit an unexpected error (OperationalError('database is locked')).History will not be written to the database.\n" - ] - } - ], + "outputs": [], "source": [ "array = np.array([[datetime.datetime(year=2020, month=2, day=20), \n", " datetime.datetime(year=2020, month=2, day=15)]])\n", diff --git a/tutorials/6.Others/6.3.Plot.ipynb b/tutorials/6.Others/6.3.Plot.ipynb index 81f1f9d..156d913 100644 --- a/tutorials/6.Others/6.3.Plot.ipynb +++ b/tutorials/6.Others/6.3.Plot.ipynb @@ -62,7 +62,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -109,7 +109,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -160,7 +160,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 8, diff --git a/tutorials/6.Others/6.4.Write_By_Timestep.ipynb b/tutorials/6.Others/6.4.Write_By_Timestep.ipynb index 9080e0c..bb6358f 100644 --- a/tutorials/6.Others/6.4.Write_By_Timestep.ipynb +++ b/tutorials/6.Others/6.4.Write_By_Timestep.ipynb @@ -54,7 +54,15 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The history saving thread hit an unexpected error (OperationalError('database is locked')).History will not be written to the database.\n" + ] + } + ], "source": [ "rotated_grid.variables = {'var1': {'data': None, 'units': 'kg.s-1', 'dtype': np.float32},\n", " 'var2': {'data': None, 'units': 'kg.s-1', 'dtype': np.float32}}" -- GitLab From d9eb0f9d97d04b1638c6e4c9df00fe0c7228301a Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Tue, 11 Apr 2023 17:52:59 +0200 Subject: [PATCH 20/21] Update gitignore --- .gitignore | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 1058f26..d58fcc0 100644 --- a/.gitignore +++ b/.gitignore @@ -1,11 +1,13 @@ .idea -log* +*.out +*.err notebooks/.ipynb_checkpoints .ipynb_checkpoints nes/__pycache__ nes/nc_projections/__pycache__ *.pyc *.nc +*.csv *.cpg *.dbf *.prj -- GitLab From 4f86abae045c0c19f9c689b983f2a59f4b21920a Mon Sep 17 00:00:00 2001 From: ctena Date: Wed, 12 Apr 2023 10:34:03 +0200 Subject: [PATCH 21/21] Update release date --- CHANGELOG.md | 2 +- nes/__init__.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 63c2076..1df7d38 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,7 +1,7 @@ # NES CHANGELOG ### 1.1.1 -* Release date: 2023/04/04 +* Release date: 2023/04/12 * Changes and new features: * Sum of Nes objects ([#48](https://earth.bsc.es/gitlab/es/NES/-/issues/48)) * Write 2D string data to save variables from shapefiles after doing a spatial join ([#49](https://earth.bsc.es/gitlab/es/NES/-/issues/49)) diff --git a/nes/__init__.py b/nes/__init__.py index c83b286..2921712 100644 --- a/nes/__init__.py +++ b/nes/__init__.py @@ -1,4 +1,4 @@ -__date__ = "2023-03-30" +__date__ = "2023-04-12" __version__ = "1.1.1" from .load_nes import open_netcdf, concatenate_netcdfs -- GitLab