diff --git a/.gitignore b/.gitignore index e5d46f5e2567228ab637f935c9d2dcdc3087f550..6f3a6192a49eb125579c4f47d9ad88445dd6b887 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,7 @@ .idea logs -tests/basic_nes_tests_alba.py -tests/test_bash_nord3v2-alba.cmd notebooks/.ipynb_checkpoints .ipynb_checkpoints nes/__pycache__ nes/nc_projections/__pycache__ -jupyter_notebooks \ No newline at end of file +*.pyc \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md index 28733133b81841ea706b37a6be2cd195c7f51d97..5017be67964bf562dddc6adc320d69a8489baf10 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,27 @@ # NES CHANGELOG +### 1.1.0 +* Release date: 2023/03/02 +* Changes and new features: + * Improve Lat-Lon to Cartesian coordinates method (used in Providentia). + * Function to_shapefile() to create shapefiles from a NES object without losing data from the original grid and being able to select the time and level. + * Function from_shapefile() to create a new grid with data from a shapefile after doing a spatial join. + * Function create_shapefile() can now be used in parallel. + * Function calculate_grid_area() to calculate the area of each cell in a grid. + * Function calculate_geometry_area() to calculate the area of each cell given a set of geometries. + * Function get_spatial_bounds_mesh_format() to get the lon-lat boundaries in a mesh format (used in pcolormesh). + * Bugs fixing: + * Correct the dimensions of the resulting points datasets from any interpolation. + * Amend the interpolation method to take into account the cases in which the distance among points equals zero. + * Correct the way we retrieve the level positive value. + * Correct how to calculate the spatial bounds of LCC and Mercator grids: the dimensions were flipped. + * Correct how to calculate the spatial bounds for all grids: use read axis limits instead of write axis limits. + * Calculate centroids from coordinates in the creation of shapefiles, instead of using the geopandas function 'centroid', that raises a warning for possible errors. + * Enable selection of variables on the creation of shapefiles. + * Correct read and write parallel limits. + * Correct data type in the parallelization of points datasets. + * Correct error that appear when trying to select coordinates and write the file. + ### 1.0.0 * Release date: 2022/11/24 * Changes and new features: @@ -12,7 +34,7 @@ * Mercator * Points * Points in GHOST format - * Points in Providentia format + * Points in PROVIDENTIA format * Parallelization: * Balanced / Unbalanced * By time axis diff --git a/nes/__init__.py b/nes/__init__.py index a15e5c1dc0bd4acf3e6df7384aabce7c0b78dcb2..29592c5be7b97ad2f8358ed8dd9ae5a94be88109 100644 --- a/nes/__init__.py +++ b/nes/__init__.py @@ -1,6 +1,7 @@ -__date__ = "2022-11-24" -__version__ = "1.0.0" +__date__ = "2023-03-02" +__version__ = "1.1.0" from .load_nes import open_netcdf, concatenate_netcdfs -from .create_nes import create_nes +from .create_nes import create_nes, from_shapefile from .nc_projections import * +from .methods.cell_measures import calculate_geometry_area diff --git a/nes/create_nes.py b/nes/create_nes.py index 41c42884f08a54b551a790b637ae58af0b55da1d..33f0bf63eb76714623e9b906770014355a96ea7d 100644 --- a/nes/create_nes.py +++ b/nes/create_nes.py @@ -3,6 +3,7 @@ import warnings from netCDF4 import num2date from mpi4py import MPI +import geopandas as gpd from .nc_projections import * @@ -60,6 +61,8 @@ def create_nes(comm=None, info=False, projection=None, parallel_method='Y', bala required_vars = ['lat', 'lon'] elif projection == 'regular': required_vars = ['lat_orig', 'lon_orig', 'inc_lat', 'inc_lon', 'n_lat', 'n_lon'] + elif projection == 'global': + required_vars = ['inc_lat', 'inc_lon'] elif projection == 'rotated': required_vars = ['centre_lat', 'centre_lon', 'west_boundary', 'south_boundary', 'inc_rlat', 'inc_rlon'] elif projection == 'lcc': @@ -71,9 +74,15 @@ def create_nes(comm=None, info=False, projection=None, parallel_method='Y', bala for var in required_vars: if var not in kwargs_list: - msg = 'WARNING!!! ' - msg += 'Variable {0} has not been defined.'.format(var) - warnings.warn(msg) + msg = 'Variable {0} has not been defined. '.format(var) + msg += 'For a {} projection, it is necessary to define {}'.format(projection, required_vars) + raise ValueError(msg) + + for var in kwargs_list: + if var not in required_vars: + msg = 'Variable {0} has been defined. '.format(var) + msg += 'For a {} projection, you can only define {}'.format(projection, required_vars) + raise ValueError(msg) if projection is None: if parallel_method == 'Y': @@ -85,7 +94,7 @@ def create_nes(comm=None, info=False, projection=None, parallel_method='Y', bala 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) - elif projection == 'regular': + 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, first_level=first_level, last_level=last_level, balanced=balanced, @@ -109,4 +118,40 @@ def create_nes(comm=None, info=False, projection=None, parallel_method='Y', bala raise NotImplementedError(projection) return nessy - \ No newline at end of file + + +def from_shapefile(path, method=None, parallel_method='Y', **kwargs): + """ + Create NES from shapefile data. + + 1. Create NES grid. + 2. Create shapefile for grid. + 3. Read shapefile from mask. + 4. Spatial join to add shapefile variables to NES variables. + + Parameters + ---------- + path : str + Path to shapefile. + method : str + Overlay method. Accepted values: ['nearest', 'intersection', None]. + parallel_method : str + Indicates the parallelization method that you want. Default: 'Y'. + accepted values: ['X', 'Y', 'T']. + kwargs : + Projection and projection dependent parameters to create it from scratch. + """ + + # Create NES + nessy = create_nes(comm=None, info=False, parallel_method=parallel_method, **kwargs) + + # Create shapefile for grid + nessy.create_shapefile() + + # Read shapefile + shapefile = gpd.read_file(path) + + # Make spatial join + nessy.spatial_join(shapefile, method=method) + + return nessy diff --git a/nes/interpolation/horizontal_interpolation.py b/nes/interpolation/horizontal_interpolation.py deleted file mode 100644 index 74dadd918532cfc0b205b13da10361380816b95e..0000000000000000000000000000000000000000 --- a/nes/interpolation/horizontal_interpolation.py +++ /dev/null @@ -1,436 +0,0 @@ -#!/usr/bin/env python - -import sys -import warnings -import numpy as np -import os -import nes -from mpi4py import MPI -from scipy import spatial -from filelock import FileLock -from datetime import datetime -from warnings import warn - - -def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='NearestNeighbour', n_neighbours=4, - info=False, to_providentia=False): - """ - Horizontal interpolation from one grid to another one. - - Parameters - ---------- - self : nes.Nes - Source projection Nes Object. - dst_grid : nes.Nes - Final projection Nes object. - weight_matrix_path : str, None - Path to the weight matrix to read/create. - kind : str - Kind of horizontal interpolation. Accepted values: ['NearestNeighbour']. - n_neighbours : int - Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. - info : bool - Indicates if you want to print extra info during the interpolation process. - to_providentia : bool - Indicates if we want the interpolated grid in Providentia format. - """ - - # Obtain weight matrix - if self.parallel_method == 'T': - weights, idx = get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours) - elif self.parallel_method in ['Y', 'X']: - weights, idx = get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours) - else: - raise NotImplemented("Parallel method {0} is not implemented yet for horizontal interpolations. Use 'T'".format( - self.parallel_method)) - - # Apply weights - final_dst = dst_grid.copy() - final_dst.set_communicator(dst_grid.comm) - # return final_dst - final_dst.lev = self.lev - final_dst._lev = self._lev - final_dst.time = self.time - final_dst._time = self._time - final_dst.hours_start = self.hours_start - final_dst.hours_end = self.hours_end - - for var_name, var_info in self.variables.items(): - if info and self.master: - print("\t{var} horizontal interpolation".format(var=var_name)) - sys.stdout.flush() - src_shape = var_info['data'].shape - if isinstance(dst_grid, nes.PointsNes): - dst_shape = (src_shape[0], src_shape[1], idx.shape[-1]) - else: - dst_shape = (src_shape[0], src_shape[1], idx.shape[-2], idx.shape[-1]) - # Creating new variable without data - final_dst.variables[var_name] = {attr_name: attr_value for attr_name, attr_value in var_info.items() - if attr_name != 'data'} - # Creating empty data - final_dst.variables[var_name]['data'] = np.empty(dst_shape) - - # src_data = var_info['data'].reshape((src_shape[0], src_shape[1], src_shape[2] * src_shape[3])) - for time in range(dst_shape[0]): - for lev in range(dst_shape[1]): - src_aux = get_src_data(self.comm, var_info['data'][time, lev], idx, self.parallel_method) - # src_aux = np.take(src_data[time, lev], idx) - final_dst.variables[var_name]['data'][time, lev] = np.sum(weights * src_aux, axis=1) - if isinstance(dst_grid, nes.PointsNes): - # Removing level axis - if src_shape[1] != 1: - raise IndexError("Data with vertical levels cannot be interpolated to points") - final_dst.variables[var_name]['data'] = final_dst.variables[var_name]['data'].reshape( - (src_shape[0], idx.shape[-1])) - if isinstance(dst_grid, nes.PointsNesGHOST) and not to_providentia: - final_dst = final_dst.to_points() - - final_dst.global_attrs = self.global_attrs - - if to_providentia: - # self = experiment to interpolate (regular, rotated, etc.) - # final_dst = interpolated experiment (points) - if isinstance(final_dst, nes.PointsNes): - model_centre_lat, model_centre_lon = self.create_providentia_exp_centre_coordinates() - grid_edge_lat, grid_edge_lon = self.create_providentia_exp_grid_edge_coordinates() - final_dst = final_dst.to_providentia(model_centre_lon=model_centre_lon, - model_centre_lat=model_centre_lat, - grid_edge_lon=grid_edge_lon, - grid_edge_lat=grid_edge_lat) - else: - msg = "The final projection must be points to interpolate an experiment and get it in Providentia format." - warnings.warn(msg) - - return final_dst - - -def get_src_data(comm, var_data, idx, parallel_method): - """ - To obtain the needed src data to interpolate. - - Parameters - ---------- - comm : MPI.Communicator. - var_data : np.array - Rank source data. - idx : np.array - Index of the needed data in a 2D flatten way. - parallel_method: str - Source parallel method. - - Returns - ------- - np.array - Flatten source needed data. - """ - - if parallel_method == 'T': - var_data = var_data.flatten() - else: - var_data = comm.gather(var_data, root=0) - if comm.Get_rank() == 0: - if parallel_method == 'Y': - axis = 0 - elif parallel_method == 'X': - axis = 1 - else: - raise NotImplementedError(parallel_method) - var_data = np.concatenate(var_data, axis=axis) - var_data = var_data.flatten() - - var_data = comm.bcast(var_data) - - var_data = np.take(var_data, idx) - - return var_data - - -# noinspection DuplicatedCode -def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours): - """ - To obtain the weights and source data index through the T axis. - - Parameters - ---------- - self : nes.Nes - Source projection Nes Object. - dst_grid : nes.Nes - Final projection Nes object. - weight_matrix_path : str, None - Path to the weight matrix to read/create. - kind : str - Kind of horizontal interpolation. Accepted values: ['NearestNeighbour']. - n_neighbours : int - Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. - - Returns - ------- - tuple - Weights and source data index. - """ - - if weight_matrix_path is not None: - with FileLock(weight_matrix_path.replace('.nc', '.lock')): - if self.master: - if os.path.isfile(weight_matrix_path): - weight_matrix = read_weight_matrix(weight_matrix_path, comm=MPI.COMM_SELF) - if len(weight_matrix.lev['data']) != n_neighbours: - warn("The selected weight matrix does not have the same number of nearest neighbours." + - "Re-calculating again but not saving it.") - if kind in ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']: - weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) - else: - raise NotImplementedError(kind) - else: - if kind in ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']: - weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) - else: - raise NotImplementedError(kind) - if weight_matrix_path is not None: - weight_matrix.to_netcdf(weight_matrix_path) - else: - weight_matrix = None - else: - if kind in ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']: - if self.master: - weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) - else: - weight_matrix = None - else: - raise NotImplementedError(kind) - - # Normalize to 1 - if self.master: - weights = np.array(np.array(weight_matrix.variables['inverse_dists']['data'], dtype=np.float64) / - np.array(weight_matrix.variables['inverse_dists']['data'], dtype=np.float64).sum(axis=1), - dtype=np.float64) - idx = np.array(weight_matrix.variables['idx']['data'][0], dtype=int) - else: - weights = None - idx = None - - weights = self.comm.bcast(weights, root=0) - idx = self.comm.bcast(idx, root=0) - - return weights, idx - - -# noinspection DuplicatedCode -def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours): - """ - To obtain the weights and source data index through the X or Y axis. - - Parameters - ---------- - self : nes.Nes - Source projection Nes Object. - dst_grid : nes.Nes - Final projection Nes object. - weight_matrix_path : str, None - Path to the weight matrix to read/create. - kind : str - Kind of horizontal interpolation. Accepted values: ['NearestNeighbour'] - n_neighbours : int - Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. - - Returns - ------- - tuple - Weights and source data index. - """ - - if isinstance(dst_grid, nes.PointsNes) and weight_matrix_path is not None: - if self.master: - warn("To point weight matrix cannot be saved.") - weight_matrix_path = None - - if weight_matrix_path is not None: - with FileLock(weight_matrix_path.replace('.nc', '.lock')): - if self.master: - if os.path.isfile(weight_matrix_path): - weight_matrix = read_weight_matrix(weight_matrix_path, comm=MPI.COMM_SELF) - if len(weight_matrix.lev['data']) != n_neighbours: - warn("The selected weight matrix does not have the same number of nearest neighbours." + - "Re-calculating again but not saving it.") - if kind in ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']: - weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) - else: - raise NotImplementedError(kind) - else: - if kind in ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']: - weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) - else: - raise NotImplementedError(kind) - if weight_matrix_path is not None: - weight_matrix.to_netcdf(weight_matrix_path) - else: - weight_matrix = None - else: - if kind in ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']: - if self.master: - weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) - else: - weight_matrix = None - else: - raise NotImplementedError(kind) - - # Normalize to 1 - if self.master: - weights = np.array(np.array(weight_matrix.variables['inverse_dists']['data'], dtype=np.float64) / - np.array(weight_matrix.variables['inverse_dists']['data'], dtype=np.float64).sum(axis=1), - dtype=np.float64) - idx = np.array(weight_matrix.variables['idx']['data'][0], dtype=int) - else: - weights = None - idx = None - - weights = self.comm.bcast(weights, root=0) - idx = self.comm.bcast(idx, root=0) - # if isinstance(dst_grid, nes.PointsNes): - # print("weights 1 ->", weights.shape) - # print("idx 1 ->", idx.shape) - # weights = weights[:, dst_grid.write_axis_limits['x_min']:dst_grid.write_axis_limits['x_max']] - # idx = idx[dst_grid.write_axis_limits['x_min']:dst_grid.write_axis_limits['x_max']] - # else: - weights = weights[:, :, dst_grid.write_axis_limits['y_min']:dst_grid.write_axis_limits['y_max'], - dst_grid.write_axis_limits['x_min']:dst_grid.write_axis_limits['x_max']] - idx = idx[:, dst_grid.write_axis_limits['y_min']:dst_grid.write_axis_limits['y_max'], - dst_grid.write_axis_limits['x_min']:dst_grid.write_axis_limits['x_max']] - # print("weights 2 ->", weights.shape) - # print("idx 2 ->", idx.shape) - - return weights, idx - - -def read_weight_matrix(weight_matrix_path, comm=None, parallel_method='T'): - """ - Read weight matrix. - - Parameters - ---------- - weight_matrix_path : str - Path of the weight matrix. - comm : MPI.Communicator - Communicator to read the weight matrix. - parallel_method : str - Nes parallel method to read the weight matrix. - - Returns - ------- - nes.Nes - Weight matrix. - """ - - weight_matrix = nes.open_netcdf(path=weight_matrix_path, comm=comm, parallel_method=parallel_method, balanced=True) - weight_matrix.load() - - return weight_matrix - - -def create_nn_weight_matrix(self, dst_grid, n_neighbours=4, info=False): - """ - To create the weight matrix with the nearest neighbours method. - - Parameters - ---------- - self : nes.Nes - Source projection Nes Object. - dst_grid : nes.Nes - Final projection Nes object. - n_neighbours : int - Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. - info: bool - Indicates if you want to print extra info during the interpolation process. - - Returns - ------- - nes.Nes - Weight matrix. - """ - - if info and self.master: - print("\tCreating Nearest Neighbour Weight Matrix with {0} neighbours".format(n_neighbours)) - sys.stdout.flush() - # Source - src_lat = np.array(self._lat['data'], dtype=np.float32) - src_lon = np.array(self._lon['data'], dtype=np.float32) - - # 1D to 2D coordinates - if len(src_lon.shape) == 1: - src_lon, src_lat = np.meshgrid(src_lon, src_lat) - - # Destination - dst_lat = np.array(dst_grid._lat['data'], dtype=np.float32) - dst_lon = np.array(dst_grid._lon['data'], dtype=np.float32) - - if isinstance(dst_grid, nes.PointsNes): - dst_lat = np.expand_dims(dst_grid._lat['data'], axis=0) - dst_lon = np.expand_dims(dst_grid._lon['data'], axis=0) - else: - # 1D to 2D coordinates - if len(dst_lon.shape) == 1: - dst_lon, dst_lat = np.meshgrid(dst_lon, dst_lat) - - # calculate N nearest neighbour inverse distance weights (and indices) - # from gridcells centres of model 1 to each gridcell centre of model 2 - # model geographic longitude/latitude coordinates are first converted - # to cartesian ECEF (Earth Centred, Earth Fixed) coordinates, before - # calculating distances. - - src_mod_xy = lon_lat_to_cartesian(src_lon.flatten(), src_lat.flatten()) - dst_mod_xy = lon_lat_to_cartesian(dst_lon.flatten(), dst_lat.flatten()) - - # generate KDtree using model 1 coordinates (i.e. the model grid you are - # interpolating from) - src_tree = spatial.cKDTree(src_mod_xy) - - # get n-neighbour nearest distances/indices (ravel form) of model 1 gridcell - # centres from each model 2 gridcell centre - - dists, idx = src_tree.query(dst_mod_xy, k=n_neighbours) - # self.nearest_neighbour_inds = \ - # np.column_stack(np.unravel_index(idx, lon.shape)) - - weight_matrix = dst_grid.copy() - weight_matrix.time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)] - weight_matrix._time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)] - weight_matrix.last_level = None - weight_matrix.first_level = 0 - weight_matrix.hours_start = 0 - weight_matrix.hours_end = 0 - - weight_matrix.set_communicator(MPI.COMM_SELF) - # take the reciprocals of the nearest neighbours distances - inverse_dists = np.reciprocal(dists) - inverse_dists_transf = inverse_dists.T.reshape((1, n_neighbours, dst_lon.shape[0], dst_lon.shape[1])) - weight_matrix.variables['inverse_dists'] = {'data': inverse_dists_transf, 'units': 'm'} - idx_transf = idx.T.reshape((1, n_neighbours, dst_lon.shape[0], dst_lon.shape[1])) - weight_matrix.variables['idx'] = {'data': idx_transf, 'units': ''} - weight_matrix.lev = {'data': np.arange(inverse_dists_transf.shape[1]), 'units': ''} - weight_matrix._lev = {'data': np.arange(inverse_dists_transf.shape[1]), 'units': ''} - - return weight_matrix - - -def lon_lat_to_cartesian(lon, lat, radius=1.0): - """ - Calculate lon, lat coordinates of a point on a sphere. - - Parameters - ---------- - lon : np.array - Longitude values. - lat : np.array - Latitude values. - radius : float - Radius of the sphere to get the distances. - """ - - lon_r = np.radians(lon) - lat_r = np.radians(lat) - - x = radius * np.cos(lat_r) * np.cos(lon_r) - y = radius * np.cos(lat_r) * np.sin(lon_r) - z = radius * np.sin(lat_r) - - return np.column_stack([x, y, z]) diff --git a/nes/load_nes.py b/nes/load_nes.py index 525e7267f45428bef4563acea7031071da9c0197..5bfd333fc4661ec1f9feffe82dfe4d726051b13e 100644 --- a/nes/load_nes.py +++ b/nes/load_nes.py @@ -112,7 +112,7 @@ def __is_rotated(dataset): Parameters ---------- dataset : Dataset - netcdf4-python opened dataset object. + netcdf4-python open dataset object. Returns ------- @@ -122,6 +122,8 @@ def __is_rotated(dataset): if 'rotated_pole' in dataset.variables.keys(): return True + elif ('rlat' in dataset.dimensions) and ('rlon' in dataset.dimensions): + return True else: return False @@ -133,7 +135,7 @@ def __is_points(dataset): Parameters ---------- dataset : Dataset - netcdf4-python opened dataset object. + netcdf4-python open dataset object. Returns ------- @@ -154,7 +156,7 @@ def __is_points_ghost(dataset): Parameters ---------- dataset : Dataset - netcdf4-python opened dataset object. + netcdf4-python open dataset object. Returns ------- @@ -175,7 +177,7 @@ def __is_points_providentia(dataset): Parameters ---------- dataset : Dataset - netcdf4-python opened dataset object. + netcdf4-python open dataset object. Returns ------- @@ -197,7 +199,7 @@ def __is_lcc(dataset): Parameters ---------- dataset : Dataset - netcdf4-python opened dataset object. + netcdf4-python open dataset object. Returns ------- @@ -218,7 +220,7 @@ def __is_mercator(dataset): Parameters ---------- dataset : Dataset - netcdf4-python opened dataset object. + netcdf4-python open dataset object. Returns ------- diff --git a/nes/interpolation/__init__.py b/nes/methods/__init__.py similarity index 100% rename from nes/interpolation/__init__.py rename to nes/methods/__init__.py diff --git a/nes/methods/cell_measures.py b/nes/methods/cell_measures.py new file mode 100644 index 0000000000000000000000000000000000000000..2866b8b9b2856898d189300a4131ef6fa0b8cd0d --- /dev/null +++ b/nes/methods/cell_measures.py @@ -0,0 +1,265 @@ +#!/usr/bin/env python + +import numpy as np +from copy import deepcopy + + +def calculate_grid_area(self): + """ + Get coordinate bounds and call function to calculate the area of each cell of a grid. + + Parameters + ---------- + self : nes.Nes + Source projection Nes Object. + """ + + # Create bounds if they do not exist + if self._lat_bnds is None or self._lon_bnds is None: + self.create_spatial_bounds() + + # Get spatial number of vertices + spatial_nv = self.lat_bnds['data'].shape[-1] + + # Reshape bounds + if spatial_nv == 2: + + aux_shape = (self.lat_bnds['data'].shape[0], self.lon_bnds['data'].shape[0], 4) + lon_bnds_aux = np.empty(aux_shape) + lon_bnds_aux[:, :, 0] = self.lon_bnds['data'][np.newaxis, :, 0] + lon_bnds_aux[:, :, 1] = self.lon_bnds['data'][np.newaxis, :, 1] + lon_bnds_aux[:, :, 2] = self.lon_bnds['data'][np.newaxis, :, 1] + lon_bnds_aux[:, :, 3] = self.lon_bnds['data'][np.newaxis, :, 0] + + lon_bnds = lon_bnds_aux + del lon_bnds_aux + + lat_bnds_aux = np.empty(aux_shape) + lat_bnds_aux[:, :, 0] = self.lat_bnds['data'][:, np.newaxis, 0] + lat_bnds_aux[:, :, 1] = self.lat_bnds['data'][:, np.newaxis, 0] + lat_bnds_aux[:, :, 2] = self.lat_bnds['data'][:, np.newaxis, 1] + lat_bnds_aux[:, :, 3] = self.lat_bnds['data'][:, np.newaxis, 1] + + lat_bnds = lat_bnds_aux + del lat_bnds_aux + + else: + lon_bnds = self.lon_bnds['data'] + lat_bnds = self.lat_bnds['data'] + + # Reshape bounds and assign as grid corner coordinates + grid_corner_lon = deepcopy(lon_bnds).reshape(lon_bnds.shape[0]*lon_bnds.shape[1], + lon_bnds.shape[2]) + grid_corner_lat = deepcopy(lat_bnds).reshape(lat_bnds.shape[0]*lat_bnds.shape[1], + lat_bnds.shape[2]) + + # Calculate cell areas + grid_area = calculate_cell_area(grid_corner_lon, grid_corner_lat, + earth_radius_minor_axis=self.earth_radius[0], + earth_radius_major_axis=self.earth_radius[1]) + + return grid_area + + +def calculate_geometry_area(geometry_list, earth_radius_minor_axis=6356752.3142, + earth_radius_major_axis=6378137.0): + """ + Get coordinate bounds and call function to calculate the area of each cell of a set of geometries. + + Parameters + ---------- + geometry_list : List + List with polygon geometries. + earth_radius_minor_axis : float + Radius of the minor axis of the Earth. + earth_radius_major_axis : float + Radius of the major axis of the Earth. + """ + + geometry_area = np.empty(shape=(len(geometry_list,))) + + for geom_ind in range(0, len(geometry_list)): + + # Calculate the area of each geometry in multipolygon and collection objects + if geometry_list[geom_ind].type in ['MultiPolygon', 'GeometryCollection']: + multi_geom_area = 0 + for multi_geom_ind in range(0, len(geometry_list[geom_ind].geoms)): + if geometry_list[geom_ind].geoms[multi_geom_ind].type == 'Point': + continue + geometry_corner_lon, geometry_corner_lat = geometry_list[geom_ind].geoms[multi_geom_ind].exterior.coords.xy + geometry_corner_lon = np.array(geometry_corner_lon) + geometry_corner_lat = np.array(geometry_corner_lat) + geom_area = mod_huiliers_area(geometry_corner_lon, geometry_corner_lat) + multi_geom_area += geom_area + geometry_area[geom_ind] = multi_geom_area * earth_radius_minor_axis * earth_radius_major_axis + + # Calculate the area of each geometry + else: + geometry_corner_lon, geometry_corner_lat = geometry_list[geom_ind].exterior.coords.xy + geometry_corner_lon = np.array(geometry_corner_lon) + geometry_corner_lat = np.array(geometry_corner_lat) + geom_area = mod_huiliers_area(geometry_corner_lon, geometry_corner_lat) + geometry_area[geom_ind] = geom_area * earth_radius_minor_axis * earth_radius_major_axis + + return geometry_area + + +def calculate_cell_area(grid_corner_lon, grid_corner_lat, + earth_radius_minor_axis=6356752.3142, + earth_radius_major_axis=6378137.0): + """ + Calculate the area of each cell of a grid. + + Parameters + ---------- + grid_corner_lon : np.array + Array with longitude bounds of grid. + grid_corner_lat : np.array + Array with longitude bounds of grid. + earth_radius_minor_axis : float + Radius of the minor axis of the Earth. + earth_radius_major_axis : float + Radius of the major axis of the Earth. + """ + + # Calculate area for each grid cell + n_cells = grid_corner_lon.shape[0] + area = np.empty(shape=(n_cells,)) + for i in range(0, n_cells): + area[i] = mod_huiliers_area(grid_corner_lon[i], grid_corner_lat[i]) + + return area*earth_radius_minor_axis*earth_radius_major_axis + + +def mod_huiliers_area(cell_corner_lon, cell_corner_lat): + """ + Calculate the area of each cell according to Huilier's theorem. + Reference: CDO (https://earth.bsc.es/gitlab/ces/cdo/) + + Parameters + ---------- + cell_corner_lon : np.array + Longitude boundaries of each cell + cell_corner_lat : np.array + Latitude boundaries of each cell + """ + + sum = 0 + + # Get points 0 (bottom left) and 1 (bottom right) in Earth coordinates + point_0 = lon_lat_to_cartesian(cell_corner_lon[0], cell_corner_lat[0], earth_radius_major_axis=1) + point_1 = lon_lat_to_cartesian(cell_corner_lon[1], cell_corner_lat[1], earth_radius_major_axis=1) + point_0, point_1 = point_0[0], point_1[0] + + # Get number of vertices + if cell_corner_lat[0] == cell_corner_lat[-1]: + spatial_nv = len(cell_corner_lon) - 1 + else: + spatial_nv = len(cell_corner_lon) + + for i in range(2, spatial_nv): + + # Get point 2 (top right) in Earth coordinates + point_2 = lon_lat_to_cartesian(cell_corner_lon[i], cell_corner_lat[i], earth_radius_major_axis=1) + point_2 = point_2[0] + + # Calculate area of triangle between points 0, 1 and 2 + sum += tri_area(point_0, point_1, point_2) + + # Copy to calculate area of next triangle + if i == (spatial_nv - 1): + point_1 = deepcopy(point_2) + + return sum + + +def tri_area(point_0, point_1, point_2): + """ + Calculate area between three points that form a triangle. + Reference: CDO (https://earth.bsc.es/gitlab/ces/cdo/) + + Parameters + ---------- + point_0 : np.array + Position of first point in cartesian coordinates. + point_1 : np.array + Position of second point in cartesian coordinates. + point_2 : np.array + Position of third point in cartesian coordinates. + """ + + # Get length of side a (between point 0 and 1) + tmp_vec = cross_product(point_0, point_1) + sina = norm(tmp_vec) + a = np.arcsin(sina) + + # Get length of side b (between point 0 and 2) + tmp_vec = cross_product(point_0, point_2) + sinb = norm(tmp_vec) + b = np.arcsin(sinb) + + # Get length of side c (between point 1 and 2) + tmp_vec = cross_product(point_2, point_1) + sinc = norm(tmp_vec) + c = np.arcsin(sinc) + + # Calculate area + s = 0.5*(a+b+c) + t = np.tan(s*0.5) * np.tan((s - a)*0.5) * np.tan((s - b)*0.5) * np.tan((s - c)*0.5) + area = np.fabs(4.0 * np.arctan(np.sqrt(np.fabs(t)))) + + return area + + +def cross_product(a, b): + """ + Calculate cross product between two points. + + Parameters + ---------- + a : np.array + Position of point a in cartesian coordinates. + b : np.array + Position of point b in cartesian coordinates. + """ + + return [a[1]*b[2] - a[2]*b[1], + a[2]*b[0] - a[0]*b[2], + a[0]*b[1] - a[1]*b[0]] + + +def norm(cp): + """ + Normalize the result of the cross product operation. + + Parameters + ---------- + cp : np.array + Cross product between two points. + """ + + return np.sqrt(cp[0]*cp[0] + cp[1]*cp[1] + cp[2]*cp[2]) + + +def lon_lat_to_cartesian(lon, lat, earth_radius_major_axis=6378137.0): + """ + Calculate lon, lat coordinates of a point on a sphere. + + Parameters + ---------- + lon : np.array + Longitude values. + lat : np.array + Latitude values. + earth_radius_major_axis : float + Radius of the major axis of the Earth. + """ + + lon_r = np.radians(lon) + lat_r = np.radians(lat) + + x = earth_radius_major_axis * np.cos(lat_r) * np.cos(lon_r) + y = earth_radius_major_axis * np.cos(lat_r) * np.sin(lon_r) + z = earth_radius_major_axis * np.sin(lat_r) + + return np.column_stack([x, y, z]) diff --git a/nes/methods/horizontal_interpolation.py b/nes/methods/horizontal_interpolation.py new file mode 100644 index 0000000000000000000000000000000000000000..ec3c2a9dcf76c9ba74ee32144d700581004b38fe --- /dev/null +++ b/nes/methods/horizontal_interpolation.py @@ -0,0 +1,722 @@ +#!/usr/bin/env python + +import sys +import warnings +import numpy as np +import pandas as pd +import os +import nes +from mpi4py import MPI +from scipy import spatial +from filelock import FileLock +from datetime import datetime +from warnings import warn +import copy +import pyproj + +# CONSTANTS +NEAREST_OPTS = ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN'] +CONSERVATIVE_OPTS = ['Conservative', 'Area_Conservative', 'cons', 'conservative', 'area'] + + +def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='NearestNeighbour', n_neighbours=4, + info=False, to_providentia=False, only_create_wm=False, wm=None): + """ + Horizontal methods from one grid to another one. + + Parameters + ---------- + self : nes.Nes + Source projection Nes Object. + dst_grid : nes.Nes + Final projection Nes object. + weight_matrix_path : str, None + Path to the weight matrix to read/create. + kind : str + Kind of horizontal methods. Accepted values: ['NearestNeighbour', 'Conservative']. + n_neighbours : int + Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. + info : bool + Indicates if you want to print extra info during the methods process. + to_providentia : bool + Indicates if we want the interpolated grid in Providentia format. + only_create_wm : bool + Indicates if you want to only create the Weight Matrix. + wm : Nes + Weight matrix Nes File + """ + if info and self.master: + print("Creating Weight Matrix") + # Obtain weight matrix + if self.parallel_method == 'T': + weights, idx = get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, + only_create_wm, wm) + elif self.parallel_method in ['Y', 'X']: + weights, idx = get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, + only_create_wm, wm) + else: + raise NotImplemented("Parallel method {0} is not implemented yet for horizontal interpolations. Use 'T'".format( + self.parallel_method)) + if info and self.master: + print("Weight Matrix done!") + if only_create_wm: + # weights for only_create is the WM NES object + return weights + + # idx[idx < 0] = np.nan + idx = np.ma.masked_array(idx, mask=idx == -999) + # idx = np.array(idx, dtype=float) + # idx[idx < 0] = np.nan + # weights[weights < 0] = np.nan + weights = np.ma.masked_array(weights, mask=weights == -999) + # weights = np.array(weights, dtype=float) + # weights[weights < 0] = np.nan + + # Copy NES + final_dst = dst_grid.copy() + final_dst.set_communicator(dst_grid.comm) + + # Remove original file information + final_dst.__ini_path = None + final_dst.netcdf = None + final_dst.dataset = None + + # Return final_dst + final_dst.lev = self.lev + final_dst._lev = self._lev + final_dst.time = self.time + final_dst._time = self._time + final_dst.hours_start = self.hours_start + final_dst.hours_end = self.hours_end + + if info and self.master: + print("Applying weights") + # Apply weights + for var_name, var_info in self.variables.items(): + if info and self.master: + print("\t{var} horizontal methods".format(var=var_name)) + sys.stdout.flush() + src_shape = var_info['data'].shape + if isinstance(dst_grid, nes.PointsNes): + dst_shape = (src_shape[0], src_shape[1], idx.shape[-1]) + else: + dst_shape = (src_shape[0], src_shape[1], idx.shape[-2], idx.shape[-1]) + # Creating new variable without data + final_dst.variables[var_name] = {attr_name: attr_value for attr_name, attr_value in var_info.items() + if attr_name != 'data'} + # Creating empty data + final_dst.variables[var_name]['data'] = np.empty(dst_shape) + + # src_data = var_info['data'].reshape((src_shape[0], src_shape[1], src_shape[2] * src_shape[3])) + for time in range(dst_shape[0]): + for lev in range(dst_shape[1]): + src_aux = get_src_data(self.comm, var_info['data'][time, lev], idx, self.parallel_method) + final_dst.variables[var_name]['data'][time, lev] = np.sum(weights * src_aux, axis=1) + + if isinstance(dst_grid, nes.PointsNes): + # Removing level axis + if src_shape[1] != 1: + raise IndexError("Data with vertical levels cannot be interpolated to points") + final_dst.variables[var_name]['data'] = final_dst.variables[var_name]['data'].reshape( + (src_shape[0], idx.shape[-1])) + if isinstance(dst_grid, nes.PointsNesGHOST) and not to_providentia: + final_dst = final_dst.to_points() + + final_dst.global_attrs = self.global_attrs + + if info and self.master: + print("Formatting") + + if to_providentia: + # self = experiment to interpolate (regular, rotated, etc.) + # final_dst = interpolated experiment (points) + if isinstance(final_dst, nes.PointsNes): + model_centre_lat, model_centre_lon = self.create_providentia_exp_centre_coordinates() + grid_edge_lat, grid_edge_lon = self.create_providentia_exp_grid_edge_coordinates() + final_dst = final_dst.to_providentia(model_centre_lon=model_centre_lon, + model_centre_lat=model_centre_lat, + grid_edge_lon=grid_edge_lon, + grid_edge_lat=grid_edge_lat) + else: + msg = "The final projection must be points to interpolate an experiment and get it in Providentia format." + warnings.warn(msg) + else: + # Convert dimensions (time, lev, lat, lon) or (time, lat, lon) to (time, station) for interpolated variables + # and reshape data + if isinstance(final_dst, nes.PointsNes): + for var_name, var_info in final_dst.variables.items(): + if len(var_info['dimensions']) != len(var_info['data'].shape): + final_dst.variables[var_name]['dimensions'] = ('time', 'station') + + return final_dst + + +def get_src_data(comm, var_data, idx, parallel_method): + """ + To obtain the needed src data to interpolate. + + Parameters + ---------- + comm : MPI.Communicator. + var_data : np.array + Rank source data. + idx : np.array + Index of the needed data in a 2D flatten way. + parallel_method: str + Source parallel method. + + Returns + ------- + np.array + Flatten source needed data. + """ + + if parallel_method == 'T': + var_data = var_data.flatten() + else: + var_data = comm.gather(var_data, root=0) + if comm.Get_rank() == 0: + if parallel_method == 'Y': + axis = 0 + elif parallel_method == 'X': + axis = 1 + else: + raise NotImplementedError(parallel_method) + var_data = np.concatenate(var_data, axis=axis) + var_data = var_data.flatten() + + var_data = comm.bcast(var_data) + + var_data = np.pad(var_data, [1, 1], 'constant', constant_values=np.nan).take(idx + 1, mode='clip') + #var_data = np.take(var_data, idx) + + return var_data + + +# noinspection DuplicatedCode +def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, only_create, wm): + """ + To obtain the weights and source data index through the T axis. + + Parameters + ---------- + self : nes.Nes + Source projection Nes Object. + dst_grid : nes.Nes + Final projection Nes object. + weight_matrix_path : str, None + Path to the weight matrix to read/create. + kind : str + Kind of horizontal methods. Accepted values: ['NearestNeighbour', 'Conservative']. + n_neighbours : int + Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. + only_create : bool + Indicates if you want to only create the Weight Matrix. + wm : Nes + Weight matrix Nes File + + Returns + ------- + tuple + Weights and source data index. + """ + if wm is not None: + weight_matrix = wm + + elif weight_matrix_path is not None: + with FileLock(weight_matrix_path + "{0:03d}.lock".format(self.rank)): + if os.path.isfile(weight_matrix_path): + if self.master: + weight_matrix = read_weight_matrix(weight_matrix_path, comm=MPI.COMM_SELF) + else: + weight_matrix = True + if kind in NEAREST_OPTS: + if self.master: + if len(weight_matrix.lev['data']) != n_neighbours: + warn("The selected weight matrix does not have the same number of nearest neighbours." + + "Re-calculating again but not saving it.") + weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) + else: + weight_matrix = True + + else: + if self.master: + if kind in NEAREST_OPTS: + weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours, + wm_path=weight_matrix_path) + elif kind in CONSERVATIVE_OPTS: + weight_matrix = create_area_conservative_weight_matrix(self, dst_grid, + wm_path=weight_matrix_path) + else: + raise NotImplementedError(kind) + else: + weight_matrix = True + + if os.path.exists(weight_matrix_path + "{0:03d}.lock".format(self.rank)): + os.remove(weight_matrix_path + "{0:03d}.lock".format(self.rank)) + else: + if self.master: + if kind in NEAREST_OPTS: + weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) + elif kind in CONSERVATIVE_OPTS: + weight_matrix = create_area_conservative_weight_matrix(self, dst_grid) + else: + raise NotImplementedError(kind) + else: + weight_matrix = True + + if only_create: + return weight_matrix, None + + if self.master: + if kind in NEAREST_OPTS: + # Normalize to 1 + weights = np.array(np.array(weight_matrix.variables['weight']['data'], dtype=np.float64) / + np.array(weight_matrix.variables['weight']['data'], dtype=np.float64).sum(axis=1), + dtype=np.float64) + else: + weights = np.array(weight_matrix.variables['weight']['data'], dtype=np.float64) + idx = np.array(weight_matrix.variables['idx']['data'][0], dtype=int) + else: + weights = None + idx = None + + weights = self.comm.bcast(weights, root=0) + idx = self.comm.bcast(idx, root=0) + + return weights, idx + + +# noinspection DuplicatedCode +def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, only_create, wm): + """ + To obtain the weights and source data index through the X or Y axis. + + Parameters + ---------- + self : nes.Nes + Source projection Nes Object. + dst_grid : nes.Nes + Final projection Nes object. + weight_matrix_path : str, None + Path to the weight matrix to read/create. + kind : str + Kind of horizontal methods. Accepted values: ['NearestNeighbour', 'Conservative']. + n_neighbours : int + Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. + only_create : bool + Indicates if you want to only create the Weight Matrix. + wm : Nes + Weight matrix Nes File + + Returns + ------- + tuple + Weights and source data index. + """ + if isinstance(dst_grid, nes.PointsNes) and weight_matrix_path is not None: + if self.master: + warn("To point weight matrix cannot be saved.") + weight_matrix_path = None + + if wm is not None: + weight_matrix = wm + + elif weight_matrix_path is not None: + with FileLock(weight_matrix_path + "{0:03d}.lock".format(self.rank)): + if os.path.isfile(weight_matrix_path): + if self.master: + weight_matrix = read_weight_matrix(weight_matrix_path, comm=MPI.COMM_SELF) + else: + weight_matrix = True + if kind in NEAREST_OPTS: + if len(weight_matrix.lev['data']) != n_neighbours: + warn("The selected weight matrix does not have the same number of nearest neighbours." + + "Re-calculating again but not saving it.") + if self.master: + weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) + else: + weight_matrix = True + else: + if kind in NEAREST_OPTS: + if self.master: + weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours, + wm_path=weight_matrix_path) + else: + weight_matrix = True + elif kind in CONSERVATIVE_OPTS: + weight_matrix = create_area_conservative_weight_matrix(self, dst_grid, wm_path=weight_matrix_path) + else: + raise NotImplementedError(kind) + + if os.path.exists(weight_matrix_path + "{0:03d}.lock".format(self.rank)): + os.remove(weight_matrix_path + "{0:03d}.lock".format(self.rank)) + else: + if kind in NEAREST_OPTS: + weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) + elif kind in CONSERVATIVE_OPTS: + weight_matrix = create_area_conservative_weight_matrix(self, dst_grid) + else: + raise NotImplementedError(kind) + + if only_create: + return weight_matrix, None + + # Normalize to 1 + if self.master: + if kind in NEAREST_OPTS: + weights = np.array(np.array(weight_matrix.variables['weight']['data'], dtype=np.float64) / + np.array(weight_matrix.variables['weight']['data'], dtype=np.float64).sum(axis=1), + dtype=np.float64) + else: + weights = np.array(weight_matrix.variables['weight']['data'], dtype=np.float64) + idx = np.array(weight_matrix.variables['idx']['data'][0], dtype=np.int64) + else: + weights = None + idx = None + + weights = self.comm.bcast(weights, root=0) + idx = self.comm.bcast(idx, root=0) + + # if isinstance(dst_grid, nes.PointsNes): + # print("weights 1 ->", weights.shape) + # print("idx 1 ->", idx.shape) + # weights = weights[:, dst_grid.write_axis_limits['x_min']:dst_grid.write_axis_limits['x_max']] + # idx = idx[dst_grid.write_axis_limits['x_min']:dst_grid.write_axis_limits['x_max']] + # else: + weights = weights[:, :, dst_grid.write_axis_limits['y_min']:dst_grid.write_axis_limits['y_max'], + dst_grid.write_axis_limits['x_min']:dst_grid.write_axis_limits['x_max']] + idx = idx[:, dst_grid.write_axis_limits['y_min']:dst_grid.write_axis_limits['y_max'], + dst_grid.write_axis_limits['x_min']:dst_grid.write_axis_limits['x_max']] + # print("weights 2 ->", weights.shape) + # print("idx 2 ->", idx.shape) + + return weights, idx + + +def read_weight_matrix(weight_matrix_path, comm=None, parallel_method='T'): + """ + Read weight matrix. + + Parameters + ---------- + weight_matrix_path : str + Path of the weight matrix. + comm : MPI.Communicator + Communicator to read the weight matrix. + parallel_method : str + Nes parallel method to read the weight matrix. + + Returns + ------- + nes.Nes + Weight matrix. + """ + weight_matrix = nes.open_netcdf(path=weight_matrix_path, comm=comm, parallel_method=parallel_method, balanced=True) + weight_matrix.load() + + # In previous versions of NES weight was called inverse_dists + if 'inverse_dists' in weight_matrix.variables.keys(): + weight_matrix.variables['weight'] = weight_matrix.variables['inverse_dists'] + + return weight_matrix + + +def create_nn_weight_matrix(self, dst_grid, n_neighbours=4, wm_path=None, info=False): + """ + To create the weight matrix with the nearest neighbours method. + + Parameters + ---------- + self : nes.Nes + Source projection Nes Object. + dst_grid : nes.Nes + Final projection Nes object. + n_neighbours : int + Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. + info: bool + Indicates if you want to print extra info during the methods process. + + Returns + ------- + nes.Nes + Weight matrix. + """ + + if info and self.master: + print("\tCreating Nearest Neighbour Weight Matrix with {0} neighbours".format(n_neighbours)) + sys.stdout.flush() + # Source + src_lat = np.array(self._lat['data'], dtype=np.float32) + src_lon = np.array(self._lon['data'], dtype=np.float32) + + # 1D to 2D coordinates + if len(src_lon.shape) == 1: + src_lon, src_lat = np.meshgrid(src_lon, src_lat) + + # Destination + dst_lat = np.array(dst_grid._lat['data'], dtype=np.float32) + dst_lon = np.array(dst_grid._lon['data'], dtype=np.float32) + + if isinstance(dst_grid, nes.PointsNes): + dst_lat = np.expand_dims(dst_grid._lat['data'], axis=0) + dst_lon = np.expand_dims(dst_grid._lon['data'], axis=0) + else: + # 1D to 2D coordinates + if len(dst_lon.shape) == 1: + dst_lon, dst_lat = np.meshgrid(dst_lon, dst_lat) + + # calculate N nearest neighbour inverse distance weights (and indices) + # from gridcells centres of model 1 to each gridcell centre of model 2 + # model geographic longitude/latitude coordinates are first converted + # to cartesian ECEF (Earth Centred, Earth Fixed) coordinates, before + # calculating distances. + + # src_mod_xy = lon_lat_to_cartesian(src_lon.flatten(), src_lat.flatten()) + # dst_mod_xy = lon_lat_to_cartesian(dst_lon.flatten(), dst_lat.flatten()) + + src_mod_xy = lon_lat_to_cartesian_ecef(src_lon.flatten(), src_lat.flatten()) + dst_mod_xy = lon_lat_to_cartesian_ecef(dst_lon.flatten(), dst_lat.flatten()) + + # generate KDtree using model 1 coordinates (i.e. the model grid you are + # interpolating from) + src_tree = spatial.cKDTree(src_mod_xy) + + # get n-neighbour nearest distances/indices (ravel form) of model 1 gridcell + # centres from each model 2 gridcell centre + + dists, idx = src_tree.query(dst_mod_xy, k=n_neighbours) + # self.nearest_neighbour_inds = \ + # np.column_stack(np.unravel_index(idx, lon.shape)) + + weight_matrix = dst_grid.copy() + weight_matrix.time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)] + weight_matrix._time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)] + weight_matrix._time_bnds = None + weight_matrix.time_bnds = None + weight_matrix.last_level = None + weight_matrix.first_level = 0 + weight_matrix.hours_start = 0 + weight_matrix.hours_end = 0 + + weight_matrix.set_communicator(MPI.COMM_SELF) + # take the reciprocals of the nearest neighbours distances + dists[dists < 1] = 1 + inverse_dists = np.reciprocal(dists) + + inverse_dists_transf = inverse_dists.T.reshape((1, n_neighbours, dst_lon.shape[0], dst_lon.shape[1])) + weight_matrix.variables['weight'] = {'data': inverse_dists_transf, 'units': 'm'} + idx_transf = idx.T.reshape((1, n_neighbours, dst_lon.shape[0], dst_lon.shape[1])) + weight_matrix.variables['idx'] = {'data': idx_transf, 'units': ''} + weight_matrix.lev = {'data': np.arange(inverse_dists_transf.shape[1]), 'units': ''} + weight_matrix._lev = {'data': np.arange(inverse_dists_transf.shape[1]), 'units': ''} + if wm_path is not None: + weight_matrix.to_netcdf(wm_path) + + return weight_matrix + + +def create_area_conservative_weight_matrix(self, dst_nes, wm_path=None, info=False): + """ + To create the weight matrix with the area conservative method. + + Parameters + ---------- + self : nes.Nes + Source projection Nes Object. + dst_nes : nes.Nes + Final projection Nes object. + wm_path : str + Path where write the weight matrix + + info: bool + Indicates if you want to print extra info during the methods process. + + Returns + ------- + nes.Nes + Weight matrix. + """ + if info and self.master: + print("\tCreating area conservative Weight Matrix") + sys.stdout.flush() + # Get a portion of the destiny grid + if dst_nes.shapefile is None: + dst_nes.create_shapefile() + dst_grid = copy.deepcopy(dst_nes.shapefile) + + # Get the complete source grid + if self.shapefile is None: + self.create_shapefile() + src_grid = copy.deepcopy(self.shapefile) + + if self.parallel_method == 'T': + # All process has the same shapefile + pass + else: + src_grid = self.comm.gather(src_grid, root=0) + if self.master: + src_grid = pd.concat(src_grid) + src_grid = self.comm.bcast(src_grid) + + my_crs = pyproj.CRS.from_proj4("+proj=latlon") + # Normalizing projections + dst_grid.to_crs(crs=my_crs, inplace=True) + dst_grid['FID_dst'] = dst_grid.index + dst_grid = dst_grid.reset_index() + + # src_grid.to_crs(crs=pyproj.Proj(proj='geocent', ellps='WGS84', datum='WGS84').crs, inplace=True) + src_grid.to_crs(crs=my_crs, inplace=True) + src_grid['FID_src'] = src_grid.index + + src_grid = src_grid.reset_index() + + if info and self.master: + print("\t\tGrids created and ready to interpolate") + sys.stdout.flush() + + # Get intersected areas + inp, res = dst_grid.sindex.query_bulk(src_grid.geometry, predicate='intersects') + + # Calculate intersected areas and fractions + intersection = pd.DataFrame(columns=["FID_src", "FID_dst"]) + intersection['INP'] = np.array(inp, dtype=np.uint32) + intersection['RES'] = np.array(res, dtype=np.uint32) + intersection['FID_src'] = np.array(src_grid.loc[inp, 'FID_src'], dtype=np.uint32) + intersection['FID_dst'] = np.array(dst_grid.loc[res, 'FID_dst'], dtype=np.uint32) + + intersection['geometry_src'] = src_grid.loc[inp, 'geometry'].values + intersection['geometry_dst'] = dst_grid.loc[res, 'geometry'].values + + if True: + # No Warnings Zone + warnings.filterwarnings('ignore') + intersection['intersect_area'] = intersection.apply( + lambda x: x['geometry_src'].intersection(x['geometry_dst']).buffer(0).area, axis=1) + intersection.drop(intersection[intersection['intersect_area'] <= 0].index, inplace=True) + + intersection["src_area"] = src_grid.loc[intersection['FID_src'], 'geometry'].area.values + + warnings.filterwarnings('default') + + intersection['weight'] = intersection['intersect_area'] / intersection["src_area"] + + # Format & Clean + intersection.drop(columns=["geometry_src", "geometry_dst", "src_area", "intersect_area"], inplace=True) + + if info and self.master: + print("\t\tWeights calculated. Formatting weight matrix.") + sys.stdout.flush() + + # Initialising weight matrix + if self.parallel_method != 'T': + intersection = self.comm.gather(intersection, root=0) + if self.master: + if self.parallel_method != 'T': + intersection = pd.concat(intersection) + intersection = intersection.set_index(['FID_dst', intersection.groupby('FID_dst').cumcount()]).rename_axis( + ('FID', 'level')).sort_index() + intersection.rename(columns={"FID_src": "idx"}, inplace=True) + weight_matrix = dst_nes.copy() + weight_matrix.time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)] + weight_matrix._time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)] + weight_matrix._time_bnds = None + weight_matrix.time_bnds = None + weight_matrix.last_level = None + weight_matrix.first_level = 0 + weight_matrix.hours_start = 0 + weight_matrix.hours_end = 0 + + weight_matrix.set_communicator(MPI.COMM_SELF) + + weight_matrix.set_levels({'data': np.arange(intersection.index.get_level_values('level').max() + 1), + 'dimensions': ('lev',), + 'units': '', + 'positive': 'up'}) + + # Creating Weight matrix empty variables + if len(weight_matrix._lat['data'].shape) == 1: + shape = (1, len(weight_matrix.lev['data']), + weight_matrix._lat['data'].shape[0], weight_matrix._lon['data'].shape[0],) + shape_flat = (1, len(weight_matrix.lev['data']), + weight_matrix._lat['data'].shape[0] * weight_matrix._lon['data'].shape[0],) + else: + shape = (1, len(weight_matrix.lev['data']), + weight_matrix._lat['data'].shape[0], weight_matrix._lat['data'].shape[1],) + shape_flat = (1, len(weight_matrix.lev['data']), + weight_matrix._lat['data'].shape[0] * weight_matrix._lat['data'].shape[1],) + + weight_matrix.variables['weight'] = {'data': np.empty(shape_flat), 'units': '-'} + weight_matrix.variables['weight']['data'][:] = -999 + weight_matrix.variables['idx'] = {'data': np.empty(shape_flat), 'units': '-'} + weight_matrix.variables['idx']['data'][:] = -999 + + # Filling Weight matrix variables + for aux_lev in weight_matrix.lev['data']: + aux_data = intersection.xs(level='level', key=aux_lev) + weight_matrix.variables['weight']['data'][0, aux_lev, aux_data.index] = aux_data.loc[:, 'weight'].values + weight_matrix.variables['idx']['data'][0, aux_lev, aux_data.index] = aux_data.loc[:, 'idx'].values + # Re-shaping + weight_matrix.variables['weight']['data'] = weight_matrix.variables['weight']['data'].reshape(shape) + weight_matrix.variables['idx']['data'] = weight_matrix.variables['idx']['data'].reshape(shape) + if wm_path is not None: + if info and self.master: + print("\t\tWeight matrix saved at {0}".format(wm_path)) + sys.stdout.flush() + weight_matrix.to_netcdf(wm_path) + else: + weight_matrix = True + return weight_matrix + + +def lon_lat_to_cartesian(lon, lat, radius=6378137.0): + """ + Calculate lon, lat coordinates of a point on a sphere. + + DEPRECATED!!!! + + Parameters + ---------- + lon : np.array + Longitude values. + lat : np.array + Latitude values. + radius : float + Radius of the sphere to get the distances. + """ + + lon_r = np.radians(lon) + lat_r = np.radians(lat) + + x = radius * np.cos(lat_r) * np.cos(lon_r) + y = radius * np.cos(lat_r) * np.sin(lon_r) + z = radius * np.sin(lat_r) + + return np.column_stack([x, y, z]) + + +def lon_lat_to_cartesian_ecef(lon, lat): + """ + # convert observational/model geographic longitude/latitude coordinates to cartesian ECEF (Earth Centred, + # Earth Fixed) coordinates, assuming WGS84 datum and ellipsoid, and that all heights = 0. + # ECEF coordiantes represent positions (in meters) as X, Y, Z coordinates, approximating the earth surface + # as an ellipsoid of revolution. + # This conversion is for the subsequent calculation of euclidean distances of the model gridcell centres + # from each observational station. + # Defining the distance between two points on the earth's surface as simply the euclidean distance + # between the two lat/lon pairs could lead to inaccurate results depending on the distance + # between two points (i.e. 1 deg. of longitude varies with latitude). + + Parameters + ---------- + lon : np.array + Longitude values. + lat : np.array + Latitude values. + """ + lla = pyproj.Proj(proj='latlong', ellps='WGS84', datum='WGS84') + ecef = pyproj.Proj(proj='geocent', ellps='WGS84', datum='WGS84') + + x, y, z = pyproj.transform(lla, ecef, lon, lat, np.zeros(lon.shape), radians=False) + + return np.column_stack([x, y, z]) diff --git a/nes/interpolation/vertical_interpolation.py b/nes/methods/vertical_interpolation.py similarity index 89% rename from nes/interpolation/vertical_interpolation.py rename to nes/methods/vertical_interpolation.py index 461dbcba90441444aa5ae779c4b35947006caeb1..620689944d335e1fd1ab3af714c0d22d96ca0cd5 100644 --- a/nes/interpolation/vertical_interpolation.py +++ b/nes/methods/vertical_interpolation.py @@ -28,23 +28,24 @@ def add_4d_vertical_info(self, info_to_add): def interpolate_vertical(self, new_levels, new_src_vertical=None, kind='linear', extrapolate=None, info=None): """ - Vertical interpolation method. + Vertical interpolation. Parameters ---------- self : Nes Source Nes object. - new_levels : list + new_levels : List List of new vertical levels. new_src_vertical kind : str - Vertical interpolation type. + Vertical methods type. extrapolate : None, tuple, str Extrapolate method (for non linear operations). info: None, bool Indicates if you want to print extra information. """ - + if len(self.lev) == 1: + raise RuntimeError("1D data cannot be vertically interpolated.") if info is None: info = self.info @@ -99,14 +100,14 @@ def interpolate_vertical(self, new_levels, new_src_vertical=None, kind='linear', # Loop over variables for var_name in self.variables.keys(): if self.variables[var_name]['data'] is None: - # Loading data if it is not loaded yet + # Load data if it is not loaded yet self.load(var_name) if var_name != self.vertical_var_name: if flip: self.variables[var_name]['data'] = np.flip(self.variables[var_name]['data'], axis=1) if info and self.master: - print("\t{var} vertical interpolation".format(var=var_name)) + print("\t{var} vertical methods".format(var=var_name)) sys.stdout.flush() nt, nz, ny, nx = self.variables[var_name]['data'].shape dst_data = np.empty((nt, nz_new, ny, nx), dtype=self.variables[var_name]['data'].dtype) @@ -119,7 +120,7 @@ def interpolate_vertical(self, new_levels, new_src_vertical=None, kind='linear', for i in range(nx): curr_level_values = src_levels[t, :, j, i] try: - # check if all values are identical or masked + # Check if all values are identical or masked if ((isinstance(curr_level_values, np.ndarray) and (curr_level_values == curr_level_values[0]).all()) or (isinstance(curr_level_values, np.ma.core.MaskedArray) and @@ -133,7 +134,7 @@ def interpolate_vertical(self, new_levels, new_src_vertical=None, kind='linear', else: fill_value = extrapolate - # We force the interpolation with float64 to avoid negative values + # We force the methods with float64 to avoid negative values # We don't know why the negatives appears with float34 if current_level: # 1D vertical component @@ -171,14 +172,24 @@ def interpolate_vertical(self, new_levels, new_src_vertical=None, kind='linear', self.variables[var_name]['data'] = copy(dst_data) # print(self.variables[var_name]['data']) - # Updating level information + # Update level information new_lev_info = {'data': new_levels} + if 'positive' in self._lev.keys(): + # Vertical level direction + if flip: + self.reverse_level_direction() + new_lev_info['positive'] = self._lev['positive'] for var_attr, attr_info in self.variables[self.vertical_var_name].items(): if var_attr not in ['data', 'dimensions', 'crs', 'grid_mapping']: new_lev_info[var_attr] = copy(attr_info) - self.lev = new_lev_info - self._lev = new_lev_info + self.set_levels(new_lev_info) + self.free_vars(self.vertical_var_name) self.vertical_var_name = None + + # Remove original file information + self.__ini_path = None + self.dataset = None + self.netcdf = None return self diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 8f03f5041cd3145b8cbd1783bf104f31b82ba374..a5cdb8af2f83571d2f81aef7e09b4e1cb9f2d313 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -2,22 +2,22 @@ import sys import gc -import os import warnings import numpy as np import pandas as pd -import datetime from xarray import open_dataset from netCDF4 import Dataset, num2date, date2num from mpi4py import MPI from cfunits import Units from numpy.ma.core import MaskError import geopandas as gpd -from shapely.geometry import Polygon +from shapely.geometry import Polygon, Point from copy import deepcopy, copy import datetime -from ..interpolation import vertical_interpolation -from ..interpolation import horizontal_interpolation +import pyproj +from ..methods import vertical_interpolation +from ..methods import horizontal_interpolation +from ..methods import cell_measures from ..nes_formats import to_netcdf_cams_ra @@ -33,7 +33,7 @@ class Nes(object): True when rank == 0. size : int Size of the communicator. - print_info : bool + info : bool Indicates if you want to print reading/writing info. is_xarray : bool (Not working) Indicates if you want to use xarray as default. @@ -52,7 +52,7 @@ class Nes(object): The variables are stored in a dictionary with the var_name as key and another dictionary with the information. The information dictionary contains the 'data' key with None (if the variable is not loaded) or the array values and the other keys are the variable attributes or description. - _time : list + _time : List Complete list of original time step values. _lev : dict Vertical level dictionary with the complete 'data' key for all the values and the rest of the attributes. @@ -69,7 +69,7 @@ class Nes(object): write_axis_limits : dict Dictionary with the 4D limits of the rank data to write. t_min, t_max, z_min, z_max, y_min, y_max, x_min and x_max. - time : list + time : List List of time steps of the rank data. lev : dict Vertical levels dictionary with the portion of 'data' corresponding to the rank values. @@ -120,7 +120,7 @@ class Nes(object): Index of the last level to use. None if it is the last. create_nes : bool Indicates if you want to create the object from scratch (True) or through an existing file. - times : list, None + times : List, None List of times to substitute the current ones while creation. kwargs : Projection dependent parameters to create it from scratch @@ -155,6 +155,9 @@ class Nes(object): # Define parallel method self.parallel_method = parallel_method + # Get minor and major axes of Earth + self.earth_radius = self.get_earth_radius('WGS84') + # NetCDF object if create_nes: @@ -182,6 +185,9 @@ class Nes(object): self.lev = deepcopy(self._lev) self.lat_bnds, self.lon_bnds = self._lat_bnds, self._lon_bnds + # Cell measures screening + self.cell_measures = self.__get_cell_measures(create_nes) + # Set NetCDF attributes self.global_attrs = self.__get_global_attributes(create_nes) @@ -213,6 +219,9 @@ class Nes(object): self._lon = self._get_coordinate_dimension(['lon', 'longitude']) self._lat_bnds, self._lon_bnds = self.__get_coordinates_bnds() + # Complete cell measures + self._cell_measures = self.__get_cell_measures() + # Set axis limits for parallel reading self.read_axis_limits = self.get_read_axis_limits() @@ -225,6 +234,9 @@ 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) + # Cell measures screening + self.cell_measures = self._get_cell_measures_values(self._cell_measures) + # Set axis limits for parallel writing self.write_axis_limits = self.get_write_axis_limits() @@ -285,7 +297,7 @@ class Nes(object): def __del__(self): """ - To delete the Nes object and close all the opened Datasets. + To delete the Nes object and close all the open datasets. """ self.close() @@ -306,6 +318,11 @@ class Nes(object): del self.lat_bnds del self._lon_bnds del self.lon_bnds + del self.shapefile + for cell_measure in self.cell_measures.keys(): + if self.cell_measures[cell_measure]['data'] is not None: + del self.cell_measures[cell_measure]['data'] + del self.cell_measures except AttributeError: pass @@ -374,6 +391,24 @@ class Nes(object): def get_full_levels(self): return self._lev + def set_level_direction(self, new_direction): + if new_direction not in ['up', 'down']: + raise ValueError("Level direction mus be up or down. '{0}' is not a valid option".format(new_direction)) + self._lev['positive'] = new_direction + self.lev['positive'] = new_direction + + return True + + def reverse_level_direction(self): + if 'positive' in self._lev.keys(): + if self._lev['positive'] == 'up': + self._lev['positive'] = 'down' + self.lev['positive'] = 'down' + else: + self._lev['positive'] = 'up' + self.lev['positive'] = 'up' + return True + def clear_communicator(self): """ Erase the communicator and the parallelization indexes. @@ -403,6 +438,7 @@ class Nes(object): self.read_axis_limits = self.get_read_axis_limits() self.write_axis_limits = self.get_write_axis_limits() + return None def set_levels(self, levels): @@ -426,7 +462,7 @@ class Nes(object): Parameters ---------- - time_bnds : list + time_bnds : List List with the new time bounds information to be set. """ @@ -463,8 +499,7 @@ class Nes(object): inc : float Increment between centre values. spatial_nv : int - Non mandatory parameter that informs the number of vertices that must have the - boundaries. Default: 2. + Non mandatory parameter that informs the number of vertices that the boundaries must have. Default: 2. inverse : bool For some grid latitudes. @@ -503,26 +538,74 @@ class Nes(object): inc_lat = np.abs(np.mean(np.diff(self._lat['data']))) lat_bnds = self.create_single_spatial_bounds(self._lat['data'], inc_lat, spatial_nv=2) - self._lat_bnds = deepcopy(lat_bnds) - self.lat_bnds = lat_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - :] - + self._lat_bnds = {} + self._lat_bnds['data'] = deepcopy(lat_bnds) + self.lat_bnds = {} + self.lat_bnds['data'] = lat_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + :] + inc_lon = np.abs(np.mean(np.diff(self._lon['data']))) lon_bnds = self.create_single_spatial_bounds(self._lon['data'], inc_lon, spatial_nv=2) - self._lon_bnds = deepcopy(lon_bnds) - self.lon_bnds = lon_bnds[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - :] + self._lon_bnds = {} + self._lon_bnds['data'] = deepcopy(lon_bnds) + self.lon_bnds = {} + self.lon_bnds['data'] = lon_bnds[self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], + :] return None + def get_spatial_bounds_mesh_format(self): + """ + Get the spatial bounds in the pcolormesh format: + + see: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pcolormesh.html + + Returns + ------- + lon_bnds_mesh : numpy.array + Longitude boundaries in the mesh format + lat_bnds_mesh : numpy.array + Latitude boundaries in the mesh format + """ + if self.size > 1: + raise RuntimeError("NES.get_spatial_bounds_mesh_format() function only works in serial mode.") + if self.lat_bnds is None: + self.create_spatial_bounds() + + if self.lat_bnds['data'].shape[-1] == 2: + # get the lat_b and lon_b first rows + lat_b_0 = np.append(self.lat_bnds['data'][:, 0], self.lat_bnds['data'][-1, -1]) + lon_b_0 = np.append(self.lon_bnds['data'][:, 0], self.lon_bnds['data'][-1, -1]) + # expand lat_band lon_b in 2D + lat_bnds_mesh = np.tile(lat_b_0, (len(self.lon['data']) + 1, 1)).transpose() + lon_bnds_mesh = np.tile(lon_b_0, (len(self.lat['data']) + 1, 1)) + + elif self.lat_bnds['data'].shape[-1] == 4: + # Irregular quadrilateral polygon cell definition + lat_bnds_mesh = np.empty((self.lat['data'].shape[0] + 1, self.lat['data'].shape[1] + 1)) + lat_bnds_mesh[:-1, :-1] = self.lat_bnds['data'][:, :, 0] + lat_bnds_mesh[:-1, 1:] = self.lat_bnds['data'][:, :, 1] + lat_bnds_mesh[1:, 1:] = self.lat_bnds['data'][:, :, 2] + lat_bnds_mesh[1:, :-1] = self.lat_bnds['data'][:, :, 3] + + lon_bnds_mesh = np.empty((self.lat['data'].shape[0] + 1, self.lat['data'].shape[1] + 1)) + lon_bnds_mesh[:-1, :-1] = self.lon_bnds['data'][:, :, 0] + lon_bnds_mesh[:-1, 1:] = self.lon_bnds['data'][:, :, 1] + lon_bnds_mesh[1:, 1:] = self.lon_bnds['data'][:, :, 2] + lon_bnds_mesh[1:, :-1] = self.lon_bnds['data'][:, :, 3] + else: + raise RuntimeError("Invalid number of vertices: {0}".format(self.lat_bnds['data'].shape[-1] )) + + return lon_bnds_mesh, lat_bnds_mesh + def free_vars(self, var_list): """ Erase the selected variables from the variables information. Parameters ---------- - var_list : list, str + var_list : List, str, list List (or single string) of the variables to be loaded. """ @@ -540,6 +623,7 @@ class Nes(object): del self.variables[var_name]['data'] del self.variables[var_name] gc.collect() + return None def keep_vars(self, var_list): @@ -548,7 +632,7 @@ class Nes(object): Parameters ---------- - var_list : list, str + var_list : List, str List (or single string) of the variables to be loaded. """ @@ -618,10 +702,12 @@ class Nes(object): aux_nessy.variables[var_name][att_name] = att_value else: aux_nessy.variables[var_name]['data'] = aux_nessy.variables[var_name]['data'][[idx_time]] + return aux_nessy def sel(self, hours_start=None, time_min=None, hours_end=None, time_max=None, lev_min=None, lev_max=None, lat_min=None, lat_max=None, lon_min=None, lon_max=None): + loaded_vars = False for var_info in self.variables.values(): if var_info['data'] is not None: @@ -664,6 +750,7 @@ class Nes(object): # New Axis limits self.read_axis_limits = self.get_read_axis_limits() + # Dimensions screening self.time = self._time[self.read_axis_limits['t_min']:self.read_axis_limits['t_max']] self.time_bnds = self._time_bnds @@ -674,17 +761,18 @@ 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) + self.filter_coordinates_selection() + # Removing complete coordinates self.write_axis_limits = self.get_write_axis_limits() - self.filter_coordinates_selection() - return None def filter_coordinates_selection(self): idx = self.get_idx_intervals() self._time = self._time[idx['idx_t_min']:idx['idx_t_max']] + self._lev['data'] = self._lev['data'][idx['idx_z_min']:idx['idx_z_max']] if len(self._lat['data'].shape) == 1: # Regular projection @@ -705,11 +793,30 @@ class Nes(object): if self._lon_bnds is not None: self._lon_bnds = self._lon_bnds[idx['idx_y_min']:idx['idx_y_max'], idx['idx_x_min']:idx['idx_x_max'], :] + self.hours_start = 0 + self.hours_end = 0 + self.last_level = None + self.first_level = None + self.lat_min = None + self.lat_max = None + self.lon_max = None + self.lon_min = None + return None def get_idx_intervals(self): + """ + Calculate the index intervals + + Returns + ------- + dict + Dictionary with the index intervals + """ idx = {'idx_t_min': self.get_time_id(self.hours_start, first=True), - 'idx_t_max': self.get_time_id(self.hours_end, first=False)} + 'idx_t_max': self.get_time_id(self.hours_end, first=False), + 'idx_z_min': self.first_level, + 'idx_z_max': self.last_level} # Axis Y if self.lat_min is None: @@ -717,7 +824,7 @@ class Nes(object): else: idx['idx_y_min'] = self.get_coordinate_id(self._lat['data'], self.lat_min, axis=0) if self.lat_max is None: - idx['idx_y_max'] = self._lat['data'].shape[0] + 1 + idx['idx_y_max'] = self._lat['data'].shape[0] else: idx['idx_y_max'] = self.get_coordinate_id(self._lat['data'], self.lat_max, axis=0) + 1 @@ -737,7 +844,7 @@ class Nes(object): axis = 1 idx['idx_x_min'] = self.get_coordinate_id(self._lon['data'], self.lon_min, axis=axis) if self.lon_max is None: - idx['idx_x_max'] = self._lon['data'].shape[-1] + 1 + idx['idx_x_max'] = self._lon['data'].shape[-1] else: if len(self._lon['data'].shape) == 1: axis = 0 @@ -947,7 +1054,6 @@ class Nes(object): Dictionary with the 4D limits of the rank data to read. t_min, t_max, z_min, z_max, y_min, y_max, x_min and x_max. """ - if self.balanced: return self.get_read_axis_limits_balanced() else: @@ -970,7 +1076,6 @@ class Nes(object): 't_min': None, 't_max': None} idx = self.get_idx_intervals() - if self.parallel_method == 'Y': y_len = idx['idx_y_max'] - idx['idx_y_min'] if y_len < self.size: @@ -978,7 +1083,7 @@ class Nes(object): self.size, y_len)) axis_limits['y_min'] = ((y_len // self.size) * self.rank) + idx['idx_y_min'] if self.rank + 1 < self.size: - axis_limits['y_max'] = ((y_len // self.size) * (self.rank + 1)) + idx['idx_y_max'] + axis_limits['y_max'] = ((y_len // self.size) * (self.rank + 1)) + idx['idx_y_min'] else: axis_limits['y_max'] = idx['idx_y_max'] @@ -996,7 +1101,7 @@ class Nes(object): self.size, x_len)) axis_limits['x_min'] = ((x_len // self.size) * self.rank) + idx['idx_x_min'] if self.rank + 1 < self.size: - axis_limits['x_max'] = ((x_len // self.size) * (self.rank + 1)) + idx['idx_x_max'] + axis_limits['x_max'] = ((x_len // self.size) * (self.rank + 1)) + idx['idx_x_min'] else: axis_limits['x_max'] = idx['idx_x_max'] @@ -1008,13 +1113,13 @@ class Nes(object): axis_limits['t_max'] = idx['idx_t_max'] elif self.parallel_method == 'T': - t_len = idx['idx_t_min'] - idx['idx_t_max'] + t_len = idx['idx_t_max'] - idx['idx_t_min'] if t_len < self.size: raise IndexError('More processors (size={0}) selected than T elements (size={1})'.format( self.size, t_len)) axis_limits['t_min'] = ((t_len // self.size) * self.rank) + idx['idx_t_min'] if self.rank + 1 < self.size: - axis_limits['t_max'] = ((t_len // self.size) * (self.rank + 1)) + idx['idx_t_max'] + axis_limits['t_max'] = ((t_len // self.size) * (self.rank + 1)) + idx['idx_t_min'] # Non parallel filters axis_limits['y_min'] = idx['idx_y_min'] @@ -1060,6 +1165,7 @@ class Nes(object): min_axis = 'y_min' max_axis = 'y_max' to_add = idx['idx_y_min'] + elif self.parallel_method == 'X': len_to_split = idx['idx_x_max'] - idx['idx_x_min'] if len_to_split < self.size: @@ -1165,17 +1271,20 @@ class Nes(object): return idx - def get_coordinate_id(self, array, value, axis=0): + @staticmethod + def get_coordinate_id(array, value, axis=0): """ Get the index of the corresponding coordinate value. Parameters ---------- + array : np.array + Array with the coordinate data value : float Coordinate value to search. - min : bool - Indicates if you want to apply the filter as minimum (True) or maximum (False) values - Default: True. + axis : int + Axis where find the value + Default: 0. Returns ------- @@ -1207,15 +1316,16 @@ class Nes(object): Returns ------- dataset : xr.Dataset - Opened dataset. + Open dataset. """ - + if self.master: warnings.filterwarnings('ignore') # Disabling warnings while reading MONARCH original file dataset = open_dataset(self.__ini_path, decode_coords='all') warnings.filterwarnings('default') # Re-activating warnings else: dataset = None + dataset = self.comm.bcast(dataset, root=0) self.dataset = dataset @@ -1233,7 +1343,7 @@ class Nes(object): Returns ------- netcdf : Dataset - Opened dataset. + Open dataset. """ if self.size == 1: @@ -1250,7 +1360,7 @@ class Nes(object): Close the NetCDF with netcdf4-python. """ - if self.netcdf is not None: + if (hasattr(self, 'netcdf')) and (self.netcdf is not None): self.netcdf.close() self.netcdf = None @@ -1265,7 +1375,7 @@ class Nes(object): Parameter --------- - time: list + time: List Original time. units: str CF compliant time units. @@ -1274,7 +1384,7 @@ class Nes(object): Returns ------- - time: list + time: List CF compliant time. """ @@ -1303,7 +1413,7 @@ class Nes(object): Parameters ---------- - time: str + time: Namespace Original time. Returns @@ -1312,7 +1422,7 @@ class Nes(object): CF compliant time. """ - units = time.units + units = self.__parse_time_unit(time.units) if not hasattr(time, 'calendar'): calendar = 'standard' else: @@ -1322,7 +1432,12 @@ class Nes(object): units = 'days since ' + time.units.split('since')[1].lstrip() time = self.__get_dates_from_months(time, units, calendar) - return time, units, calendar + time_data = time[:] + + if len(time_data) == 1 and np.isnan(time_data[0]): + time_data[0] = 0 + + return time_data, units, calendar @staticmethod def __parse_time_unit(t_units): @@ -1352,7 +1467,7 @@ class Nes(object): Returns ------- - time : list + time : List List of times (datetime.datetime) of the NetCDF data. """ @@ -1361,8 +1476,9 @@ class Nes(object): else: if self.master: nc_var = self.netcdf.variables['time'] - nc_var, units, calendar = self.__parse_time(nc_var) - time = num2date(nc_var[:], self.__parse_time_unit(units), calendar=calendar) + time_data, units, calendar = self.__parse_time(nc_var) + + time = num2date(time_data, units, calendar=calendar) time = [aux.replace(second=0, microsecond=0) for aux in time] else: time = None @@ -1382,7 +1498,7 @@ class Nes(object): Returns ------- - time_bnds : list + time_bnds : List List of time bounds (datetime) of the NetCDF data. """ @@ -1419,9 +1535,9 @@ class Nes(object): Returns ------- - lat_bnds : list + lat_bnds : List List of latitude bounds of the NetCDF data. - lon_bnds : list + lon_bnds : List List of longitude bounds of the NetCDF data. """ @@ -1432,11 +1548,13 @@ class Nes(object): if self.master: if not create_nes: if 'lat_bnds' in self.netcdf.variables.keys(): - lat_bnds = self.netcdf.variables['lat_bnds'][:] + lat_bnds = {} + lat_bnds['data'] = self.netcdf.variables['lat_bnds'][:] else: lat_bnds = None if 'lon_bnds' in self.netcdf.variables.keys(): - lon_bnds = self.netcdf.variables['lon_bnds'][:] + lon_bnds = {} + lon_bnds['data'] = self.netcdf.variables['lon_bnds'][:] else: lon_bnds = None else: @@ -1452,6 +1570,33 @@ class Nes(object): return lat_bnds, lon_bnds + def __get_cell_measures(self, create_nes=False): + """ + Get the NetCDF cell measures values. + + Parameters + ---------- + create_nes : bool + Indicates if you want to create the object from scratch (True) or through an existing file. + + Returns + ------- + cell_measures : dict + Dictionary of cell measures of the NetCDF data. + """ + + cell_measures = {} + if self.master: + if not create_nes: + if 'cell_area' in self.netcdf.variables.keys(): + cell_measures['cell_area'] = {} + cell_measures['cell_area']['data'] = self.netcdf.variables['cell_area'][:] + cell_measures = self.comm.bcast(cell_measures, root=0) + + self.free_vars(['cell_area']) + + return cell_measures + def _get_coordinate_dimension(self, possible_names): """ Read the coordinate dimension data. @@ -1460,7 +1605,7 @@ class Nes(object): Parameters ---------- - possible_names: list, str + possible_names: List, str, list List (or single string) of the possible names of the coordinate (e.g. ['lat', 'latitude']). Returns @@ -1485,8 +1630,7 @@ class Nes(object): self.free_vars(dimension_name) except KeyError: nc_var = {'data': np.array([0]), - 'units': '' - } + 'units': ''} return nc_var @@ -1500,11 +1644,14 @@ class Nes(object): Dictionary with the 'data' key with the coordinate variable values. and the attributes as other keys. coordinate_axis : str Name of the coordinate to extract. Accepted values: ['Z', 'Y', 'X']. + bounds : bool + Boolean variable to know if there are coordinate bounds. Returns ------- values : dict Dictionary with the portion of data corresponding to the rank. """ + if coordinate_info is None: return None @@ -1521,7 +1668,8 @@ class Nes(object): if coordinate_len == 1: values['data'] = values['data'][self.read_axis_limits['y_min']:self.read_axis_limits['y_max']] elif coordinate_len == 2: - values['data'] = values['data'][self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] + values['data'] = values['data'][self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] else: raise NotImplementedError("The coordinate has wrong dimensions: {dim}".format( dim=values['data'].shape)) @@ -1529,7 +1677,8 @@ class Nes(object): if coordinate_len == 1: 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['y_min']:self.read_axis_limits['y_max'], self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] + values['data'] = values['data'][self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] else: raise NotImplementedError("The coordinate has wrong dimensions: {dim}".format( dim=values['data'].shape)) @@ -1542,6 +1691,44 @@ class Nes(object): return values + def _get_cell_measures_values(self, cell_measures_info): + """ + Get the cell measures data of the current portion. + + Parameters + ---------- + cell_measures_info : dict, list + Dictionary with the 'data' key with the cell measures variable values. and the attributes as other keys. + + Returns + ------- + values : dict + Dictionary with the portion of data corresponding to the rank. + """ + + if cell_measures_info is None: + return None + + cell_measures_values = {} + + for cell_measures_var in cell_measures_info.keys(): + + values = deepcopy(cell_measures_info[cell_measures_var]) + coordinate_len = len(values['data'].shape) + + if coordinate_len == 1: + 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['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] + else: + raise NotImplementedError("The coordinate has wrong dimensions: {dim}".format( + dim=values['data'].shape)) + + cell_measures_values[cell_measures_var] = values + + return cell_measures_values + def _get_lazy_variables(self): """ Get all the variables information. @@ -1563,13 +1750,14 @@ class Nes(object): else: if self.master: variables = {} + # Initialise data for var_name, var_info in self.netcdf.variables.items(): variables[var_name] = {} variables[var_name]['data'] = None variables[var_name]['dimensions'] = var_info.dimensions + # Avoid some attributes for attrname in var_info.ncattrs(): - # Avoiding some attributes if attrname not in ['missing_value', '_FillValue']: value = getattr(var_info, attrname) if value in ['unitless', '-']: @@ -1627,7 +1815,7 @@ class Nes(object): var_name)) # Missing to nan try: - data[data.mask == True] = np.nan + data[data.shapefile == True] = np.nan except (AttributeError, MaskError, ValueError): pass @@ -1641,9 +1829,12 @@ class Nes(object): Parameters ---------- - var_list : list, str + var_list : List, str, None List (or single string) of the variables to be loaded. """ + + if (self.__ini_path is None) and (self.dataset is None) and (self.netcdf is None): + raise RuntimeError('Only data from existing files can be loaded.') if self.netcdf is None: self.__open_dataset() @@ -1661,6 +1852,9 @@ class Nes(object): print("Rank {0:03d}: Loading {1} var ({2}/{3})".format(self.rank, var_name, i + 1, len(var_list))) if self.variables[var_name]['data'] is None: self.variables[var_name]['data'] = self._read_variable(var_name) + else: + if self.master: + print("Data for {0} was previously loaded. Skipping variable.".format(var_name)) if self.info: print("Rank {0:03d}: Loaded {1} var ({2})".format( self.rank, var_name, self.variables[var_name]['data'].shape)) @@ -1866,7 +2060,7 @@ class Nes(object): Parameters ---------- netcdf : Dataset - netcdf4-python opened Dataset. + netcdf4-python open dataset. """ # Create time dimension @@ -1876,10 +2070,6 @@ class Nes(object): if self._time_bnds is not None: netcdf.createDimension('time_nv', 2) - # Create spatial_nv (number of vertices) dimension - if (self._lat_bnds is not None) and (self._lon_bnds is not None): - netcdf.createDimension('spatial_nv', 2) - # Create lev, lon and lat dimensions netcdf.createDimension('lev', len(self.lev['data'])) netcdf.createDimension('lon', len(self._lon['data'])) @@ -1894,13 +2084,12 @@ class Nes(object): Parameters ---------- netcdf : Dataset - netcdf4-python opened Dataset. + netcdf4-python open dataset. """ # TIMES time_var = netcdf.createVariable('time', np.float64, ('time',), zlib=self.zip_lvl > 0, complevel=self.zip_lvl) - time_var.units = 'hours since {0}'.format( - self._time[self.get_time_id(self.hours_start, first=True)].strftime('%Y-%m-%d %H:%M:%S')) + time_var.units = 'hours since {0}'.format( self._time[0].strftime('%Y-%m-%d %H:%M:%S')) time_var.standard_name = 'time' time_var.calendar = 'standard' time_var.long_name = 'time' @@ -1908,9 +2097,7 @@ class Nes(object): time_var.bounds = 'time_bnds' if self.size > 1: time_var.set_collective(True) - time_var[:] = date2num(self._time[self.get_time_id(self.hours_start, first=True): - self.get_time_id(self.hours_end, first=False)], - time_var.units, time_var.calendar) + time_var[:] = date2num(self._time[:], time_var.units, time_var.calendar) # TIME BOUNDS if self._time_bnds is not None: @@ -1926,7 +2113,9 @@ class Nes(object): lev.units = Units(self._lev['units'], formatted=True).units else: lev.units = '' - lev.positive = 'up' + if 'positive' in self._lev.keys(): + lev.positive = self._lev['positive'] + if self.size > 1: lev.set_collective(True) lev[:] = self._lev['data'] @@ -1949,7 +2138,7 @@ class Nes(object): zlib=self.zip_lvl > 0, complevel=self.zip_lvl) if self.size > 1: lat_bnds_var.set_collective(True) - lat_bnds_var[:] = self._lat_bnds[:] + lat_bnds_var[:] = self._lat_bnds['data'] # LONGITUDES lon = netcdf.createVariable('lon', np.float64, self._lon_dim, zlib=self.zip_lvl > 0, complevel=self.zip_lvl) @@ -1969,10 +2158,29 @@ class Nes(object): zlib=self.zip_lvl > 0, complevel=self.zip_lvl) if self.size > 1: lon_bnds_var.set_collective(True) - lon_bnds_var[:] = self._lon_bnds[:] + lon_bnds_var[:] = self._lon_bnds['data'] return None + def _create_cell_measures(self, netcdf): + + # CELL AREA + if 'cell_area' in self.cell_measures.keys(): + cell_area = netcdf.createVariable('cell_area', np.float64, self._var_dim, + zlib=self.zip_lvl > 0, complevel=self.zip_lvl) + if self.size > 1: + cell_area.set_collective(True) + cell_area[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] = self.cell_measures[ + 'cell_area']['data'] + + cell_area.long_name = 'area of grid cell' + cell_area.standard_name = 'cell_area' + cell_area.units = 'm2' + + for var_name in self.variables.keys(): + self.variables[var_name]['cell_measures'] = 'area: cell_area' + def _create_variables(self, netcdf, chunking=False): """ Create the netCDF file variables. @@ -1980,7 +2188,7 @@ class Nes(object): Parameters ---------- netcdf : Dataset - netcdf4-python opened Dataset. + netcdf4-python open dataset. chunking : bool Indicates if you want to chunk the output netCDF. """ @@ -1988,7 +2196,8 @@ class Nes(object): for i, (var_name, var_dict) in enumerate(self.variables.items()): if var_dict['data'] is not None: if self.info: - print("Rank {0:03d}: Writing {1} var ({2}/{3})".format(self.rank, var_name, i + 1, len(self.variables))) + 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_dict['data'].dtype, ('time', 'lev',) + self._var_dim, @@ -2002,7 +2211,8 @@ class Nes(object): chunk_size = None chunk_size = self.comm.bcast(chunk_size, root=0) var = netcdf.createVariable(var_name, var_dict['data'].dtype, ('time', 'lev',) + self._var_dim, - zlib=self.zip_lvl > 0, complevel=self.zip_lvl, chunksizes=chunk_size) + 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))) @@ -2126,6 +2336,11 @@ class Nes(object): if self.info: print("Rank {0:03d}: Dimensions done".format(self.rank)) + # Create cell measures + self._create_cell_measures(netcdf) + if self.info: + print("Rank {0:03d}: Cell measures done".format(self.rank)) + # Create variables self._create_variables(netcdf, chunking=chunking) @@ -2327,27 +2542,32 @@ class Nes(object): def create_shapefile(self): """ Create spatial geodataframe (shapefile). + + Returns + ------- + shapefile : GeoPandasDataFrame + Shapefile dataframe. """ if self._lat_bnds is None or self._lon_bnds is None: self.create_spatial_bounds() # Reshape arrays to create geometry - aux_shape = (self.lat_bnds.shape[0], self.lon_bnds.shape[0], 4) + aux_shape = (self.lat_bnds['data'].shape[0], self.lon_bnds['data'].shape[0], 4) lon_bnds_aux = np.empty(aux_shape) - lon_bnds_aux[:, :, 0] = self.lon_bnds[np.newaxis, :, 0] - lon_bnds_aux[:, :, 1] = self.lon_bnds[np.newaxis, :, 1] - lon_bnds_aux[:, :, 2] = self.lon_bnds[np.newaxis, :, 1] - lon_bnds_aux[:, :, 3] = self.lon_bnds[np.newaxis, :, 0] + lon_bnds_aux[:, :, 0] = self.lon_bnds['data'][np.newaxis, :, 0] + lon_bnds_aux[:, :, 1] = self.lon_bnds['data'][np.newaxis, :, 1] + lon_bnds_aux[:, :, 2] = self.lon_bnds['data'][np.newaxis, :, 1] + lon_bnds_aux[:, :, 3] = self.lon_bnds['data'][np.newaxis, :, 0] lon_bnds = lon_bnds_aux del lon_bnds_aux lat_bnds_aux = np.empty(aux_shape) - lat_bnds_aux[:, :, 0] = self.lat_bnds[:, np.newaxis, 0] - lat_bnds_aux[:, :, 1] = self.lat_bnds[:, np.newaxis, 0] - lat_bnds_aux[:, :, 2] = self.lat_bnds[:, np.newaxis, 1] - lat_bnds_aux[:, :, 3] = self.lat_bnds[:, np.newaxis, 1] + lat_bnds_aux[:, :, 0] = self.lat_bnds['data'][:, np.newaxis, 0] + lat_bnds_aux[:, :, 1] = self.lat_bnds['data'][:, np.newaxis, 0] + lat_bnds_aux[:, :, 2] = self.lat_bnds['data'][:, np.newaxis, 1] + lat_bnds_aux[:, :, 3] = self.lat_bnds['data'][:, np.newaxis, 1] lat_bnds = lat_bnds_aux del lat_bnds_aux @@ -2375,7 +2595,8 @@ class Nes(object): return gdf def write_shapefile(self, path): - """Save spatial geodataframe (shapefile). + """ + Save spatial geodataframe (shapefile). Parameters ---------- @@ -2398,126 +2619,240 @@ class Nes(object): return None - def spatial_join(self, mask, method=None): + def to_shapefile(self, path, time=None, lev=None, var_list=None): + """ + Create shapefile from NES data. + + 1. Create grid shapefile. + 2. Add variables to shapefile (as independent function). + 3. Write shapefile. + + Parameters + ---------- + path : str + Path to the output file. + time : datetime.datetime + Time stamp to select. + lev : int + Vertical level to select. + var_list : List, str, None + List (or single string) of the variables to be loaded and saved in the shapefile. + """ + + # If list is not defined, get all variables + if var_list is None: + var_list = list(self.variables.keys()) + else: + if isinstance(var_list, str): + var_list = [var_list] + + # Add warning for unloaded variables + unloaded_vars = [] + for var_name in var_list: + if self.variables[var_name]['data'] is None: + unloaded_vars.append(var_name) + if len(unloaded_vars) > 0: + raise ValueError('The variables {0} need to be loaded/created before using to_shapefile.'.format( + unloaded_vars)) + + # Select first vertical level (if needed) + if lev is None: + msg = 'No vertical level has been specified. The first one will be selected.' + warnings.warn(msg) + idx_lev = 0 + else: + if lev not in self.lev['data']: + raise ValueError('Level {} is not available. Choose from {}'.format(lev, self.lev['data'])) + idx_lev = lev + + # Select first time (if needed) + if time is None: + msg = 'No time has been specified. The first one will be selected.' + warnings.warn(msg) + idx_time = 0 + else: + if time not in self.time: + raise ValueError('Time {} is not available. Choose from {}'.format(time, self.time)) + idx_time = self.time.index(time) + + # Create shapefile + self.create_shapefile() + + # Load variables from original file and get data for selected time / level + self.add_variables_to_shapefile(var_list, idx_lev, idx_time) + + # Write shapefile + self.write_shapefile(path) + + return None + + def add_variables_to_shapefile(self, var_list, idx_lev=0, idx_time=0): + """ + Add variables data to shapefile. + + var_list : List + List (or single string) of the variables to be loaded and saved in the shapefile. + idx_lev : int + Index of vertical level for which the data will be saved in the shapefile. + idx_time : int + Index of time for which the data will be saved in the shapefile. + """ + + for var_name in var_list: + self.shapefile[var_name] = self.variables[var_name]['data'][idx_time, idx_lev, :].ravel() + + return None + + def spatial_join(self, ext_shp, method=None, var_list=None, info=False): """ - Compute overlay intersection of two GeoPandasDataFrames + Compute overlay intersection of two GeoPandasDataFrames. Parameters ---------- - mask : GeoPandasDataFrame - File from where the data will be obtained on the intersection. + ext_shp : GeoPandasDataFrame, str + File or path from where the data will be obtained on the intersection. method : str - Overlay method. Accepted values: ['nearest', 'intersection', None]. + Overlay method. Accepted values: ['nearest', 'intersection', 'centroid']. + var_list : List, None + Variables that will be included in the resulting shapefile. """ + if isinstance(ext_shp, str): + ext_shp = gpd.read_file(ext_shp) - # Nearest centroids to the mask polygons + # Create shapefile if it does not exist + if self.shapefile is None: + msg = 'Shapefile does not exist. It will be created now.' + warnings.warn(msg) + grid_shp = self.create_shapefile() + else: + grid_shp = self.shapefile + grid_shp = copy(grid_shp) + + # Get variables of interest from shapefile + if var_list is not None: + if isinstance(var_list, str): + var_list = [var_list] + ext_shp = ext_shp.loc[:, var_list + ['geometry']] + else: + var_list = ext_shp.columns.to_list() + var_list.remove('geometry') + + ext_shp = ext_shp.to_crs(grid_shp.crs) + + # Nearest centroids to the shapefile polygons if method == 'nearest': + if self.master and info: + print("Nearest spatial joint") + # Get centroids + centroids_gdf = 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/userwarning-when-trying-to-get-centroid-from-a-polygon-geopandas + ext_shp = ext_shp.to_crs('EPSG:4328') + centroids_gdf = centroids_gdf.to_crs('EPSG:4328') - # Get centroids of shapefile to mask - shapefile_aux = deepcopy(self.shapefile) - shapefile_aux.geometry = self.shapefile.centroid - # Calculate spatial joint by distance - shapefile_aux = gpd.sjoin_nearest(shapefile_aux, mask.to_crs(self.shapefile.crs), distance_col='distance') - + aux_grid = gpd.sjoin_nearest(centroids_gdf, ext_shp, distance_col='distance') + # Get data from closest shapes to centroids - del shapefile_aux['geometry'], shapefile_aux['index_right'] - self.shapefile.loc[shapefile_aux.index, shapefile_aux.columns] = shapefile_aux - - # Intersect the areas of the mask polygons, outside of the mask there will be NaN + del aux_grid['geometry'], aux_grid['index_right'] + self.shapefile.loc[aux_grid.index, aux_grid.columns] = aux_grid + + # Intersect the areas of the shapefile polygons, outside the shapefile there will be NaN elif method == 'intersection': - + if self.master and info: + print("Intersection spatial joint") + grid_shp['FID_grid'] = grid_shp.index + grid_shp = grid_shp.reset_index() # Get intersected areas - inp, res = mask.sindex.query_bulk(self.shapefile.geometry, predicate='intersects') - print('Rank {0:03d}: {1} intersected areas found'.format(self.rank, len(inp))) - + # inp, res = shapefile.sindex.query_bulk(self.shapefile.geometry, predicate='intersects') + inp, res = grid_shp.sindex.query_bulk(ext_shp.geometry, predicate='intersects') + + if self.master and info: + print('Rank {0:03d}: {1} intersected areas found'.format(self.rank, len(inp))) + # Calculate intersected areas and fractions - intersection = pd.concat([self.shapefile.geometry[inp].reset_index(), mask.geometry[res].reset_index()], - axis=1, ignore_index=True) - intersection.columns = (list(self.shapefile.geometry[inp].reset_index().columns) + - list(mask.geometry[res].reset_index().rename(columns={'geometry': 'geometry_mask', - 'index': 'index_mask'}).columns)) - intersection['area'] = intersection.apply(lambda x: x['geometry'].intersection(x['geometry_mask']).buffer(0).area, - axis=1) - intersection['fraction'] = intersection.apply(lambda x: x['area'] / x['geometry'].area, axis=1) - - # Choose biggest area from intersected areas with multiple options - intersection.sort_values('fraction', ascending=False, inplace=True) + intersection = pd.DataFrame() + intersection['FID'] = np.array(grid_shp.loc[res, 'FID_grid'], dtype=np.uint32) + intersection['ext_shp_id'] = np.array(inp, dtype=np.uint32) + + # intersection['geometry_ext'] = ext_shp.loc[inp, 'geometry'].values + # intersection['geometry_grid'] = grid_shp.loc[res, 'geometry'].values + if True: + # No Warnings Zone + counts = intersection['FID'].value_counts() + warnings.filterwarnings('ignore') + + intersection.loc[:, 'weight'] = 1. + for i, row in intersection.iterrows(): + if i % 1000 == 0 and self.master and info: + print('\tRank {0:03d}: {1:.3f} %'.format(self.rank, i*100 / len(intersection))) + # Filter to do not calculate percentages over 100% grid cells spatial joint + if counts[row['FID']] > 1: + intersection.loc[i, 'weight'] = grid_shp.loc[res[i], 'geometry'].intersection( + ext_shp.loc[inp[i], 'geometry']).area / grid_shp.loc[res[i], 'geometry'].area + # intersection['intersect_area'] = intersection.apply( + # lambda x: x['geometry_grid'].intersection(x['geometry_ext']).area, axis=1) + intersection.drop(intersection[intersection['weight'] <= 0].index, inplace=True) + + warnings.filterwarnings('default') + + # Choose the biggest area from intersected areas with multiple options + intersection.sort_values('weight', ascending=False, inplace=True) intersection = intersection.drop_duplicates(subset='FID', keep="first") intersection = intersection.sort_values('FID').set_index('FID') - # Get data from mask - del mask['geometry'] - self.shapefile.loc[intersection.index, mask.columns] = np.array(mask.loc[intersection.index_mask, :]) + for var_name in var_list: + self.shapefile.loc[intersection.index, var_name] = np.array( + ext_shp.loc[intersection['ext_shp_id'], var_name]) - # Centroids that fall on the mask polygons, outside of the mask there will be NaN - elif method is None: + # Centroids that fall on the shapefile polygons, outside the shapefile there will be NaN + elif method == 'centroid': - # Get centroids of shapefile to mask - shapefile_aux = deepcopy(self.shapefile) - shapefile_aux.geometry = self.shapefile.centroid + # Get centroids + centroids_gdf = self.get_centroids_from_coordinates() # Calculate spatial joint - shapefile_aux = gpd.sjoin(shapefile_aux, mask.to_crs(self.shapefile.crs)) - + aux_grid = gpd.sjoin(centroids_gdf, ext_shp, predicate='within') + # Get data from shapes where there are centroids, rest will be NaN - del shapefile_aux['geometry'], shapefile_aux['index_right'] - self.shapefile.loc[shapefile_aux.index, shapefile_aux.columns] = shapefile_aux - + del aux_grid['geometry'], aux_grid['index_right'] + self.shapefile.loc[aux_grid.index, aux_grid.columns] = aux_grid + + else: + accepted_values = ['nearest', 'intersection', 'centroid'] + raise NotImplementedError('{0} is not implemented. Choose from: {1}'.format(method, accepted_values)) + return None - @staticmethod - def spatial_overlays(df1, df2, how='intersection'): - """ - Compute overlay intersection of two GeoPandasDataFrames df1 and df2 - - https://github.com/geopandas/geopandas/issues/400 - - :param df1: GeoDataFrame - :param df2: GeoDataFrame - :param how: Operation to do - :return: GeoDataFrame - """ - from functools import reduce - - df1 = df1.copy() - df2 = df2.copy() - df1['geometry'] = df1.geometry.buffer(0) - df2['geometry'] = df2.geometry.buffer(0) - if how == 'intersection': - # Spatial Index to create intersections - spatial_index = df2.sindex - df1['bbox'] = df1.geometry.apply(lambda x: x.bounds) - df1['histreg'] = df1.bbox.apply(lambda x: list(spatial_index.intersection(x))) - pairs = df1['histreg'].to_dict() - nei = [] - for i, j in pairs.items(): - for k in j: - nei.append([i, k]) - pairs = pd.DataFrame(nei, columns=['idx1', 'idx2']) - pairs = pairs.merge(df1, left_on='idx1', right_index=True) - pairs = pairs.merge(df2, left_on='idx2', right_index=True, suffixes=['_1', '_2']) - pairs['geometry'] = pairs.apply(lambda x: (x['geometry_1'].intersection(x['geometry_2'])).buffer(0), axis=1) - - pairs.drop(columns=['geometry_1', 'geometry_2', 'histreg', 'bbox'], inplace=True) - pairs = gpd.GeoDataFrame(pairs, columns=pairs.columns, crs=df1.crs) - pairs = pairs.loc[~pairs.geometry.is_empty] - - return_value = pairs - elif how == 'difference': - spatial_index = df2.sindex - df1['bbox'] = df1.geometry.apply(lambda x: x.bounds) - df1['histreg'] = df1.bbox.apply(lambda x: list(spatial_index.intersection(x))) - df1['new_g'] = df1.apply(lambda x: reduce(lambda x, y: x.difference(y).buffer(0), - [x.geometry] + list(df2.iloc[x.histreg].geometry)), axis=1) - df1.geometry = df1.new_g - df1 = df1.loc[~df1.geometry.is_empty].copy() - df1.drop(['bbox', 'histreg', 'new_g'], axis=1, inplace=True) - return_value = df1 - else: - raise NotImplementedError(how) + def get_centroids_from_coordinates(self): + """ + Get centroids from geographical coordinates. - return return_value + Returns + ------- + centroids_gdf: GeoPandasDataFrame + Centroids dataframe. + """ + + # Get centroids from coordinates + centroids = [] + for lat_ind in range(0, len(self.lat['data'])): + for lon_ind in range(0, len(self.lon['data'])): + centroids.append(Point(self.lon['data'][lon_ind], + self.lat['data'][lat_ind])) + + # Create dataframe cointaining all points + 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']] + centroids_gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), + geometry=centroids, + crs="EPSG:4326") + + return centroids_gdf def __gather_data_py_object(self): """ @@ -2578,7 +2913,8 @@ class Nes(object): 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)) + sys.stderr.write("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format( + var_name)) print(e) sys.stderr.write(str(e)) # print(e, file=sys.stderr) @@ -2663,7 +2999,8 @@ class Nes(object): data_list[var_name]['data'] = np.concatenate(recvbuf, 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)) + sys.stderr.write("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format( + var_name)) print(e) sys.stderr.write(str(e)) # print(e, file=sys.stderr) @@ -2676,7 +3013,33 @@ class Nes(object): # ================================================================================================================== # Extra Methods # ================================================================================================================== - + @staticmethod + def lon_lat_to_cartesian_ecef(lon, lat): + """ + # Convert observational/model geographic longitude/latitude coordinates to cartesian ECEF (Earth Centred, + # Earth Fixed) coordinates, assuming WGS84 datum and ellipsoid, and that all heights = 0. + # ECEF coordiantes represent positions (in meters) as X, Y, Z coordinates, approximating the earth surface + # as an ellipsoid of revolution. + # This conversion is for the subsequent calculation of euclidean distances of the model gridcell centres + # from each observational station. + # Defining the distance between two points on the earth's surface as simply the euclidean distance + # between the two lat/lon pairs could lead to inaccurate results depending on the distance + # between two points (i.e. 1 deg. of longitude varies with latitude). + + Parameters + ---------- + lon : np.array + Longitude values. + lat : np.array + Latitude values. + """ + + lla = pyproj.Proj(proj='latlong', ellps='WGS84', datum='WGS84') + ecef = pyproj.Proj(proj='geocent', ellps='WGS84', datum='WGS84') + x, y, z = pyproj.transform(lla, ecef, lon, lat, np.zeros(lon.shape), radians=False) + + return np.column_stack([x, y, z]) + def add_4d_vertical_info(self, info_to_add): """ To add the vertical information from other source. @@ -2692,17 +3055,17 @@ class Nes(object): def interpolate_vertical(self, new_levels, new_src_vertical=None, kind='linear', extrapolate=None, info=None): """ - Vertical interpolation method. + Vertical interpolation function. Parameters ---------- self : Nes Source Nes object. - new_levels : list + new_levels : List List of new vertical levels. new_src_vertical kind : str - Vertical interpolation type. + Vertical methods type. extrapolate : None, tuple, str Extrapolate method (for non linear operations). info: None, bool @@ -2713,9 +3076,9 @@ class Nes(object): self, new_levels, new_src_vertical=new_src_vertical, kind=kind, extrapolate=extrapolate, info=info) def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='NearestNeighbour', n_neighbours=4, - info=False, to_providentia=False): + info=False, to_providentia=False, only_create_wm=False, wm=None): """ - Horizontal interpolation from the current grid to another one. + Horizontal methods from the current grid to another one. Parameters ---------- @@ -2724,15 +3087,68 @@ class Nes(object): weight_matrix_path : str, None Path to the weight matrix to read/create. kind : str - Kind of horizontal interpolation. choices = ['NearestNeighbour']. + Kind of horizontal methods. choices = ['NearestNeighbour', 'Conservative']. n_neighbours: int Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. info: bool - Indicates if you want to print extra info during the interpolation process. + Indicates if you want to print extra info during the methods process. to_providentia : bool Indicates if we want the interpolated grid in Providentia format. + only_create_wm : bool + Indicates if you want to only create the Weight Matrix. + wm : Nes + Weight matrix Nes File """ return horizontal_interpolation.interpolate_horizontal( self, dst_grid, weight_matrix_path=weight_matrix_path, kind=kind, n_neighbours=n_neighbours, info=info, - to_providentia=to_providentia) + to_providentia=to_providentia, only_create_wm=only_create_wm, wm=wm) + + def calculate_grid_area(self): + """ + Get coordinate bounds and call function to calculate the area of each cell of a grid. + + Parameters + ---------- + self : nes.Nes + Source projection Nes Object. + """ + + grid_area = cell_measures.calculate_grid_area(self) + + return grid_area + + @staticmethod + def calculate_geometry_area(geometry_list, earth_radius_minor_axis=6356752.3142, + earth_radius_major_axis=6378137.0): + """ + Get coordinate bounds and call function to calculate the area of each cell of a set of geometries. + + Parameters + ---------- + geometry_list : List + List with polygon geometries. + earth_radius_minor_axis : float + Radius of the minor axis of the Earth. + earth_radius_major_axis : float + Radius of the major axis of the Earth. + """ + + return cell_measures.calculate_geometry_area(geometry_list, earth_radius_minor_axis=earth_radius_minor_axis, + earth_radius_major_axis=earth_radius_major_axis) + + @staticmethod + def get_earth_radius(ellps): + """ + Get minor and major axis of Earth. + + Parameters + ---------- + ellps : str + Spatial reference system. + """ + + # WGS84 with radius defined in Cartopy source code + earth_radius_dict = {'WGS84': [6356752.3142, 6378137.0]} + + return earth_radius_dict[ellps] diff --git a/nes/nc_projections/latlon_nes.py b/nes/nc_projections/latlon_nes.py index b1e996f4780f4a4a31d3c4b3c8174d016675b71e..cfcb11dae3619e336abbc0978e18ede3be9bfd2f 100644 --- a/nes/nc_projections/latlon_nes.py +++ b/nes/nc_projections/latlon_nes.py @@ -99,6 +99,24 @@ class LatLonNes(Nes): return new + def _create_dimensions(self, netcdf): + """ + Create 'spatial_nv' dimensions and the super dimensions 'lev', 'time', 'time_nv', 'lon' and 'lat'. + + Parameters + ---------- + netcdf : Dataset + NetCDF object. + """ + + super(LatLonNes, self)._create_dimensions(netcdf) + + # Create spatial_nv (number of vertices) dimension + if (self._lat_bnds is not None) and (self._lon_bnds is not None): + netcdf.createDimension('spatial_nv', 2) + + return None + def _create_centre_coordinates(self, **kwargs): """ Calculate centre latitudes and longitudes from grid details. @@ -111,19 +129,32 @@ class LatLonNes(Nes): Dictionary with data of centre longitudes in 1D """ + inc_lat = kwargs['inc_lat'] + inc_lon = kwargs['inc_lon'] + + # Global domain + if len(kwargs) == 2: + lat_orig = -90 + lon_orig = -180 + n_lat = int(180 // inc_lat) + n_lon = int(360 // inc_lon) + + # Other domains + else: + lat_orig = kwargs['lat_orig'] + lon_orig = kwargs['lon_orig'] + n_lat = kwargs['n_lat'] + n_lon = kwargs['n_lon'] + # Calculate centre latitudes - lat_c_orig = kwargs['lat_orig'] + (kwargs['inc_lat'] / 2) - centre_lat_data = np.linspace( - lat_c_orig, lat_c_orig + (kwargs['inc_lat'] * (kwargs['n_lat'] - 1)), kwargs['n_lat']) - centre_lat = {'data': centre_lat_data} + lat_c_orig = lat_orig + (inc_lat / 2) + centre_lat = np.linspace(lat_c_orig, lat_c_orig + (inc_lat * (n_lat - 1)), n_lat) # Calculate centre longitudes - lon_c_orig = kwargs['lon_orig'] + (kwargs['inc_lon'] / 2) - centre_lon_data = np.linspace( - lon_c_orig, lon_c_orig + (kwargs['inc_lon'] * (kwargs['n_lon'] - 1)), kwargs['n_lon']) - centre_lon = {'data': centre_lon_data} + lon_c_orig = lon_orig + (inc_lon / 2) + centre_lon = np.linspace(lon_c_orig, lon_c_orig + (inc_lon * (n_lon - 1)), n_lon) - return centre_lat, centre_lon + return {'data': centre_lat}, {'data': centre_lon} def create_providentia_exp_centre_coordinates(self): """ @@ -228,6 +259,8 @@ class LatLonNes(Nes): Parameters ---------- + lat_flip : bool + Indicates if the latitudes have to be flipped path : str Path to the output file. grib_keys : dict @@ -239,3 +272,14 @@ class LatLonNes(Nes): """ return super(LatLonNes, self).to_grib2(path, grib_keys, grib_template_path, lat_flip=lat_flip, info=info) + + def calculate_grid_area(self): + """ + Get coordinate bounds and call function to calculate the area of each cell of a grid. + + """ + grid_area = super().calculate_grid_area() + self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0], + self.lon_bnds['data'].shape[1]])} + + return None diff --git a/nes/nc_projections/lcc_nes.py b/nes/nc_projections/lcc_nes.py index 35a825a59303a764bdaf2fc06c0fdf87b5b396a3..585acbe11a338e65a382ddb1a3d2c544d4325725 100644 --- a/nes/nc_projections/lcc_nes.py +++ b/nes/nc_projections/lcc_nes.py @@ -1,12 +1,13 @@ #!/usr/bin/env python +import warnings import numpy as np import pandas as pd from cfunits import Units from pyproj import Proj from copy import deepcopy import geopandas as gpd -from shapely.geometry import Polygon +from shapely.geometry import Polygon, Point from .default_nes import Nes @@ -152,22 +153,28 @@ class LCCNes(Nes): projection_data = {'data': None, 'dimensions': (), 'grid_mapping_name': 'lambert_conformal_conic', - 'standard_parallel': [kwargs['lat_1'], kwargs['lat_2']], - 'longitude_of_central_meridian': kwargs['lon_0'], - 'latitude_of_projection_origin': kwargs['lat_0'], + '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']), } else: - projection_data = self.variables['Lambert_conformal'] - projection_data['standard_parallel'] = [projection_data['standard_parallel'].split(', ')[0], - projection_data['standard_parallel'].split(', ')[1]] - self.free_vars('Lambert_conformal') + 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) + projection_data = None return projection_data def _create_dimensions(self, netcdf): """ - Create 'y', 'x' and 'spatial_nv' dimensions and the super dimensions ('lev', 'time'). + Create 'y', 'x' and 'spatial_nv' dimensions and the super dimensions 'lev', 'time', 'time_nv', 'lon' and 'lat' Parameters ---------- @@ -239,11 +246,11 @@ class LCCNes(Nes): self._x = {'data': np.linspace(kwargs['x_0'] + (kwargs['inc_x'] / 2), kwargs['x_0'] + (kwargs['inc_x'] / 2) + (kwargs['inc_x'] * (kwargs['nx'] - 1)), kwargs['nx'], - dtype=np.float)} + dtype=np.float64)} self._y = {'data': np.linspace(kwargs['y_0'] + (kwargs['inc_y'] / 2), kwargs['y_0'] + (kwargs['inc_y'] / 2) + (kwargs['inc_y'] * (kwargs['ny'] - 1)), kwargs['ny'], - dtype=np.float)} + dtype=np.float64)} # Create a regular grid in metres (1D to 2D) x = np.array([self._x['data']] * len(self._y['data'])) @@ -252,7 +259,7 @@ class LCCNes(Nes): self.projection = Proj( proj='lcc', ellps='WGS84', - R=6356752.3142, # WGS84_SEMIMINOR_AXIS (as defined in Cartopy source code) + R=self.earth_radius[0], lat_1=kwargs['lat_1'], lat_2=kwargs['lat_2'], lon_0=kwargs['lon_0'], @@ -260,16 +267,14 @@ class LCCNes(Nes): to_meter=1, x_0=0, y_0=0, - a=6378137.0, # WGS84_SEMIMAJOR_AXIS (as defined in Cartopy source code) + a=self.earth_radius[1], k_0=1.0 ) # Calculate centre latitudes and longitudes (UTM to LCC) - centre_lon_data, centre_lat_data = self.projection(x, y, inverse=True) - centre_lat = {'data': centre_lat_data} - centre_lon = {'data': centre_lon_data} + centre_lon, centre_lat = self.projection(x, y, inverse=True) - return centre_lat, centre_lon + return {'data': centre_lat}, {'data': centre_lon} def create_providentia_exp_centre_coordinates(self): """ @@ -329,7 +334,7 @@ class LCCNes(Nes): self.projection = Proj( proj='lcc', ellps='WGS84', - R=6356752.3142, # WGS84_SEMIMINOR_AXIS (as defined in Cartopy source code) + 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']), @@ -337,7 +342,7 @@ class LCCNes(Nes): to_meter=1, x_0=0, y_0=0, - a=6378137.0, # WGS84_SEMIMAJOR_AXIS (as defined in Cartopy source code) + 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) @@ -356,18 +361,18 @@ class LCCNes(Nes): # Calculate LCC coordinates bounds inc_x = np.abs(np.mean(np.diff(self._x['data']))) - x_bnds = self.create_single_spatial_bounds(np.array([self._y['data']] * len(self._x['data'])).T, - inc_x, spatial_nv=4, inverse=True) + x_bnds = self.create_single_spatial_bounds(np.array([self._x['data']] * len(self._y['data'])), + inc_x, spatial_nv=4) inc_y = np.abs(np.mean(np.diff(self._y['data']))) - y_bnds = self.create_single_spatial_bounds(np.array([self._x['data']] * len(self._y['data'])), - inc_y, spatial_nv=4) + y_bnds = self.create_single_spatial_bounds(np.array([self._y['data']] * len(self._x['data'])).T, + inc_y, spatial_nv=4, inverse=True) # Transform LCC bounds to regular bounds self.projection = Proj( proj='lcc', ellps='WGS84', - R=6356752.3142, # WGS84_SEMIMINOR_AXIS (as defined in Cartopy source code) + 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']), @@ -375,21 +380,25 @@ class LCCNes(Nes): to_meter=1, x_0=0, y_0=0, - a=6378137.0, # WGS84_SEMIMAJOR_AXIS (as defined in Cartopy source code) + 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 - self._lat_bnds = deepcopy(lat_bnds) - self.lat_bnds = lat_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - :] - - self._lon_bnds = deepcopy(lon_bnds) - self.lon_bnds = lon_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - :] + self._lat_bnds = {} + self._lat_bnds['data'] = deepcopy(lat_bnds) + self.lat_bnds = {} + self.lat_bnds['data'] = lat_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], + :] + + self._lon_bnds = {} + self._lon_bnds['data'] = deepcopy(lon_bnds) + self.lon_bnds = {} + self.lon_bnds['data'] = lon_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], + :] return None @@ -419,11 +428,12 @@ class LCCNes(Nes): netcdf4-python Dataset """ - mapping = netcdf.createVariable('Lambert_conformal', 'c') - mapping.grid_mapping_name = self.projection_data['grid_mapping_name'] - mapping.standard_parallel = self.projection_data['standard_parallel'] - mapping.longitude_of_central_meridian = self.projection_data['longitude_of_central_meridian'] - mapping.latitude_of_projection_origin = self.projection_data['latitude_of_projection_origin'] + if self.projection_data is not None: + mapping = netcdf.createVariable('Lambert_conformal', 'c') + mapping.grid_mapping_name = self.projection_data['grid_mapping_name'] + mapping.standard_parallel = self.projection_data['standard_parallel'] + mapping.longitude_of_central_meridian = self.projection_data['longitude_of_central_meridian'] + mapping.latitude_of_projection_origin = self.projection_data['latitude_of_projection_origin'] return None @@ -448,6 +458,11 @@ class LCCNes(Nes): def create_shapefile(self): """ Create spatial geodataframe (shapefile). + + Returns + ------- + shapefile : GeoPandasDataFrame + Shapefile dataframe. """ # Get latitude and longitude cell boundaries @@ -455,10 +470,12 @@ class LCCNes(Nes): self.create_spatial_bounds() # Reshape arrays to create geometry - aux_b_lats = self.lat_bnds.reshape((self.lat_bnds.shape[0] * self.lat_bnds.shape[1], self.lat_bnds.shape[2])) - aux_b_lons = self.lon_bnds.reshape((self.lon_bnds.shape[0] * self.lon_bnds.shape[1], self.lon_bnds.shape[2])) + aux_b_lats = self.lat_bnds['data'].reshape((self.lat_bnds['data'].shape[0] * self.lat_bnds['data'].shape[1], + self.lat_bnds['data'].shape[2])) + aux_b_lons = self.lon_bnds['data'].reshape((self.lon_bnds['data'].shape[0] * self.lon_bnds['data'].shape[1], + self.lon_bnds['data'].shape[2])) - # Create dataframe cointaining all polygons + # Get polygons from bounds geometry = [] for i in range(aux_b_lons.shape[0]): geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), @@ -466,8 +483,10 @@ class LCCNes(Nes): (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]) - fids = fids.reshape((self._lat['data'].shape[0],self._lat['data'].shape[1])) + fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1])) 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()), @@ -476,3 +495,41 @@ class LCCNes(Nes): self.shapefile = gdf return gdf + + def get_centroids_from_coordinates(self): + """ + Get centroids from geographical coordinates. + + Returns + ------- + centroids_gdf: GeoPandasDataFrame + Centroids dataframe. + """ + + # Get centroids from coordinates + centroids = [] + for lat_ind in range(0, self.lon['data'].shape[0]): + for lon_ind in range(0, self.lon['data'].shape[1]): + centroids.append(Point(self.lon['data'][lat_ind, lon_ind], + self.lat['data'][lat_ind, lon_ind])) + + # Create dataframe cointaining all points + fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1]) + fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1])) + 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']] + centroids_gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), + geometry=centroids, + crs="EPSG:4326") + + return centroids_gdf + + def calculate_grid_area(self): + """ + Get coordinate bounds and call function to calculate the area of each cell of a grid. + """ + grid_area = super(LCCNes, self).calculate_grid_area() + self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0], + self.lon_bnds['data'].shape[1]])} + + return None diff --git a/nes/nc_projections/mercator_nes.py b/nes/nc_projections/mercator_nes.py index 8b79b9e6aa2c5dc7e8605ba45ac74635db31563f..e6ccf187b683ff7d2f17db3cd7657545b024ace7 100644 --- a/nes/nc_projections/mercator_nes.py +++ b/nes/nc_projections/mercator_nes.py @@ -1,12 +1,13 @@ #!/usr/bin/env python +import warnings import numpy as np import pandas as pd from cfunits import Units from pyproj import Proj from copy import deepcopy import geopandas as gpd -from shapely.geometry import Polygon +from shapely.geometry import Polygon, Point from nes.nc_projections.default_nes import Nes @@ -157,14 +158,19 @@ class MercatorNes(Nes): } else: - projection_data = self.variables['mercator'] - self.free_vars('mercator') + 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) + projection_data = None return projection_data def _create_dimensions(self, netcdf): """ - Create 'y', 'x' and 'spatial_nv' dimensions and the super dimensions ('lev', 'time'). + Create 'y', 'x' and 'spatial_nv' dimensions and the super dimensions 'lev', 'time', 'time_nv', 'lon' and 'lat' Parameters ---------- @@ -236,13 +242,13 @@ class MercatorNes(Nes): self._x = {'data': np.linspace(kwargs['x_0'] + (kwargs['inc_x'] / 2), kwargs['x_0'] + (kwargs['inc_x'] / 2) + (kwargs['inc_x'] * (kwargs['nx'] - 1)), kwargs['nx'], - dtype=np.float)} + dtype=np.float64)} self._y = {'data': np.linspace(kwargs['y_0'] + (kwargs['inc_y'] / 2), kwargs['y_0'] + (kwargs['inc_y'] / 2) + (kwargs['inc_y'] * (kwargs['ny'] - 1)), kwargs['ny'], - dtype=np.float)} + dtype=np.float64)} # Create a regular grid in metres (1D to 2D) x = np.array([self._x['data']] * len(self._y['data'])) @@ -250,18 +256,16 @@ class MercatorNes(Nes): self.projection = Proj( proj='merc', - a=6378137.0, # WGS84_SEMIMAJOR_AXIS (as defined in Cartopy source code) - b=6356752.3142, # WGS84_SEMIMINOR_AXIS (as defined in Cartopy source code) + 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_data, centre_lat_data = self.projection(x, y, inverse=True) - centre_lat = {'data': centre_lat_data} - centre_lon = {'data': centre_lon_data} + centre_lon, centre_lat = self.projection(x, y, inverse=True) - return centre_lat, centre_lon + return {'data': centre_lat}, {'data': centre_lon} def create_providentia_exp_centre_coordinates(self): """ @@ -320,8 +324,8 @@ class MercatorNes(Nes): # Get edges for regular coordinates self.projection = Proj( proj='merc', - a=6378137.0, # WGS84_SEMIMAJOR_AXIS (as defined in Cartopy source code) - b=6356752.3142, # WGS84_SEMIMINOR_AXIS (as defined in Cartopy source code) + 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']), ) @@ -341,33 +345,37 @@ class MercatorNes(Nes): # Calculate Mercator coordinates bounds inc_x = np.abs(np.mean(np.diff(self._x['data']))) - x_bnds = self.create_single_spatial_bounds(np.array([self._y['data']] * len(self._x['data'])).T, - inc_x, spatial_nv=4, inverse=True) + x_bnds = self.create_single_spatial_bounds(np.array([self._x['data']] * len(self._y['data'])), + inc_x, spatial_nv=4) inc_y = np.abs(np.mean(np.diff(self._y['data']))) - y_bnds = self.create_single_spatial_bounds(np.array([self._x['data']] * len(self._y['data'])), - inc_y, spatial_nv=4) + y_bnds = self.create_single_spatial_bounds(np.array([self._y['data']] * len(self._x['data'])).T, + inc_y, spatial_nv=4, inverse=True) # Transform Mercator bounds to regular bounds self.projection = Proj( proj='merc', - a=6378137.0, # WGS84_SEMIMAJOR_AXIS (as defined in Cartopy source code) - b=6356752.3142, # WGS84_SEMIMINOR_AXIS (as defined in Cartopy source code) + 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 - self._lat_bnds = deepcopy(lat_bnds) - self.lat_bnds = lat_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - :] - - self._lon_bnds = deepcopy(lon_bnds) - self.lon_bnds = lon_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - :] + self._lat_bnds = {} + self._lat_bnds['data'] = deepcopy(lat_bnds) + self.lat_bnds = {} + self.lat_bnds['data'] = lat_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], + :] + + self._lon_bnds = {} + self._lon_bnds['data'] = deepcopy(lon_bnds) + self.lon_bnds = {} + self.lon_bnds['data'] = lon_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], + :] return None @@ -397,10 +405,11 @@ class MercatorNes(Nes): netcdf4-python Dataset. """ - mapping = netcdf.createVariable('mercator', 'c') - mapping.grid_mapping_name = self.projection_data['grid_mapping_name'] - mapping.standard_parallel = self.projection_data['standard_parallel'] - mapping.longitude_of_projection_origin = self.projection_data['longitude_of_projection_origin'] + if self.projection_data is not None: + mapping = netcdf.createVariable('mercator', 'c') + mapping.grid_mapping_name = self.projection_data['grid_mapping_name'] + mapping.standard_parallel = self.projection_data['standard_parallel'] + mapping.longitude_of_projection_origin = self.projection_data['longitude_of_projection_origin'] return None @@ -425,6 +434,11 @@ class MercatorNes(Nes): def create_shapefile(self): """ Create spatial geodataframe (shapefile). + + Returns + ------- + shapefile : GeoPandasDataFrame + Shapefile dataframe. """ # Get latitude and longitude cell boundaries @@ -432,10 +446,12 @@ class MercatorNes(Nes): self.create_spatial_bounds() # Reshape arrays to create geometry - aux_b_lats = self.lat_bnds.reshape((self.lat_bnds.shape[0] * self.lat_bnds.shape[1], self.lat_bnds.shape[2])) - aux_b_lons = self.lon_bnds.reshape((self.lon_bnds.shape[0] * self.lon_bnds.shape[1], self.lon_bnds.shape[2])) + aux_b_lats = self.lat_bnds['data'].reshape((self.lat_bnds['data'].shape[0] * self.lat_bnds['data'].shape[1], + self.lat_bnds['data'].shape[2])) + aux_b_lons = self.lon_bnds['data'].reshape((self.lon_bnds['data'].shape[0] * self.lon_bnds['data'].shape[1], + self.lon_bnds['data'].shape[2])) - # Create dataframe cointaining all polygons + # Get polygons from bounds geometry = [] for i in range(aux_b_lons.shape[0]): geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), @@ -443,8 +459,10 @@ class MercatorNes(Nes): (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]) - fids = fids.reshape((self._lat['data'].shape[0],self._lat['data'].shape[1])) + fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1])) 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()), @@ -453,3 +471,42 @@ class MercatorNes(Nes): self.shapefile = gdf return gdf + + def get_centroids_from_coordinates(self): + """ + Get centroids from geographical coordinates. + + Returns + ------- + centroids_gdf: GeoPandasDataFrame + Centroids dataframe. + """ + + # Get centroids from coordinates + centroids = [] + for lat_ind in range(0, self.lon['data'].shape[0]): + for lon_ind in range(0, self.lon['data'].shape[1]): + centroids.append(Point(self.lon['data'][lat_ind, lon_ind], + self.lat['data'][lat_ind, lon_ind])) + + # Create dataframe cointaining all points + fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1]) + fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1])) + 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']] + centroids_gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), + geometry=centroids, + crs="EPSG:4326") + + return centroids_gdf + + def calculate_grid_area(self): + """ + Get coordinate bounds and call function to calculate the area of each cell of a grid. + """ + + grid_area = super(MercatorNes, self).calculate_grid_area() + self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0], + self.lon_bnds['data'].shape[1]])} + + return None diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index d5ef1f2457f9dbfb8af23fcf7e463e7d976c6617..9c3d859b48ad1c157095d42b570e9977ddd1f0e0 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -121,7 +121,7 @@ class PointsNes(Nes): def _create_dimensions(self, netcdf): """ - Create 'lev', 'time_nv', 'station', 'spatial_nv' and 'strlen' dimensions. + Create 'time', 'time_nv', 'station' and 'strlen' dimensions. Parameters ---------- @@ -219,7 +219,7 @@ class PointsNes(Nes): return None - def _get_coordinate_values(self, coordinate_info, coordinate_axis): + def _get_coordinate_values(self, coordinate_info, coordinate_axis, bounds=False): """ Get the coordinate data of the current portion. @@ -229,16 +229,25 @@ class PointsNes(Nes): Dictionary with the 'data' key with the coordinate variable values. and the attributes as other keys. coordinate_axis : str Name of the coordinate to extract. Accepted values: ['X']. + bounds : bool + Boolean variable to know if there are coordinate bounds. Returns ------- values : dict Dictionary with the portion of data corresponding to the rank. """ - values = deepcopy(coordinate_info) - if isinstance(coordinate_info, list): + if coordinate_info is None: + return None + + if not isinstance(coordinate_info, dict): values = {'data': deepcopy(coordinate_info)} + else: + values = deepcopy(coordinate_info) + coordinate_len = len(values['data'].shape) + if bounds: + coordinate_len -= 1 if coordinate_axis == 'X': if coordinate_len == 1: @@ -291,9 +300,9 @@ class PointsNes(Nes): if len(var_dims) < 2: data = nc_var[self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] elif len(var_dims) == 2: - if 'strlen' in nc_var.dimensions: + 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) for i in data]) + data = np.array([''.join(i.tostring().decode('ascii').replace('\x00', '')) for i in data]) 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']] @@ -316,7 +325,7 @@ class PointsNes(Nes): Parameters ---------- netcdf : Dataset - netcdf4-python opened Dataset. + netcdf4-python open Dataset. chunking : bool Indicates if you want to chunk the output netCDF. """ @@ -330,8 +339,8 @@ class PointsNes(Nes): 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, + 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) try: @@ -368,8 +377,9 @@ class PointsNes(Nes): if 'strlen' not in var_dims: var_dims += ('strlen',) var_dict['data'] = stringtochar(np.array([v.encode('ascii', 'ignore') - for v in var_dict['data']]).astype('S' + str(self.strlen))) - var_dtype = 'S' + str(self.strlen) + for v in var_dict['data']]).astype('S' + + str(self.strlen))) + var_dtype = 'S1' except: pass @@ -543,14 +553,12 @@ class PointsNes(Nes): """ # Calculate centre latitudes - centre_lat_data = kwargs['lat'] - centre_lat = {'data': centre_lat_data} + centre_lat = kwargs['lat'] # Calculate centre longitudes - centre_lon_data = kwargs['lon'] - centre_lon = {'data': centre_lon_data} + centre_lon = kwargs['lon'] - return centre_lat, centre_lon + return {'data': centre_lat}, {'data': centre_lon} def _create_metadata(self, netcdf): """ @@ -601,12 +609,13 @@ class PointsNes(Nes): lon=self.lon['data'] ) - # Convert dimensions (time, lat, lon) to (time, station) for interpolated variables and reshape data + # Convert dimensions (time, lev, lat, lon) to (station, time) for interpolated variables and reshape data variables = {} interpolated_variables = deepcopy(self.variables) for var_name, var_info in interpolated_variables.items(): variables[var_name] = {} - if var_info['dimensions'] == ('time', 'lat', 'lon') and len(var_info['data'].shape) == 2: + # ('time', 'lev', 'lat', 'lon') or ('time', 'lat', 'lon') to ('station', 'time') + if len(var_info['dimensions']) != len(var_info['data'].shape): variables[var_name]['data'] = var_info['data'].T variables[var_name]['dimensions'] = ('station', 'time') else: @@ -639,19 +648,62 @@ class PointsNes(Nes): def create_shapefile(self): """ Create spatial geodataframe (shapefile). + + Returns + ------- + shapefile : GeoPandasDataFrame + Shapefile dataframe. """ # Create dataframe cointaining all points - geometry = gpd.points_from_xy(self.lon['data'], self.lat['data']) - fids = np.arange(len(self._lon['data'])) - fids = fids[self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] - gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids), - geometry=geometry, - crs="EPSG:4326") + gdf = self.get_centroids_from_coordinates() self.shapefile = gdf return gdf + def get_centroids_from_coordinates(self): + """ + Get centroids from geographical coordinates. + + Returns + ------- + centroids_gdf: GeoPandasDataFrame + Centroids dataframe. + """ + + # Get centroids from coordinates + centroids = gpd.points_from_xy(self.lon['data'], self.lat['data']) + + # Create dataframe cointaining all points + fids = np.arange(len(self._lon['data'])) + fids = fids[self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] + centroids_gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids), + geometry=centroids, + crs="EPSG:4326") + + return centroids_gdf + + def add_variables_to_shapefile(self, var_list, idx_lev=0, idx_time=0): + """ + Add variables data to shapefile. + + var_list : list, str + List (or single string) of the variables to be loaded and saved in the shapefile. + idx_lev : int + Index of vertical level for which the data will be saved in the shapefile. + idx_time : int + Index of time for which the data will be saved in the shapefile. + """ + + if idx_lev != 0: + msg = 'Error: Points dataset has no level (Level: {0}).'.format(idx_lev) + raise ValueError(msg) + + for var_name in var_list: + self.shapefile[var_name] = self.variables[var_name]['data'][idx_time, :].ravel() + + return None + @staticmethod def _get_axis_index_(axis): if axis == 'T': diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py index 7951bcfe77fbea75d2b68e82635f2435ef085fc7..1d95fa18baa925d5b930d411cea97d1737e7c5dd 100644 --- a/nes/nc_projections/points_nes_ghost.py +++ b/nes/nc_projections/points_nes_ghost.py @@ -130,7 +130,7 @@ class PointsNesGHOST(PointsNes): def _create_dimensions(self, netcdf): """ Create 'N_flag_codes' and 'N_qa_codes' dimensions and the super dimensions - ('lev', 'time_nv', 'station', 'spatial_nv' and 'strlen'). + 'time', 'time_nv', 'station', and 'strlen'. Parameters ---------- @@ -227,7 +227,7 @@ class PointsNesGHOST(PointsNes): return None - def _get_coordinate_values(self, coordinate_info, coordinate_axis): + def _get_coordinate_values(self, coordinate_info, coordinate_axis, bounds=False): """ Get the coordinate data of the current portion. @@ -237,16 +237,25 @@ class PointsNesGHOST(PointsNes): Dictionary with the 'data' key with the coordinate variable values. and the attributes as other keys. coordinate_axis : str Name of the coordinate to extract. Accepted values: ['X']. + bounds : bool + Boolean variable to know if there are coordinate bounds. Returns ------- values : dict Dictionary with the portion of data corresponding to the rank. """ - values = deepcopy(coordinate_info) - if isinstance(coordinate_info, list): + if coordinate_info is None: + return None + + if not isinstance(coordinate_info, dict): values = {'data': deepcopy(coordinate_info)} + else: + values = deepcopy(coordinate_info) + coordinate_len = len(values['data'].shape) + if bounds: + coordinate_len -= 1 if coordinate_axis == 'X': if coordinate_len == 1: @@ -286,11 +295,11 @@ class PointsNesGHOST(PointsNes): data = nc_var[self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] elif len(var_dims) == 2: data = nc_var[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 len(var_dims) == 3: data = nc_var[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('Error with {0}. Only can be read netCDF with 3 dimensions or less'.format( var_name)) @@ -310,7 +319,7 @@ class PointsNesGHOST(PointsNes): Parameters ---------- netcdf : Dataset - netcdf4-python opened Dataset. + netcdf4-python open Dataset. chunking : bool Indicates if you want to chunk the output netCDF. """ @@ -352,6 +361,7 @@ class PointsNesGHOST(PointsNes): var_dict['data'] = stringtochar(np.array([v.encode('ascii', 'ignore') for v in var_dict['data']]).astype('S' + str(self.strlen))) + var_dtype = 'S1' except: pass @@ -576,11 +586,12 @@ class PointsNesGHOST(PointsNes): Indicates if you want a chunked netCDF output. Only available with non serial writes. Default: False. """ - if not serial: + if (not serial) and (self.size > 1): msg = 'WARNING!!! ' msg += 'GHOST datasets cannot be written in parallel yet. ' msg += 'Changing to serial mode.' warnings.warn(msg) + super(PointsNesGHOST, self).to_netcdf(path, compression_level=compression_level, serial=True, info=info, chunking=chunking) @@ -609,8 +620,12 @@ class PointsNesGHOST(PointsNes): lon=self.lon['data'], times=self.time ) - - GHOST_version = str(float(np.unique(self.variables['GHOST_version']['data']))) + + # The version attribute in GHOST files prior to 1.3.3 is called data_version, after it is version + if 'version' in self.global_attrs: + GHOST_version = self.global_attrs['version'] + elif 'data_version' in self.global_attrs: + GHOST_version = self.global_attrs['data_version'] metadata_variables = self.get_standard_metadata(GHOST_version) self.free_vars(metadata_variables) self.free_vars('station') @@ -752,6 +767,27 @@ class PointsNesGHOST(PointsNes): return metadata_variables[GHOST_version] + def add_variables_to_shapefile(self, var_list, idx_lev=0, idx_time=0): + """ + Add variables data to shapefile. + + var_list : list, str + List (or single string) of the variables to be loaded and saved in the shapefile. + idx_lev : int + Index of vertical level for which the data will be saved in the shapefile. + idx_time : int + Index of time for which the data will be saved in the shapefile. + """ + + if idx_lev != 0: + msg = 'Error: Points dataset has no level (Level: {0}).'.format(idx_lev) + raise ValueError(msg) + + for var_name in var_list: + self.shapefile[var_name] = self.variables[var_name]['data'][:, idx_time].ravel() + + return None + @staticmethod def _get_axis_index_(axis): if axis == 'T': diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py index 499cd7af261e3fc43250ae905547d731d0b81f7e..737392f31da97abc6ca00aa3075127dc36305b51 100644 --- a/nes/nc_projections/points_nes_providentia.py +++ b/nes/nc_projections/points_nes_providentia.py @@ -172,7 +172,7 @@ class PointsNesProvidentia(PointsNes): def _create_dimensions(self, netcdf): """ Create 'grid_edge', 'model_latitude' and 'model_longitude' dimensions and the super dimensions - ('lev', 'time_nv', 'station', 'spatial_nv', 'strlen'). + 'time', 'time_nv', 'station', and 'strlen'. Parameters ---------- @@ -260,7 +260,7 @@ class PointsNesProvidentia(PointsNes): self.free_vars(('model_centre_longitude', 'model_centre_latitude', 'grid_edge_longitude', 'grid_edge_latitude')) - def _get_coordinate_values(self, coordinate_info, coordinate_axis): + def _get_coordinate_values(self, coordinate_info, coordinate_axis, bounds=False): """ Get the coordinate data of the current portion. @@ -270,16 +270,25 @@ class PointsNesProvidentia(PointsNes): Dictionary with the 'data' key with the coordinate variable values. and the attributes as other keys. coordinate_axis : str Name of the coordinate to extract. Accepted values: ['X']. + bounds : bool + Boolean variable to know if there are coordinate bounds. Returns ------- values : dict Dictionary with the portion of data corresponding to the rank. """ - values = deepcopy(coordinate_info) - if isinstance(coordinate_info, list): + if coordinate_info is None: + return None + + if not isinstance(coordinate_info, dict): values = {'data': deepcopy(coordinate_info)} + else: + values = deepcopy(coordinate_info) + coordinate_len = len(values['data'].shape) + if bounds: + coordinate_len -= 1 if coordinate_axis == 'X': if coordinate_len == 1: @@ -322,11 +331,11 @@ class PointsNesProvidentia(PointsNes): data = nc_var[self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] elif len(var_dims) == 2: data = nc_var[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 len(var_dims) == 3: data = nc_var[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('Error with {0}. Only can be read netCDF with 3 dimensions or less'.format( var_name)) @@ -346,7 +355,7 @@ class PointsNesProvidentia(PointsNes): Parameters ---------- netcdf : Dataset - netcdf4-python opened Dataset. + netcdf4-python open Dataset. chunking : bool Indicates if you want to chunk the output netCDF. """ @@ -388,6 +397,7 @@ class PointsNesProvidentia(PointsNes): var_dict['data'] = stringtochar(np.array([v.encode('ascii', 'ignore') for v in var_dict['data']]).astype('S' + str(self.strlen))) + var_dtype = 'S1' except: pass @@ -578,7 +588,7 @@ class PointsNesProvidentia(PointsNes): Indicates if you want a chunked netCDF output. Only available with non serial writes. Default: False. """ - if not serial: + if (not serial) and (self.size > 1): msg = 'WARNING!!! ' msg += 'Providentia datasets cannot be written in parallel yet. ' msg += 'Changing to serial mode.' @@ -589,6 +599,27 @@ class PointsNesProvidentia(PointsNes): return None + def add_variables_to_shapefile(self, var_list, idx_lev=0, idx_time=0): + """ + Add variables data to shapefile. + + var_list : list, str + List (or single string) of the variables to be loaded and saved in the shapefile. + idx_lev : int + Index of vertical level for which the data will be saved in the shapefile. + idx_time : int + Index of time for which the data will be saved in the shapefile. + """ + + if idx_lev != 0: + msg = 'Error: Points dataset has no level (Level: {0}).'.format(idx_lev) + raise ValueError(msg) + + for var_name in var_list: + self.shapefile[var_name] = self.variables[var_name]['data'][:, idx_time].ravel() + + return None + @staticmethod def _get_axis_index_(axis): if axis == 'T': diff --git a/nes/nc_projections/rotated_nes.py b/nes/nc_projections/rotated_nes.py index b968dd724f49a5577ca5654a84a1ae1a87c596ba..ac66c76a1825e466119da770def6cff7f33ac414 100644 --- a/nes/nc_projections/rotated_nes.py +++ b/nes/nc_projections/rotated_nes.py @@ -1,12 +1,13 @@ #!/usr/bin/env python +import warnings import numpy as np import pandas as pd import math from cfunits import Units from copy import deepcopy import geopandas as gpd -from shapely.geometry import Polygon +from shapely.geometry import Polygon, Point from .default_nes import Nes @@ -156,16 +157,20 @@ class RotatedNes(Nes): 'grid_north_pole_latitude': 90 - kwargs['centre_lat'], 'grid_north_pole_longitude': -180 + kwargs['centre_lon'], } - else: - projection_data = self.variables['rotated_pole'] - self.free_vars('rotated_pole') - + 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) + projection_data = None + return projection_data def _create_dimensions(self, netcdf): """ - Create 'rlat', 'rlon' and 'spatial_nv' dimensions and the super dimensions ('lev', 'time'). + Create 'rlat', 'rlon' and 'spatial_nv' dimensions and the super dimensions 'lev', 'time', 'time_nv', 'lon' and 'lat'. Parameters ---------- @@ -182,6 +187,7 @@ class RotatedNes(Nes): # Create spatial_nv (number of vertices) dimension if (self._lat_bnds is not None) and (self._lon_bnds is not None): netcdf.createDimension('spatial_nv', 4) + pass return None @@ -234,15 +240,16 @@ class RotatedNes(Nes): """ # Calculate rotated latitudes - self.n_lat = int((abs(kwargs['south_boundary']) / kwargs['inc_rlat']) * 2 + 1) - self.rotated_lat = np.linspace(kwargs['south_boundary'], kwargs['south_boundary'] + - (kwargs['inc_rlat'] * (self.n_lat - 1)), self.n_lat) + n_lat = int((abs(kwargs['south_boundary']) / kwargs['inc_rlat']) * 2 + 1) + rlat = np.linspace(kwargs['south_boundary'], kwargs['south_boundary'] + + (kwargs['inc_rlat'] * (n_lat - 1)), n_lat) + # Calculate rotated longitudes - self.n_lon = int((abs(kwargs['west_boundary']) / kwargs['inc_rlon']) * 2 + 1) - self.rotated_lon = np.linspace(kwargs['west_boundary'], kwargs['west_boundary'] + - (kwargs['inc_rlon'] * (self.n_lon - 1)), self.n_lon) + n_lon = int((abs(kwargs['west_boundary']) / kwargs['inc_rlon']) * 2 + 1) + rlon = np.linspace(kwargs['west_boundary'], kwargs['west_boundary'] + + (kwargs['inc_rlon'] * (n_lon - 1)), n_lon) - return {'data': self.rotated_lat}, {'data': self.rotated_lon} + return {'data': rlat}, {'data': rlon} def rotated2latlon(self, lon_deg, lat_deg, lon_min=-180, **kwargs): """ @@ -318,13 +325,11 @@ class RotatedNes(Nes): self._rlat, self._rlon = self._create_rotated_coordinates(**kwargs) # Calculate centre latitudes and longitudes (1D to 2D) - centre_lon_data, centre_lat_data = self.rotated2latlon(np.array([self.rotated_lon] * len(self.rotated_lat)), - np.array([self.rotated_lat] * len(self.rotated_lon)).T, - **kwargs) - centre_lon = {'data': centre_lon_data} - centre_lat = {'data': centre_lat_data} + centre_lon, centre_lat = self.rotated2latlon(np.array([self._rlon['data']] * len(self._rlat['data'])), + np.array([self._rlat['data']] * len(self._rlon['data'])).T, + **kwargs) - return centre_lat, centre_lon + return {'data': centre_lat}, {'data': centre_lon} def create_providentia_exp_centre_coordinates(self): """ @@ -409,15 +414,19 @@ class RotatedNes(Nes): lon_bnds, lat_bnds = self.rotated2latlon(rlon_bnds, rlat_bnds) # Obtain regular coordinates bounds - self._lat_bnds = deepcopy(lat_bnds) - self.lat_bnds = lat_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - :] - - self._lon_bnds = deepcopy(lon_bnds) - self.lon_bnds = lon_bnds[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - :] + self._lat_bnds = {} + self._lat_bnds['data'] = deepcopy(lat_bnds) + self.lat_bnds = {} + self.lat_bnds['data'] = lat_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], + :] + + self._lon_bnds = {} + self._lon_bnds['data'] = deepcopy(lon_bnds) + self.lon_bnds = {} + self.lon_bnds['data']= lon_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], + :] return None @@ -447,10 +456,11 @@ class RotatedNes(Nes): netcdf4-python Dataset. """ - mapping = netcdf.createVariable('rotated_pole', 'c') - mapping.grid_mapping_name = self.projection_data['grid_mapping_name'] - mapping.grid_north_pole_latitude = self.projection_data['grid_north_pole_latitude'] - mapping.grid_north_pole_longitude = self.projection_data['grid_north_pole_longitude'] + if self.projection_data is not None: + mapping = netcdf.createVariable('rotated_pole', 'c') + mapping.grid_mapping_name = self.projection_data['grid_mapping_name'] + mapping.grid_north_pole_latitude = self.projection_data['grid_north_pole_latitude'] + mapping.grid_north_pole_longitude = self.projection_data['grid_north_pole_longitude'] return None @@ -475,16 +485,23 @@ class RotatedNes(Nes): def create_shapefile(self): """ Create spatial geodataframe (shapefile). + + Returns + ------- + shapefile : GeoPandasDataFrame + Shapefile dataframe. """ if self._lat_bnds is None or self._lon_bnds is None: self.create_spatial_bounds() # Reshape arrays to create geometry - aux_b_lats = self.lat_bnds.reshape((self.lat_bnds.shape[0] * self.lat_bnds.shape[1], self.lat_bnds.shape[2])) - aux_b_lons = self.lon_bnds.reshape((self.lon_bnds.shape[0] * self.lon_bnds.shape[1], self.lon_bnds.shape[2])) - - # Create dataframe cointaining all polygons + aux_b_lats = self.lat_bnds['data'].reshape((self.lat_bnds['data'].shape[0] * self.lat_bnds['data'].shape[1], + self.lat_bnds['data'].shape[2])) + aux_b_lons = self.lon_bnds['data'].reshape((self.lon_bnds['data'].shape[0] * self.lon_bnds['data'].shape[1], + self.lon_bnds['data'].shape[2])) + + # Get polygons from bounds geometry = [] for i in range(aux_b_lons.shape[0]): geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), @@ -492,8 +509,10 @@ class RotatedNes(Nes): (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]) - fids = fids.reshape((self._lat['data'].shape[0],self._lat['data'].shape[1])) + fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1])) 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()), @@ -502,3 +521,42 @@ class RotatedNes(Nes): self.shapefile = gdf return gdf + + def get_centroids_from_coordinates(self): + """ + Get centroids from geographical coordinates. + + Returns + ------- + centroids_gdf: GeoPandasDataFrame + Centroids dataframe. + """ + + # Get centroids from coordinates + centroids = [] + for lat_ind in range(0, self.lon['data'].shape[0]): + for lon_ind in range(0, self.lon['data'].shape[1]): + centroids.append(Point(self.lon['data'][lat_ind, lon_ind], + self.lat['data'][lat_ind, lon_ind])) + + # Create dataframe cointaining all points + fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1]) + fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1])) + 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']] + centroids_gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), + geometry=centroids, + crs="EPSG:4326") + + return centroids_gdf + + def calculate_grid_area(self): + """ + Get coordinate bounds and call function to calculate the area of each cell of a grid. + """ + + grid_area = super(RotatedNes, self).calculate_grid_area() + self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0], + self.lon_bnds['data'].shape[1]])} + + return None diff --git a/nes/nes_formats/cams_ra_format.py b/nes/nes_formats/cams_ra_format.py index 86bd49db0ad470b34176f9ee96135d506cb139db..a286d5dc0a5237d9c12854afd3ed134edd29e58c 100644 --- a/nes/nes_formats/cams_ra_format.py +++ b/nes/nes_formats/cams_ra_format.py @@ -12,7 +12,7 @@ from copy import copy def to_netcdf_cams_ra(self, path): """ - Horizontal interpolation from one grid to another one. + Horizontal methods from one grid to another one. Parameters ---------- @@ -21,11 +21,14 @@ def to_netcdf_cams_ra(self, path): path : str Path to the output netCDF file. """ + if not isinstance(self, nes.LatLonNes): raise TypeError("CAMS Re-Analysis format must have Regular Lat-Lon projection") if '' not in path: raise ValueError("AMS Re-Analysis path must contain '' as pattern; current: '{0}'".format(path)) + orig_path = copy(path) + for i_lev, level in enumerate(self.lev['data']): path = orig_path.replace('', 'l{0}'.format(i_lev)) # Open NetCDF @@ -69,7 +72,7 @@ def create_dimensions(self, netcdf): self : nes.Nes Source projection Nes Object. netcdf : Dataset - netcdf4-python opened Dataset. + netcdf4-python open dataset. """ # Create time dimension @@ -91,8 +94,9 @@ def create_dimension_variables(self, netcdf): self : nes.Nes Source projection Nes Object. netcdf : Dataset - netcdf4-python opened Dataset. + netcdf4-python open dataset. """ + # LATITUDES lat = netcdf.createVariable('lat', np.float64, ('lat',)) lat.standard_name = 'latitude' @@ -138,8 +142,7 @@ def create_variables(self, netcdf, i_lev): self : nes.Nes Source projection Nes Object. netcdf : Dataset - netcdf4-python opened Dataset. - + netcdf4-python open dataset. """ for i, (var_name, var_dict) in enumerate(self.variables.items()): @@ -189,8 +192,10 @@ def create_variables(self, netcdf, i_lev): def date2num(time_array, time_units=None, time_calendar=None): + time_res = [] for aux_time in time_array: time_res.append(float(aux_time.strftime("%Y%m%d")) + (float(aux_time.strftime("%H")) / 24)) time_res = np.array(time_res, dtype=np.float64) + return time_res diff --git a/requirements.txt b/requirements.txt index 74a35d2b0177f006ea26eeb08b997d3cc0c4bc1f..a40856e17a26c8879f41f335d629cc759a16e843 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,14 +1,16 @@ -pycodestyle~=2.8.0 -geopandas~=0.10.2 -pandas~=1.2.2 -netcdf4 -numpy~=1.21.5 -timezonefinder~=5.2.0 +pycodestyle>=2.10.0 +geopandas>=0.10.2 +rtree>=0.9.0 +pandas>=1.3.5 +netcdf4>=1.6.2 +numpy>=1.20.0 pyproj~=3.2.1 -setuptools~=47.1.0 -pytest~=6.2.5 -Shapely~=1.8.0 -scipy -filelock -pyproj~=3.2.1 -eccodes-python~=0.9.5 \ No newline at end of file +setuptools>=66.1.1 +pytest>=7.2.1 +Shapely>=2.0.0 +scipy>=1.7.3 +filelock>=3.9.0 +eccodes-python~=0.9.5 +cfunits>=3.3.5 +xarray>=0.20.2 +mpi4py>=3.1.4 \ No newline at end of file diff --git a/tests/1-test_read_write_size.py b/tests/1-test_read_write_size.py deleted file mode 100644 index e545fadf190480843312c1797f9d0e10a1752d2e..0000000000000000000000000000000000000000 --- a/tests/1-test_read_write_size.py +++ /dev/null @@ -1,106 +0,0 @@ -#!/usr/bin/env python -import timeit -import sys -from nes import * -import pandas as pd -# from mpi4py import MPI - -# test_list = [{'in_file': "/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2008123100.nc", -# 'prefix': 'small'}, -# {'in_file': "/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2021080212.nc", -# 'prefix': 'medium'}, -# {'in_file': "/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2017123000.nc", -# 'prefix': 'large'}, -# ] -test_list = [{'in_file': "/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2017123000.nc", - 'prefix': 'large'}, - ] -# parallel_method = 'X' -# parallel_method = 'Y' -parallel_method = 'T' - - -times = pd.DataFrame(index=[0]) -# size = MPI.COMM_WORLD.Get_size() - -for test in test_list: - # ====================================================================================================================== - - st_time = timeit.default_timer() - # ===== Reading - nessy = open_netcdf(path=test['in_file'], parallel_method=parallel_method) - size = nessy.size - if nessy.master: - print("\n=====", test['prefix'], "=====") - - nessy.keep_vars(['O3', 'T', 'U', 'V', 'layer_thickness']) - # nessy.keep_vars(['O3']) - # ===== END Reading - spent_time = timeit.default_timer() - st_time - if nessy.master: - print("Init time {0:02d}:{1:02.3f} (tot: {2:.3f} s)".format( - int(spent_time // 60), spent_time - (int(spent_time // 60) * 60), spent_time)) - sys.stdout.flush() - times["{0}_Init".format(test['prefix'])] = spent_time - - # ====================================================================================================================== - - st_time = timeit.default_timer() - # ===== Loading - nessy.load() - # ===== END Loading - spent_time = timeit.default_timer() - st_time - if nessy.master: - print("Loading time {min:02d}:{sec:02.3f} (tot: {tot:.3f} s ; var: {var:.3f}s/var)".format( - min=int(spent_time // 60), sec=spent_time - (int(spent_time // 60) * 60), - var=spent_time / len(nessy.variables.keys()), tot=spent_time)) - sys.stdout.flush() - times["{0}_Load".format(test['prefix'])] = spent_time - - # ====================================================================================================================== - - if test['prefix'] not in ['large']: - st_time = timeit.default_timer() - # ===== Serial Write - nessy.to_netcdf('nc_test_{0}_{1}_serial.nc'.format(test['prefix'], nessy.size), serial=True) - # ===== END Serial Write - spent_time = timeit.default_timer() - st_time - if nessy.master: - print("Writing in serial time {min:02d}:{sec:02.3f} (tot: {tot:.3f} s ; var: {var:.3f}s/var)".format( - min=int(spent_time // 60), sec=spent_time - (int(spent_time // 60) * 60), - var=spent_time / len(nessy.variables.keys()), tot=spent_time)) - sys.stdout.flush() - times["{0}_Serial".format(test['prefix'])] = spent_time - - # ====================================================================================================================== - - st_time = timeit.default_timer() - # ===== Parallel Chunk Write - nessy.to_netcdf('nc_test_{0}_{1}_parallel_chunked.nc'.format(test['prefix'], nessy.size), chunking=True) - # ===== END Parallel Chunk Write - spent_time = timeit.default_timer() - st_time - if nessy.master: - print("Writing in chunked parallel time {min:02d}:{sec:02.3f} (tot: {tot:.3f} s ; var: {var:.3f}s/var)".format( - min=int(spent_time // 60), sec=spent_time - (int(spent_time // 60) * 60), - var=spent_time / len(nessy.variables.keys()), tot=spent_time)) - sys.stdout.flush() - times["{0}_Chunk".format(test['prefix'])] = spent_time - - # ====================================================================================================================== - - st_time = timeit.default_timer() - # ===== Parallel Write - nessy.to_netcdf('nc_test_{0}_{1}_parallel.nc'.format(test['prefix'], nessy.size)) - # ===== END Parallel Write - spent_time = timeit.default_timer() - st_time - if nessy.master: - print("Writing in parallel time {min:02d}:{sec:02.3f} (tot: {tot:.3f} s ; var: {var:.3f}s/var)".format( - min=int(spent_time // 60), sec=spent_time - (int(spent_time // 60) * 60), - var=spent_time / len(nessy.variables.keys()), tot=spent_time)) - sys.stdout.flush() - times["{0}_Parallel".format(test['prefix'])] = spent_time - - # ====================================================================================================================== - - -times.to_csv("times_{0}.csv".format(str(size).zfill(3))) diff --git a/tests/1.1-test_read_write_projection.py b/tests/1.1-test_read_write_projection.py new file mode 100644 index 0000000000000000000000000000000000000000..37fd9cd6d57cc6ebea829a624f809ee2e3fcac26 --- /dev/null +++ b/tests/1.1-test_read_write_projection.py @@ -0,0 +1,216 @@ +#!/usr/bin/env python + +import sys +import timeit +import pandas as pd +from mpi4py import MPI +from nes import * + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'Y' + +result_path = "Times_test_1.1_read_write_projection_{0}_{1:03d}.csv".format(parallel_method, size) +result = pd.DataFrame(index=['read', 'write'], + columns=['1.1.1.Regular', '1.1.2.Rotated', '1.1.3.Points', '1.1.4.Points_GHOST', + '1.1.5.LCC', '1.1.6.Mercator']) + +# ====================================================================================================================== +# ============================================= REGULAR ======================================================== +# ====================================================================================================================== + +test_name = '1.1.1.Regular' +if rank == 0: + print(test_name) + +# Original path: /gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc +# Regular lat-lon grid from MONARCH +path = '/gpfs/projects/bsc32/models/NES_tutorial_data/franco_interp.nc' + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# LOAD VARIABLES +variables = ['O3'] +nessy.keep_vars(variables) +nessy.load() + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ============================================= ROTATED ======================================================== +# ====================================================================================================================== + +test_name = '1.1.2.Rotated' +if rank == 0: + print(test_name) + +# Original path: /gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc +# Rotated grid from MONARCH +path = '/gpfs/projects/bsc32/models/NES_tutorial_data/O3_all-000_2021080300.nc' + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# LOAD VARIABLES +variables = ['O3_all'] +nessy.keep_vars(variables) +nessy.load() + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + + +# ====================================================================================================================== +# ============================================= LCC ============================================================ +# ====================================================================================================================== + +test_name = '1.1.5.LCC' +if rank == 0: + print(test_name) + +# Original path: /esarchive/exp/snes/a5g1/ip/daily_max/sconco3/sconco3_2022111500.nc +# LCC grid with a coverage over the Iberian Peninsula (4x4km) +path = '/gpfs/projects/bsc32/models/NES_tutorial_data/sconco3_2022111500.nc' + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# LOAD VARIABLES +nessy.load() + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ============================================= MERCATOR ======================================================== +# ====================================================================================================================== + +test_name = '1.1.6.Mercator' +if rank == 0: + print(test_name) + +# Original path: None (generated with NES) +# Mercator grid +path = '/gpfs/projects/bsc32/models/NES_tutorial_data/mercator_grid.nc' + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# LOAD VARIABLES +nessy.load() + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) + +# ====================================================================================================================== +# ============================================= POINTS ========================================================= +# ====================================================================================================================== + +test_name = '1.1.3.Points' +if rank == 0: + print(test_name) + +# Original path: /esarchive/obs/nilu/ebas/daily/pm10/pm10_201507.nc +# Points grid from EBAS network +path = '/gpfs/projects/bsc32/models/NES_tutorial_data/pm10_201507.nc' + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# LOAD VARIABLES +nessy.load() + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ============================================= POINTS GHOST =================================================== +# ====================================================================================================================== + +test_name = '1.1.4.Points_GHOST' +if rank == 0: + print(test_name) + +path = '/gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.4/hourly/sconco3/sconco3_201906.nc' + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# LOAD VARIABLES +nessy.load() + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() diff --git a/tests/1.2-test_create_projection.py b/tests/1.2-test_create_projection.py new file mode 100644 index 0000000000000000000000000000000000000000..f6e41254bcd23035df7ea9333687f311e569f142 --- /dev/null +++ b/tests/1.2-test_create_projection.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python + +import sys +from mpi4py import MPI +import pandas as pd +import timeit +from nes import * + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'Y' + +result_path = "Times_test_1.2_create_projection_{0}_{1:03d}.csv".format(parallel_method, size) +result = pd.DataFrame(index=['create', 'write'], + columns=['1.2.1.Regular', '1.2.2.Rotated', '1.2.3.LCC', '1.2.4.Mercator', '1.2.5.Global']) + +# ====================================================================================================================== +# ============================================= REGULAR ======================================================== +# ====================================================================================================================== + +test_name = '1.2.1.Regular' +if rank == 0: + print(test_name) + +# CREATE GRID +st_time = timeit.default_timer() +lat_orig = 41.1 +lon_orig = 1.8 +inc_lat = 0.01 +inc_lon = 0.01 +n_lat = 100 +n_lon = 100 +nessy = create_nes(projection='regular', parallel_method=parallel_method, + lat_orig=lat_orig, lon_orig=lon_orig, inc_lat=inc_lat, inc_lon=inc_lon, n_lat=n_lat, n_lon=n_lon) + +comm.Barrier() +result.loc['create', test_name] = timeit.default_timer() - st_time + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ============================================= ROTATED ======================================================== +# ====================================================================================================================== + +test_name = '1.2.2.Rotated' +if rank == 0: + print(test_name) + +# CREATE GRID +st_time = timeit.default_timer() +centre_lat = 51 +centre_lon = 10 +west_boundary = -35 +south_boundary = -27 +inc_rlat = 0.2 +inc_rlon = 0.2 +nessy = create_nes(projection='rotated', parallel_method=parallel_method, + centre_lat=centre_lat, centre_lon=centre_lon, + west_boundary=west_boundary, south_boundary=south_boundary, + inc_rlat=inc_rlat, inc_rlon=inc_rlon) + +comm.Barrier() +result.loc['create', test_name] = timeit.default_timer() - st_time + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ============================================= LCC ======================================================== +# ====================================================================================================================== + +test_name = '1.2.3.LCC' +if rank == 0: + print(test_name) + +# CREATE GRID +st_time = timeit.default_timer() +lat_1 = 37 +lat_2 = 43 +lon_0 = -3 +lat_0 = 40 +nx = 397 +ny = 397 +inc_x = 4000 +inc_y = 4000 +x_0 = -807847.688 +y_0 = -797137.125 +nessy = create_nes(projection='lcc', parallel_method=parallel_method, + lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, + nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0) + +comm.Barrier() +result.loc['create', test_name] = timeit.default_timer() - st_time + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ============================================= MERCATOR ======================================================== +# ====================================================================================================================== + +test_name = '1.2.4.Mercator' +if rank == 0: + print(test_name) + +# CREATE GRID +st_time = timeit.default_timer() +lat_ts = -1.5 +lon_0 = -18.0 +nx = 210 +ny = 236 +inc_x = 50000 +inc_y = 50000 +x_0 = -126017.5 +y_0 = -5407460.0 +nessy = create_nes(projection='mercator', parallel_method=parallel_method, + lat_ts=lat_ts, lon_0=lon_0, nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0) + +comm.Barrier() +result.loc['create', test_name] = timeit.default_timer() - st_time + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ============================================== GLOBAL ======================================================== +# ====================================================================================================================== + +test_name = '1.2.5.Global' +if rank == 0: + print(test_name) + +# CREATE GRID +st_time = timeit.default_timer() +inc_lat = 0.1 +inc_lon = 0.1 +nessy = create_nes(projection='global', parallel_method=parallel_method, inc_lat=inc_lat, inc_lon=inc_lon) + +comm.Barrier() +result.loc['create', test_name] = timeit.default_timer() - st_time + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) diff --git a/tests/1.3-test_selecting.py b/tests/1.3-test_selecting.py new file mode 100644 index 0000000000000000000000000000000000000000..72ab997edfd221cc944309136e194ba57e04a40f --- /dev/null +++ b/tests/1.3-test_selecting.py @@ -0,0 +1,184 @@ +#!/usr/bin/env python +import sys +import timeit +import pandas as pd +from mpi4py import MPI +from nes import * +import os, sys +from datetime import datetime + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'Y' +serial_write = True + +result_path = "Times_test_1.3.Selecting_{0}_{1:03d}.csv".format(parallel_method, size) + +result = pd.DataFrame(index=['read', 'calcul', 'write'], + columns=['1.3.1.LatLon', '1.3.2.Level', '1.3.3.Time', '1.3.4.Time_min', '1.3.5.Time_max']) + +# NAMEE +src_path = "/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc" +var_list = ['O3'] + +# ====================================================================================================================== +# ====================================== '1.3.1.LatLon' ===================================================== +# ====================================================================================================================== +test_name = '1.3.1.LatLon' + +if rank == 0: + print(test_name) + +st_time = timeit.default_timer() + +# Source data +nessy = open_netcdf(src_path, parallel_method=parallel_method, balanced=True) +nessy.keep_vars(var_list) +nessy.sel(lat_min=35, lat_max=45, lon_min=-9, lon_max=5) + +nessy.load() + +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size), serial=serial_write) + +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ====================================== 1.3.2.Level ===================================================== +# ====================================================================================================================== +test_name = '1.3.2.Level' + +if rank == 0: + print(test_name) + +st_time = timeit.default_timer() + +# Source data +nessy = open_netcdf(src_path, parallel_method=parallel_method) +nessy.keep_vars(var_list) +nessy.sel(lev_min=3, lev_max=5) + +nessy.load() + +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size), serial=serial_write) + +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ====================================== 1.3.3.Time ===================================================== +# ====================================================================================================================== +test_name = '1.3.3.Time' + +if rank == 0: + print(test_name) + +st_time = timeit.default_timer() + +# Source data +nessy = open_netcdf(src_path, parallel_method=parallel_method) +nessy.keep_vars(var_list) +nessy.sel(time_min=datetime(year=2022, month=11, day=16, hour=0), + time_max=datetime(year=2022, month=11, day=16, hour=0)) + +nessy.load() + +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size), serial=serial_write) + +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ====================================== '1.3.4.Time_min' ===================================================== +# ====================================================================================================================== +test_name = '1.3.4.Time_min' + +if rank == 0: + print(test_name) + +st_time = timeit.default_timer() + +# Source data +nessy = open_netcdf(src_path, parallel_method=parallel_method) +nessy.keep_vars(var_list) +nessy.sel(time_min=datetime(year=2022, month=11, day=16, hour=0)) + +nessy.load() + +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size), serial=serial_write) + +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ====================================== '1.3.5.Time_max' ===================================================== +# ====================================================================================================================== +test_name = '1.3.5.Time_max' + +if rank == 0: + print(test_name) + +st_time = timeit.default_timer() + +# Source data +nessy = open_netcdf(src_path, parallel_method=parallel_method) +nessy.keep_vars(var_list) +nessy.sel(time_max=datetime(year=2022, month=11, day=16, hour=0)) + +nessy.load() + +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size), serial=serial_write) + +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) + print("TEST PASSED SUCCESSFULLY") diff --git a/tests/2-test_read_write_projection.py b/tests/2-test_read_write_projection.py deleted file mode 100644 index 5f4590c1482f5e2690643caeaaf765ab9db2cbb2..0000000000000000000000000000000000000000 --- a/tests/2-test_read_write_projection.py +++ /dev/null @@ -1,135 +0,0 @@ -#!/usr/bin/env python -import sys -import timeit -import pandas as pd -from mpi4py import MPI -from nes import * - -paths = {'regular_file': {'path': '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2008123100.nc', - 'projection': 'regular', - 'variables': ['O3'], - 'parallel_methods': ['X', 'Y', 'T']}, - 'rotated_file': {'path': '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc', - 'projection': 'rotated', - 'variables': ['O3_all'], - 'parallel_methods': ['X', 'Y', 'T']}, - 'points_file': {'path': '/esarchive/obs/eea/eionet/hourly/pm10/pm10_202107.nc', - 'projection': 'points', - 'variables': [], # all - 'parallel_methods': ['X', 'T']}, - 'points_ghost_file': {'path': '/gpfs/projects/bsc32/AC_cache/obs/ghost/EANET/1.4/daily/sconcso4/sconcso4_201911.nc', - 'projection': 'points_ghost', - 'variables': [], # all - 'parallel_methods': ['X', 'T']}, - 'lcc_file': {'path': '/esarchive/exp/wrf-hermes-cmaq/b075/eu/hourly/pm10/pm10_2022062600.nc', - 'projection': 'lcc', - 'variables': [], # all - 'parallel_methods': ['X', 'Y', 'T']}, - 'mercator_file': {'path': '/esarchive/scratch/avilanova/software/NES/tutorials/data/mercator_grid_example.nc', - 'projection': 'mercator', - 'variables': [], # all - 'parallel_methods': ['X', 'Y', 'T']} - } - -results = [] - -comm = MPI.COMM_WORLD -rank = comm.Get_rank() -size = comm.Get_size() - -for name, dict in paths.items(): - - path = dict['path'] - projection = dict['projection'] - variables = dict['variables'] - parallel_methods = dict['parallel_methods'] - - for parallel_method in parallel_methods: - - if rank == 0: - print('TEST TO USE {0} GRID IN {1} FOR {2} USING {3} NODES'.format( - projection.upper(), parallel_method, path, size)) - sys.stdout.flush() - - try: - - # Read - start_time = timeit.default_timer() - nessy_1 = open_netcdf(path=path, comm=comm, info=True, parallel_method=parallel_method) - open_time = timeit.default_timer() - start_time - - # Select variables and load - start_time = timeit.default_timer() - if len(variables) > 0: - nessy_1.keep_vars(variables) - nessy_1.load() - load_time = timeit.default_timer() - start_time - comm.Barrier() - - # Write in serial - if rank == 0: - print('WRITE IN SERIAL') - sys.stdout.flush() - start_time = timeit.default_timer() - nessy_1.to_netcdf('{0}_{1}_file_{2}_serial.nc'.format(size, projection, parallel_method), - info=True, serial=True) - serial_time = timeit.default_timer() - start_time - comm.Barrier() - - # Write in parallel - if rank == 0: - print('WRITE IN PARALLEL') - sys.stdout.flush() - start_time = timeit.default_timer() - nessy_1.to_netcdf('{0}_{1}_file_{2}_parallel.nc'.format(size, projection, parallel_method), - info=True) - parallel_time = timeit.default_timer() - start_time - comm.Barrier() - - # Write in chunks - if rank == 0: - print('WRITE IN CHUNKS') - sys.stdout.flush() - start_time = timeit.default_timer() - nessy_1.to_netcdf('{0}_{1}_file_{2}_chunking.nc'.format(size, projection, parallel_method), - info=True, chunking=True) - chunking_time = timeit.default_timer() - start_time - comm.Barrier() - - # Close everything - del nessy_1 - - if rank == 0: - print('Test was successful for {0} projection in {1}'.format(projection, parallel_method)) - sys.stdout.flush() - - # End timer and save results - results.append({'Projection': projection, - 'Method': parallel_method, - 'Open': '{min:02d}:{sec:02.3f}'.format( - min=int(open_time // 60), sec=open_time - (int(open_time // 60) * 60)), - 'Load': '{min:02d}:{sec:02.3f}'.format( - min=int(load_time // 60), sec=load_time - (int(load_time // 60) * 60)), - 'Serial': '{min:02d}:{sec:02.3f}'.format( - min=int(serial_time // 60), sec=serial_time - (int(serial_time // 60) * 60)), - 'Chunking': '{min:02d}:{sec:02.3f}'.format( - min=int(chunking_time // 60), sec=chunking_time - (int(chunking_time // 60) * 60)), - 'Parallel': '{min:02d}:{sec:02.3f}'.format( - min=int(parallel_time // 60), sec=parallel_time - (int(parallel_time // 60) * 60)) - }) - - comm.Barrier() - - except Exception as e: - print(e) - - sys.stdout.flush() - -comm.Barrier() - -if rank == 0: - table = pd.DataFrame(results) - print('RESULTS TABLE') - print(table) - table.to_csv('{0}_results.csv'.format(size)) - sys.stdout.flush() diff --git a/tests/2-test_read_write_projection_nord3v2.bash b/tests/2-test_read_write_projection_nord3v2.bash deleted file mode 100644 index 4f7eac151bfa80b6a16328289db257bb4d9952d4..0000000000000000000000000000000000000000 --- a/tests/2-test_read_write_projection_nord3v2.bash +++ /dev/null @@ -1,24 +0,0 @@ -#!/bin/bash - -#EXPORTPATH="/esarchive/scratch/avilanova/software/NES" -EXPORTPATH="/gpfs/projects/bsc32/models/NES" -SRCPATH="/gpfs/projects/bsc32/models/NES/tests" -EXE="2-test_read_write_projection.py" - -module purge -module load Python/3.7.4-GCCcore-8.3.0 -module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4 -module load cfunits/1.8-foss-2019b-Python-3.7.4 -module load xarray/0.17.0-foss-2019b-Python-3.7.4 -module load pandas/1.2.4-foss-2019b-Python-3.7.4 -module load mpi4py/3.0.3-foss-2019b-Python-3.7.4 -module load filelock/3.7.1-foss-2019b-Python-3.7.4 -module load pyproj/2.5.0-foss-2019b-Python-3.7.4 -module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4 -module load geopandas/0.8.1-foss-2019b-Python-3.7.4 -module load Shapely/1.7.1-foss-2019b-Python-3.7.4 - -for nprocs in 1 2 4 8 16 32 -do - JOB_ID=`sbatch --ntasks=${nprocs} --exclusive --job-name=nes_${nprocs} --output=./log_nord3v2_NES_${nprocs}_%J.out --error=./log_nord3v2_NES_${nprocs}_%J.err -D . --time=02:00:00 --wrap="export PYTHONPATH=${EXPORTPATH}:${PYTHONPATH}; cd ${SRCPATH}; mpirun --mca mpi_warn_on_fork 0 -np ${nprocs} python ${SRCPATH}/${EXE}"` -done \ No newline at end of file diff --git a/tests/2.1-test_spatial_join.py b/tests/2.1-test_spatial_join.py new file mode 100644 index 0000000000000000000000000000000000000000..8aedaba3c58dda1b7daa9da3de868f0f8621ad59 --- /dev/null +++ b/tests/2.1-test_spatial_join.py @@ -0,0 +1,214 @@ +#!/usr/bin/env python + +import sys +from mpi4py import MPI +import pandas as pd +import timeit +from nes import * + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'Y' + +result_path = "Times_test_2.1_spatial_join_{0}_{1:03d}.csv".format(parallel_method, size) +result = pd.DataFrame(index=['read', 'calculate', 'write'], + columns=['2.1.1.Existing_file_centroid', '2.1.2.New_file_centroid', + '2.1.3.Existing_file_nearest', '2.1.4.New_file_nearest', + '2.1.5.Existing_file_intersection', '2.1.6.New_file_intersection']) + +# ===== PATH TO MASK ===== # +shapefile_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/timezones_2021c/timezones_2021c.shp' + +# Original path: /gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc +# Regular lat-lon grid from MONARCH +original_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/franco_interp.nc' + +# ====================================================================================================================== +# =================================== CENTROID EXISTING FILE =================================================== +# ====================================================================================================================== + +test_name = '2.1.1.Existing_file_centroid' +if rank == 0: + print(test_name) + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(original_path, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# SPATIAL JOIN +# Method can be centroid, nearest and intersection +st_time = timeit.default_timer() +nessy.spatial_join(shapefile_path, method='centroid') +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time +print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# =================================== CENTROID FROM NEW FILE =================================================== +# ====================================================================================================================== + +test_name = '2.1.2.New_file_centroid' +if rank == 0: + print(test_name) + +# DEFINE PROJECTION +st_time = timeit.default_timer() +projection = 'regular' +lat_orig = 41.1 +lon_orig = 1.8 +inc_lat = 0.2 +inc_lon = 0.2 +n_lat = 100 +n_lon = 100 + +# SPATIAL JOIN +# Method can be centroid, nearest and intersection +nessy = from_shapefile(shapefile_path, method='centroid', projection=projection, + lat_orig=lat_orig, lon_orig=lon_orig, + inc_lat=inc_lat, inc_lon=inc_lon, + n_lat=n_lat, n_lon=n_lon) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# =================================== NEAREST EXISTING FILE =================================================== +# ====================================================================================================================== + +test_name = '2.1.3.Existing_file_nearest' +if rank == 0: + print(test_name) + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(original_path, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# SPATIAL JOIN +# Method can be centroid, nearest and intersection +st_time = timeit.default_timer() +nessy.spatial_join(shapefile_path, method='nearest') +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time +print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# =================================== NEAREST FROM NEW FILE =================================================== +# ====================================================================================================================== + +test_name = '2.1.4.New_file_nearest' +if rank == 0: + print(test_name) + +# DEFINE PROJECTION +st_time = timeit.default_timer() +projection = 'regular' +lat_orig = 41.1 +lon_orig = 1.8 +inc_lat = 0.2 +inc_lon = 0.2 +n_lat = 100 +n_lon = 100 + +# SPATIAL JOIN +# Method can be centroid, nearest and intersection +nessy = from_shapefile(shapefile_path, method='nearest', projection=projection, + lat_orig=lat_orig, lon_orig=lon_orig, + inc_lat=inc_lat, inc_lon=inc_lon, + n_lat=n_lat, n_lon=n_lon) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + + +# ====================================================================================================================== +# =================================== INTERSECTION EXISTING FILE =================================================== +# ====================================================================================================================== + +test_name = '2.1.5.Existing_file_intersection' +if rank == 0: + print(test_name) + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(original_path, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# SPATIAL JOIN +# Method can be centroid, nearest and intersection +st_time = timeit.default_timer() +nessy.spatial_join(shapefile_path, method='intersection') +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time +print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + + +# ====================================================================================================================== +# =================================== INTERSECTION FROM NEW FILE =================================================== +# ====================================================================================================================== + +test_name = '2.1.6.New_file_intersection' +if rank == 0: + print(test_name) + +# DEFINE PROJECTION +st_time = timeit.default_timer() +projection = 'regular' +lat_orig = 41.1 +lon_orig = 1.8 +inc_lat = 0.2 +inc_lon = 0.2 +n_lat = 100 +n_lon = 100 + +# SPATIAL JOIN +# Method can be centroid, nearest and intersection +nessy = from_shapefile(shapefile_path, method='intersection', projection=projection, + lat_orig=lat_orig, lon_orig=lon_orig, + inc_lat=inc_lat, inc_lon=inc_lon, + n_lat=n_lat, n_lon=n_lon) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) diff --git a/tests/2.2-test_create_shapefile.py b/tests/2.2-test_create_shapefile.py new file mode 100644 index 0000000000000000000000000000000000000000..d41b59304d5158ffb1f9d53f4f45615e57a41574 --- /dev/null +++ b/tests/2.2-test_create_shapefile.py @@ -0,0 +1,200 @@ +#!/usr/bin/env python + +import sys +import timeit +import pandas as pd +from mpi4py import MPI +from nes import * +import datetime + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'Y' + +result_path = "Times_test_2.2_create_shapefile_{0}_{1:03d}.csv".format(parallel_method, size) +result = pd.DataFrame(index=['read', 'calculate'], + columns=['2.2.1.Existing', '2.2.2.New_Regular', + '2.2.3.New_Rotated', '2.2.4.New_LCC', '2.2.5.New_Mercator']) + +# ====================================================================================================================== +# ===================================== CREATE SHAPEFILE FROM EXISTING GRID ========================================== +# ====================================================================================================================== + +test_name = '2.2.1.Existing' +if rank == 0: + print(test_name) + +# Original path: /gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc +# Regular lat-lon grid from MONARCH +path = '/gpfs/projects/bsc32/models/NES_tutorial_data/franco_interp.nc' + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(path=path, info=True, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# LOAD VARIABLES +nessy.load() + +# CREATE SHAPEFILE +st_time = timeit.default_timer() +nessy.to_shapefile(path='regular_shp', + time=datetime.datetime(2019, 1, 1, 10, 0), + lev=0, var_list=['sconcno2']) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time +print('FROM EXISTING GRID - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ===================================== CREATE SHAPEFILE FROM NEW REGULAR GRID ======================================= +# ====================================================================================================================== + +test_name = '2.2.2.New_Regular' +if rank == 0: + print(test_name) + +# CREATE GRID +st_time = timeit.default_timer() +lat_orig = 41.1 +lon_orig = 1.8 +inc_lat = 0.1 +inc_lon = 0.1 +n_lat = 50 +n_lon = 100 +nessy = create_nes(comm=None, info=False, projection='regular', + lat_orig=lat_orig, lon_orig=lon_orig, inc_lat=inc_lat, inc_lon=inc_lon, + n_lat=n_lat, n_lon=n_lon) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# CREATE SHAPEFILE +st_time = timeit.default_timer() +nessy.create_shapefile() +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time +print('FROM NEW REGULAR GRID - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ===================================== CREATE SHAPEFILE FROM NEW ROTATED GRID ======================================= +# ====================================================================================================================== + +test_name = '2.2.3.New_Rotated' +if rank == 0: + print(test_name) + +# CREATE GRID +st_time = timeit.default_timer() +centre_lat = 51 +centre_lon = 10 +west_boundary = -35 +south_boundary = -27 +inc_rlat = 0.2 +inc_rlon = 0.2 +nessy = create_nes(comm=None, info=False, projection='rotated', + centre_lat=centre_lat, centre_lon=centre_lon, + west_boundary=west_boundary, south_boundary=south_boundary, + inc_rlat=inc_rlat, inc_rlon=inc_rlon) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# CREATE SHAPEFILE +st_time = timeit.default_timer() +nessy.create_shapefile() +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time +print('FROM NEW ROTATED GRID - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ===================================== CREATE SHAPEFILE FROM NEW LCC GRID =========================================== +# ====================================================================================================================== + +test_name = '2.2.4.New_LCC' +if rank == 0: + print(test_name) + +# CREATE GRID +st_time = timeit.default_timer() +lat_1 = 37 +lat_2 = 43 +lon_0 = -3 +lat_0 = 40 +nx = 100 +ny = 200 +inc_x = 4000 +inc_y = 4000 +x_0 = -807847.688 +y_0 = -797137.125 +nessy = create_nes(comm=None, info=False, projection='lcc', + lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, + nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# CREATE SHAPEFILE +st_time = timeit.default_timer() +nessy.create_shapefile() +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time +print('FROM NEW LCC GRID - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ===================================== CREATE SHAPEFILE FROM NEW MERCATOR GRID ====================================== +# ====================================================================================================================== + +test_name = '2-2.5.New_Mercator' +if rank == 0: + print(test_name) + +# CREATE GRID +st_time = timeit.default_timer() +lat_ts = -1.5 +lon_0 = -18.0 +nx = 100 +ny = 50 +inc_x = 50000 +inc_y = 50000 +x_0 = -126017.5 +y_0 = -5407460.0 +nessy = create_nes(comm=None, info=False, projection='mercator', + lat_ts=lat_ts, lon_0=lon_0, nx=nx, ny=ny, + inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# CREATE SHAPEFILE +st_time = timeit.default_timer() +nessy.create_shapefile() +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time +print('FROM NEW MERCATOR GRID - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) diff --git a/tests/2.3-test_bounds.py b/tests/2.3-test_bounds.py new file mode 100644 index 0000000000000000000000000000000000000000..3bee2ede76cee4e96dd3e9031ce4209505e292a7 --- /dev/null +++ b/tests/2.3-test_bounds.py @@ -0,0 +1,160 @@ +#!/usr/bin/env python + +import sys +import timeit +import pandas as pd +from mpi4py import MPI +from nes import * + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'Y' + +result_path = "Times_test_2.3_bounds_{0}_{1:03d}.csv".format(parallel_method, size) +result = pd.DataFrame(index=['read', 'calculate', 'write'], + columns=['2.3.1.With_bounds', '2.3.2.Without_bounds', "2.3.3.Create_new"]) + +# ====================================================================================================================== +# ===================================== FILE WITH EXISTING BOUNDS ==================================================== +# ====================================================================================================================== + +test_name = "2.3.1.With_bounds" +if rank == 0: + print(test_name) + +# 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' +nessy_1 = open_netcdf(path=path_1, parallel_method=parallel_method, info=True) + +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# EXPLORE BOUNDS +st_time = timeit.default_timer() +print('FILE WITH EXISTING BOUNDS - Rank', rank, '-', 'Lat bounds', nessy_1.lat_bnds) +print('FILE WITH EXISTING BOUNDS - Rank', rank, '-', 'Lon bounds', nessy_1.lon_bnds) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# WRITE +st_time = timeit.default_timer() +nessy_1.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +# REOPEN +nessy_2 = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) + +# LOAD DATA AND EXPLORE BOUNDS +print('FILE WITH EXISTING BOUNDS AFTER WRITE - Rank', rank, '-', 'Lat bounds', nessy_2.lat_bnds) +print('FILE WITH EXISTING BOUNDS AFTER WRITE - Rank', rank, '-', 'Lon bounds', nessy_2.lon_bnds) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# =================================== FILE WITHOUT EXISTING BOUNDS =================================================== +# ====================================================================================================================== + +test_name = '2.3.2.Without_bounds' +if rank == 0: + print(test_name) + +# Original path: /gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc +# Rotated grid from MONARCH +st_time = timeit.default_timer() +path_3 = "/gpfs/projects/bsc32/models/NES_tutorial_data/O3_all-000_2021080300.nc" +nessy_3 = open_netcdf(path=path_3, parallel_method=parallel_method, info=True) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# CREATE BOUNDS +st_time = timeit.default_timer() +nessy_3.create_spatial_bounds() +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# EXPLORE BOUNDS +print('FILE WITHOUT EXISTING BOUNDS - Rank', rank, '-', 'Lat bounds', nessy_3.lat_bnds) +print('FILE WITHOUT EXISTING BOUNDS - Rank', rank, '-', 'Lon bounds', nessy_3.lon_bnds) + +# WRITE +st_time = timeit.default_timer() +nessy_3.to_netcdf('/tmp/bounds_file_2.nc', info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +# REOPEN +nessy_4 = open_netcdf('/tmp/bounds_file_2.nc', info=True) + +# LOAD DATA AND EXPLORE BOUNDS +print('FILE WITH EXISTING BOUNDS AFTER WRITE - Rank', rank, '-', 'Lat bounds', nessy_4.lat_bnds) +print('FILE WITH EXISTING BOUNDS AFTER WRITE - Rank', rank, '-', 'Lon bounds', nessy_4.lon_bnds) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ==================================== CREATE NES REGULAR LAT-LON ==================================================== +# ====================================================================================================================== + +test_name = "2.3.3.Create_new" +if rank == 0: + print(test_name) + +# CREATE GRID +st_time = timeit.default_timer() +lat_orig = 41.1 +lon_orig = 1.8 +inc_lat = 0.2 +inc_lon = 0.2 +n_lat = 100 +n_lon = 100 +nessy_5 = create_nes(comm=None, parallel_method=parallel_method, info=True, projection='regular', + lat_orig=lat_orig, lon_orig=lon_orig, + inc_lat=inc_lat, inc_lon=inc_lon, + n_lat=n_lat, n_lon=n_lon) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# CREATE BOUNDS +st_time = timeit.default_timer() +nessy_5.create_spatial_bounds() +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# EXPLORE BOUNDS +print('FROM NEW GRID - Rank', rank, '-', 'Lat bounds', nessy_5.lat_bnds) +print('FROM NEW GRID - Rank', rank, '-', 'Lon bounds', nessy_5.lon_bnds) + +# WRITE +st_time = timeit.default_timer() +nessy_5.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +# REOPENcomm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +nessy_6 = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) + +# LOAD DATA AND EXPLORE BOUNDS +print('FROM NEW GRID AFTER WRITE - Rank', rank, '-', 'Lat bounds', nessy_6.lat_bnds) +print('FROM NEW GRID AFTER WRITE - Rank', rank, '-', 'Lon bounds', nessy_6.lon_bnds) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) diff --git a/tests/2.4-test_cell_area.py b/tests/2.4-test_cell_area.py new file mode 100644 index 0000000000000000000000000000000000000000..4c9b152c2c1b7e7dc9c0d25b06eaa10a8b79a4cb --- /dev/null +++ b/tests/2.4-test_cell_area.py @@ -0,0 +1,194 @@ +#!/usr/bin/env python + +import sys +import timeit +import pandas as pd +from mpi4py import MPI +from nes import * + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'Y' + +result_path = "Times_test_2.4_cell_area_{0}_{1:03d}.csv".format(parallel_method, size) +result = pd.DataFrame(index=['read', 'calculate', 'write'], + columns=['2.4.1.New_file_grid_area', '2.4.2.New_file_geometry_area', + '2.4.3.Existing_file_grid_area', '2.4.4.Existing_file_geometry_area']) + +# ====================================================================================================================== +# ===================================== CALCULATE CELLS AREA FROM NEW GRID =========================================== +# ====================================================================================================================== + +test_name = "2.4.1.New_file_grid_area" +if rank == 0: + print(test_name) + +# CREATE GRID +st_time = timeit.default_timer() +lat_1 = 37 +lat_2 = 43 +lon_0 = -3 +lat_0 = 40 +nx = 20 +ny = 40 +inc_x = 4000 +inc_y = 4000 +x_0 = -807847.688 +y_0 = -797137.125 +nessy = create_nes(comm=None, info=False, projection='lcc', + lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, + nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# CALCULATE AREA OF EACH CELL IN GRID +st_time = timeit.default_timer() +nessy.calculate_grid_area() +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# EXPLORE GRID AREA +print('Rank {0:03d}: Calculate grid cell area: {1}'.format(rank, nessy.cell_measures['cell_area'])) + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +# REOPEN +# nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) + +# EXPLORE GRID AREA +print('Rank {0:03d}: Write grid cell area: {1}'.format(rank, nessy.cell_measures['cell_area'])) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +del nessy + +# ====================================================================================================================== +# ===================================== CALCULATE CELLS AREA FROM GEOMETRIES ========================================= +# ====================================================================================================================== + +test_name = "2.4.2.New_file_geometry_area" +if rank == 0: + print(test_name) + +# CREATE GRID +st_time = timeit.default_timer() +lat_1 = 37 +lat_2 = 43 +lon_0 = -3 +lat_0 = 40 +nx = 20 +ny = 40 +inc_x = 4000 +inc_y = 4000 +x_0 = -807847.688 +y_0 = -797137.125 +nessy = create_nes(comm=None, info=False, projection='lcc', + lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, + nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# CALCULATE AREA OF EACH CELL POLYGON +st_time = timeit.default_timer() +nessy.create_shapefile() +geometry_list = nessy.shapefile['geometry'].values +geometry_area = calculate_geometry_area(geometry_list) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# EXPLORE GEOMETRIES AREA +print('Rank {0:03d}: Calculate geometry cell area: {1}'.format(rank, geometry_area)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ===================================== CALCULATE CELLS AREA FROM EXISTING GRID ====================================== +# ====================================================================================================================== + +test_name = '2.4.3.Existing_file_grid_area' +if rank == 0: + print(test_name) + +# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc +# Rotated grid from MONARCH +original_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc' + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(original_path, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# CALCULATE AREA OF EACH CELL IN GRID +st_time = timeit.default_timer() +nessy.calculate_grid_area() +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# EXPLORE GRID AREA +print('Rank {0:03d}: Calculate grid cell area: {1}'.format(rank, nessy.cell_measures['cell_area'])) + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +# REOPEN +# nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() +del nessy + +# ====================================================================================================================== +# ===================================== CALCULATE CELLS AREA FROM GEOMETRIES FROM EXISTING GRID ====================== +# ====================================================================================================================== + +test_name = '2.4.4.Existing_file_geometry_area' +if rank == 0: + print(test_name) + +# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc +# Rotated grid from MONARCH +original_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc' + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(original_path, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# CALCULATE AREA OF EACH CELL POLYGON +st_time = timeit.default_timer() +nessy.create_shapefile() +geometry_list = nessy.shapefile['geometry'].values +geometry_area = calculate_geometry_area(geometry_list) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# EXPLORE GEOMETRIES AREA +print('Rank {0:03d}: Calculate geometry cell area: {1}'.format(rank, geometry_area)) + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() +del nessy + +if rank == 0: + result.to_csv(result_path) diff --git a/tests/3-test_spatial_join.py b/tests/3-test_spatial_join.py deleted file mode 100644 index e456c64ee2cd1ea24e63e864f851678d37cc5936..0000000000000000000000000000000000000000 --- a/tests/3-test_spatial_join.py +++ /dev/null @@ -1,124 +0,0 @@ -#!/usr/bin/env python -import geopandas as gpd -import pandas as pd -import timeit -import sys -from mpi4py import MPI -from nes import * - -# Hide warning -pd.options.mode.chained_assignment = None - -comm = MPI.COMM_WORLD -rank = comm.Get_rank() -size = comm.Get_size() - -results = [] -for method in ['spatial_overlay', 'spatial_join']: - for projection in ['regular', 'rotated']: - for projection_type in ['created', 'read']: - - # Regular projection - if projection == 'regular': - # Create dataset and get shapefile - if projection_type == 'created': - lat_orig = 41.1 - lon_orig = 1.8 - inc_lat = 0.2 - inc_lon = 0.2 - n_lat = 100 - n_lon = 100 - coordinates = create_nes(comm=None, info=True, - projection='regular', - lat_orig=lat_orig, - lon_orig=lon_orig, - inc_lat=inc_lat, - inc_lon=inc_lon, - n_lat=n_lat, - n_lon=n_lon) - coordinates.create_shapefile() - - # Open dataset and get shapefile - elif projection_type == 'read': - coordinates_path = '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2008123100.nc' - coordinates = open_netcdf(path=coordinates_path, info=True) - coordinates.create_shapefile() - coordinates.keep_vars(['O3']) - coordinates.load() - coordinates.shapefile['O3'] = coordinates.variables['O3']['data'][-1, -1, :].ravel() - - # Rotated projection - elif projection == 'rotated': - # Create dataset and get shapefile - if projection_type == 'created': - centre_lat = 51 - centre_lon = 10 - west_boundary = -35 - south_boundary = -27 - inc_rlat = 0.2 - inc_rlon = 0.2 - coordinates = create_nes(comm=None, info=True, - projection='rotated', - centre_lat=centre_lat, - centre_lon=centre_lon, - west_boundary=west_boundary, - south_boundary=south_boundary, - inc_rlat=inc_rlat, - inc_rlon=inc_rlon) - coordinates.create_shapefile() - - # Open dataset and get shapefile - elif projection_type == 'read': - coordinates_path = '/gpfs/scratch/bsc32/bsc32538/original_files/CAMS_MONARCH_d01_2022070412.nc' - coordinates = open_netcdf(path=coordinates_path, info=True) - coordinates.create_shapefile() - coordinates.keep_vars(['O3']) - coordinates.load() - coordinates.shapefile['O3'] = coordinates.variables['O3']['data'][-1, -1, :].ravel() - - coordinates.write_shapefile('coordinates_{0}_{1}'.format(projection, - projection_type)) - - mask_path = '/esarchive/scratch/avilanova/software/NES/tutorials/data/timezones_2021c/timezones_2021c.shp' - mask = gpd.read_file(mask_path) - - # Spatial overlay (old method) - if method == 'spatial_overlay': - start_time = timeit.default_timer() - intersection = coordinates.shapefile.copy() - #intersection['area'] = intersection.geometry.area - intersection = coordinates.spatial_overlays(intersection, mask) - #intersection.rename(columns={'idx1': 'FID', 'idx2': 'shp_id'}, inplace=True) - #intersection['fraction'] = intersection.geometry.area / intersection['area'] - #intersection.sort_values('fraction', ascending=False, inplace=True) - #intersection = intersection.drop_duplicates(subset='FID', keep="first") - #intersection.set_index('FID', inplace=True) - #coordinates.loc[intersection.index, coordinates.shp_colname] = intersection[coordinates.shp_colname] - time = timeit.default_timer() - start_time - - # Spatial join (new method) - elif method == 'spatial_join': - start_time = timeit.default_timer() - coordinates.spatial_join(mask, method='intersection') - time = timeit.default_timer() - start_time - - coordinates.write_shapefile('masked_coordinates_{0}_{1}_{2}'.format(projection, - projection_type, - method)) - - results = [] - results.append({'Projection': projection, - 'Projection type': projection_type, - 'Method': '{min:02d}:{sec:02.3f}'.format( - min=int(time // 60), sec=time - (int(time // 60) * 60)) - }) - - comm.Barrier() - -comm.Barrier() - -if rank == 0: - table = pd.DataFrame(results) - print('RESULTS TABLE') - print(table) - sys.stdout.flush() diff --git a/tests/3-test_spatial_join_nord3v2.bash b/tests/3-test_spatial_join_nord3v2.bash deleted file mode 100644 index 079aa7928696599a280f2c555704dbd543b65385..0000000000000000000000000000000000000000 --- a/tests/3-test_spatial_join_nord3v2.bash +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash - -EXPORTPATH="/esarchive/scratch/avilanova/software/NES" -SRCPATH="/esarchive/scratch/avilanova/software/NES/tests" -EXE="3-test_spatial_join.py" - -module purge -module load Python/3.7.4-GCCcore-8.3.0 -module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4 -module load cfunits/1.8-foss-2019b-Python-3.7.4 -module load xarray/0.17.0-foss-2019b-Python-3.7.4 -module load pandas/1.2.4-foss-2019b-Python-3.7.4 -module load mpi4py/3.0.3-foss-2019b-Python-3.7.4 -module load filelock/3.7.1-foss-2019b-Python-3.7.4 -module load pyproj/2.5.0-foss-2019b-Python-3.7.4 -module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4 -module load geopandas/0.8.1-foss-2019b-Python-3.7.4 -module load Shapely/1.7.1-foss-2019b-Python-3.7.4 - -for nprocs in 1 -do - JOB_ID=`sbatch --ntasks=${nprocs} --exclusive --job-name=nes_${nprocs} --output=./log_nord3v2_NES_${nprocs}_%J.out --error=./log_nord3v2_NES_${nprocs}_%J.err -D . --time=02:00:00 --wrap="export PYTHONPATH=${EXPORTPATH}:${PYTHONPATH}; cd ${SRCPATH}; mpirun --mca mpi_warn_on_fork 0 -np ${nprocs} python ${SRCPATH}/${EXE}"` -done \ No newline at end of file diff --git a/tests/3.1-test_vertical_interp.py b/tests/3.1-test_vertical_interp.py new file mode 100644 index 0000000000000000000000000000000000000000..d7bcfed201572bdbe10fb50c1c1314f8367398f8 --- /dev/null +++ b/tests/3.1-test_vertical_interp.py @@ -0,0 +1,65 @@ +#!/usr/bin/env python +import sys +import timeit +import pandas as pd +from mpi4py import MPI +from nes import * +import os, sys +from datetime import datetime + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'T' + +result_path = "Times_test_3.1_vertical_interp_{0}_{1:03d}.csv".format(parallel_method, size) +result = pd.DataFrame(index=['read', 'calculate'], + columns=['3.1.1.Interp']) + +# ====================================================================================================================== +# =============================================== VERTICAL INTERPOLATION ============================================= +# ====================================================================================================================== + +test_name = '3.1.1.Interp' +if rank == 0: + print(test_name) + +# READ +st_time = timeit.default_timer() + +# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc +# Rotated grid from MONARCH +source_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc' + +# Read source data +source_data = open_netcdf(path=source_path, info=True) + +# Select time and load variables +source_data.keep_vars(['O3', 'mid_layer_height_agl']) +source_data.load() + +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# INTERPOLATE +st_time = timeit.default_timer() +source_data.vertical_var_name = 'mid_layer_height_agl' +level_list = [0., 50., 100., 250., 500., 750., 1000., 2000., 3000., 5000.] +interp_nes = source_data.interpolate_vertical(level_list, info=True, kind='linear', extrapolate=None) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# WRITE +st_time = timeit.default_timer() +interp_nes.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) diff --git a/tests/3.2-test_horiz_interp_bilinear.py b/tests/3.2-test_horiz_interp_bilinear.py new file mode 100644 index 0000000000000000000000000000000000000000..097085a7b85704be793ec60831419c652453dd56 --- /dev/null +++ b/tests/3.2-test_horiz_interp_bilinear.py @@ -0,0 +1,220 @@ +#!/usr/bin/env python +import sys +import timeit +import pandas as pd +from mpi4py import MPI +from nes import * +import os, sys + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'T' + +result_path = "Times_test_3.2_horiz_interp_bilinear_{0}_{1:03d}.csv".format(parallel_method, size) +result = pd.DataFrame(index=['read', 'calculate', 'write'], + columns=['3.2.1.Only interp', '3.2.2.Create_WM', "3.2.3.Use_WM", "3.2.4.Read_WM"]) + +# NAMEE +src_path = "/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc" +var_list = ['O3'] + +# ====================================================================================================================== +# ====================================== Only interp ===================================================== +# ====================================================================================================================== +test_name = '3.2.1.NN_Only interp' +if rank == 0: + print(test_name) + +# READING +st_time = timeit.default_timer() + +# Source data +src_nes = open_netcdf(src_path, parallel_method=parallel_method) +src_nes.keep_vars(var_list) +src_nes.load() + +# Destination Grid +lat_1 = 37 +lat_2 = 43 +lon_0 = -3 +lat_0 = 40 +nx = 397 +ny = 397 +inc_x = 4000 +inc_y = 4000 +x_0 = -807847.688 +y_0 = -797137.125 +dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, + nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method, + times=src_nes.get_full_times()) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() + +interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='NN') +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() +interp_nes.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ====================================== Create_WM ===================================================== +# ====================================================================================================================== +test_name = '3.2.2.NN_Create_WM' +if rank == 0: + print(test_name) + +# READ +st_time = timeit.default_timer() + +# Read source data +src_nes = open_netcdf(src_path, parallel_method=parallel_method) +src_nes.keep_vars(var_list) +src_nes.load() + +# Destination Grid +lat_1 = 37 +lat_2 = 43 +lon_0 = -3 +lat_0 = 40 +nx = 397 +ny = 397 +inc_x = 4000 +inc_y = 4000 +x_0 = -807847.688 +y_0 = -797137.125 +dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, + nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method, + times=src_nes.get_full_times()) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# Cleaning WM +if os.path.exists("NN_WM_NAMEE_to_IP.nc") and rank == 0: + os.remove("NN_WM_NAMEE_to_IP.nc") +comm.Barrier() + +st_time = timeit.default_timer() + +wm_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='NN', info=True, + weight_matrix_path="NN_WM_NAMEE_to_IP.nc", only_create_wm=True) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ====================================== Use_WM ===================================================== +# ====================================================================================================================== +test_name = "3.2.3.NN_Use_WM" +if rank == 0: + print(test_name) + +# READING +st_time = timeit.default_timer() + +# Source data +src_nes = open_netcdf(src_path, parallel_method=parallel_method) +src_nes.keep_vars(var_list) +src_nes.load() + +# Destination Grid +lat_1 = 37 +lat_2 = 43 +lon_0 = -3 +lat_0 = 40 +nx = 397 +ny = 397 +inc_x = 4000 +inc_y = 4000 +x_0 = -807847.688 +y_0 = -797137.125 + +dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, + nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method, + times=src_nes.get_full_times()) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() + +interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='NN', wm=wm_nes) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() +interp_nes.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ====================================== Read_WM ===================================================== +# ====================================================================================================================== +test_name = "3.2.4.NN_Read_WM" +if rank == 0: + print(test_name) + +# READING +st_time = timeit.default_timer() + +# Source data +src_nes = open_netcdf(src_path, parallel_method=parallel_method) +src_nes.keep_vars(var_list) +src_nes.load() + +# Destination Grid +lat_1 = 37 +lat_2 = 43 +lon_0 = -3 +lat_0 = 40 +nx = 397 +ny = 397 +inc_x = 4000 +inc_y = 4000 +x_0 = -807847.688 +y_0 = -797137.125 + +dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, + nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method, + times=src_nes.get_full_times()) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() + +interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='NN', + weight_matrix_path="NN_WM_NAMEE_to_IP.nc") +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() +interp_nes.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) diff --git a/tests/3.3-test_horiz_interp_conservative.py b/tests/3.3-test_horiz_interp_conservative.py new file mode 100644 index 0000000000000000000000000000000000000000..51981244a7e9a54a3cbc38defe32f4fe9f000f44 --- /dev/null +++ b/tests/3.3-test_horiz_interp_conservative.py @@ -0,0 +1,234 @@ +#!/usr/bin/env python +import sys +import timeit +import pandas as pd +from mpi4py import MPI +from nes import * +import os, sys + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'Y' + +result_path = "Times_test_3.3_horiz_interp_conservative.py_{0}_{1:03d}.csv".format(parallel_method, size) +result = pd.DataFrame(index=['read', 'calculate', 'write'], + columns=['3.3.1.Only interp', '3.3.2.Create_WM', "3.3.3.Use_WM", "3.3.4.Read_WM"]) + +# NAMEE +src_path = "/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc" +var_list = ['O3'] + +# ====================================================================================================================== +# ====================================== Only interp ===================================================== +# ====================================================================================================================== + +test_name = '3.3.1.Only interp' +if rank == 0: + print(test_name) + +# READ +# final_dst.variables[var_name]['data'][time, lev] = np.sum(weights * src_aux, axis=1) + +st_time = timeit.default_timer() + +# Read source data +src_nes = open_netcdf(src_path, parallel_method=parallel_method) +src_nes.keep_vars(var_list) +src_nes.load() + +# Create destination grid +lat_1 = 37 +lat_2 = 43 +lon_0 = -3 +lat_0 = 40 +nx = 397 +ny = 397 +inc_x = 4000 +inc_y = 4000 +x_0 = -807847.688 +y_0 = -797137.125 +dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, + nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method, + times=src_nes.get_full_times()) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() + +# INTERPOLATE +interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative') +# interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', weight_matrix_path='T_WM.nc') +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# WRITE +st_time = timeit.default_timer() +interp_nes.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size), serial=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ====================================== Create_WM ===================================================== +# ====================================================================================================================== + +test_name = '3.3.2.Create_WM' +if rank == 0: + print(test_name) + +# READING +st_time = timeit.default_timer() + +# Read source data +src_nes = open_netcdf(src_path, parallel_method=parallel_method) +src_nes.keep_vars(var_list) +src_nes.load() + +# Create destination grid +lat_1 = 37 +lat_2 = 43 +lon_0 = -3 +lat_0 = 40 +nx = 397 +ny = 397 +inc_x = 4000 +inc_y = 4000 +x_0 = -807847.688 +y_0 = -797137.125 +dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, + nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method, + times=src_nes.get_full_times()) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# Cleaning WM +if os.path.exists("CONS_WM_NAMEE_to_IP.nc") and rank == 0: + os.remove("CONS_WM_NAMEE_to_IP.nc") +comm.Barrier() + +# INTERPOLATE +st_time = timeit.default_timer() +wm_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', info=True, + weight_matrix_path="CONS_WM_NAMEE_to_IP.nc", only_create_wm=True) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# WRITE +# st_time = timeit.default_timer() +# interp_nes.to_netcdf(test_name.replace(' ', '_') + ".nc") +# comm.Barrier() +# result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ====================================== Use_WM ===================================================== +# ====================================================================================================================== + +test_name = "3.3.3.Use_WM" +if rank == 0: + print(test_name) + +# READ +st_time = timeit.default_timer() + +# Read source data +src_nes = open_netcdf(src_path, parallel_method=parallel_method) +src_nes.keep_vars(var_list) +src_nes.load() + +# Create destination grid +lat_1 = 37 +lat_2 = 43 +lon_0 = -3 +lat_0 = 40 +nx = 397 +ny = 397 +inc_x = 4000 +inc_y = 4000 +x_0 = -807847.688 +y_0 = -797137.125 +dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, + nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method, + times=src_nes.get_full_times()) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# INTERPOLATE +st_time = timeit.default_timer() +interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', wm=wm_nes) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# WRITE +st_time = timeit.default_timer() +interp_nes.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# ====================================== Read_WM ===================================================== +# ====================================================================================================================== + +test_name = "3.3.4.Read_WM" +if rank == 0: + print(test_name) + +# READ +st_time = timeit.default_timer() + +# Read source data +src_nes = open_netcdf(src_path, parallel_method=parallel_method) +src_nes.keep_vars(var_list) +src_nes.load() + +# Create destination grid +lat_1 = 37 +lat_2 = 43 +lon_0 = -3 +lat_0 = 40 +nx = 397 +ny = 397 +inc_x = 4000 +inc_y = 4000 +x_0 = -807847.688 +y_0 = -797137.125 +dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, + nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method, + times=src_nes.get_full_times()) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# INTERPOLATE +st_time = timeit.default_timer() +interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', weight_matrix_path="CONS_WM_NAMEE_to_IP.nc") +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# WRITE +st_time = timeit.default_timer() +interp_nes.to_netcdf(test_name.replace(' ', '_') + "{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) diff --git a/tests/4-test_bounds.py b/tests/4-test_bounds.py deleted file mode 100644 index e3d3063fddd238f0dc601a6a1c2837e1bcd98e94..0000000000000000000000000000000000000000 --- a/tests/4-test_bounds.py +++ /dev/null @@ -1,41 +0,0 @@ -#!/usr/bin/env python -import sys -import timeit -import pandas as pd -from mpi4py import MPI -from nes import * - -comm = MPI.COMM_WORLD -rank = comm.Get_rank() - -for projection_type in ['read', 'created']: - - # Open dataset - if projection_type == 'read': - test_path = "/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc" - nessy = open_netcdf(path=test_path, info=True) - - # Create dataset - elif projection_type == 'created': - lat_orig = 41.1 - lon_orig = 1.8 - inc_lat = 0.2 - inc_lon = 0.2 - n_lat = 100 - n_lon = 100 - nessy = create_nes(comm=None, info=True, projection='regular', - lat_orig=lat_orig, lon_orig=lon_orig, - inc_lat=inc_lat, inc_lon=inc_lon, - n_lat=n_lat, n_lon=n_lon) - - # Add bounds - nessy.create_spatial_bounds() - - print('NES', projection_type, '-', 'Rank', rank, '-', nessy) - print('NES', projection_type, '-', 'Rank', rank, '-', 'Lat bounds', - nessy.lat_bnds) - print('NES', projection_type, '-', 'Rank', rank, '-', 'Lon bounds', - nessy.lon_bnds) - - comm.Barrier() - sys.stdout.flush() \ No newline at end of file diff --git a/tests/4-test_bounds_nord3v2.bash b/tests/4-test_bounds_nord3v2.bash deleted file mode 100644 index fc6e06b0824d8682c80372867da0d429df1a80b6..0000000000000000000000000000000000000000 --- a/tests/4-test_bounds_nord3v2.bash +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash - -EXPORTPATH="/esarchive/scratch/avilanova/software/NES" -SRCPATH="/esarchive/scratch/avilanova/software/NES/tests" -EXE="4-test_bounds.py" - -module purge -module load Python/3.7.4-GCCcore-8.3.0 -module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4 -module load cfunits/1.8-foss-2019b-Python-3.7.4 -module load xarray/0.17.0-foss-2019b-Python-3.7.4 -module load pandas/1.2.4-foss-2019b-Python-3.7.4 -module load mpi4py/3.0.3-foss-2019b-Python-3.7.4 -module load filelock/3.7.1-foss-2019b-Python-3.7.4 -module load pyproj/2.5.0-foss-2019b-Python-3.7.4 -module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4 -module load geopandas/0.8.1-foss-2019b-Python-3.7.4 -module load Shapely/1.7.1-foss-2019b-Python-3.7.4 - -for nprocs in 1 2 -do - JOB_ID=`sbatch --ntasks=${nprocs} --exclusive --job-name=nes_${nprocs} --output=./log_nord3v2_NES_${nprocs}_%J.out --error=./log_nord3v2_NES_${nprocs}_%J.err -D . --time=02:00:00 --wrap="export PYTHONPATH=${EXPORTPATH}:${PYTHONPATH}; cd ${SRCPATH}; mpirun --mca mpi_warn_on_fork 0 -np ${nprocs} python ${SRCPATH}/${EXE}"` -done \ No newline at end of file diff --git a/tests/4.1-test_daily_stats.py b/tests/4.1-test_daily_stats.py new file mode 100644 index 0000000000000000000000000000000000000000..dd00407b3206e3927de1bc3eb0dd5755b7c47ea7 --- /dev/null +++ b/tests/4.1-test_daily_stats.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python + +import sys +import timeit +import pandas as pd +from mpi4py import MPI +from nes import * + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'Y' + +result_path = "Times_test_4.1_daily_stats_{0}_{1:03d}.csv".format(parallel_method, size) +result = pd.DataFrame(index=['read', 'calculate', 'write'], + columns=['4.1.1.Mean']) + +# ====================================================================================================================== +# ================================================== CALCULATE MEAN ================================================== +# ====================================================================================================================== + +test_name = '4.1.1.Mean' +if rank == 0: + print(test_name) + +# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc +# Rotated grid from MONARCH +cams_file = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc' + +# READ +st_time = timeit.default_timer() +nessy = open_netcdf(path=cams_file, info=True, parallel_method=parallel_method) +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# LOAD VARIABLES +nessy.keep_vars('O3') +nessy.load() + +# CALCULATE MEAN +st_time = timeit.default_timer() +nessy.daily_statistic(op="mean") +print(nessy.variables['O3']['cell_methods']) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) diff --git a/tests/run_scalability_tests_nord3v2.sh b/tests/run_scalability_tests_nord3v2.sh new file mode 100644 index 0000000000000000000000000000000000000000..4e6a8fe63f92342b78e262d19dff9e70cf68cdd1 --- /dev/null +++ b/tests/run_scalability_tests_nord3v2.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +EXPORTPATH="/gpfs/scratch/bsc32/bsc32538/NES_tests/NES" +SRCPATH="/gpfs/scratch/bsc32/bsc32538/NES_tests/NES/tests" + +module purge +module load Python/3.7.4-GCCcore-8.3.0 +module load NES/1.0.0-nord3-v2-foss-2019b-Python-3.7.4 + + +for EXE in "1.1-test_read_write_projection.py" "1.2-test_create_projection.py" "1.3-test_selecting.py" "2.1-test_spatial_join.py" "2.2-test_create_shapefile.py" "2.3-test_bounds.py" "2.4-test_cell_area.py" "3.1-test_vertical_interp.py" "3.2-test_horiz_interp_bilinear.py" "3.3-test_horiz_interp_conservative.py" "4.1-test_daily_stats.py" + do + for nprocs in 1 2 4 8 16 + do + JOB_ID=`sbatch --ntasks=${nprocs} --qos=debug --exclusive --job-name=NES_${EXE}_${nprocs} --output=./log_NES-${EXE}_nord3v2_${nprocs}_%J.out --error=./log_NES-${EXE}_nord3v2_${nprocs}_%J.err -D . --time=02:00:00 --wrap="export PYTHONPATH=${EXPORTPATH}:${PYTHONPATH}; cd ${SRCPATH}; mpirun --mca mpi_warn_on_fork 0 -np ${nprocs} python ${SRCPATH}/${EXE}"` + done + done diff --git a/tests/test_bash_mn4.cmd b/tests/test_bash_mn4.cmd index db2758fcffd54ce03e81b5a2b4e4d8af072ea07e..f8663a1d6f5d79192ab77f1418acb5e596cbe98c 100644 --- a/tests/test_bash_mn4.cmd +++ b/tests/test_bash_mn4.cmd @@ -4,10 +4,10 @@ #SBATCH -A bsc32 #SBATCH --cpus-per-task=1 #SBATCH -n 4 -#SBATCH -t 00:30:00 -#SBATCH -J test_nes -#SBATCH --output=log_mn4_NES_%j.out -#SBATCH --error=log_mn4_NES_%j.err +#SBATCH -t 02:00:00 +#SBATCH -J NES-test +#SBATCH --output=log_NES-tests_mn4_%j.out +#SBATCH --error=log_NES-tests_mn4_%j.err #SBATCH --exclusive ### ulimit -s 128000 @@ -15,18 +15,22 @@ module purge module use /gpfs/projects/bsc32/software/suselinux/11/modules/all -module load Python/3.7.4-GCCcore-8.3.0 -module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4 -module load cfunits/1.8-foss-2019b-Python-3.7.4 -module load xarray/0.17.0-foss-2019b-Python-3.7.4 +module load NES/1.1.0-mn4-foss-2019b-Python-3.7.4 module load OpenMPI/4.0.5-GCC-8.3.0-mn4 -module load filelock/3.7.1-foss-2019b-Python-3.7.4 -module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4 -module load pyproj/2.5.0-foss-2019b-Python-3.7.4 -module load geopandas/0.8.1-foss-2019b-Python-3.7.4 -module load Shapely/1.7.1-foss-2019b-Python-3.7.4 -export PYTHONPATH=/gpfs/projects/bsc32/models/NES:${PYTHONPATH} -cd /gpfs/projects/bsc32/models/NES/tests +cd /gpfs/projects/bsc32/models/NES_master/tests -mpirun --mca mpi_warn_on_fork 0 -np 4 python basic_nes_tests.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 1.1-test_read_write_projection.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 1.2-test_create_projection.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 1.3-test_selecting.py + +mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.1-test_spatial_join.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.2-test_create_shapefile.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.3-test_bounds.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.4-test_cell_area.py + +mpirun --mca mpi_warn_on_fork 0 -np 4 python 3.1-test_vertical_interp.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 3.2-test_horiz_interp_bilinear.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 3.3-test_horiz_interp_conservative.py + +mpirun --mca mpi_warn_on_fork 0 -np 4 python 4.1-test_daily_stats.py diff --git a/tests/test_bash_nord3v2.cmd b/tests/test_bash_nord3v2.cmd index da0f4d7bf1829f732d82e21237872333048060c8..15f22c9d30942e23ebb094f55a508c8944f70c45 100644 --- a/tests/test_bash_nord3v2.cmd +++ b/tests/test_bash_nord3v2.cmd @@ -1,32 +1,34 @@ #!/bin/bash -####SBATCH --qos=debug +#SBATCH --qos=debug #SBATCH -A bsc32 #SBATCH --cpus-per-task=1 #SBATCH -n 4 -#SBATCH -t 00:10:00 -#SBATCH -J test_nes -#SBATCH --output=log_nord3v2_NES_%j.out -#SBATCH --error=log_nord3v2_NES_%j.err +#SBATCH -t 02:00:00 +#SBATCH -J NES-test +#SBATCH --output=log_NES-tests_nord3v2_%j.out +#SBATCH --error=log_NES-tests_nord3v2_%j.err #SBATCH --exclusive ### ulimit -s 128000 module purge -module load Python/3.7.4-GCCcore-8.3.0 -module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4 -module load cfunits/1.8-foss-2019b-Python-3.7.4 -module load xarray/0.17.0-foss-2019b-Python-3.7.4 -module load pandas/1.2.4-foss-2019b-Python-3.7.4 -module load mpi4py/3.0.3-foss-2019b-Python-3.7.4 -module load filelock/3.7.1-foss-2019b-Python-3.7.4 -module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4 -module load pyproj/2.5.0-foss-2019b-Python-3.7.4 -module load geopandas/0.8.1-foss-2019b-Python-3.7.4 -module load Shapely/1.7.1-foss-2019b-Python-3.7.4 - -export PYTHONPATH=/gpfs/projects/bsc32/models/NES:${PYTHONPATH} -cd /gpfs/projects/bsc32/models/NES/tests - -mpirun --mca mpi_warn_on_fork 0 -np 4 python 2-nes_tests_by_projection.py +module load NES/1.1.0-nord3-v2-foss-2019b-Python-3.7.4 + +cd /gpfs/projects/bsc32/models/NES_master/tests + +mpirun --mca mpi_warn_on_fork 0 -np 4 python 1.1-test_read_write_projection.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 1.2-test_create_projection.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 1.3-test_selecting.py + +mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.1-test_spatial_join.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.2-test_create_shapefile.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.3-test_bounds.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 2.4-test_cell_area.py + +mpirun --mca mpi_warn_on_fork 0 -np 4 python 3.1-test_vertical_interp.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 3.2-test_horiz_interp_bilinear.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 3.3-test_horiz_interp_conservative.py + +mpirun --mca mpi_warn_on_fork 0 -np 4 python 4.1-test_daily_stats.py diff --git a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb index 90d1d0cb6f733e4624976371a0694ab5918bb2e2..06542ff4bd7c67656035b23c128e50d3a662da03 100644 --- a/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb +++ b/tutorials/1.Introduction/1.1.Read_Write_Regular.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# How to read and write regular grids" + "# How to read and write regular lat-lon grids" ] }, { @@ -13,8 +13,6 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", - "from netCDF4 import Dataset\n", "from nes import *" ] }, @@ -24,7 +22,9 @@ "metadata": {}, "outputs": [], "source": [ - "nc_path_1 = '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2008123100.nc'" + "# Original path: /gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc\n", + "# Regular lat-lon grid from MONARCH\n", + "nc_path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/franco_interp.nc'" ] }, { @@ -34,1207 +34,15 @@ "## 1. Read dataset" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Open with xarray" - ] - }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'IM' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'JM' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'LM' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'IHRST' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'I_PAR_STA' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'J_PAR_STA' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'NPHS' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'NCLOD' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'NHEAT' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'NPREC' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'NRDLW' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'NRDSW' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'NSRFC' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'AVGMAXLEN' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'MDRMINout' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'MDRMAXout' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'MDIMINout' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'MDIMAXout' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'DXH' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SG1' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SG2' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'DSG1' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'DSG2' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SGML1' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SGML2' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SLDPTH' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ISLTYP' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'IVGTYP' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'NCFRCV' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'NCFRST' has multiple fill values {-999999, -32767}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'FIS' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'GLAT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'GLON' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'PD' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'VLAT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'VLON' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ACFRCV' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ACFRST' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ACPREC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ACSNOM' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ACSNOW' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'AKHSAVG' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'AKMSAVG' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ALBASE' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ALBEDO' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ALWIN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ALWOUT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ALWTOA' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ASWIN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ASWOUT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ASWTOA' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'BGROFF' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CFRACH' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CFRACL' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CFRACM' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CLDEFI' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CMC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CNVBOT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CNVTOP' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CPRATE' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CUPPT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CUPREC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CZEN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CZMEAN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'DNVVELMAX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'EPSR' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'GRNFLX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'HBOTD' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'HBOTS' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'HTOPD' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'HTOPS' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'MIXHT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'MXSNAL' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'PBLH' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'POTEVP' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'PREC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'PSFCAVG' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'PSHLTR' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'RH02MAX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'RH02MIN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'T02MAX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'T02MIN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'T10' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'T10AVG' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'Q10' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'QSH' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'QSHLTR' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'QWBS' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'QZ0' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'RADOT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'REFDMAX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'RLWIN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'RLWTOA' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'RSWIN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'RSWINC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'RSWOUT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SFCEVP' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SFCEXC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SFCLHX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SFCSHX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SI' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SICE' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SIGT4' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SM' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SMSTAV' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SMSTOT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SNO' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SNOAVG' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SNOPCX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SOILTB' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SR' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SSROFF' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SST' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SUBSHX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'TG' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'TH10' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'THS' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'THZ0' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'TSHLTR' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'TWBS' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'UPHLMAX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'UPVVELMAX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'U10' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'U10MAX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'USTAR' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'UZ0' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'V10' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'V10MAX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'VEGFRC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'VZ0' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'Z0' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'RSWTOA' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'POTFLX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'T2' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'PSFC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'TLMIN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'TLMAX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'LSPA' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ACUTIM' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'APHTIM' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ARDLW' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ARDSW' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ASRFC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'AVRAIN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'AVCNVC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ROUGHCOR' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SMOISCOR' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'relative_humidity_2m' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'W' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'W_TOT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'OMGALF' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'O3' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CLDFRA' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'CW' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'EXCH_H' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'Q' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'Q2' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'RLWTT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'RSWTT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'PINT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'DWDT' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'T' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'TCUCN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'TRAIN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'U' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'V' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'XLEN_MIX' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'F_ICE' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'F_RIMEF' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'F_RAIN' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SH2O' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'SMC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'STC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'AERO_ACPREC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'AERO_CUPREC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'AERO_DEPDRY' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'AERO_OPT_R' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'DRE_SW_TOA' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'DRE_SW_SFC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'DRE_LW_TOA' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'DRE_LW_SFC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ENG_SW_SFC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'EMISS_AERO' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'ADRYDEP' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'WETDEP' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'PH_NO2' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'AEROSSA' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aerosol_optical_depth' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'satellite_AOD' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aerosol_loading' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'clear_sky_AOD' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'layer_thickness' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'mid_layer_pressure' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'interface_pressure' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'relative_humidity' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'mid_layer_height' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'mid_layer_height_agl' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'air_density' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'dry_pm10_mass' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'dry_pm2p5_mass' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'QC' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'QR' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'QS' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'QG' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_dust_001' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_dust_002' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_dust_003' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_dust_004' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_dust_005' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_dust_006' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_dust_007' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_dust_008' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_001' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_002' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_003' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_004' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_005' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_006' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_007' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_008' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_009' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_010' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_011' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_012' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_013' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_014' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_015' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_016' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_017' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_018' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_019' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_020' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_021' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_022' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_023' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_024' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_025' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_026' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_027' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_028' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_029' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_030' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_031' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_032' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_033' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_034' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_035' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_036' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_037' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_038' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_039' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_040' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_041' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_042' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_043' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_044' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_045' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_046' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_047' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_048' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_049' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_050' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_051' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_052' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_053' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_054' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_055' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_056' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_057' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_058' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_059' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_060' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_061' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_062' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_063' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_064' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_065' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_066' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_067' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_068' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_069' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_070' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_071' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_072' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_073' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_074' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_075' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_076' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_077' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_078' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_079' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aero_flex_080' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'MPRATES_001' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aerosol_extinction_DUST_1' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aerosol_extinction_DUST_2' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aerosol_extinction_DUST_3' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aerosol_extinction_DUST_4' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aerosol_extinction_DUST_5' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aerosol_extinction_DUST_6' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aerosol_extinction_DUST_7' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n", - "/gpfs/projects/bsc32/software/suselinux/11/software/xarray/0.19.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/xarray/conventions.py:520: SerializationWarning: variable 'aerosol_extinction_DUST_8' has multiple fill values {-999999.0, -32767.0}, decoding all values to NaN.\n", - " decode_timedelta=decode_timedelta,\n" - ] - }, { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:                    (time: 9, lm: 48, lmp: 49, lon: 257, lat: 181, idat: 3, soil_lm: 4, num_aero: 88, num_aero_r: 3, num_aero_1: 89, num_engy: 7, num_gas_total: 1, aerosol_optical_depth_dim: 8, satellite_AOD_dim: 2, aerosol_loading_dim: 8, clear_sky_AOD_dim: 8, aerosol_extinction_dim: 8)\n",
-       "Coordinates:\n",
-       "  * time                       (time) datetime64[ns] 2008-12-31 ... 2009-01-01\n",
-       "  * lm                         (lm) int32 0 1 2 3 4 5 6 ... 41 42 43 44 45 46 47\n",
-       "  * lmp                        (lmp) int32 0 1 2 3 4 5 6 ... 43 44 45 46 47 48\n",
-       "  * lon                        (lon) float32 -180.0 -178.6 ... 178.6 180.0\n",
-       "  * lat                        (lat) float32 -90.0 -89.0 -88.0 ... 89.0 90.0\n",
-       "  * aerosol_optical_depth_dim  (aerosol_optical_depth_dim) |S100 b'DUST_1    ...\n",
-       "  * satellite_AOD_dim          (satellite_AOD_dim) |S100 b'MODIS TERRA 550 nm...\n",
-       "  * aerosol_loading_dim        (aerosol_loading_dim) |S100 b'DUST_1          ...\n",
-       "  * clear_sky_AOD_dim          (clear_sky_AOD_dim) |S100 b'DUST_1            ...\n",
-       "  * aerosol_extinction_dim     (aerosol_extinction_dim) |S100 b'DUST_1       ...\n",
-       "Dimensions without coordinates: idat, soil_lm, num_aero, num_aero_r, num_aero_1, num_engy, num_gas_total\n",
-       "Data variables: (12/302)\n",
-       "    IM                         float64 257.0\n",
-       "    JM                         float64 181.0\n",
-       "    LM                         float64 48.0\n",
-       "    IHRST                      float64 0.0\n",
-       "    I_PAR_STA                  float64 1.0\n",
-       "    J_PAR_STA                  float64 1.0\n",
-       "    ...                         ...\n",
-       "    aerosol_extinction_DUST_3  (time, lm, lat, lon) float32 ...\n",
-       "    aerosol_extinction_DUST_4  (time, lm, lat, lon) float32 ...\n",
-       "    aerosol_extinction_DUST_5  (time, lm, lat, lon) float32 ...\n",
-       "    aerosol_extinction_DUST_6  (time, lm, lat, lon) float32 ...\n",
-       "    aerosol_extinction_DUST_7  (time, lm, lat, lon) float32 ...\n",
-       "    aerosol_extinction_DUST_8  (time, lm, lat, lon) float32 ...\n",
-       "Attributes:\n",
-       "    Domain:       Global\n",
-       "    Conventions:  None\n",
-       "    history:      MONARCHv1.0 netcdf file.\n",
-       "    comment:      Generated on marenostrum4
" - ], "text/plain": [ - "\n", - "Dimensions: (time: 9, lm: 48, lmp: 49, lon: 257, lat: 181, idat: 3, soil_lm: 4, num_aero: 88, num_aero_r: 3, num_aero_1: 89, num_engy: 7, num_gas_total: 1, aerosol_optical_depth_dim: 8, satellite_AOD_dim: 2, aerosol_loading_dim: 8, clear_sky_AOD_dim: 8, aerosol_extinction_dim: 8)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2008-12-31 ... 2009-01-01\n", - " * lm (lm) int32 0 1 2 3 4 5 6 ... 41 42 43 44 45 46 47\n", - " * lmp (lmp) int32 0 1 2 3 4 5 6 ... 43 44 45 46 47 48\n", - " * lon (lon) float32 -180.0 -178.6 ... 178.6 180.0\n", - " * lat (lat) float32 -90.0 -89.0 -88.0 ... 89.0 90.0\n", - " * aerosol_optical_depth_dim (aerosol_optical_depth_dim) |S100 b'DUST_1 ...\n", - " * satellite_AOD_dim (satellite_AOD_dim) |S100 b'MODIS TERRA 550 nm...\n", - " * aerosol_loading_dim (aerosol_loading_dim) |S100 b'DUST_1 ...\n", - " * clear_sky_AOD_dim (clear_sky_AOD_dim) |S100 b'DUST_1 ...\n", - " * aerosol_extinction_dim (aerosol_extinction_dim) |S100 b'DUST_1 ...\n", - "Dimensions without coordinates: idat, soil_lm, num_aero, num_aero_r, num_aero_1, num_engy, num_gas_total\n", - "Data variables: (12/302)\n", - " IM float64 ...\n", - " JM float64 ...\n", - " LM float64 ...\n", - " IHRST float64 ...\n", - " I_PAR_STA float64 ...\n", - " J_PAR_STA float64 ...\n", - " ... ...\n", - " aerosol_extinction_DUST_3 (time, lm, lat, lon) float32 ...\n", - " aerosol_extinction_DUST_4 (time, lm, lat, lon) float32 ...\n", - " aerosol_extinction_DUST_5 (time, lm, lat, lon) float32 ...\n", - " aerosol_extinction_DUST_6 (time, lm, lat, lon) float32 ...\n", - " aerosol_extinction_DUST_7 (time, lm, lat, lon) float32 ...\n", - " aerosol_extinction_DUST_8 (time, lm, lat, lon) float32 ...\n", - "Attributes:\n", - " Domain: Global\n", - " Conventions: None\n", - " history: MONARCHv1.0 netcdf file.\n", - " comment: Generated on marenostrum4" + "" ] }, "execution_count": 4, @@ -1242,33 +50,6 @@ "output_type": "execute_result" } ], - "source": [ - "xr.open_dataset(nc_path_1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Open with NES" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "nessy_1 = open_netcdf(path=nc_path_1, info=True)\n", "nessy_1" @@ -1276,24 +57,40 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[datetime.datetime(2008, 12, 31, 0, 0),\n", - " datetime.datetime(2008, 12, 31, 3, 0),\n", - " datetime.datetime(2008, 12, 31, 6, 0),\n", - " datetime.datetime(2008, 12, 31, 9, 0),\n", - " datetime.datetime(2008, 12, 31, 12, 0),\n", - " datetime.datetime(2008, 12, 31, 15, 0),\n", - " datetime.datetime(2008, 12, 31, 18, 0),\n", - " datetime.datetime(2008, 12, 31, 21, 0),\n", - " datetime.datetime(2009, 1, 1, 0, 0)]" + "[datetime.datetime(2019, 1, 1, 0, 0),\n", + " datetime.datetime(2019, 1, 1, 1, 0),\n", + " datetime.datetime(2019, 1, 1, 2, 0),\n", + " datetime.datetime(2019, 1, 1, 3, 0),\n", + " datetime.datetime(2019, 1, 1, 4, 0),\n", + " datetime.datetime(2019, 1, 1, 5, 0),\n", + " datetime.datetime(2019, 1, 1, 6, 0),\n", + " datetime.datetime(2019, 1, 1, 7, 0),\n", + " datetime.datetime(2019, 1, 1, 8, 0),\n", + " datetime.datetime(2019, 1, 1, 9, 0),\n", + " datetime.datetime(2019, 1, 1, 10, 0),\n", + " datetime.datetime(2019, 1, 1, 11, 0),\n", + " datetime.datetime(2019, 1, 1, 12, 0),\n", + " datetime.datetime(2019, 1, 1, 13, 0),\n", + " datetime.datetime(2019, 1, 1, 14, 0),\n", + " datetime.datetime(2019, 1, 1, 15, 0),\n", + " datetime.datetime(2019, 1, 1, 16, 0),\n", + " datetime.datetime(2019, 1, 1, 17, 0),\n", + " datetime.datetime(2019, 1, 1, 18, 0),\n", + " datetime.datetime(2019, 1, 1, 19, 0),\n", + " datetime.datetime(2019, 1, 1, 20, 0),\n", + " datetime.datetime(2019, 1, 1, 21, 0),\n", + " datetime.datetime(2019, 1, 1, 22, 0),\n", + " datetime.datetime(2019, 1, 1, 23, 0),\n", + " datetime.datetime(2019, 1, 2, 0, 0)]" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1304,25 +101,21 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'data': masked_array(data=[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,\n", - " 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,\n", - " 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,\n", - " 42, 43, 44, 45, 46, 47],\n", + "{'data': masked_array(data=[0.],\n", " mask=False,\n", - " fill_value=999999,\n", - " dtype=int32),\n", - " 'dimensions': ('lm',),\n", - " 'units': '',\n", - " 'long_name': 'layer id'}" + " fill_value=1e+20),\n", + " 'dimensions': ('lev',),\n", + " 'units': '1',\n", + " 'positive': 'up'}" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1333,43 +126,35 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'data': masked_array(data=[-90., -89., -88., -87., -86., -85., -84., -83., -82.,\n", - " -81., -80., -79., -78., -77., -76., -75., -74., -73.,\n", - " -72., -71., -70., -69., -68., -67., -66., -65., -64.,\n", - " -63., -62., -61., -60., -59., -58., -57., -56., -55.,\n", - " -54., -53., -52., -51., -50., -49., -48., -47., -46.,\n", - " -45., -44., -43., -42., -41., -40., -39., -38., -37.,\n", - " -36., -35., -34., -33., -32., -31., -30., -29., -28.,\n", - " -27., -26., -25., -24., -23., -22., -21., -20., -19.,\n", - " -18., -17., -16., -15., -14., -13., -12., -11., -10.,\n", - " -9., -8., -7., -6., -5., -4., -3., -2., -1.,\n", - " 0., 1., 2., 3., 4., 5., 6., 7., 8.,\n", - " 9., 10., 11., 12., 13., 14., 15., 16., 17.,\n", - " 18., 19., 20., 21., 22., 23., 24., 25., 26.,\n", - " 27., 28., 29., 30., 31., 32., 33., 34., 35.,\n", - " 36., 37., 38., 39., 40., 41., 42., 43., 44.,\n", - " 45., 46., 47., 48., 49., 50., 51., 52., 53.,\n", - " 54., 55., 56., 57., 58., 59., 60., 61., 62.,\n", - " 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n", - " 72., 73., 74., 75., 76., 77., 78., 79., 80.,\n", - " 81., 82., 83., 84., 85., 86., 87., 88., 89.,\n", - " 90.],\n", + "{'data': masked_array(data=[33.9, 34. , 34.1, 34.2, 34.3, 34.4, 34.5, 34.6, 34.7,\n", + " 34.8, 34.9, 35. , 35.1, 35.2, 35.3, 35.4, 35.5, 35.6,\n", + " 35.7, 35.8, 35.9, 36. , 36.1, 36.2, 36.3, 36.4, 36.5,\n", + " 36.6, 36.7, 36.8, 36.9, 37. , 37.1, 37.2, 37.3, 37.4,\n", + " 37.5, 37.6, 37.7, 37.8, 37.9, 38. , 38.1, 38.2, 38.3,\n", + " 38.4, 38.5, 38.6, 38.7, 38.8, 38.9, 39. , 39.1, 39.2,\n", + " 39.3, 39.4, 39.5, 39.6, 39.7, 39.8, 39.9, 40. , 40.1,\n", + " 40.2, 40.3, 40.4, 40.5, 40.6, 40.7, 40.8, 40.9, 41. ,\n", + " 41.1, 41.2, 41.3, 41.4, 41.5, 41.6, 41.7, 41.8, 41.9,\n", + " 42. , 42.1, 42.2, 42.3, 42.4, 42.5, 42.6, 42.7, 42.8,\n", + " 42.9, 43. , 43.1, 43.2, 43.3, 43.4, 43.5, 43.6, 43.7,\n", + " 43.8, 43.9, 44. , 44.1, 44.2, 44.3, 44.4, 44.5, 44.6,\n", + " 44.7, 44.8, 44.9, 45. , 45.1, 45.2, 45.3],\n", " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", + " fill_value=1e+20),\n", " 'dimensions': ('lat',),\n", - " 'long_name': 'latitude',\n", " 'units': 'degrees_north',\n", - " 'standard_name': 'grid_latitude'}" + " 'axis': 'Y',\n", + " 'long_name': 'latitude coordinate',\n", + " 'standard_name': 'latitude'}" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1380,87 +165,77 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'data': masked_array(data=[-180. , -178.59375, -177.1875 , -175.78125,\n", - " -174.375 , -172.96875, -171.5625 , -170.15625,\n", - " -168.75 , -167.34375, -165.9375 , -164.53125,\n", - " -163.125 , -161.71875, -160.3125 , -158.90625,\n", - " -157.5 , -156.09375, -154.6875 , -153.28125,\n", - " -151.875 , -150.46875, -149.0625 , -147.65625,\n", - " -146.25 , -144.84375, -143.4375 , -142.03125,\n", - " -140.625 , -139.21875, -137.8125 , -136.40625,\n", - " -135. , -133.59375, -132.1875 , -130.78125,\n", - " -129.375 , -127.96875, -126.5625 , -125.15625,\n", - " -123.75 , -122.34375, -120.9375 , -119.53125,\n", - " -118.125 , -116.71875, -115.3125 , -113.90625,\n", - " -112.5 , -111.09375, -109.6875 , -108.28125,\n", - " -106.875 , -105.46875, -104.0625 , -102.65625,\n", - " -101.25 , -99.84375, -98.4375 , -97.03125,\n", - " -95.625 , -94.21875, -92.8125 , -91.40625,\n", - " -90. , -88.59375, -87.1875 , -85.78125,\n", - " -84.375 , -82.96875, -81.5625 , -80.15625,\n", - " -78.75 , -77.34375, -75.9375 , -74.53125,\n", - " -73.125 , -71.71875, -70.3125 , -68.90625,\n", - " -67.5 , -66.09375, -64.6875 , -63.28125,\n", - " -61.875 , -60.46875, -59.0625 , -57.65625,\n", - " -56.25 , -54.84375, -53.4375 , -52.03125,\n", - " -50.625 , -49.21875, -47.8125 , -46.40625,\n", - " -45. , -43.59375, -42.1875 , -40.78125,\n", - " -39.375 , -37.96875, -36.5625 , -35.15625,\n", - " -33.75 , -32.34375, -30.9375 , -29.53125,\n", - " -28.125 , -26.71875, -25.3125 , -23.90625,\n", - " -22.5 , -21.09375, -19.6875 , -18.28125,\n", - " -16.875 , -15.46875, -14.0625 , -12.65625,\n", - " -11.25 , -9.84375, -8.4375 , -7.03125,\n", - " -5.625 , -4.21875, -2.8125 , -1.40625,\n", - " 0. , 1.40625, 2.8125 , 4.21875,\n", - " 5.625 , 7.03125, 8.4375 , 9.84375,\n", - " 11.25 , 12.65625, 14.0625 , 15.46875,\n", - " 16.875 , 18.28125, 19.6875 , 21.09375,\n", - " 22.5 , 23.90625, 25.3125 , 26.71875,\n", - " 28.125 , 29.53125, 30.9375 , 32.34375,\n", - " 33.75 , 35.15625, 36.5625 , 37.96875,\n", - " 39.375 , 40.78125, 42.1875 , 43.59375,\n", - " 45. , 46.40625, 47.8125 , 49.21875,\n", - " 50.625 , 52.03125, 53.4375 , 54.84375,\n", - " 56.25 , 57.65625, 59.0625 , 60.46875,\n", - " 61.875 , 63.28125, 64.6875 , 66.09375,\n", - " 67.5 , 68.90625, 70.3125 , 71.71875,\n", - " 73.125 , 74.53125, 75.9375 , 77.34375,\n", - " 78.75 , 80.15625, 81.5625 , 82.96875,\n", - " 84.375 , 85.78125, 87.1875 , 88.59375,\n", - " 90. , 91.40625, 92.8125 , 94.21875,\n", - " 95.625 , 97.03125, 98.4375 , 99.84375,\n", - " 101.25 , 102.65625, 104.0625 , 105.46875,\n", - " 106.875 , 108.28125, 109.6875 , 111.09375,\n", - " 112.5 , 113.90625, 115.3125 , 116.71875,\n", - " 118.125 , 119.53125, 120.9375 , 122.34375,\n", - " 123.75 , 125.15625, 126.5625 , 127.96875,\n", - " 129.375 , 130.78125, 132.1875 , 133.59375,\n", - " 135. , 136.40625, 137.8125 , 139.21875,\n", - " 140.625 , 142.03125, 143.4375 , 144.84375,\n", - " 146.25 , 147.65625, 149.0625 , 150.46875,\n", - " 151.875 , 153.28125, 154.6875 , 156.09375,\n", - " 157.5 , 158.90625, 160.3125 , 161.71875,\n", - " 163.125 , 164.53125, 165.9375 , 167.34375,\n", - " 168.75 , 170.15625, 171.5625 , 172.96875,\n", - " 174.375 , 175.78125, 177.1875 , 178.59375,\n", - " 180. ],\n", + "{'data': masked_array(data=[-1.18000000e+01, -1.17000000e+01, -1.16000000e+01,\n", + " -1.15000000e+01, -1.14000000e+01, -1.13000000e+01,\n", + " -1.12000000e+01, -1.11000000e+01, -1.10000000e+01,\n", + " -1.09000000e+01, -1.08000000e+01, -1.07000000e+01,\n", + " -1.06000000e+01, -1.05000000e+01, -1.04000000e+01,\n", + " -1.03000000e+01, -1.02000000e+01, -1.01000000e+01,\n", + " -1.00000000e+01, -9.90000000e+00, -9.80000000e+00,\n", + " -9.70000000e+00, -9.60000000e+00, -9.50000000e+00,\n", + " -9.40000000e+00, -9.30000000e+00, -9.20000000e+00,\n", + " -9.10000000e+00, -9.00000000e+00, -8.90000000e+00,\n", + " -8.80000000e+00, -8.70000000e+00, -8.60000000e+00,\n", + " -8.50000000e+00, -8.40000000e+00, -8.30000000e+00,\n", + " -8.20000000e+00, -8.10000000e+00, -8.00000000e+00,\n", + " -7.90000000e+00, -7.80000000e+00, -7.70000000e+00,\n", + " -7.60000000e+00, -7.50000000e+00, -7.40000000e+00,\n", + " -7.30000000e+00, -7.20000000e+00, -7.10000000e+00,\n", + " -7.00000000e+00, -6.90000000e+00, -6.80000000e+00,\n", + " -6.70000000e+00, -6.60000000e+00, -6.50000000e+00,\n", + " -6.40000000e+00, -6.30000000e+00, -6.20000000e+00,\n", + " -6.10000000e+00, -6.00000000e+00, -5.90000000e+00,\n", + " -5.80000000e+00, -5.70000000e+00, -5.60000000e+00,\n", + " -5.50000000e+00, -5.40000000e+00, -5.30000000e+00,\n", + " -5.20000000e+00, -5.10000000e+00, -5.00000000e+00,\n", + " -4.90000000e+00, -4.80000000e+00, -4.70000000e+00,\n", + " -4.60000000e+00, -4.50000000e+00, -4.40000000e+00,\n", + " -4.30000000e+00, -4.20000000e+00, -4.10000000e+00,\n", + " -4.00000000e+00, -3.90000000e+00, -3.80000000e+00,\n", + " -3.70000000e+00, -3.60000000e+00, -3.50000000e+00,\n", + " -3.40000000e+00, -3.30000000e+00, -3.20000000e+00,\n", + " -3.10000000e+00, -3.00000000e+00, -2.90000000e+00,\n", + " -2.80000000e+00, -2.70000000e+00, -2.60000000e+00,\n", + " -2.50000000e+00, -2.40000000e+00, -2.30000000e+00,\n", + " -2.20000000e+00, -2.10000000e+00, -2.00000000e+00,\n", + " -1.90000000e+00, -1.80000000e+00, -1.70000000e+00,\n", + " -1.60000000e+00, -1.50000000e+00, -1.40000000e+00,\n", + " -1.30000000e+00, -1.20000000e+00, -1.10000000e+00,\n", + " -1.00000000e+00, -9.00000000e-01, -8.00000000e-01,\n", + " -7.00000000e-01, -6.00000000e-01, -5.00000000e-01,\n", + " -4.00000000e-01, -3.00000000e-01, -2.00000000e-01,\n", + " -1.00000000e-01, 3.55271368e-15, 1.00000000e-01,\n", + " 2.00000000e-01, 3.00000000e-01, 4.00000000e-01,\n", + " 5.00000000e-01, 6.00000000e-01, 7.00000000e-01,\n", + " 8.00000000e-01, 9.00000000e-01, 1.00000000e+00,\n", + " 1.10000000e+00, 1.20000000e+00, 1.30000000e+00,\n", + " 1.40000000e+00, 1.50000000e+00, 1.60000000e+00,\n", + " 1.70000000e+00, 1.80000000e+00, 1.90000000e+00,\n", + " 2.00000000e+00, 2.10000000e+00, 2.20000000e+00,\n", + " 2.30000000e+00, 2.40000000e+00, 2.50000000e+00,\n", + " 2.60000000e+00, 2.70000000e+00, 2.80000000e+00,\n", + " 2.90000000e+00, 3.00000000e+00, 3.10000000e+00,\n", + " 3.20000000e+00, 3.30000000e+00, 3.40000000e+00,\n", + " 3.50000000e+00, 3.60000000e+00, 3.70000000e+00,\n", + " 3.80000000e+00, 3.90000000e+00, 4.00000000e+00,\n", + " 4.10000000e+00, 4.20000000e+00, 4.30000000e+00,\n", + " 4.40000000e+00, 4.50000000e+00, 4.60000000e+00],\n", " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", + " fill_value=1e+20),\n", " 'dimensions': ('lon',),\n", - " 'long_name': 'longitude',\n", " 'units': 'degrees_east',\n", + " 'axis': 'X',\n", + " 'long_name': 'longitude coordinate',\n", " 'standard_name': 'longitude'}" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1471,24 +246,24 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "nessy_1.keep_vars('O3')" + "nessy_1.keep_vars('sconcno2')" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Rank 000: Loading O3 var (1/1)\n", - "Rank 000: Loaded O3 var ((9, 48, 181, 257))\n" + "Rank 000: Loading sconcno2 var (1/1)\n", + "Rank 000: Loaded sconcno2 var ((25, 1, 115, 165))\n" ] } ], @@ -1498,330 +273,114 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'O3': {'data': masked_array(\n", - " data=[[[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " ...,\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]],\n", - " \n", - " \n", - " [[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " ...,\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]],\n", - " \n", - " \n", - " [[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " ...,\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]],\n", + "{'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", + " \n", + " \n", + " [[[0.00037584, 0.0003461 , 0.0003287 , ..., 0.00119401,\n", + " 0.00108016, 0.00106187],\n", + " [0.0003455 , 0.00032571, 0.00031077, ..., 0.00106491,\n", + " 0.00102893, 0.00102203],\n", + " [0.0003218 , 0.00031359, 0.00030532, ..., 0.00102925,\n", + " 0.0009996 , 0.00102999],\n", + " ...,\n", + " [0.00147903, 0.00146823, 0.00138363, ..., 0.00352023,\n", + " 0.00391141, 0.00457858],\n", + " [0.00131989, 0.00132148, 0.00125568, ..., 0.00400305,\n", + " 0.00434196, 0.00503274],\n", + " [0.00120665, 0.00120067, 0.00116059, ..., 0.00445699,\n", + " 0.00476228, 0.00503062]]],\n", + " \n", + " \n", + " [[[0.00033761, 0.00031193, 0.00029469, ..., 0.0012606 ,\n", + " 0.00115534, 0.00112762],\n", + " [0.00031313, 0.00029929, 0.00028769, ..., 0.00112673,\n", + " 0.00108856, 0.00107317],\n", + " [0.00029702, 0.00029466, 0.00028725, ..., 0.00104992,\n", + " 0.00100861, 0.00103601],\n", + " ...,\n", + " [0.00140104, 0.00139278, 0.00131915, ..., 0.00326939,\n", + " 0.00364619, 0.0043329 ],\n", + " [0.00127264, 0.00124479, 0.00116947, ..., 0.0037481 ,\n", + " 0.00409988, 0.0048346 ],\n", + " [0.00115889, 0.00114078, 0.00109094, ..., 0.00419286,\n", + " 0.00453428, 0.00482921]]],\n", " \n", " \n", " ...,\n", " \n", " \n", - " [[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " ...,\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]],\n", - " \n", - " \n", - " [[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " ...,\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]],\n", - " \n", - " \n", - " [[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " ...,\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]],\n", - " \n", - " [[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]]],\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, 0.00333072]]],\n", + " \n", + " \n", + " [[[0.00038904, 0.00037653, 0.00034856, ..., 0.00085969,\n", + " 0.00084833, 0.00092002],\n", + " [0.00039004, 0.00038412, 0.00036637, ..., 0.00089948,\n", + " 0.00090467, 0.00095847],\n", + " [0.00040008, 0.00040883, 0.00038797, ..., 0.00104462,\n", + " 0.00107986, 0.00110756],\n", + " ...,\n", + " [0.00113757, 0.00110313, 0.00105139, ..., 0.00177507,\n", + " 0.00188169, 0.00223301],\n", + " [0.00112182, 0.00107407, 0.00102277, ..., 0.00191701,\n", + " 0.00212489, 0.00262882],\n", + " [0.00109371, 0.00106941, 0.00101557, ..., 0.00215019,\n", + " 0.00244743, 0.00267947]]],\n", + " \n", + " \n", + " [[[0.00036001, 0.00034841, 0.00032176, ..., 0.000967 ,\n", + " 0.00090925, 0.00093248],\n", + " [0.00036436, 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]]]],\n", " mask=False,\n", " fill_value=1e+20,\n", " dtype=float32),\n", - " 'dimensions': ('time', 'lm', 'lat', 'lon'),\n", - " 'long_name': 'O3',\n", - " 'units': 'unknown',\n", - " 'standard_name': 'O3'}}" + " 'dimensions': ('time', 'lev', 'lat', 'lon'),\n", + " 'units': 'ppmV',\n", + " 'grid_mapping': 'crs',\n", + " 'coordinates': 'lat lon'}}" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1837,16 +396,9 @@ "## 2. Write dataset" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Write with NES" - ] - }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1856,11 +408,11 @@ "Rank 000: Creating regular_file_1.nc\n", "Rank 000: NetCDF ready to write\n", "Rank 000: Dimensions done\n", - "Rank 000: Writing O3 var (1/1)\n", - "Rank 000: Var O3 created (1/1)\n", - "Rank 000: Filling O3)\n", - "Rank 000: Var O3 data (1/1)\n", - "Rank 000: Var O3 completed (1/1)\n" + "Rank 000: Writing sconcno2 var (1/1)\n", + "Rank 000: Var sconcno2 created (1/1)\n", + "Rank 000: Filling sconcno2)\n", + "Rank 000: Var sconcno2 data (1/1)\n", + "Rank 000: Var sconcno2 completed (1/1)\n" ] } ], @@ -1874,21 +426,21 @@ "toc-hr-collapsed": true }, "source": [ - "### Reopen with NES" + "## 3. Reopen dataset" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1897,438 +449,6 @@ "nessy_2 = open_netcdf('regular_file_1.nc', info=True)\n", "nessy_2" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reopen with xarray" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:  (time: 9, lev: 48, lat: 181, lon: 257)\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 2008-12-31 2008-12-31T03:00:00 ... 2009-01-01\n",
-       "  * lev      (lev) float64 0.0 1.0 2.0 3.0 4.0 5.0 ... 43.0 44.0 45.0 46.0 47.0\n",
-       "  * lat      (lat) float64 -90.0 -89.0 -88.0 -87.0 -86.0 ... 87.0 88.0 89.0 90.0\n",
-       "  * lon      (lon) float64 -180.0 -178.6 -177.2 -175.8 ... 177.2 178.6 180.0\n",
-       "Data variables:\n",
-       "    O3       (time, lev, lat, lon) float32 ...\n",
-       "    crs      |S1 b''\n",
-       "Attributes:\n",
-       "    Domain:       Global\n",
-       "    Conventions:  CF-1.7\n",
-       "    history:      MONARCHv1.0 netcdf file.\n",
-       "    comment:      Generated on marenostrum4
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 9, lev: 48, lat: 181, lon: 257)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2008-12-31 2008-12-31T03:00:00 ... 2009-01-01\n", - " * lev (lev) float64 0.0 1.0 2.0 3.0 4.0 5.0 ... 43.0 44.0 45.0 46.0 47.0\n", - " * lat (lat) float64 -90.0 -89.0 -88.0 -87.0 -86.0 ... 87.0 88.0 89.0 90.0\n", - " * lon (lon) float64 -180.0 -178.6 -177.2 -175.8 ... 177.2 178.6 180.0\n", - "Data variables:\n", - " O3 (time, lev, lat, lon) float32 ...\n", - " crs |S1 ...\n", - "Attributes:\n", - " Domain: Global\n", - " Conventions: CF-1.7\n", - " history: MONARCHv1.0 netcdf file.\n", - " comment: Generated on marenostrum4" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('regular_file_1.nc')" - ] } ], "metadata": { diff --git a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb index 87ab4d22710cc11a032e21aefab605ef8358d692..ccb839d0f18cf53a49b23fddf7ccbaa0167d3e59 100644 --- a/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb +++ b/tutorials/1.Introduction/1.2.Read_Write_Rotated.ipynb @@ -13,7 +13,6 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", "from nes import *" ] }, @@ -23,7 +22,9 @@ "metadata": {}, "outputs": [], "source": [ - "nc_path_1 = '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc'" + "# 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", + "nc_path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/O3_all-000_2021080300.nc'" ] }, { @@ -33,13 +34,6 @@ "## 1. Read dataset" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Open with xarray" - ] - }, { "cell_type": "code", "execution_count": 3, @@ -47,416 +41,8 @@ "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:       (time: 1, nv: 2, lev: 24, rlat: 271, rlon: 351)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2021-08-03\n",
-       "  * lev           (lev) float64 0.0 1.0 2.0 3.0 4.0 ... 19.0 20.0 21.0 22.0 23.0\n",
-       "    lat           (rlat, rlon) float64 16.35 16.43 16.52 ... 58.83 58.68 58.53\n",
-       "    lon           (rlat, rlon) float64 -22.18 -22.02 -21.85 ... 88.05 88.23\n",
-       "  * rlat          (rlat) float64 -27.0 -26.8 -26.6 -26.4 ... 26.4 26.6 26.8 27.0\n",
-       "  * rlon          (rlon) float64 -35.0 -34.8 -34.6 -34.4 ... 34.4 34.6 34.8 35.0\n",
-       "Dimensions without coordinates: nv\n",
-       "Data variables:\n",
-       "    time_bnds     (time, nv) datetime64[ns] 2021-08-03 2021-08-07\n",
-       "    O3_all        (time, lev, rlat, rlon) float32 ...\n",
-       "    rotated_pole  |S1 b''\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7\n",
-       "    comment:      Generated on marenostrum4
" - ], "text/plain": [ - "\n", - "Dimensions: (time: 1, nv: 2, lev: 24, rlat: 271, rlon: 351)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2021-08-03\n", - " * lev (lev) float64 0.0 1.0 2.0 3.0 4.0 ... 19.0 20.0 21.0 22.0 23.0\n", - " lat (rlat, rlon) float64 ...\n", - " lon (rlat, rlon) float64 ...\n", - " * rlat (rlat) float64 -27.0 -26.8 -26.6 -26.4 ... 26.4 26.6 26.8 27.0\n", - " * rlon (rlon) float64 -35.0 -34.8 -34.6 -34.4 ... 34.4 34.6 34.8 35.0\n", - "Dimensions without coordinates: nv\n", - "Data variables:\n", - " time_bnds (time, nv) datetime64[ns] ...\n", - " O3_all (time, lev, rlat, rlon) float32 ...\n", - " rotated_pole |S1 ...\n", - "Attributes:\n", - " Conventions: CF-1.7\n", - " comment: Generated on marenostrum4" + "" ] }, "execution_count": 3, @@ -464,33 +50,6 @@ "output_type": "execute_result" } ], - "source": [ - "xr.open_dataset(nc_path_1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Open with NES" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "nessy_1 = open_netcdf(path=nc_path_1, info=True)\n", "nessy_1" @@ -498,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -507,7 +66,7 @@ "[datetime.datetime(2021, 8, 3, 0, 0)]" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -518,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -534,7 +93,7 @@ " 'positive': 'up'}" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -545,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -574,7 +133,7 @@ " 'standard_name': 'latitude'}" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -585,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -614,7 +173,7 @@ " 'standard_name': 'longitude'}" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -625,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -643,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -746,7 +305,7 @@ " 'grid_mapping': 'rotated_pole'}}" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -762,16 +321,9 @@ "## 2. Write dataset" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Write with NES" - ] - }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -781,6 +333,7 @@ "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", @@ -799,21 +352,21 @@ "toc-hr-collapsed": true }, "source": [ - "### Reopen with NES" + "## 3. Reopen dataset" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -822,441 +375,6 @@ "nessy_2 = open_netcdf('rotated_file_1.nc', info=True)\n", "nessy_2" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reopen with xarray" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:       (time: 1, time_nv: 2, lev: 24, rlat: 271, rlon: 351)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2021-08-03\n",
-       "  * lev           (lev) float64 0.0 1.0 2.0 3.0 4.0 ... 19.0 20.0 21.0 22.0 23.0\n",
-       "    lat           (rlat, rlon) float64 16.35 16.43 16.52 ... 58.83 58.68 58.53\n",
-       "    lon           (rlat, rlon) float64 -22.18 -22.02 -21.85 ... 88.05 88.23\n",
-       "  * rlat          (rlat) float64 -27.0 -26.8 -26.6 -26.4 ... 26.4 26.6 26.8 27.0\n",
-       "  * rlon          (rlon) float64 -35.0 -34.8 -34.6 -34.4 ... 34.4 34.6 34.8 35.0\n",
-       "Dimensions without coordinates: time_nv\n",
-       "Data variables:\n",
-       "    time_bnds     (time, time_nv) datetime64[ns] 2021-08-03 2021-08-07\n",
-       "    O3_all        (time, lev, rlat, rlon) float32 ...\n",
-       "    rotated_pole  |S1 b''\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7\n",
-       "    comment:      Generated on marenostrum4
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 1, time_nv: 2, lev: 24, rlat: 271, rlon: 351)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2021-08-03\n", - " * lev (lev) float64 0.0 1.0 2.0 3.0 4.0 ... 19.0 20.0 21.0 22.0 23.0\n", - " lat (rlat, rlon) float64 ...\n", - " lon (rlat, rlon) float64 ...\n", - " * rlat (rlat) float64 -27.0 -26.8 -26.6 -26.4 ... 26.4 26.6 26.8 27.0\n", - " * rlon (rlon) float64 -35.0 -34.8 -34.6 -34.4 ... 34.4 34.6 34.8 35.0\n", - "Dimensions without coordinates: time_nv\n", - "Data variables:\n", - " time_bnds (time, time_nv) datetime64[ns] ...\n", - " O3_all (time, lev, rlat, rlon) float32 ...\n", - " rotated_pole |S1 ...\n", - "Attributes:\n", - " Conventions: CF-1.7\n", - " comment: Generated on marenostrum4" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('rotated_file_1.nc')" - ] } ], "metadata": { diff --git a/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb b/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb index 582007ae500a62734a4f35e80ca893c7864f68ff..80dcc1d691fd4c8179fd4e0542f4ef085737425c 100644 --- a/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb +++ b/tutorials/1.Introduction/1.3.Read_Write_Points.ipynb @@ -13,8 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "from nes import *\n", - "import xarray as xr" + "from nes import *" ] }, { @@ -23,22 +22,23 @@ "metadata": {}, "outputs": [], "source": [ - "# nc_path_1 = '/esarchive/obs/eea/eionet/hourly/pm10/pm10_202107.nc' # EIONET\n", - "nc_path_1 = '/esarchive/obs/nilu/ebas/daily/pm10/pm10_201507.nc' # EBAS" + "# Original path: /esarchive/obs/nilu/ebas/daily/pm10/pm10_201507.nc\n", + "# Points grid from EBAS network\n", + "nc_path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/pm10_201507.nc'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Read and write - Non-GHOST type" + "## 1. Non-GHOST" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Open with xarray" + "### 1.1. Read dataset" ] }, { @@ -48,642 +48,8 @@ "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:                       (station: 84, time: 31)\n",
-       "Coordinates:\n",
-       "  * time                          (time) datetime64[ns] 2015-07-01 ... 2015-0...\n",
-       "Dimensions without coordinates: station\n",
-       "Data variables: (12/19)\n",
-       "    station_start_date            (station) |S75 b'1980-01-01' ... b'nan'\n",
-       "    station_zone                  (station) |S75 b'nan' b'nan' ... b'nan' b'nan'\n",
-       "    street_type                   (station) |S75 b'nan' b'nan' ... b'nan' b'nan'\n",
-       "    country_code                  (station) |S75 b'CH' b'CH' ... b'NL' b'IT'\n",
-       "    ccaa                          (station) |S75 b'nan' b'nan' ... b'nan' b'nan'\n",
-       "    station_name                  (station) |S75 b'payerne' ... b'lamezia terme'\n",
-       "    ...                            ...\n",
-       "    station_code                  (station) |S75 b'CH0002R' ... b'IT0016R'\n",
-       "    longitude                     (station) float32 6.944 8.905 ... 6.277 16.23\n",
-       "    station_end_date              (station) |S75 b'nan' b'nan' ... b'nan' b'nan'\n",
-       "    station_rural_back            (station) |S75 b'nan' b'nan' ... b'nan' b'nan'\n",
-       "    latitude                      (station) float32 46.81 47.48 ... 53.33 38.88\n",
-       "    station_ozone_classification  (station) |S75 b'rural' b'rural' ... b'nan'
" - ], "text/plain": [ - "\n", - "Dimensions: (station: 84, time: 31)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2015-07-01 ... 2015-0...\n", - "Dimensions without coordinates: station\n", - "Data variables: (12/19)\n", - " station_start_date (station) |S75 ...\n", - " station_zone (station) |S75 ...\n", - " street_type (station) |S75 ...\n", - " country_code (station) |S75 ...\n", - " ccaa (station) |S75 ...\n", - " station_name (station) |S75 ...\n", - " ... ...\n", - " station_code (station) |S75 ...\n", - " longitude (station) float32 ...\n", - " station_end_date (station) |S75 ...\n", - " station_rural_back (station) |S75 ...\n", - " latitude (station) float32 ...\n", - " station_ozone_classification (station) |S75 ..." + "" ] }, "execution_count": 3, @@ -691,33 +57,6 @@ "output_type": "execute_result" } ], - "source": [ - "xr.open_dataset(nc_path_1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Open with NES" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "nessy_1 = open_netcdf(path=nc_path_1, info=True, parallel_method='X')\n", "nessy_1" @@ -725,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -764,7 +103,7 @@ " datetime.datetime(2015, 7, 2, 6, 0)]" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -775,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -784,7 +123,7 @@ "{'data': array([0]), 'units': ''}" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -795,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -808,7 +147,7 @@ " 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83])}" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -819,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -852,7 +191,7 @@ " 'axis': 'Y'}" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -863,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -900,7 +239,7 @@ " 'axis': 'X'}" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -911,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -961,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1243,7 +582,7 @@ " 'standard_name': ''}}" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1254,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1288,7 +627,7 @@ " 'long_name': 'pm10_mass pm10'}" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1301,12 +640,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Write with NES" + "### 1.2. Write dataset" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -1316,6 +655,7 @@ "Rank 000: Creating points_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 station_start_date var (1/17)\n", "Rank 000: Var station_start_date created (1/17)\n", "Rank 000: Var station_start_date data (1/17)\n", @@ -1397,21 +737,21 @@ "toc-hr-collapsed": true }, "source": [ - "### Reopen with NES" + "### 1.3. Reopen dataset" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1425,591 +765,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Reopen with xarray" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:                       (time: 31, station: 84, strlen: 75)\n",
-       "Coordinates:\n",
-       "  * time                          (time) datetime64[ns] 2015-07-01 ... 2015-0...\n",
-       "  * station                       (station) float64 0.0 1.0 2.0 ... 82.0 83.0\n",
-       "Dimensions without coordinates: strlen\n",
-       "Data variables: (12/19)\n",
-       "    lat                           (station) float64 46.81 47.48 ... 53.33 38.88\n",
-       "    lon                           (station) float64 6.944 8.905 ... 6.277 16.23\n",
-       "    station_start_date            (station, strlen) object '1' '9' '8' ... '' ''\n",
-       "    station_zone                  (station, strlen) object 'n' 'a' 'n' ... '' ''\n",
-       "    street_type                   (station, strlen) object 'n' 'a' 'n' ... '' ''\n",
-       "    country_code                  (station, strlen) object 'C' 'H' '' ... '' ''\n",
-       "    ...                            ...\n",
-       "    country                       (station, strlen) object 's' 'w' 'i' ... '' ''\n",
-       "    altitude                      (station) float32 489.0 538.0 ... 1.0 6.0\n",
-       "    station_code                  (station, strlen) object 'C' 'H' '0' ... '' ''\n",
-       "    station_end_date              (station, strlen) object 'n' 'a' 'n' ... '' ''\n",
-       "    station_rural_back            (station, strlen) object 'n' 'a' 'n' ... '' ''\n",
-       "    station_ozone_classification  (station, strlen) object 'r' 'u' 'r' ... '' ''\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 31, station: 84, strlen: 75)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2015-07-01 ... 2015-0...\n", - " * station (station) float64 0.0 1.0 2.0 ... 82.0 83.0\n", - "Dimensions without coordinates: strlen\n", - "Data variables: (12/19)\n", - " lat (station) float64 ...\n", - " lon (station) float64 ...\n", - " station_start_date (station, strlen) object ...\n", - " station_zone (station, strlen) object ...\n", - " street_type (station, strlen) object ...\n", - " country_code (station, strlen) object ...\n", - " ... ...\n", - " country (station, strlen) object ...\n", - " altitude (station) float32 ...\n", - " station_code (station, strlen) object ...\n", - " station_end_date (station, strlen) object ...\n", - " station_rural_back (station, strlen) object ...\n", - " station_ozone_classification (station, strlen) object ...\n", - "Attributes:\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('points_file_1.nc')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Read and write - GHOST type" + "## 2. GHOST" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -2024,623 +785,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Open with xarray" + "### 2.1. Read dataset" ] }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:                                                           (station: 3, time: 30, N_flag_codes: 190, N_qa_codes: 77)\n",
-       "Coordinates:\n",
-       "  * time                                                              (time) datetime64[ns] ...\n",
-       "Dimensions without coordinates: station, N_flag_codes, N_qa_codes\n",
-       "Data variables: (12/177)\n",
-       "    ASTER_v3_altitude                                                 (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_BC_emissions                          (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_CO_emissions                          (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_NH3_emissions                         (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_NMVOC_emissions                       (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_NOx_emissions                         (station) float32 ...\n",
-       "    ...                                                                ...\n",
-       "    street_type                                                       (station) object ...\n",
-       "    street_width                                                      (station) float32 ...\n",
-       "    terrain                                                           (station) object ...\n",
-       "    vertical_datum                                                    (station) object ...\n",
-       "    weekday_weekend_code                                              (station, time) uint8 ...\n",
-       "    sconcso4_prefiltered_defaultqa                                    (station, time) float32 ...\n",
-       "Attributes:\n",
-       "    title:          Surface sulphate data in the EANET network in 2019-11.\n",
-       "    institution:    Barcelona Supercomputing Center\n",
-       "    source:         Surface observations\n",
-       "    creator_name:   Dene R. Bowdalo\n",
-       "    creator_email:  dene.bowdalo@bsc.es\n",
-       "    version:        1.4
" - ], - "text/plain": [ - "\n", - "Dimensions: (station: 3, time: 30, N_flag_codes: 190, N_qa_codes: 77)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] ...\n", - "Dimensions without coordinates: station, N_flag_codes, N_qa_codes\n", - "Data variables: (12/177)\n", - " ASTER_v3_altitude (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_NMVOC_emissions (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_NOx_emissions (station) float32 ...\n", - " ... ...\n", - " street_type (station) object ...\n", - " street_width (station) float32 ...\n", - " terrain (station) object ...\n", - " vertical_datum (station) object ...\n", - " weekday_weekend_code (station, time) uint8 ...\n", - " sconcso4_prefiltered_defaultqa (station, time) float32 ...\n", - "Attributes:\n", - " title: Surface sulphate data in the EANET network in 2019-11.\n", - " institution: Barcelona Supercomputing Center\n", - " source: Surface observations\n", - " creator_name: Dene R. Bowdalo\n", - " creator_email: dene.bowdalo@bsc.es\n", - " version: 1.4" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset(nc_path_2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Open with NES" - ] - }, - { - "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -2652,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -2690,7 +849,7 @@ " datetime.datetime(2019, 11, 30, 0, 0)]" ] }, - "execution_count": 19, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -2701,7 +860,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -2710,7 +869,7 @@ "{'data': array([0]), 'units': ''}" ] }, - "execution_count": 20, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -2721,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -2730,7 +889,7 @@ "{'data': array([0, 1, 2])}" ] }, - "execution_count": 21, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2741,7 +900,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -2758,7 +917,7 @@ " 'axis': 'Y'}" ] }, - "execution_count": 22, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2769,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -2786,7 +945,7 @@ " 'axis': 'X'}" ] }, - "execution_count": 23, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2797,7 +956,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -3161,12 +1320,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Write with NES" + "### 2.2. Write dataset" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -3174,8 +1333,23 @@ "output_type": "stream", "text": [ "Rank 000: Creating points_file_2.nc\n", - "Rank 000: NetCDF ready to write\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: 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", @@ -3392,21 +1566,7 @@ "Rank 000: Var NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km created (54/173)\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" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_ghost.py:570: 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: 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: 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", @@ -3893,12 +2053,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Reopen with NES" + "### 2.3. Reopen dataset" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -4258,10 +2418,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -4276,627 +2436,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Reopen with xarray" + "### 2.4. Transform to points" ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:                                                           (time: 30, station: 3, N_flag_codes: 190, N_qa_codes: 77)\n",
-       "Coordinates:\n",
-       "  * time                                                              (time) datetime64[ns] ...\n",
-       "  * station                                                           (station) float64 ...\n",
-       "Dimensions without coordinates: N_flag_codes, N_qa_codes\n",
-       "Data variables: (12/177)\n",
-       "    latitude                                                          (station) float64 ...\n",
-       "    longitude                                                         (station) float64 ...\n",
-       "    ASTER_v3_altitude                                                 (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_BC_emissions                          (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_CO_emissions                          (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_NH3_emissions                         (station) float32 ...\n",
-       "    ...                                                                ...\n",
-       "    terrain                                                           (station) object ...\n",
-       "    vertical_datum                                                    (station) object ...\n",
-       "    weekday_weekend_code                                              (station, time) uint8 ...\n",
-       "    sconcso4_prefiltered_defaultqa                                    (station, time) float32 ...\n",
-       "    flag                                                              (station, time, N_flag_codes) int64 ...\n",
-       "    qa                                                                (station, time, N_qa_codes) int64 ...\n",
-       "Attributes:\n",
-       "    title:          Surface sulphate data in the EANET network in 2019-11.\n",
-       "    institution:    Barcelona Supercomputing Center\n",
-       "    source:         Surface observations\n",
-       "    creator_name:   Dene R. Bowdalo\n",
-       "    creator_email:  dene.bowdalo@bsc.es\n",
-       "    version:        1.4\n",
-       "    Conventions:    CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 30, station: 3, N_flag_codes: 190, N_qa_codes: 77)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] ...\n", - " * station (station) float64 ...\n", - "Dimensions without coordinates: N_flag_codes, N_qa_codes\n", - "Data variables: (12/177)\n", - " latitude (station) float64 ...\n", - " longitude (station) float64 ...\n", - " ASTER_v3_altitude (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n", - " ... ...\n", - " terrain (station) object ...\n", - " vertical_datum (station) object ...\n", - " weekday_weekend_code (station, time) uint8 ...\n", - " sconcso4_prefiltered_defaultqa (station, time) float32 ...\n", - " flag (station, time, N_flag_codes) int64 ...\n", - " qa (station, time, N_qa_codes) int64 ...\n", - "Attributes:\n", - " title: Surface sulphate data in the EANET network in 2019-11.\n", - " institution: Barcelona Supercomputing Center\n", - " source: Surface observations\n", - " creator_name: Dene R. Bowdalo\n", - " creator_email: dene.bowdalo@bsc.es\n", - " version: 1.4\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('points_file_2.nc')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Transform to points" - ] - }, - { - "cell_type": "code", - "execution_count": 28, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 28, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -4908,7 +2462,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -4970,7 +2524,7 @@ " 'description': 'Measured value of surface sulphate for the stated temporal resolution. Prefiltered by default QA.'}}" ] }, - "execution_count": 29, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -4978,33 +2532,6 @@ "source": [ "nessy_ghost_3.variables" ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rank 000: Creating points_file_3.nc\n", - "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n", - "Rank 000: Writing sconcso4 var (1/2)\n", - "Rank 000: Var sconcso4 created (1/2)\n", - "Rank 000: Var sconcso4 data (1/2)\n", - "Rank 000: Var sconcso4 completed (1/2)\n", - "Rank 000: Writing sconcso4_prefiltered_defaultqa var (2/2)\n", - "Rank 000: Var sconcso4_prefiltered_defaultqa created (2/2)\n", - "Rank 000: Var sconcso4_prefiltered_defaultqa data (2/2)\n", - "Rank 000: Var sconcso4_prefiltered_defaultqa completed (2/2)\n" - ] - } - ], - "source": [ - "nessy_ghost_3.to_netcdf('points_file_3.nc', info=True)" - ] } ], "metadata": { diff --git a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb index e1a4f6499b0a6f23a9246da4d36c9882e2c0f04c..8baf84be11b50a545ada9c0726c264e781d5d388 100644 --- a/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb +++ b/tutorials/1.Introduction/1.4.Read_Write_LCC.ipynb @@ -13,8 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "from nes import *\n", - "import xarray as xr" + "from nes import *" ] }, { @@ -23,450 +22,16 @@ "metadata": {}, "outputs": [], "source": [ - "nc_path_1 = '/esarchive/exp/wrf-hermes-cmaq/b075/eu/hourly/sconco3/sconco3_2021010100.nc'" + "# Original path: /esarchive/exp/snes/a5g1/ip/daily_max/sconco3/sconco3_2022111500.nc\n", + "# LCC grid with a coverage over the Iberian Peninsula (4x4km)\n", + "nc_path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/sconco3_2022111500.nc'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Read and write" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Open with xarray" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:            (time: 48, y: 398, x: 478, lev: 1)\n",
-       "Coordinates:\n",
-       "  * time               (time) datetime64[ns] 2021-01-01 ... 2021-01-02T23:00:00\n",
-       "    lat                (y, x) float32 ...\n",
-       "    lon                (y, x) float32 ...\n",
-       "  * x                  (x) float64 -2.126e+06 -2.114e+06 ... 3.586e+06 3.598e+06\n",
-       "  * y                  (y) float64 -2.067e+06 -2.055e+06 ... 2.685e+06 2.697e+06\n",
-       "  * lev                (lev) float32 0.0\n",
-       "Data variables:\n",
-       "    sconco3            (time, lev, y, x) float32 ...\n",
-       "    Lambert_conformal  int32 -2147483647
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 48, y: 398, x: 478, lev: 1)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2021-01-01 ... 2021-01-02T23:00:00\n", - " lat (y, x) float32 ...\n", - " lon (y, x) float32 ...\n", - " * x (x) float64 -2.126e+06 -2.114e+06 ... 3.586e+06 3.598e+06\n", - " * y (y) float64 -2.067e+06 -2.055e+06 ... 2.685e+06 2.697e+06\n", - " * lev (lev) float32 0.0\n", - "Data variables:\n", - " sconco3 (time, lev, y, x) float32 ...\n", - " Lambert_conformal int32 ..." - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset(nc_path_1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Open with NES" + "## 1. Read dataset" ] }, { @@ -477,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -498,54 +63,7 @@ { "data": { "text/plain": [ - "[datetime.datetime(2021, 1, 1, 0, 0),\n", - " datetime.datetime(2021, 1, 1, 1, 0),\n", - " datetime.datetime(2021, 1, 1, 2, 0),\n", - " datetime.datetime(2021, 1, 1, 3, 0),\n", - " datetime.datetime(2021, 1, 1, 4, 0),\n", - " datetime.datetime(2021, 1, 1, 5, 0),\n", - " datetime.datetime(2021, 1, 1, 6, 0),\n", - " datetime.datetime(2021, 1, 1, 7, 0),\n", - " datetime.datetime(2021, 1, 1, 8, 0),\n", - " datetime.datetime(2021, 1, 1, 9, 0),\n", - " datetime.datetime(2021, 1, 1, 10, 0),\n", - " datetime.datetime(2021, 1, 1, 11, 0),\n", - " datetime.datetime(2021, 1, 1, 12, 0),\n", - " datetime.datetime(2021, 1, 1, 13, 0),\n", - " datetime.datetime(2021, 1, 1, 14, 0),\n", - " datetime.datetime(2021, 1, 1, 15, 0),\n", - " datetime.datetime(2021, 1, 1, 16, 0),\n", - " datetime.datetime(2021, 1, 1, 17, 0),\n", - " datetime.datetime(2021, 1, 1, 18, 0),\n", - " datetime.datetime(2021, 1, 1, 19, 0),\n", - " datetime.datetime(2021, 1, 1, 20, 0),\n", - " datetime.datetime(2021, 1, 1, 21, 0),\n", - " datetime.datetime(2021, 1, 1, 22, 0),\n", - " datetime.datetime(2021, 1, 1, 23, 0),\n", - " datetime.datetime(2021, 1, 2, 0, 0),\n", - " datetime.datetime(2021, 1, 2, 1, 0),\n", - " datetime.datetime(2021, 1, 2, 2, 0),\n", - " datetime.datetime(2021, 1, 2, 3, 0),\n", - " datetime.datetime(2021, 1, 2, 4, 0),\n", - " datetime.datetime(2021, 1, 2, 5, 0),\n", - " datetime.datetime(2021, 1, 2, 6, 0),\n", - " datetime.datetime(2021, 1, 2, 7, 0),\n", - " datetime.datetime(2021, 1, 2, 8, 0),\n", - " datetime.datetime(2021, 1, 2, 9, 0),\n", - " datetime.datetime(2021, 1, 2, 10, 0),\n", - " datetime.datetime(2021, 1, 2, 11, 0),\n", - " datetime.datetime(2021, 1, 2, 12, 0),\n", - " datetime.datetime(2021, 1, 2, 13, 0),\n", - " datetime.datetime(2021, 1, 2, 14, 0),\n", - " datetime.datetime(2021, 1, 2, 15, 0),\n", - " datetime.datetime(2021, 1, 2, 16, 0),\n", - " datetime.datetime(2021, 1, 2, 17, 0),\n", - " datetime.datetime(2021, 1, 2, 18, 0),\n", - " datetime.datetime(2021, 1, 2, 19, 0),\n", - " datetime.datetime(2021, 1, 2, 20, 0),\n", - " datetime.datetime(2021, 1, 2, 21, 0),\n", - " datetime.datetime(2021, 1, 2, 22, 0),\n", - " datetime.datetime(2021, 1, 2, 23, 0)]" + "[datetime.datetime(2022, 11, 15, 0, 0), datetime.datetime(2022, 11, 16, 0, 0)]" ] }, "execution_count": 5, @@ -567,8 +85,10 @@ "text/plain": [ "{'data': masked_array(data=[0.],\n", " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32), 'dimensions': ('lev',), 'positive': 'up'}" + " fill_value=1e+20),\n", + " 'dimensions': ('lev',),\n", + " 'units': '',\n", + " 'positive': 'up'}" ] }, "execution_count": 6, @@ -588,171 +108,144 @@ { "data": { "text/plain": [ - "{'data': masked_array(data=[-2.12584753e+06, -2.11384761e+06, -2.10184701e+06,\n", - " -2.08984901e+06, -2.07784716e+06, -2.06584784e+06,\n", - " -2.05384791e+06, -2.04185027e+06, -2.02984883e+06,\n", - " -2.01784924e+06, -2.00584929e+06, -1.99384819e+06,\n", - " -1.98184942e+06, -1.96984999e+06, -1.95784950e+06,\n", - " -1.94584776e+06, -1.93384820e+06, -1.92184804e+06,\n", - " -1.90984965e+06, -1.89784727e+06, -1.88584974e+06,\n", - " -1.87384798e+06, -1.86184856e+06, -1.84984781e+06,\n", - " -1.83784882e+06, -1.82584878e+06, -1.81384730e+06,\n", - " -1.80184766e+06, -1.78985000e+06, -1.77784784e+06,\n", - " -1.76584709e+06, -1.75384839e+06, -1.74184822e+06,\n", - " -1.72984970e+06, -1.71784950e+06, -1.70584804e+06,\n", - " -1.69384799e+06, -1.68184961e+06, -1.66984941e+06,\n", - " -1.65784782e+06, -1.64584735e+06, -1.63384873e+06,\n", - " -1.62184786e+06, -1.60984877e+06, -1.59784781e+06,\n", - " -1.58584811e+06, -1.57384978e+06, -1.56184949e+06,\n", - " -1.54984706e+06, -1.53784949e+06, -1.52584971e+06,\n", - " -1.51384784e+06, -1.50185029e+06, -1.48984741e+06,\n", - " -1.47784881e+06, -1.46584796e+06, -1.45384829e+06,\n", - " -1.44184935e+06, -1.42984834e+06, -1.41784813e+06,\n", - " -1.40584895e+06, -1.39384732e+06, -1.38184958e+06,\n", - " -1.36984970e+06, -1.35784703e+06, -1.34584852e+06,\n", - " -1.33384739e+06, -1.32185023e+06, -1.30984708e+06,\n", - " -1.29784782e+06, -1.28584924e+06, -1.27384787e+06,\n", - " -1.26185017e+06, -1.24984974e+06, -1.23784984e+06,\n", - " -1.22584679e+06, -1.21384762e+06, -1.20184864e+06,\n", - " -1.18985017e+06, -1.17784863e+06, -1.16584730e+06,\n", - " -1.15384933e+06, -1.14184842e+06, -1.12984761e+06,\n", - " -1.11785016e+06, -1.10584945e+06, -1.09384884e+06,\n", - " -1.08184809e+06, -1.06984737e+06, -1.05784994e+06,\n", - " -1.04584897e+06, -1.03384802e+06, -1.02185016e+06,\n", - " -1.00984874e+06, -9.97847138e+05, -9.85848430e+05,\n", - " -9.73849460e+05, -9.61850193e+05, -9.49847198e+05,\n", - " -9.37847136e+05, -9.25849883e+05, -9.13848882e+05,\n", - " -9.01847317e+05, -8.89848619e+05, -8.77849203e+05,\n", - " -8.65849037e+05, -8.53848483e+05, -8.41850255e+05,\n", - " -8.29848103e+05, -8.17848443e+05, -8.05848159e+05,\n", - " -7.93850146e+05, -7.81847983e+05, -7.69848242e+05,\n", - " -7.57847649e+05, -7.45849126e+05, -7.33849816e+05,\n", - " -7.21849273e+05, -7.09847665e+05, -6.97848323e+05,\n", - " -6.85847775e+05, -6.73849354e+05, -6.61849656e+05,\n", - " -6.49848767e+05, -6.37849773e+05, -6.25849455e+05,\n", - " -6.13847832e+05, -6.01848000e+05, -5.89850037e+05,\n", - " -5.77847297e+05, -5.65849601e+05, -5.53850360e+05,\n", - " -5.41849485e+05, -5.29850392e+05, -5.17849446e+05,\n", - " -5.05850300e+05, -4.93849326e+05, -4.81849796e+05,\n", - " -4.69848463e+05, -4.57848676e+05, -4.45850309e+05,\n", - " -4.33849861e+05, -4.21847579e+05, -4.09849797e+05,\n", - " -3.97850061e+05, -3.85848258e+05, -3.73847618e+05,\n", - " -3.61848107e+05, -3.49849786e+05, -3.37849091e+05,\n", - " -3.25849544e+05, -3.13847764e+05, -3.01850147e+05,\n", - " -2.89850223e+05, -2.77847869e+05, -2.65849682e+05,\n", - " -2.53848995e+05, -2.41849099e+05, -2.29849935e+05,\n", - " -2.17848225e+05, -2.05850456e+05, -1.93850007e+05,\n", - " -1.81850198e+05, -1.69847633e+05, -1.57848901e+05,\n", - " -1.45847355e+05, -1.33849541e+05, -1.21848866e+05,\n", - " -1.09848557e+05, -9.78485812e+04, -8.58489110e+04,\n", - " -7.38495157e+04, -6.18503428e+04, -4.98480588e+04,\n", - " -3.78492294e+04, -2.58472156e+04, -1.38485865e+04,\n", - " -1.85000093e+03, 1.01518839e+04, 2.21504974e+04,\n", - " 3.41524846e+04, 4.61512675e+04, 5.81502025e+04,\n", - " 7.01526052e+04, 8.21519418e+04, 9.41515115e+04,\n", - " 1.06151401e+05, 1.18151593e+05, 1.30152150e+05,\n", - " 1.42149821e+05, 1.54151239e+05, 1.66149805e+05,\n", - " 1.78152206e+05, 1.90151841e+05, 2.02152103e+05,\n", - " 2.14149676e+05, 2.26151179e+05, 2.38150101e+05,\n", - " 2.50149789e+05, 2.62150163e+05, 2.74151423e+05,\n", - " 2.86150208e+05, 2.98149857e+05, 3.10150460e+05,\n", - " 3.22152060e+05, 3.34151271e+05, 3.46151519e+05,\n", - " 3.58149605e+05, 3.70152044e+05, 3.82152331e+05,\n", - " 3.94150502e+05, 4.06149863e+05, 4.18150448e+05,\n", - " 4.30152410e+05, 4.42152326e+05, 4.54150354e+05,\n", - " 4.66149752e+05, 4.78150600e+05, 4.90149630e+05,\n", - " 5.02150092e+05, 5.14152159e+05, 5.26152471e+05,\n", - " 5.38151163e+05, 5.50151477e+05, 5.62150197e+05,\n", - " 5.74150715e+05, 5.86149501e+05, 5.98150157e+05,\n", - " 6.10149476e+05, 6.22150579e+05, 6.34150250e+05,\n", - " 6.46151947e+05, 6.58152226e+05, 6.70151301e+05,\n", - " 6.82152394e+05, 6.94152233e+05, 7.06151050e+05,\n", - " 7.18151992e+05, 7.30151725e+05, 7.42150415e+05,\n", - " 7.54151335e+05, 7.66151356e+05, 7.78150447e+05,\n", - " 7.90151805e+05, 8.02152305e+05, 8.14151909e+05,\n", - " 8.26150803e+05, 8.38152179e+05, 8.50149463e+05,\n", - " 8.62152600e+05, 8.74151875e+05, 8.86150469e+05,\n", - " 8.98151720e+05, 9.10152442e+05, 9.22152590e+05,\n", - " 9.34152281e+05, 9.46151555e+05, 9.58150185e+05,\n", - " 9.70151947e+05, 9.82150008e+05, 9.94151009e+05,\n", - " 1.00615168e+06, 1.01815215e+06, 1.03015237e+06,\n", - " 1.04215255e+06, 1.05415245e+06, 1.06615219e+06,\n", - " 1.07815192e+06, 1.09015146e+06, 1.10215106e+06,\n", - " 1.11415084e+06, 1.12615055e+06, 1.13815031e+06,\n", - " 1.15015027e+06, 1.16215026e+06, 1.17415050e+06,\n", - " 1.18615117e+06, 1.19815184e+06, 1.21014970e+06,\n", - " 1.22215106e+06, 1.23414955e+06, 1.24615161e+06,\n", - " 1.25815089e+06, 1.27015072e+06, 1.28215091e+06,\n", - " 1.29415184e+06, 1.30614966e+06, 1.31815171e+06,\n", - " 1.33015072e+06, 1.34215086e+06, 1.35415143e+06,\n", - " 1.36614929e+06, 1.37815146e+06, 1.39015111e+06,\n", - " 1.40215157e+06, 1.41414946e+06, 1.42615154e+06,\n", - " 1.43815149e+06, 1.45015214e+06, 1.46215061e+06,\n", - " 1.47414997e+06, 1.48615052e+06, 1.49815230e+06,\n", - " 1.51015149e+06, 1.52215197e+06, 1.53415035e+06,\n", - " 1.54615023e+06, 1.55815110e+06, 1.57015010e+06,\n", - " 1.58215014e+06, 1.59415154e+06, 1.60615118e+06,\n", - " 1.61815225e+06, 1.63015134e+06, 1.64215177e+06,\n", - " 1.65415073e+06, 1.66615110e+06, 1.67814931e+06,\n", - " 1.69014976e+06, 1.70215157e+06, 1.71415178e+06,\n", - " 1.72615026e+06, 1.73815066e+06, 1.75015254e+06,\n", - " 1.76214949e+06, 1.77415192e+06, 1.78614949e+06,\n", - " 1.79815196e+06, 1.81015012e+06, 1.82214994e+06,\n", - " 1.83415163e+06, 1.84615225e+06, 1.85815149e+06,\n", - " 1.87014939e+06, 1.88214927e+06, 1.89415152e+06,\n", - " 1.90615253e+06, 1.91815231e+06, 1.93015091e+06,\n", - " 1.94215219e+06, 1.95415199e+06, 1.96615088e+06,\n", - " 1.97815184e+06, 1.99015212e+06, 2.00215141e+06,\n", - " 2.01414974e+06, 2.02615043e+06, 2.03815022e+06,\n", - " 2.05014932e+06, 2.06215089e+06, 2.07415183e+06,\n", - " 2.08615200e+06, 2.09815142e+06, 2.11015031e+06,\n", - " 2.12215125e+06, 2.13415212e+06, 2.14615216e+06,\n", - " 2.15815181e+06, 2.17015090e+06, 2.18215236e+06,\n", - " 2.19415062e+06, 2.20615132e+06, 2.21815157e+06,\n", - " 2.23015162e+06, 2.24215129e+06, 2.25415041e+06,\n", - " 2.26614942e+06, 2.27815103e+06, 2.29014950e+06,\n", - " 2.30215084e+06, 2.31415199e+06, 2.32614967e+06,\n", - " 2.33815052e+06, 2.35015105e+06, 2.36215151e+06,\n", - " 2.37415171e+06, 2.38615211e+06, 2.39815209e+06,\n", - " 2.41014925e+06, 2.42214936e+06, 2.43414979e+06,\n", - " 2.44615014e+06, 2.45815065e+06, 2.47015092e+06,\n", - " 2.48215186e+06, 2.49414885e+06, 2.50615033e+06,\n", - " 2.51815099e+06, 2.53014892e+06, 2.54215075e+06,\n", - " 2.55414946e+06, 2.56615190e+06, 2.57815081e+06,\n", - " 2.59015090e+06, 2.60215059e+06, 2.61415105e+06,\n", - " 2.62615163e+06, 2.63814974e+06, 2.65015084e+06,\n", - " 2.66215003e+06, 2.67414918e+06, 2.68614955e+06,\n", - " 2.69814946e+06, 2.71015063e+06, 2.72215238e+06,\n", - " 2.73415141e+06, 2.74615082e+06, 2.75815088e+06,\n", - " 2.77015212e+06, 2.78215001e+06, 2.79414938e+06,\n", - " 2.80614927e+06, 2.81814994e+06, 2.83015143e+06,\n", - " 2.84215046e+06, 2.85415010e+06, 2.86615064e+06,\n", - " 2.87815237e+06, 2.89015096e+06, 2.90215132e+06,\n", - " 2.91415163e+06, 2.92615073e+06, 2.93815062e+06,\n", - " 2.95015108e+06, 2.96214987e+06, 2.97415232e+06,\n", - " 2.98614959e+06, 2.99815110e+06, 3.01015052e+06,\n", - " 3.02215092e+06, 3.03414984e+06, 3.04615228e+06,\n", - " 3.05814999e+06, 3.07015153e+06, 3.08215198e+06,\n", - " 3.09414943e+06, 3.10615189e+06, 3.11815252e+06,\n", - " 3.13015077e+06, 3.14215026e+06, 3.15415157e+06,\n", - " 3.16615058e+06, 3.17815090e+06, 3.19015010e+06,\n", - " 3.20214982e+06, 3.21415120e+06, 3.22615069e+06,\n", - " 3.23815162e+06, 3.25015100e+06, 3.26215215e+06,\n", - " 3.27415093e+06, 3.28615124e+06, 3.29815011e+06,\n", - " 3.31015056e+06, 3.32214902e+06, 3.33414971e+06,\n", - " 3.34615147e+06, 3.35815161e+06, 3.37014985e+06,\n", - " 3.38215073e+06, 3.39415246e+06, 3.40614937e+06,\n", - " 3.41815139e+06, 3.43015224e+06, 3.44215134e+06,\n", - " 3.45415201e+06, 3.46615129e+06, 3.47814888e+06,\n", - " 3.49015204e+06, 3.50214995e+06, 3.51415017e+06,\n", - " 3.52615242e+06, 3.53814916e+06, 3.55015129e+06,\n", - " 3.56214954e+06, 3.57415226e+06, 3.58615021e+06,\n", - " 3.59815064e+06],\n", + "{'data': masked_array(data=[-805847.93512686, -801847.95626194, -797848.36116962,\n", + " -793846.40078545, -789847.5662888 , -785846.12161903,\n", + " -781847.9213502 , -777847.26711017, -773846.90615214,\n", + " -769846.99396972, -765847.29384614, -761847.92217455,\n", + " -757846.1635423 , -753847.52001723, -749846.52217576,\n", + " -745848.52234501, -741847.97471659, -737847.82062838,\n", + " -733848.16920624, -729848.64509128, -725846.4925582 ,\n", + " -721847.77716311, -717846.4291098 , -713848.18385516,\n", + " -709847.59391026, -705847.0556987 , -701847.07129347,\n", + " -697847.20935111, -693847.78875693, -689848.70038656,\n", + " -685846.96966084, -681848.43600654, -677847.46490525,\n", + " -673846.61202228, -669846.11722529, -665848.81181576,\n", + " -661846.09003088, -657846.48649904, -653847.1656709 ,\n", + " -649848.22514489, -645846.62952115, -641848.3753264 ,\n", + " -637847.33246165, -633846.66099372, -629846.16542218,\n", + " -625849.06399662, -621846.24075126, -617846.742536 ,\n", + " -613847.47778944, -609848.50762818, -605846.96204506,\n", + " -601848.48284466, -597847.42518251, -593846.68501473,\n", + " -589846.10959814, -585848.71610943, -581848.79564948,\n", + " -577846.10972474, -573846.7167133 , -569847.54064889,\n", + " -565848.60940359, -561847.05357627, -557848.69254475,\n", + " -553847.58996162, -549846.86638894, -545846.29508862,\n", + " -541848.88208792, -537848.88888833, -533849.01764933,\n", + " -529846.64404749, -525847.31197283, -521848.2063491 ,\n", + " -517846.51047709, -513847.87892857, -509846.65286731,\n", + " -505848.46233897, -501847.72451902, -497847.15130423,\n", + " -493846.81779096, -489846.74677709, -485846.78715095,\n", + " -481847.16055064, -477847.71647316, -473848.40576421,\n", + " -469846.57609158, -465847.7446169 , -461846.34045261,\n", + " -457847.90894114, -453846.85385685, -449848.90821442,\n", + " -445848.3789051 , -441847.83741869, -437847.6443723 ,\n", + " -433847.59518169, -429847.7551937 , -425848.12227221,\n", + " -421848.6728026 , -417846.57992853, -413847.51344757,\n", + " -409848.64629979, -405847.08780312, -401848.59265191,\n", + " -397847.34191435, -393846.34416041, -389848.4235981 ,\n", + " -385847.7238277 , -381847.30809541, -377846.97879215,\n", + " -373846.83202364, -369846.84681622, -365847.0775374 ,\n", + " -361847.42868547, -357847.99107807, -353848.67124696,\n", + " -349846.63664111, -345847.69195027, -341848.86085595,\n", + " -337847.36121013, -333848.87439829, -329847.61272787,\n", + " -325846.49417387, -321848.45066265, -317847.67770099,\n", + " -313847.00966464, -309846.55672976, -305846.15851356,\n", + " -301848.80743028, -297848.73401306, -293848.80400067,\n", + " -289848.95618854, -285846.42493523, -281846.89895013,\n", + " -277847.46574728, -273848.20872659, -269846.17354669,\n", + " -265847.10920286, -261848.18691689, -257846.52291925,\n", + " -253847.79909431, -249846.38182197, -245847.88954112,\n", + " -241846.71062923, -237848.4302232 , -233847.50534012,\n", + " -229846.57370064, -225848.6682608 , -221847.96954579,\n", + " -217847.40062777, -213846.91455502, -209846.54248561,\n", + " -205846.23970871, -201848.92522171, -197848.80949076,\n", + " -193848.78972017, -189848.89278964, -185846.18054808,\n", + " -181846.43539071, -177846.75949393, -173847.16965507,\n", + " -169847.67238598, -165848.22267293, -161848.86205713,\n", + " -157846.73599205, -153847.50569577, -149848.37354183,\n", + " -145846.43794118, -141847.43312064, -137848.48966761,\n", + " -133846.73134824, -129847.93270571, -125846.31490803,\n", + " -121847.60658359, -117848.95756046, -113847.49781463,\n", + " -109848.96096089, -105847.60370073, -101846.30276213,\n", + " -97847.91828188, -93846.68365713, -89848.3819641 ,\n", + " -85847.25535105, -81849.02074623, -77847.97046565,\n", + " -73846.9571176 , -69848.82507724, -65847.87702698,\n", + " -61846.94727318, -57848.91579031, -53848.04741895,\n", + " -49847.20474114, -45846.38561176, -41848.44936691,\n", + " -37847.66572384, -33846.89869206, -29849.0176602 ,\n", + " -25848.28147743, -21847.55537625, -17846.84480235,\n", + " -13849.00537462, -9848.30375156, -5847.60778563,\n", + " -1846.91443742, 2150.90966469, 6151.60303648,\n", + " 10152.29900308, 14153.00065035, 18153.70795947,\n", + " 22151.55623066, 26152.28336887, 30153.01595515,\n", + " 34150.89912272, 38151.66872149, 42152.45103505,\n", + " 46153.25578283, 50151.21272946, 54152.06744801,\n", + " 58152.92875182, 62150.96147069, 66151.89584031,\n", + " 70152.84528477, 74150.97124616, 78151.98558124,\n", + " 82153.05360533, 86151.27690254, 90152.41390478,\n", + " 94153.5898915 , 98151.94351874, 102153.2074843 ,\n", + " 106151.65297211, 110153.01227233, 114151.55697637,\n", + " 118153.01294512, 122151.66401699, 126153.22941959,\n", + " 130152.00748048, 134153.6900796 , 138152.56931872,\n", + " 142151.52246648, 146153.39036518, 150152.45286934,\n", + " 154151.61081517, 158153.70382188, 162152.98939895,\n", + " 166152.33634753, 170151.76293096, 174151.28016777,\n", + " 178150.88173441, 182153.40052491, 186153.19533963,\n", + " 190153.06235028, 194152.96290358, 198152.99644881,\n", + " 202153.07486465, 206153.27077183, 210153.56611891,\n", + " 214151.10608157, 218151.60694126, 222152.16839161,\n", + " 226152.87079379, 230153.64776205, 234151.70331023,\n", + " 238152.70436732, 242150.92782145, 246152.13543546,\n", + " 250153.52519863, 254152.10493862, 258153.60950245,\n", + " 262152.43578044, 266151.38904911, 270153.31180315,\n", + " 274152.49713737, 278151.8150729 , 282151.22462874,\n", + " 286153.65230465, 290153.33553062, 294153.18867164,\n", + " 298153.07839344, 302153.21805122, 306153.42691301,\n", + " 310150.93098161, 314151.4847698 , 318152.11031704,\n", + " 322152.92157806, 326150.95435571, 330152.04500714,\n", + " 334153.31199356, 338151.8040678 , 342153.37867622,\n", + " 346152.21587801, 350151.18447167, 354153.22540454,\n", + " 358152.55155502, 362151.97699927, 366151.63196773,\n", + " 370151.46387702, 374151.4177032 , 378151.57078458,\n", + " 382151.88694777, 386152.36772088, 390153.05436712,\n", + " 394151.06168363, 398152.12756765, 402153.304367 ,\n", + " 406151.8273034 , 410153.37223178, 414152.2043099 ,\n", + " 418151.27749738, 422153.46302984, 426152.94171617,\n", + " 430152.56045283, 434152.43006433, 438152.46467961,\n", + " 442152.64270581, 446153.12344062, 450150.92899765,\n", + " 454151.72730472, 458152.83675802, 462151.25289819,\n", + " 466152.75985787, 470151.52828603, 474153.43844753,\n", + " 478152.70926043, 482152.16165718, 486151.74706637,\n", + " 490151.73959925, 494151.84385635, 498152.23639489,\n", + " 502152.79278782, 506153.5906173 , 510151.76413657,\n", + " 514153.02507329, 518151.66555909, 522153.39691557,\n", + " 526152.45813349, 530151.79893534, 534151.44924002,\n", + " 538151.24810644, 542151.30556965, 546151.65154799,\n", + " 550152.1208407 , 554152.93807898, 558151.18254747,\n", + " 562152.47835114, 566151.14763776, 570152.89868486,\n", + " 574152.17188498, 578151.66322488, 582151.40322951,\n", + " 586151.33379136, 590151.57528313, 594152.13044127,\n", + " 598152.9411577 , 602151.20232222, 606152.49829395,\n", + " 610151.27918863, 614153.09671507, 618152.43485402,\n", + " 622151.97561113, 626151.78297035, 630151.95457719,\n", + " 634152.42928344, 638153.11187408, 642151.13477367,\n", + " 646152.49685929, 650151.17004599, 654153.02281532,\n", + " 658152.38845054, 662152.03718631, 666151.80122378,\n", + " 670152.05376699, 674152.62863479, 678153.42454061,\n", + " 682151.53923575, 686153.02204595, 690151.89683146,\n", + " 694150.997103 , 698153.36844712, 702153.10002477,\n", + " 706153.31121357, 710153.6096021 , 714151.59694722,\n", + " 718152.57687259, 722151.14234101, 726152.88547008,\n", + " 730152.03491755, 734151.53406926, 738151.42254562,\n", + " 742151.62766628, 746152.11304004, 750152.8795744 ,\n", + " 754151.17522194, 758152.58442569, 762151.68142302,\n", + " 766150.98901518, 770153.53123988, 774153.41718595,\n", + " 778150.92318215],\n", " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('x',),\n", - " 'units': '1000 m',\n", " 'long_name': 'x coordinate of projection',\n", + " 'units': '1000 m',\n", " 'standard_name': 'projection_x_coordinate'}" ] }, @@ -773,210 +266,144 @@ { "data": { "text/plain": [ - "{'data': masked_array(data=[-2067138.47948561, -2055138.33742867,\n", - " -2043137.54665598, -2031137.11044915,\n", - " -2019137.8807928 , -2007138.08379361,\n", - " -1995137.55201761, -1983138.30318791,\n", - " -1971139.14829968, -1959137.5926525 ,\n", - " -1947137.10543589, -1935138.53320712,\n", - " -1923137.4951208 , -1911137.45400782,\n", - " -1899138.2432851 , -1887137.51394029,\n", - " -1875138.43810423, -1863136.92956926,\n", - " -1851137.89552799, -1839137.1121049 ,\n", - " -1827137.15927377, -1815138.01203912,\n", - " -1803138.7795864 , -1791137.70775766,\n", - " -1779137.3704284 , -1767137.74254063,\n", - " -1755137.93518378, -1743137.06138263,\n", - " -1731137.6875659 , -1719137.19999768,\n", - " -1707136.43834552, -1695137.96319842,\n", - " -1683137.44174755, -1671137.43454641,\n", - " -1659137.056387 , -1647138.00256504,\n", - " -1635137.66921584, -1623136.89251186,\n", - " -1611137.36489549, -1599137.34432241,\n", - " -1587137.79320469, -1575137.57084194,\n", - " -1563136.9109904 , -1551137.37235623,\n", - " -1539137.94554037, -1527137.1502086 ,\n", - " -1515138.25410466, -1503138.66687657,\n", - " -1491138.4899473 , -1479136.59453581,\n", - " -1467137.47233746, -1455137.55905622,\n", - " -1443137.68244934, -1431137.81703208,\n", - " -1419138.78837709, -1407138.99088641,\n", - " -1395137.42673134, -1383137.47212092,\n", - " -1371137.40019024, -1359138.03433592,\n", - " -1347136.9224302 , -1335137.31271794,\n", - " -1323137.4816182 , -1311137.94634304,\n", - " -1299137.29022577, -1287137.1818736 ,\n", - " -1275137.59439096, -1263136.92697495,\n", - " -1251137.57275311, -1239136.12380517,\n", - " -1227137.6241066 , -1215137.51625937,\n", - " -1203137.04074528, -1191137.62692217,\n", - " -1179138.21301879, -1167137.50751754,\n", - " -1155137.89726681, -1143138.09129864,\n", - " -1131138.06316848, -1119137.05825655,\n", - " -1107137.15205236, -1095138.20305536,\n", - " -1083138.08298794, -1071137.18627622,\n", - " -1059138.64697368, -1047138.74478058,\n", - " -1035138.51485085, -1023137.92955771,\n", - " -1011137.6894263 , -999137.45744737,\n", - " -987137.51499851, -975137.83442915,\n", - " -963137.76831308, -951139.05433923,\n", - " -939137.39017683, -927137.54863286,\n", - " -915138.66481437, -903137.47725183,\n", - " -891138.44385041, -879137.99257723,\n", - " -867138.69715018, -855137.92810361,\n", - " -843137.53065969, -831137.99786638,\n", - " -819138.36299108, -807137.87013262,\n", - " -795137.32354617, -783137.83855245,\n", - " -771137.41075253, -759138.09010529,\n", - " -747137.76958547, -735137.98006167,\n", - " -723137.5495894 , -711137.6947773 ,\n", - " -699137.86979959, -687138.5622281 ,\n", - " -675137.66992397, -663138.06610574,\n", - " -651137.23497902, -639136.80506847,\n", - " -627138.30290647, -615137.65946231,\n", - " -603138.15712078, -591136.87075576,\n", - " -579137.08014637, -567137.82964577,\n", - " -555137.63384505, -543137.50696179,\n", - " -531138.0283756 , -519137.73451411,\n", - " -507137.93240193, -495137.45153707,\n", - " -483137.30641057, -471137.15203014,\n", - " -459138.19530608, -447138.75770337,\n", - " -435136.43449817, -423137.59544964,\n", - " -411136.54082615, -399138.59271965,\n", - " -387138.05393281, -375137.17535161,\n", - " -363137.35013412, -351138.04215929,\n", - " -339138.30575139, -327138.29921261,\n", - " -315137.89921544, -303137.58006488,\n", - " -291137.12571846, -279137.5132003 ,\n", - " -267137.79855472, -255137.95212261,\n", - " -243138.35433354, -231138.15527529,\n", - " -219137.41721347, -207138.06770319,\n", - " -195138.02753911, -183138.17757006,\n", - " -171137.98702931, -159138.24510045,\n", - " -147137.37488813, -135137.71176692,\n", - " -123137.99774014, -111137.06580801,\n", - " -99137.65919525, -87138.52051766,\n", - " -75137.66532798, -63138.24447606,\n", - " -51137.45613798, -39138.44989177,\n", - " -27137.19935201, -15137.26193513,\n", - " -3138.2870129 , 8862.70158754,\n", - " 20863.19613188, 32862.09109349,\n", - " 44862.1850711 , 56862.28417564,\n", - " 68862.09833787, 80862.79348443,\n", - " 92862.04079618, 104861.82177242,\n", - " 116861.75894296, 128861.96917478,\n", - " 140861.98836512, 152861.43960268,\n", - " 164861.6613091 , 176862.10360022,\n", - " 188862.1547975 , 200862.57312437,\n", - " 212861.43960038, 224862.6824761 ,\n", - " 236862.02704646, 248862.67280663,\n", - " 260861.80232975, 272862.69968633,\n", - " 284863.03968892, 296862.27594177,\n", - " 308863.36959344, 320862.7767121 ,\n", - " 332862.79683064, 344861.59736628,\n", - " 356861.88372131, 368861.82305626,\n", - " 380861.27672775, 392861.25573027,\n", - " 404862.83978888, 416862.48790187,\n", - " 428862.26071291, 440862.59403697,\n", - " 452861.89377259, 464861.81366973,\n", - " 476862.70563957, 488861.51974816,\n", - " 500861.77140022, 512861.95044987,\n", - " 524862.40904252, 536862.85449188,\n", - " 548862.09945016, 560862.20196287,\n", - " 572862.70276656, 584862.09217358,\n", - " 596862.4276693 , 608861.6280585 ,\n", - " 620861.42797071, 632862.17940113,\n", - " 644862.3726426 , 656861.95444672,\n", - " 668862.65863139, 680862.81014377,\n", - " 692862.52099743, 704861.41511084,\n", - " 716862.19408409, 728863.02536633,\n", - " 740861.50621775, 752862.36459892,\n", - " 764861.74143799, 776862.50273099,\n", - " 788863.2968091 , 800862.6963584 ,\n", - " 812861.94605002, 824861.7205462 ,\n", - " 836861.80803198, 848862.4778583 ,\n", - " 860861.89719402, 872862.84895905,\n", - " 884862.52520112, 896861.84719109,\n", - " 908861.8945184 , 920862.37268268,\n", - " 932862.58191684, 944862.38597276,\n", - " 956862.70585726, 968862.75932956,\n", - " 980862.97978011, 992862.99006537,\n", - " 1004862.00768959, 1016862.08692122,\n", - " 1028861.63462963, 1040862.21737941,\n", - " 1052861.91891392, 1064861.98284895,\n", - " 1076862.35437825, 1088862.01009992,\n", - " 1100862.02850585, 1112862.4370279 ,\n", - " 1124863.3456949 , 1136862.34934454,\n", - " 1148862.23038066, 1160861.47692509,\n", - " 1172862.46616204, 1184862.06404513,\n", - " 1196861.91951627, 1208861.89356904,\n", - " 1220862.17889049, 1232861.99106498,\n", - " 1244862.49052547, 1256862.89251711,\n", - " 1268862.41196453, 1280861.96984232,\n", - " 1292861.10334917, 1304861.8676576 ,\n", - " 1316861.85408047, 1328862.71199087,\n", - " 1340862.92749166, 1352862.44262531,\n", - " 1364861.60505252, 1376863.04434379,\n", - " 1388861.42362458, 1400862.3692699 ,\n", - " 1412861.20141946, 1424861.92291089,\n", - " 1436862.85005413, 1448862.46695994,\n", - " 1460862.42366287, 1472861.93234365,\n", - " 1484862.55875226, 1496862.0583845 ,\n", - " 1508862.3186023 , 1520862.95731279,\n", - " 1532862.13580359, 1544862.14789759,\n", - " 1556862.20427556, 1568862.0074687 ,\n", - " 1580861.9026419 , 1592861.99921494,\n", - " 1604861.10001189, 1616862.31297015,\n", - " 1628862.98422931, 1640862.32262325,\n", - " 1652862.62179676, 1664862.12766606,\n", - " 1676862.23304256, 1688862.2324209 ,\n", - " 1700861.42018878, 1712862.49792768,\n", - " 1724861.99388639, 1736861.79393994,\n", - " 1748861.68713378, 1760862.01617442,\n", - " 1772861.8994825 , 1784862.35010491,\n", - " 1796861.98991304, 1808861.01601485,\n", - " 1820862.85914197, 1832862.09010331,\n", - " 1844861.90765863, 1856861.83581031,\n", - " 1868862.30366401, 1880861.87554469,\n", - " 1892861.61954572, 1904861.96465242,\n", - " 1916862.20298818, 1928862.26487282,\n", - " 1940862.25984512, 1952861.70845873,\n", - " 1964862.6760201 , 1976862.40781729,\n", - " 1988862.0601863 , 2000862.06152662,\n", - " 2012861.61160637, 2024861.54817743,\n", - " 2036862.29978874, 2048862.33737603,\n", - " 2060862.31546611, 2072862.34356724,\n", - " 2084862.02940992, 2096862.21104696,\n", - " 2108862.08564468, 2120862.80904264,\n", - " 2132861.29995525, 2144863.04431031,\n", - " 2156861.7680832 , 2168862.22975281,\n", - " 2180862.0745934 , 2192862.45785697,\n", - " 2204862.98450722, 2216862.11947367,\n", - " 2228862.25144406, 2240861.84524301,\n", - " 2252861.64424617, 2264862.07524215,\n", - " 2276862.64609012, 2288862.83419518,\n", - " 2300861.95483109, 2312863.00214068,\n", - " 2324862.2811275 , 2336862.69096942,\n", - " 2348862.5961938 , 2360862.83510549,\n", - " 2372862.49844879, 2384862.20620674,\n", - " 2396862.09153175, 2408862.0470545 ,\n", - " 2420862.20439962, 2432861.21701588,\n", - " 2444862.20681598, 2456862.29183621,\n", - " 2468862.30979652, 2480862.37103315,\n", - " 2492861.55935617, 2504862.36887632,\n", - " 2516862.32624744, 2528862.6837446 ,\n", - " 2540862.62276294, 2552862.56717894,\n", - " 2564862.52648303, 2576862.09514914,\n", - " 2588861.69682198, 2600862.48360977,\n", - " 2612862.07561471, 2624862.03895504,\n", - " 2636862.06879401, 2648862.48574049,\n", - " 2660862.15334881, 2672862.22245282,\n", - " 2684862.70004968, 2696862.03251993],\n", + "{'data': masked_array(data=[-795136.61639098, -791136.41059064, -787136.95398677,\n", + " -783136.54507254, -779137.45651076, -775138.11398099,\n", + " -771137.96423217, -767137.28208224, -763137.76563627,\n", + " -759136.86451451, -755137.97579632, -751137.84855236,\n", + " -747137.88310773, -743137.52610199, -739137.32826948,\n", + " -735138.01003527, -731136.44930963, -727136.46604007,\n", + " -723136.9335004 , -719136.70436349, -715136.7747824 ,\n", + " -711138.1405258 , -707137.25658184, -703136.24380026,\n", + " -699138.07101214, -695136.37209306, -691135.81316616,\n", + " -687136.96503205, -683136.56030037, -679137.01503691,\n", + " -675136.48305301, -671136.51150421, -667137.11902392,\n", + " -663137.43595774, -659137.1850433 , -655136.51298831,\n", + " -651137.66627877, -647136.6998782 , -643136.00824864,\n", + " -639136.43767724, -635137.1389174 , -631137.26295508,\n", + " -627136.53251755, -623137.61709821, -619136.57328355,\n", + " -615137.48924267, -611137.25000216, -607137.14453586,\n", + " -603137.57580866, -599136.57208816, -595136.39751736,\n", + " -591137.60255377, -587136.66913773, -583137.25996956,\n", + " -579136.83276977, -575136.65657788, -571137.15334795,\n", + " -567137.62219384, -563137.76707809, -559137.88128765,\n", + " -555136.96958736, -551137.57028668, -547136.29592197,\n", + " -543136.10784507, -539137.42764623, -535137.86189828,\n", + " -531136.83909752, -527136.74963176, -523137.03994595,\n", + " -519137.13845806, -515136.34476016, -511136.47861553,\n", + " -507137.2624934 , -503137.57295892, -499136.83881607,\n", + " -495136.75046434, -491138.15216017, -487136.81367617,\n", + " -483138.23091907, -479136.20664698, -475137.63385457,\n", + " -471136.88538445, -467136.77262358, -463137.29406559,\n", + " -459136.33471237, -455137.27480186, -451137.30068009,\n", + " -447137.1096951 , -443136.97649607, -439136.77024811,\n", + " -435137.61066859, -431137.80600265, -427137.07891212,\n", + " -423137.67014482, -419136.91366753, -415136.77409801,\n", + " -411138.3708385 , -407137.77083851, -403137.50736794,\n", + " -399136.44144162, -395137.82100067, -391136.28350233,\n", + " -387137.61075983, -383137.8539102 , -379137.01168091,\n", + " -375137.19411908, -371137.97741569, -367138.23958832,\n", + " -363136.56643527, -359137.032515 , -355137.39542249,\n", + " -351137.50757774, -347137.09156023, -343137.12013648,\n", + " -339137.46190547, -335137.4011641 , -331137.22864502,\n", + " -327136.79687085, -323137.7925582 , -319137.68202128,\n", + " -315136.60992979, -311137.38282789, -307136.89938064,\n", + " -303137.98185358, -299136.68531379, -295137.22773734,\n", + " -291136.8000591 , -287137.21861107, -283137.50797208,\n", + " -279136.25529925, -275136.83400805, -271136.28970518,\n", + " -267136.73009672, -263136.46646462, -259136.90851916,\n", + " -255137.48720864, -251138.05534607, -247137.63772891,\n", + " -243137.07641722, -239137.34369933, -235136.89696686,\n", + " -231137.988885 , -227137.52052539, -223137.17719019,\n", + " -219137.80068959, -215137.28134621, -211137.30426063,\n", + " -207136.74869985, -203137.99719673, -199136.41079586,\n", + " -195137.46861417, -191137.79654696, -187137.39319426,\n", + " -183137.24571539, -179137.48313279, -175137.55192516,\n", + " -171137.72664199, -167137.16284194, -163138.11192593,\n", + " -159136.90971936, -155137.63885172, -151137.47821038,\n", + " -147136.29579376, -143136.89472954, -139136.89034263,\n", + " -135137.67603237, -131137.43406953, -127137.55784189,\n", + " -123137.91519273, -119137.09518443, -115138.19122442,\n", + " -111137.54029239, -107137.39303131, -103137.47190727,\n", + " -99137.63011412, -95137.44485548, -91137.33597317,\n", + " -87138.28984298, -83136.64407898, -79136.90066737,\n", + " -75136.38512162, -71136.78105858, -67137.24442899,\n", + " -63137.35249174, -59137.67024233, -55136.64202745,\n", + " -51137.08423766, -47137.1652011 , -43137.7258817 ,\n", + " -39137.22510488, -35137.05587385, -31136.66464553,\n", + " -27137.58953575, -23137.30185574, -19137.06371283,\n", + " -15137.71596995, -11137.29637321, -7137.61901295,\n", + " -3137.28799547, 863.00098038, 4863.24941572,\n", + " 8862.89252879, 12863.06439672, 16863.05510887,\n", + " 20863.4324407 , 24862.09219132, 28862.40499 ,\n", + " 32862.26664082, 36862.79694174, 40862.45798722,\n", + " 44862.93579718, 48863.38962098, 52862.13649057,\n", + " 56862.5468791 , 60861.95048882, 64862.59950388,\n", + " 68862.95606085, 72862.58641333, 76862.76955963,\n", + " 80863.36192165, 84863.37771449, 88863.80575256,\n", + " 92862.96312886, 96863.7990964 , 100862.10412526,\n", + " 104862.78790487, 108863.1943838 , 112862.61381734,\n", + " 116862.03501245, 120863.14390749, 124863.27031676,\n", + " 128863.12702731, 132862.42539513, 136863.41756142,\n", + " 140862.73619004, 144862.48834325, 148861.68823736,\n", + " 152862.7331382 , 156863.64995215, 160862.47970676,\n", + " 164862.59285906, 168862.58249175, 172862.31920476,\n", + " 176862.35657583, 180862.42011234, 184862.93247983,\n", + " 188862.63181013, 192863.0590615 , 196861.97915599,\n", + " 200862.4725277 , 204863.00126842, 208862.3034493 ,\n", + " 212862.34123212, 216862.56411947, 220862.26220624,\n", + " 224862.42449941, 228863.47373106, 232862.88437566,\n", + " 236863.03987784, 240862.96855469, 244863.22399709,\n", + " 248862.26811232, 252862.62965461, 256862.48016488,\n", + " 260862.9539953 , 264862.91986326, 268862.52449022,\n", + " 272862.19064672, 276863.03840288, 280863.25353619,\n", + " 284862.1258368 , 288863.59344583, 292862.60271927,\n", + " 296863.36792621, 300862.37510111, 304862.86534092,\n", + " 308862.86449151, 312862.51935428, 316861.83146148,\n", + " 320862.76370655, 324862.51364452, 328862.9134924 ,\n", + " 332861.98874568, 336862.69044933, 340862.36130204,\n", + " 344861.97624768, 348861.82757176, 352863.73314478,\n", + " 356863.20443515, 360862.62599938, 364862.71164663,\n", + " 368862.32925202, 372862.46868182, 376862.28859325,\n", + " 380862.90951545, 384862.51652108, 388862.50617549,\n", + " 392863.02554629, 396863.23318331, 400862.70908473,\n", + " 404862.99544012, 408862.55314023, 412862.79397973,\n", + " 416863.28290963, 420862.77179626, 424863.07904209,\n", + " 428862.38932847, 432862.94278066, 436862.77840975,\n", + " 440863.01708396, 444862.68665592, 448863.18411451,\n", + " 452862.54821401, 456862.46732199, 460862.52132826,\n", + " 464862.98784592, 468863.17067095, 472863.49313601,\n", + " 476863.1132311 , 480862.45427433, 484863.05933278,\n", + " 488862.5448325 , 492862.59968152, 496862.80364446,\n", + " 500862.31452776, 504862.67559245, 508862.34667757,\n", + " 512863.01706504, 516862.57864618, 520862.29880874,\n", + " 524862.45517937, 528862.77333847, 532862.41082865,\n", + " 536863.0573465 , 540862.45822825, 544862.59532854,\n", + " 548862.75610294, 552862.24396131, 556863.44712821,\n", + " 560862.43806453, 564862.72539344, 568862.34603078,\n", + " 572862.990341 , 576862.40266614, 580862.98812753,\n", + " 584862.34464282, 588862.30904336, 592862.4606934 ,\n", + " 596863.07723751, 600862.34118273, 604862.91789654,\n", + " 608862.42111689, 612863.24039535, 616862.29080023,\n", + " 620862.66051503, 624862.80782521, 628863.15679884,\n", + " 632862.71751253, 636863.05209059, 640862.60149526,\n", + " 644861.93631965, 648863.02461258, 652862.78033962,\n", + " 656863.02496098, 660863.06140619, 664863.31395051,\n", + " 668862.51604284, 672863.05890353, 676862.13162718,\n", + " 680862.69517888, 684862.63729034, 688862.65833449,\n", + " 692862.63071 , 696862.26231303, 700862.40048983,\n", + " 704862.91778939, 708862.67608951, 712863.51578514,\n", + " 716862.47750864, 720862.52388042, 724862.66350396,\n", + " 728862.34590843, 732862.97097581, 736862.42466877,\n", + " 740862.69536738, 744863.49052236, 748861.99636145,\n", + " 752862.44667911, 756862.303728 , 760863.53829826,\n", + " 764862.06609825, 768862.12193212, 772862.43751676,\n", + " 776863.29054439, 780862.86026755, 784863.39408875,\n", + " 788863.62322432],\n", " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('y',),\n", - " 'units': '1000 m',\n", " 'long_name': 'y coordinate of projection',\n", + " 'units': '1000 m',\n", " 'standard_name': 'projection_y_coordinate'}" ] }, @@ -998,22 +425,21 @@ "data": { "text/plain": [ "{'data': masked_array(\n", - " data=[[19.706436, 19.728317, 19.750084, ..., 16.264694, 16.22998 ,\n", - " 16.19516 ],\n", - " [19.805984, 19.827904, 19.849716, ..., 16.358276, 16.323502,\n", - " 16.288643],\n", - " [19.905594, 19.927544, 19.949394, ..., 16.45192 , 16.417084,\n", - " 16.382141],\n", + " data=[[32.51078415, 32.51420212, 32.51760101, ..., 32.54065323,\n", + " 32.53738403, 32.53408432],\n", + " [32.54635239, 32.54976654, 32.55315399, ..., 32.57624817,\n", + " 32.57295609, 32.56965637],\n", + " [32.58191681, 32.58533096, 32.58874512, ..., 32.61181641,\n", + " 32.60853195, 32.60523224],\n", " ...,\n", - " [59.66961 , 59.70968 , 59.74953 , ..., 53.457195, 53.39534 ,\n", - " 53.33333 ],\n", - " [59.76223 , 59.802353, 59.842262, ..., 53.541912, 53.47999 ,\n", - " 53.417908],\n", - " [59.854744, 59.89492 , 59.934883, ..., 53.62653 , 53.56453 ,\n", - " 53.502373]],\n", + " [46.58963013, 46.59383011, 46.59801102, ..., 46.62640381,\n", + " 46.62236404, 46.61830521],\n", + " [46.62516785, 46.62936783, 46.63354874, ..., 46.66195679,\n", + " 46.65791702, 46.65385818],\n", + " [46.66069794, 46.66490173, 46.66908264, ..., 46.69750214,\n", + " 46.69345856, 46.6893959 ]],\n", " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", + " fill_value=1e+20),\n", " 'dimensions': ('y', 'x'),\n", " 'units': 'degrees_north',\n", " 'axis': 'Y',\n", @@ -1039,22 +465,21 @@ "data": { "text/plain": [ "{'data': masked_array(\n", - " data=[[-22.32898 , -22.223236, -22.117432, ..., 28.619202, 28.716614,\n", - " 28.813965],\n", - " [-22.352325, -22.24646 , -22.140533, ..., 28.655334, 28.752869,\n", - " 28.850311],\n", - " [-22.375702, -22.269714, -22.163696, ..., 28.69159 , 28.789185,\n", - " 28.886719],\n", + " data=[[-11.54882812, -11.50665283, -11.46447754, ..., 5.17233276,\n", + " 5.21453857, 5.25671387],\n", + " [-11.55288696, -11.51068115, -11.46847534, ..., 5.1762085 ,\n", + " 5.21844482, 5.26065063],\n", + " [-11.5569458 , -11.51473999, -11.47250366, ..., 5.18008423,\n", + " 5.22235107, 5.2645874 ],\n", " ...,\n", - " [-39.438995, -39.25531 , -39.07132 , ..., 53.106964, 53.249176,\n", - " 53.391052],\n", - " [-39.518707, -39.334717, -39.15039 , ..., 53.210876, 53.35321 ,\n", - " 53.49518 ],\n", - " [-39.598724, -39.41443 , -39.229828, ..., 53.315125, 53.45755 ,\n", - " 53.59964 ]],\n", + " [-13.51443481, -13.46273804, -13.41101074, ..., 7.05273438,\n", + " 7.10449219, 7.15625 ],\n", + " [-13.52056885, -13.46884155, -13.41708374, ..., 7.05859375,\n", + " 7.1104126 , 7.16220093],\n", + " [-13.5267334 , -13.47494507, -13.42315674, ..., 7.06448364,\n", + " 7.11630249, 7.16812134]],\n", " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", + " fill_value=1e+20),\n", " 'dimensions': ('y', 'x'),\n", " 'units': 'degrees_east',\n", " 'axis': 'X',\n", @@ -1081,7 +506,7 @@ "output_type": "stream", "text": [ "Rank 000: Loading sconco3 var (1/1)\n", - "Rank 000: Loaded sconco3 var ((48, 1, 398, 478))\n" + "Rank 000: Loaded sconco3 var ((2, 1, 397, 397))\n" ] } ], @@ -1098,104 +523,42 @@ "data": { "text/plain": [ "{'sconco3': {'data': masked_array(\n", - " data=[[[[0.03952214, 0.0395867 , 0.03965145, ..., 0.03198181,\n", - " 0.03125041, 0.03153064],\n", - " [0.03945881, 0.03952107, 0.03958255, ..., 0.03184386,\n", - " 0.03127047, 0.03173521],\n", - " [0.03940514, 0.03945867, 0.03951972, ..., 0.03147388,\n", - " 0.031453 , 0.03222592],\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", - " [0.03757998, 0.03767523, 0.03775784, ..., 0.02404686,\n", - " 0.02617675, 0.02804444],\n", - " [0.03747029, 0.03767779, 0.03778836, ..., 0.02497317,\n", - " 0.02633872, 0.02821054],\n", - " [0.03744229, 0.03762932, 0.03774261, ..., 0.02464963,\n", - " 0.02615741, 0.02828379]]],\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", - " [[[0.03965769, 0.03971414, 0.03978673, ..., 0.03103125,\n", - " 0.03015577, 0.03036901],\n", - " [0.03960922, 0.03966256, 0.03972849, ..., 0.03101462,\n", - " 0.03021824, 0.03060081],\n", - " [0.03957865, 0.03962931, 0.03968912, ..., 0.03126283,\n", - " 0.03049327, 0.0311233 ],\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", - " [0.03786875, 0.03777716, 0.0376072 , ..., 0.02531419,\n", - " 0.02676462, 0.02868378],\n", - " [0.03781448, 0.03783737, 0.03778312, ..., 0.02607518,\n", - " 0.02714914, 0.02904003],\n", - " [0.03779451, 0.03781895, 0.03778093, ..., 0.02577951,\n", - " 0.02662436, 0.02910396]]],\n", - " \n", - " \n", - " [[[0.03982776, 0.03989794, 0.04000261, ..., 0.03058425,\n", - " 0.02924641, 0.02938778],\n", - " [0.03979794, 0.03985743, 0.03995337, ..., 0.03089899,\n", - " 0.02948623, 0.02973373],\n", - " [0.03978007, 0.03982415, 0.03991285, ..., 0.03140561,\n", - " 0.03000267, 0.0304553 ],\n", - " ...,\n", - " [0.03821166, 0.03814133, 0.0379876 , ..., 0.02608518,\n", - " 0.02720294, 0.02940145],\n", - " [0.03820223, 0.03822928, 0.03817524, ..., 0.02666686,\n", - " 0.02796096, 0.02975006],\n", - " [0.03819539, 0.03822353, 0.03819501, ..., 0.02689083,\n", - " 0.02732235, 0.02981647]]],\n", - " \n", - " \n", - " ...,\n", - " \n", - " \n", - " [[[0.04243098, 0.04246398, 0.0425217 , ..., 0.03174707,\n", - " 0.02986301, 0.02911444],\n", - " [0.04233237, 0.0423856 , 0.0424809 , ..., 0.03209906,\n", - " 0.03045077, 0.02993134],\n", - " [0.04244579, 0.04248011, 0.04255648, ..., 0.03262969,\n", - " 0.03137314, 0.03106195],\n", - " ...,\n", - " [0.03979023, 0.03997482, 0.04005315, ..., 0.03456535,\n", - " 0.03498862, 0.0354754 ],\n", - " [0.03967334, 0.0398716 , 0.04000713, ..., 0.03471798,\n", - " 0.03485566, 0.03500457],\n", - " [0.03964297, 0.03981132, 0.03993002, ..., 0.03497454,\n", - " 0.03504148, 0.03509094]]],\n", - " \n", - " \n", - " [[[0.04243221, 0.04248913, 0.04257801, ..., 0.03141348,\n", - " 0.02992571, 0.02943683],\n", - " [0.04236476, 0.0424497 , 0.04255884, ..., 0.03173555,\n", - " 0.03054 , 0.03031359],\n", - " [0.04247259, 0.04253475, 0.0426126 , ..., 0.03197961,\n", - " 0.03136487, 0.03140653],\n", - " ...,\n", - " [0.03982663, 0.04001996, 0.04009991, ..., 0.03445901,\n", - " 0.0349366 , 0.03539021],\n", - " [0.03969444, 0.03990471, 0.04005693, ..., 0.03466238,\n", - " 0.03480245, 0.03491453],\n", - " [0.0396612 , 0.03983796, 0.03996958, ..., 0.03486277,\n", - " 0.03491549, 0.03492822]]],\n", - " \n", - " \n", - " [[[0.04249088, 0.04256602, 0.04264675, ..., 0.03092884,\n", - " 0.02980646, 0.02965403],\n", - " [0.042451 , 0.04252698, 0.04259988, ..., 0.03106567,\n", - " 0.03033506, 0.03051317],\n", - " [0.04252941, 0.04258011, 0.04264118, ..., 0.0312073 ,\n", - " 0.03103344, 0.03165624],\n", - " ...,\n", - " [0.03986304, 0.04006038, 0.04014875, ..., 0.03444035,\n", - " 0.03486974, 0.03531818],\n", - " [0.03971234, 0.03994204, 0.04009009, ..., 0.03459954,\n", - " 0.0347504 , 0.0348453 ],\n", - " [0.03967606, 0.03986803, 0.03999919, ..., 0.03475965,\n", - " 0.03481083, 0.03478444]]]],\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", " 'dimensions': ('time', 'lev', 'y', 'x'),\n", " 'units': 'ppm',\n", - " 'coordinates': 'lat lon',\n", - " 'grid_mapping': 'Lambert_conformal'}}" + " 'cell_methods': 'time: max (interval: 1hr)',\n", + " 'grid_mapping': 'Lambert_conformal',\n", + " 'coordinates': 'lat lon'}}" ] }, "execution_count": 12, @@ -1207,6 +570,13 @@ "nessy_1.variables" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Write dataset" + ] + }, { "cell_type": "code", "execution_count": 13, @@ -1235,7 +605,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Reopen with NES" + "## 3. Reopen dataset" ] }, { @@ -1246,7 +616,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -1258,439 +628,6 @@ "nessy_2 = open_netcdf('lcc_file_1.nc', info=True)\n", "nessy_2" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reopen with xarray" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:            (time: 48, lev: 1, y: 398, x: 478)\n",
-       "Coordinates:\n",
-       "  * time               (time) datetime64[ns] 2021-01-01 ... 2021-01-02T23:00:00\n",
-       "  * lev                (lev) float64 0.0\n",
-       "    lat                (y, x) float64 ...\n",
-       "    lon                (y, x) float64 ...\n",
-       "  * y                  (y) float64 -2.067e+06 -2.055e+06 ... 2.685e+06 2.697e+06\n",
-       "  * x                  (x) float64 -2.126e+06 -2.114e+06 ... 3.586e+06 3.598e+06\n",
-       "Data variables:\n",
-       "    sconco3            (time, lev, y, x) float32 ...\n",
-       "    Lambert_conformal  |S1 b''\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 48, lev: 1, y: 398, x: 478)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2021-01-01 ... 2021-01-02T23:00:00\n", - " * lev (lev) float64 0.0\n", - " lat (y, x) float64 ...\n", - " lon (y, x) float64 ...\n", - " * y (y) float64 -2.067e+06 -2.055e+06 ... 2.685e+06 2.697e+06\n", - " * x (x) float64 -2.126e+06 -2.114e+06 ... 3.586e+06 3.598e+06\n", - "Data variables:\n", - " sconco3 (time, lev, y, x) float32 ...\n", - " Lambert_conformal |S1 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('lcc_file_1.nc')" - ] } ], "metadata": { diff --git a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb index d4cb3f39016ba56ef84ad85a5204515898be6e1d..47357c7f42b5970c08a5624b358841c951416f2f 100644 --- a/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb +++ b/tutorials/1.Introduction/1.5.Read_Write_Mercator.ipynb @@ -13,8 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "from nes import *\n", - "import xarray as xr" + "from nes import *" ] }, { @@ -23,21 +22,16 @@ "metadata": {}, "outputs": [], "source": [ - "nc_path_1 = '/gpfs/scratch/bsc32/bsc32538/a4mg/HERMESv3/auxiliar_files/d01/temporal_coords.nc'" + "# Original path: None (generated with NES)\n", + "# Mercator grid\n", + "nc_path_1 = '/gpfs/projects/bsc32/models/NES_tutorial_data/mercator_grid.nc'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 1. Read and write" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Open with xarray" + "## 1. Read dataset" ] }, { @@ -47,431 +41,8 @@ "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:    (time: 1, y: 236, x: 210, nv: 4)\n",
-       "Coordinates:\n",
-       "  * time       (time) float64 0.0\n",
-       "    lat        (y, x) float32 -43.52 -43.52 -43.52 -43.52 ... 49.6 49.6 49.6\n",
-       "    lon        (y, x) float32 -18.91 -18.46 -18.01 -17.56 ... 74.22 74.67 75.12\n",
-       "  * x          (x) float64 -1.01e+05 -5.101e+04 ... 1.03e+07 1.035e+07\n",
-       "  * y          (y) float64 -5.382e+06 -5.332e+06 ... 6.318e+06 6.368e+06\n",
-       "Dimensions without coordinates: nv\n",
-       "Data variables:\n",
-       "    lat_bnds   (y, x, nv) float32 ...\n",
-       "    lon_bnds   (y, x, nv) float32 ...\n",
-       "    var_aux    (time, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
-       "    mercator   int32 -2147483647\n",
-       "    cell_area  (y, x) float32 1.316e+09 1.316e+09 ... 1.051e+09 1.051e+09
" - ], "text/plain": [ - "\n", - "Dimensions: (time: 1, y: 236, x: 210, nv: 4)\n", - "Coordinates:\n", - " * time (time) float64 0.0\n", - " lat (y, x) float32 ...\n", - " lon (y, x) float32 ...\n", - " * x (x) float64 -1.01e+05 -5.101e+04 ... 1.03e+07 1.035e+07\n", - " * y (y) float64 -5.382e+06 -5.332e+06 ... 6.318e+06 6.368e+06\n", - "Dimensions without coordinates: nv\n", - "Data variables:\n", - " lat_bnds (y, x, nv) float32 ...\n", - " lon_bnds (y, x, nv) float32 ...\n", - " var_aux (time, y, x) float32 ...\n", - " mercator int32 ...\n", - " cell_area (y, x) float32 ..." + "" ] }, "execution_count": 3, @@ -480,14 +51,8 @@ } ], "source": [ - "xr.open_dataset(nc_path_1, decode_times=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Open with NES" + "nessy_1 = open_netcdf(nc_path_1, info=True)\n", + "nessy_1" ] }, { @@ -498,7 +63,7 @@ { "data": { "text/plain": [ - "" + "[datetime.datetime(1996, 12, 31, 0, 0)]" ] }, "execution_count": 4, @@ -507,8 +72,7 @@ } ], "source": [ - "nessy_1 = open_netcdf(nc_path_1, info=True)\n", - "nessy_1" + "nessy_1.time" ] }, { @@ -519,7 +83,12 @@ { "data": { "text/plain": [ - "[datetime.datetime(2000, 1, 1, 0, 0)]" + "{'data': masked_array(data=[0.],\n", + " mask=False,\n", + " fill_value=1e+20),\n", + " 'dimensions': ('lev',),\n", + " 'units': '1',\n", + " 'positive': 'up'}" ] }, "execution_count": 5, @@ -527,117 +96,97 @@ "output_type": "execute_result" } ], - "source": [ - "nessy_1.time" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': array([0]), 'units': ''}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "nessy_1.lev" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'data': masked_array(data=[-1.01014500e+05, -5.10145000e+04, -1.01450000e+03,\n", - " 4.89855000e+04, 9.89855000e+04, 1.48985500e+05,\n", - " 1.98985500e+05, 2.48985500e+05, 2.98985500e+05,\n", - " 3.48985500e+05, 3.98985500e+05, 4.48985500e+05,\n", - " 4.98985500e+05, 5.48985500e+05, 5.98985500e+05,\n", - " 6.48985500e+05, 6.98985500e+05, 7.48985500e+05,\n", - " 7.98985500e+05, 8.48985500e+05, 8.98985500e+05,\n", - " 9.48985500e+05, 9.98985500e+05, 1.04898550e+06,\n", - " 1.09898550e+06, 1.14898550e+06, 1.19898550e+06,\n", - " 1.24898550e+06, 1.29898550e+06, 1.34898550e+06,\n", - " 1.39898550e+06, 1.44898550e+06, 1.49898550e+06,\n", - " 1.54898550e+06, 1.59898550e+06, 1.64898550e+06,\n", - " 1.69898550e+06, 1.74898550e+06, 1.79898550e+06,\n", - " 1.84898550e+06, 1.89898550e+06, 1.94898550e+06,\n", - " 1.99898550e+06, 2.04898550e+06, 2.09898550e+06,\n", - " 2.14898550e+06, 2.19898550e+06, 2.24898550e+06,\n", - " 2.29898550e+06, 2.34898550e+06, 2.39898550e+06,\n", - " 2.44898550e+06, 2.49898550e+06, 2.54898550e+06,\n", - " 2.59898550e+06, 2.64898550e+06, 2.69898550e+06,\n", - " 2.74898550e+06, 2.79898550e+06, 2.84898550e+06,\n", - " 2.89898550e+06, 2.94898550e+06, 2.99898550e+06,\n", - " 3.04898550e+06, 3.09898550e+06, 3.14898550e+06,\n", - " 3.19898550e+06, 3.24898550e+06, 3.29898550e+06,\n", - " 3.34898550e+06, 3.39898550e+06, 3.44898550e+06,\n", - " 3.49898550e+06, 3.54898550e+06, 3.59898550e+06,\n", - " 3.64898550e+06, 3.69898550e+06, 3.74898550e+06,\n", - " 3.79898550e+06, 3.84898550e+06, 3.89898550e+06,\n", - " 3.94898550e+06, 3.99898550e+06, 4.04898550e+06,\n", - " 4.09898550e+06, 4.14898550e+06, 4.19898550e+06,\n", - " 4.24898550e+06, 4.29898550e+06, 4.34898550e+06,\n", - " 4.39898550e+06, 4.44898550e+06, 4.49898550e+06,\n", - " 4.54898550e+06, 4.59898550e+06, 4.64898550e+06,\n", - " 4.69898550e+06, 4.74898550e+06, 4.79898550e+06,\n", - " 4.84898550e+06, 4.89898550e+06, 4.94898550e+06,\n", - " 4.99898550e+06, 5.04898550e+06, 5.09898550e+06,\n", - " 5.14898550e+06, 5.19898550e+06, 5.24898550e+06,\n", - " 5.29898550e+06, 5.34898550e+06, 5.39898550e+06,\n", - " 5.44898550e+06, 5.49898550e+06, 5.54898550e+06,\n", - " 5.59898550e+06, 5.64898550e+06, 5.69898550e+06,\n", - " 5.74898550e+06, 5.79898550e+06, 5.84898550e+06,\n", - " 5.89898550e+06, 5.94898550e+06, 5.99898550e+06,\n", - " 6.04898550e+06, 6.09898550e+06, 6.14898550e+06,\n", - " 6.19898550e+06, 6.24898550e+06, 6.29898550e+06,\n", - " 6.34898550e+06, 6.39898550e+06, 6.44898550e+06,\n", - " 6.49898550e+06, 6.54898550e+06, 6.59898550e+06,\n", - " 6.64898550e+06, 6.69898550e+06, 6.74898550e+06,\n", - " 6.79898550e+06, 6.84898550e+06, 6.89898550e+06,\n", - " 6.94898550e+06, 6.99898550e+06, 7.04898550e+06,\n", - " 7.09898550e+06, 7.14898550e+06, 7.19898550e+06,\n", - " 7.24898550e+06, 7.29898550e+06, 7.34898550e+06,\n", - " 7.39898550e+06, 7.44898550e+06, 7.49898550e+06,\n", - " 7.54898550e+06, 7.59898550e+06, 7.64898550e+06,\n", - " 7.69898550e+06, 7.74898550e+06, 7.79898550e+06,\n", - " 7.84898550e+06, 7.89898550e+06, 7.94898550e+06,\n", - " 7.99898550e+06, 8.04898550e+06, 8.09898550e+06,\n", - " 8.14898550e+06, 8.19898550e+06, 8.24898550e+06,\n", - " 8.29898550e+06, 8.34898550e+06, 8.39898550e+06,\n", - " 8.44898550e+06, 8.49898550e+06, 8.54898550e+06,\n", - " 8.59898550e+06, 8.64898550e+06, 8.69898550e+06,\n", - " 8.74898550e+06, 8.79898550e+06, 8.84898550e+06,\n", - " 8.89898550e+06, 8.94898550e+06, 8.99898550e+06,\n", - " 9.04898550e+06, 9.09898550e+06, 9.14898550e+06,\n", - " 9.19898550e+06, 9.24898550e+06, 9.29898550e+06,\n", - " 9.34898550e+06, 9.39898550e+06, 9.44898550e+06,\n", - " 9.49898550e+06, 9.54898550e+06, 9.59898550e+06,\n", - " 9.64898550e+06, 9.69898550e+06, 9.74898550e+06,\n", - " 9.79898550e+06, 9.84898550e+06, 9.89898550e+06,\n", - " 9.94898550e+06, 9.99898550e+06, 1.00489855e+07,\n", - " 1.00989855e+07, 1.01489855e+07, 1.01989855e+07,\n", - " 1.02489855e+07, 1.02989855e+07, 1.03489855e+07],\n", + "{'data': masked_array(data=[-1.01017500e+05, -5.10175000e+04, -1.01750000e+03,\n", + " 4.89825000e+04, 9.89825000e+04, 1.48982500e+05,\n", + " 1.98982500e+05, 2.48982500e+05, 2.98982500e+05,\n", + " 3.48982500e+05, 3.98982500e+05, 4.48982500e+05,\n", + " 4.98982500e+05, 5.48982500e+05, 5.98982500e+05,\n", + " 6.48982500e+05, 6.98982500e+05, 7.48982500e+05,\n", + " 7.98982500e+05, 8.48982500e+05, 8.98982500e+05,\n", + " 9.48982500e+05, 9.98982500e+05, 1.04898250e+06,\n", + " 1.09898250e+06, 1.14898250e+06, 1.19898250e+06,\n", + " 1.24898250e+06, 1.29898250e+06, 1.34898250e+06,\n", + " 1.39898250e+06, 1.44898250e+06, 1.49898250e+06,\n", + " 1.54898250e+06, 1.59898250e+06, 1.64898250e+06,\n", + " 1.69898250e+06, 1.74898250e+06, 1.79898250e+06,\n", + " 1.84898250e+06, 1.89898250e+06, 1.94898250e+06,\n", + " 1.99898250e+06, 2.04898250e+06, 2.09898250e+06,\n", + " 2.14898250e+06, 2.19898250e+06, 2.24898250e+06,\n", + " 2.29898250e+06, 2.34898250e+06, 2.39898250e+06,\n", + " 2.44898250e+06, 2.49898250e+06, 2.54898250e+06,\n", + " 2.59898250e+06, 2.64898250e+06, 2.69898250e+06,\n", + " 2.74898250e+06, 2.79898250e+06, 2.84898250e+06,\n", + " 2.89898250e+06, 2.94898250e+06, 2.99898250e+06,\n", + " 3.04898250e+06, 3.09898250e+06, 3.14898250e+06,\n", + " 3.19898250e+06, 3.24898250e+06, 3.29898250e+06,\n", + " 3.34898250e+06, 3.39898250e+06, 3.44898250e+06,\n", + " 3.49898250e+06, 3.54898250e+06, 3.59898250e+06,\n", + " 3.64898250e+06, 3.69898250e+06, 3.74898250e+06,\n", + " 3.79898250e+06, 3.84898250e+06, 3.89898250e+06,\n", + " 3.94898250e+06, 3.99898250e+06, 4.04898250e+06,\n", + " 4.09898250e+06, 4.14898250e+06, 4.19898250e+06,\n", + " 4.24898250e+06, 4.29898250e+06, 4.34898250e+06,\n", + " 4.39898250e+06, 4.44898250e+06, 4.49898250e+06,\n", + " 4.54898250e+06, 4.59898250e+06, 4.64898250e+06,\n", + " 4.69898250e+06, 4.74898250e+06, 4.79898250e+06,\n", + " 4.84898250e+06, 4.89898250e+06, 4.94898250e+06,\n", + " 4.99898250e+06, 5.04898250e+06, 5.09898250e+06,\n", + " 5.14898250e+06, 5.19898250e+06, 5.24898250e+06,\n", + " 5.29898250e+06, 5.34898250e+06, 5.39898250e+06,\n", + " 5.44898250e+06, 5.49898250e+06, 5.54898250e+06,\n", + " 5.59898250e+06, 5.64898250e+06, 5.69898250e+06,\n", + " 5.74898250e+06, 5.79898250e+06, 5.84898250e+06,\n", + " 5.89898250e+06, 5.94898250e+06, 5.99898250e+06,\n", + " 6.04898250e+06, 6.09898250e+06, 6.14898250e+06,\n", + " 6.19898250e+06, 6.24898250e+06, 6.29898250e+06,\n", + " 6.34898250e+06, 6.39898250e+06, 6.44898250e+06,\n", + " 6.49898250e+06, 6.54898250e+06, 6.59898250e+06,\n", + " 6.64898250e+06, 6.69898250e+06, 6.74898250e+06,\n", + " 6.79898250e+06, 6.84898250e+06, 6.89898250e+06,\n", + " 6.94898250e+06, 6.99898250e+06, 7.04898250e+06,\n", + " 7.09898250e+06, 7.14898250e+06, 7.19898250e+06,\n", + " 7.24898250e+06, 7.29898250e+06, 7.34898250e+06,\n", + " 7.39898250e+06, 7.44898250e+06, 7.49898250e+06,\n", + " 7.54898250e+06, 7.59898250e+06, 7.64898250e+06,\n", + " 7.69898250e+06, 7.74898250e+06, 7.79898250e+06,\n", + " 7.84898250e+06, 7.89898250e+06, 7.94898250e+06,\n", + " 7.99898250e+06, 8.04898250e+06, 8.09898250e+06,\n", + " 8.14898250e+06, 8.19898250e+06, 8.24898250e+06,\n", + " 8.29898250e+06, 8.34898250e+06, 8.39898250e+06,\n", + " 8.44898250e+06, 8.49898250e+06, 8.54898250e+06,\n", + " 8.59898250e+06, 8.64898250e+06, 8.69898250e+06,\n", + " 8.74898250e+06, 8.79898250e+06, 8.84898250e+06,\n", + " 8.89898250e+06, 8.94898250e+06, 8.99898250e+06,\n", + " 9.04898250e+06, 9.09898250e+06, 9.14898250e+06,\n", + " 9.19898250e+06, 9.24898250e+06, 9.29898250e+06,\n", + " 9.34898250e+06, 9.39898250e+06, 9.44898250e+06,\n", + " 9.49898250e+06, 9.54898250e+06, 9.59898250e+06,\n", + " 9.64898250e+06, 9.69898250e+06, 9.74898250e+06,\n", + " 9.79898250e+06, 9.84898250e+06, 9.89898250e+06,\n", + " 9.94898250e+06, 9.99898250e+06, 1.00489825e+07,\n", + " 1.00989825e+07, 1.01489825e+07, 1.01989825e+07,\n", + " 1.02489825e+07, 1.02989825e+07, 1.03489825e+07],\n", " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('x',),\n", - " 'units': '1000 m',\n", " 'long_name': 'x coordinate of projection',\n", + " 'units': 'm',\n", " 'standard_name': 'projection_x_coordinate'}" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -648,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -705,12 +254,12 @@ " mask=False,\n", " fill_value=1e+20),\n", " 'dimensions': ('y',),\n", - " 'units': '1000 m',\n", " 'long_name': 'y coordinate of projection',\n", + " 'units': 'm',\n", " 'standard_name': 'projection_y_coordinate'}" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -721,38 +270,36 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'data': masked_array(\n", - " data=[[-43.5182 , -43.5182 , -43.5182 , ..., -43.5182 , -43.5182 ,\n", - " -43.5182 ],\n", - " [-43.191082, -43.191082, -43.191082, ..., -43.191082, -43.191082,\n", - " -43.191082],\n", - " [-42.8622 , -42.8622 , -42.8622 , ..., -42.8622 , -42.8622 ,\n", - " -42.8622 ],\n", + " data=[[-43.6653826 , -43.6653826 , -43.6653826 , ..., -43.6653826 ,\n", + " -43.6653826 , -43.6653826 ],\n", + " [-43.33831983, -43.33831983, -43.33831983, ..., -43.33831983,\n", + " -43.33831983, -43.33831983],\n", + " [-43.00947316, -43.00947316, -43.00947316, ..., -43.00947316,\n", + " -43.00947316, -43.00947316],\n", " ...,\n", - " [ 49.016712, 49.016712, 49.016712, ..., 49.016712, 49.016712,\n", - " 49.016712],\n", - " [ 49.31089 , 49.31089 , 49.31089 , ..., 49.31089 , 49.31089 ,\n", - " 49.31089 ],\n", - " [ 49.60332 , 49.60332 , 49.60332 , ..., 49.60332 , 49.60332 ,\n", - " 49.60332 ]],\n", + " [ 49.15978847, 49.15978847, 49.15978847, ..., 49.15978847,\n", + " 49.15978847, 49.15978847],\n", + " [ 49.45358158, 49.45358158, 49.45358158, ..., 49.45358158,\n", + " 49.45358158, 49.45358158],\n", + " [ 49.74561434, 49.74561434, 49.74561434, ..., 49.74561434,\n", + " 49.74561434, 49.74561434]],\n", " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", + " fill_value=1e+20),\n", " 'dimensions': ('y', 'x'),\n", " 'units': 'degrees_north',\n", " 'axis': 'Y',\n", " 'long_name': 'latitude coordinate',\n", - " 'standard_name': 'latitude',\n", - " 'bounds': 'lat_bnds'}" + " 'standard_name': 'latitude'}" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -763,38 +310,36 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'data': masked_array(\n", - " data=[[-18.9089 , -18.459013, -18.009129, ..., 74.217415, 74.6673 ,\n", - " 75.11718 ],\n", - " [-18.9089 , -18.459013, -18.009129, ..., 74.217415, 74.6673 ,\n", - " 75.11718 ],\n", - " [-18.9089 , -18.459013, -18.009129, ..., 74.217415, 74.6673 ,\n", - " 75.11718 ],\n", + " data=[[-18.90776463, -18.45845405, -18.00914347, ..., 74.0995253 ,\n", + " 74.54883588, 74.99814646],\n", + " [-18.90776463, -18.45845405, -18.00914347, ..., 74.0995253 ,\n", + " 74.54883588, 74.99814646],\n", + " [-18.90776463, -18.45845405, -18.00914347, ..., 74.0995253 ,\n", + " 74.54883588, 74.99814646],\n", " ...,\n", - " [-18.9089 , -18.459013, -18.009129, ..., 74.217415, 74.6673 ,\n", - " 75.11718 ],\n", - " [-18.9089 , -18.459013, -18.009129, ..., 74.217415, 74.6673 ,\n", - " 75.11718 ],\n", - " [-18.9089 , -18.459013, -18.009129, ..., 74.217415, 74.6673 ,\n", - " 75.11718 ]],\n", + " [-18.90776463, -18.45845405, -18.00914347, ..., 74.0995253 ,\n", + " 74.54883588, 74.99814646],\n", + " [-18.90776463, -18.45845405, -18.00914347, ..., 74.0995253 ,\n", + " 74.54883588, 74.99814646],\n", + " [-18.90776463, -18.45845405, -18.00914347, ..., 74.0995253 ,\n", + " 74.54883588, 74.99814646]],\n", " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", + " fill_value=1e+20),\n", " 'dimensions': ('y', 'x'),\n", " 'units': 'degrees_east',\n", " 'axis': 'X',\n", " 'long_name': 'longitude coordinate',\n", - " 'standard_name': 'longitude',\n", - " 'bounds': 'lon_bnds'}" + " 'standard_name': 'longitude'}" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -804,397 +349,16 @@ ] }, { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "masked_array(\n", - " data=[[[-43.68109893798828, -43.68109893798828, -43.354862213134766,\n", - " -43.354862213134766],\n", - " [-43.68109893798828, -43.68109893798828, -43.354862213134766,\n", - " -43.354862213134766],\n", - " [-43.68109893798828, -43.68109893798828, -43.354862213134766,\n", - " -43.354862213134766],\n", - " ...,\n", - " [-43.68109893798828, -43.68109893798828, -43.354862213134766,\n", - " -43.354862213134766],\n", - " [-43.68109893798828, -43.68109893798828, -43.354862213134766,\n", - " -43.354862213134766],\n", - " [-43.68109893798828, -43.68109893798828, -43.354862213134766,\n", - " -43.354862213134766]],\n", - "\n", - " [[-43.354862213134766, -43.354862213134766, -43.02686309814453,\n", - " -43.02686309814453],\n", - " [-43.354862213134766, -43.354862213134766, -43.02686309814453,\n", - " -43.02686309814453],\n", - " [-43.354862213134766, -43.354862213134766, -43.02686309814453,\n", - " -43.02686309814453],\n", - " ...,\n", - " [-43.354862213134766, -43.354862213134766, -43.02686309814453,\n", - " -43.02686309814453],\n", - " [-43.354862213134766, -43.354862213134766, -43.02686309814453,\n", - " -43.02686309814453],\n", - " [-43.354862213134766, -43.354862213134766, -43.02686309814453,\n", - " -43.02686309814453]],\n", - "\n", - " [[-43.02686309814453, -43.02686309814453, -42.69709777832031,\n", - " -42.69709777832031],\n", - " [-43.02686309814453, -43.02686309814453, -42.69709777832031,\n", - " -42.69709777832031],\n", - " [-43.02686309814453, -43.02686309814453, -42.69709777832031,\n", - " -42.69709777832031],\n", - " ...,\n", - " [-43.02686309814453, -43.02686309814453, -42.69709777832031,\n", - " -42.69709777832031],\n", - " [-43.02686309814453, -43.02686309814453, -42.69709777832031,\n", - " -42.69709777832031],\n", - " [-43.02686309814453, -43.02686309814453, -42.69709777832031,\n", - " -42.69709777832031]],\n", - "\n", - " ...,\n", - "\n", - " [[48.86896514892578, 48.86896514892578, 49.16401672363281,\n", - " 49.16401672363281],\n", - " [48.86896514892578, 48.86896514892578, 49.16401672363281,\n", - " 49.16401672363281],\n", - " [48.86896514892578, 48.86896514892578, 49.16401672363281,\n", - " 49.16401672363281],\n", - " ...,\n", - " [48.86896514892578, 48.86896514892578, 49.16401672363281,\n", - " 49.16401672363281],\n", - " [48.86896514892578, 48.86896514892578, 49.16401672363281,\n", - " 49.16401672363281],\n", - " [48.86896514892578, 48.86896514892578, 49.16401672363281,\n", - " 49.16401672363281]],\n", - "\n", - " [[49.16401672363281, 49.16401672363281, 49.45732498168945,\n", - " 49.45732498168945],\n", - " [49.16401672363281, 49.16401672363281, 49.45732498168945,\n", - " 49.45732498168945],\n", - " [49.16401672363281, 49.16401672363281, 49.45732498168945,\n", - " 49.45732498168945],\n", - " ...,\n", - " [49.16401672363281, 49.16401672363281, 49.45732498168945,\n", - " 49.45732498168945],\n", - " [49.16401672363281, 49.16401672363281, 49.45732498168945,\n", - " 49.45732498168945],\n", - " [49.16401672363281, 49.16401672363281, 49.45732498168945,\n", - " 49.45732498168945]],\n", - "\n", - " [[49.45732498168945, 49.45732498168945, 49.74888229370117,\n", - " 49.74888229370117],\n", - " [49.45732498168945, 49.45732498168945, 49.74888229370117,\n", - " 49.74888229370117],\n", - " [49.45732498168945, 49.45732498168945, 49.74888229370117,\n", - " 49.74888229370117],\n", - " ...,\n", - " [49.45732498168945, 49.45732498168945, 49.74888229370117,\n", - " 49.74888229370117],\n", - " [49.45732498168945, 49.45732498168945, 49.74888229370117,\n", - " 49.74888229370117],\n", - " [49.45732498168945, 49.45732498168945, 49.74888229370117,\n", - " 49.74888229370117]]],\n", - " mask=[[[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", - "\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,\n", - " dtype=float32)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nessy_1.lat_bnds" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "masked_array(\n", - " data=[[[-19.13384246826172, -18.683956146240234, -18.683956146240234,\n", - " -19.13384246826172],\n", - " [-18.683956146240234, -18.234071731567383, -18.234071731567383,\n", - " -18.683956146240234],\n", - " [-18.234071731567383, -17.7841854095459, -17.7841854095459,\n", - " -18.234071731567383],\n", - " ...,\n", - " [73.99246978759766, 74.44235229492188, 74.44235229492188,\n", - " 73.99246978759766],\n", - " [74.44235229492188, 74.89224243164062, 74.89224243164062,\n", - " 74.44235229492188],\n", - " [74.89224243164062, 75.34212493896484, 75.34212493896484,\n", - " 74.89224243164062]],\n", - "\n", - " [[-19.13384246826172, -18.683956146240234, -18.683956146240234,\n", - " -19.13384246826172],\n", - " [-18.683956146240234, -18.234071731567383, -18.234071731567383,\n", - " -18.683956146240234],\n", - " [-18.234071731567383, -17.7841854095459, -17.7841854095459,\n", - " -18.234071731567383],\n", - " ...,\n", - " [73.99246978759766, 74.44235229492188, 74.44235229492188,\n", - " 73.99246978759766],\n", - " [74.44235229492188, 74.89224243164062, 74.89224243164062,\n", - " 74.44235229492188],\n", - " [74.89224243164062, 75.34212493896484, 75.34212493896484,\n", - " 74.89224243164062]],\n", - "\n", - " [[-19.13384246826172, -18.683956146240234, -18.683956146240234,\n", - " -19.13384246826172],\n", - " [-18.683956146240234, -18.234071731567383, -18.234071731567383,\n", - " -18.683956146240234],\n", - " [-18.234071731567383, -17.7841854095459, -17.7841854095459,\n", - " -18.234071731567383],\n", - " ...,\n", - " [73.99246978759766, 74.44235229492188, 74.44235229492188,\n", - " 73.99246978759766],\n", - " [74.44235229492188, 74.89224243164062, 74.89224243164062,\n", - " 74.44235229492188],\n", - " [74.89224243164062, 75.34212493896484, 75.34212493896484,\n", - " 74.89224243164062]],\n", - "\n", - " ...,\n", - "\n", - " [[-19.13384246826172, -18.683956146240234, -18.683956146240234,\n", - " -19.13384246826172],\n", - " [-18.683956146240234, -18.234071731567383, -18.234071731567383,\n", - " -18.683956146240234],\n", - " [-18.234071731567383, -17.7841854095459, -17.7841854095459,\n", - " -18.234071731567383],\n", - " ...,\n", - " [73.99246978759766, 74.44235229492188, 74.44235229492188,\n", - " 73.99246978759766],\n", - " [74.44235229492188, 74.89224243164062, 74.89224243164062,\n", - " 74.44235229492188],\n", - " [74.89224243164062, 75.34212493896484, 75.34212493896484,\n", - " 74.89224243164062]],\n", - "\n", - " [[-19.13384246826172, -18.683956146240234, -18.683956146240234,\n", - " -19.13384246826172],\n", - " [-18.683956146240234, -18.234071731567383, -18.234071731567383,\n", - " -18.683956146240234],\n", - " [-18.234071731567383, -17.7841854095459, -17.7841854095459,\n", - " -18.234071731567383],\n", - " ...,\n", - " [73.99246978759766, 74.44235229492188, 74.44235229492188,\n", - " 73.99246978759766],\n", - " [74.44235229492188, 74.89224243164062, 74.89224243164062,\n", - " 74.44235229492188],\n", - " [74.89224243164062, 75.34212493896484, 75.34212493896484,\n", - " 74.89224243164062]],\n", - "\n", - " [[-19.13384246826172, -18.683956146240234, -18.683956146240234,\n", - " -19.13384246826172],\n", - " [-18.683956146240234, -18.234071731567383, -18.234071731567383,\n", - " -18.683956146240234],\n", - " [-18.234071731567383, -17.7841854095459, -17.7841854095459,\n", - " -18.234071731567383],\n", - " ...,\n", - " [73.99246978759766, 74.44235229492188, 74.44235229492188,\n", - " 73.99246978759766],\n", - " [74.44235229492188, 74.89224243164062, 74.89224243164062,\n", - " 74.44235229492188],\n", - " [74.89224243164062, 75.34212493896484, 75.34212493896484,\n", - " 74.89224243164062]]],\n", - " mask=[[[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", - "\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,\n", - " dtype=float32)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nessy_1.lon_bnds" - ] - }, - { - "cell_type": "code", - "execution_count": 13, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rank 000: Loading var_aux var (1/2)\n", - "Rank 000: Loaded var_aux var ((1, 1, 236, 210))\n", - "Rank 000: Loading cell_area var (2/2)\n", - "Rank 000: Loaded cell_area var ((1, 1, 236, 210))\n" - ] - } - ], "source": [ - "nessy_1.load()" + "## 2. Write dataset" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'var_aux': {'data': masked_array(\n", - " data=[[[[0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]]]],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", - " 'dimensions': ('time', 'y', 'x'),\n", - " 'units': '?',\n", - " 'coordinates': 'lat lon',\n", - " 'cell_measures': 'area: cell_area',\n", - " 'grid_mapping': 'mercator'},\n", - " 'cell_area': {'data': masked_array(\n", - " data=[[[[1.31594240e+09, 1.31593690e+09, 1.31594240e+09, ...,\n", - " 1.31593126e+09, 1.31595354e+09, 1.31593126e+09],\n", - " [1.33020250e+09, 1.33019686e+09, 1.33020250e+09, ...,\n", - " 1.33019123e+09, 1.33021389e+09, 1.33019123e+09],\n", - " [1.34454989e+09, 1.34454426e+09, 1.34454989e+09, ...,\n", - " 1.34453850e+09, 1.34456128e+09, 1.34453850e+09],\n", - " ...,\n", - " [1.07638515e+09, 1.07638054e+09, 1.07638515e+09, ...,\n", - " 1.07637606e+09, 1.07639424e+09, 1.07637606e+09],\n", - " [1.06368742e+09, 1.06368288e+09, 1.06368742e+09, ...,\n", - " 1.06367840e+09, 1.06369645e+09, 1.06367840e+09],\n", - " [1.05104730e+09, 1.05104288e+09, 1.05104730e+09, ...,\n", - " 1.05103840e+09, 1.05105626e+09, 1.05103840e+09]]]],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", - " 'dimensions': ('y', 'x'),\n", - " 'long_name': 'area of the grid cell',\n", - " 'standard_name': 'cell_area',\n", - " 'units': 'm2'}}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nessy_1.variables" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -1203,16 +367,7 @@ "Rank 000: Creating mercator_file_1.nc\n", "Rank 000: NetCDF ready to write\n", "Rank 000: Dimensions done\n", - "Rank 000: Writing var_aux var (1/2)\n", - "Rank 000: Var var_aux created (1/2)\n", - "Rank 000: Filling var_aux)\n", - "Rank 000: Var var_aux data (1/2)\n", - "Rank 000: Var var_aux completed (1/2)\n", - "Rank 000: Writing cell_area var (2/2)\n", - "Rank 000: Var cell_area created (2/2)\n", - "Rank 000: Filling cell_area)\n", - "Rank 000: Var cell_area data (2/2)\n", - "Rank 000: Var cell_area completed (2/2)\n" + "Rank 000: Cell measures done\n" ] } ], @@ -1224,21 +379,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Reopen with NES" + "## 3. Reopen dataset" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1247,450 +402,6 @@ "nessy_2 = open_netcdf('mercator_file_1.nc', info=True)\n", "nessy_2" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reopen with xarray" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:    (time: 1, lev: 1, y: 236, x: 210, spatial_nv: 4)\n",
-       "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 2000-01-01\n",
-       "  * lev        (lev) float64 0.0\n",
-       "    lat        (y, x) float64 -43.52 -43.52 -43.52 -43.52 ... 49.6 49.6 49.6\n",
-       "    lon        (y, x) float64 -18.91 -18.46 -18.01 -17.56 ... 74.22 74.67 75.12\n",
-       "  * y          (y) float64 -5.382e+06 -5.332e+06 ... 6.318e+06 6.368e+06\n",
-       "  * x          (x) float64 -1.01e+05 -5.101e+04 ... 1.03e+07 1.035e+07\n",
-       "Dimensions without coordinates: spatial_nv\n",
-       "Data variables:\n",
-       "    lat_bnds   (y, x, spatial_nv) float64 ...\n",
-       "    lon_bnds   (y, x, spatial_nv) float64 ...\n",
-       "    var_aux    (time, lev, y, x) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
-       "    cell_area  (time, lev, y, x) float32 1.316e+09 1.316e+09 ... 1.051e+09\n",
-       "    mercator   |S1 b''\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 1, lev: 1, y: 236, x: 210, spatial_nv: 4)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2000-01-01\n", - " * lev (lev) float64 0.0\n", - " lat (y, x) float64 ...\n", - " lon (y, x) float64 ...\n", - " * y (y) float64 -5.382e+06 -5.332e+06 ... 6.318e+06 6.368e+06\n", - " * x (x) float64 -1.01e+05 -5.101e+04 ... 1.03e+07 1.035e+07\n", - "Dimensions without coordinates: spatial_nv\n", - "Data variables:\n", - " lat_bnds (y, x, spatial_nv) float64 ...\n", - " lon_bnds (y, x, spatial_nv) float64 ...\n", - " var_aux (time, lev, y, x) float32 ...\n", - " cell_area (time, lev, y, x) float32 ...\n", - " mercator |S1 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('mercator_file_1.nc')" - ] } ], "metadata": { diff --git a/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb b/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb index 11985c0eb74d8c6ad76077bbeff0d5b9e08230c8..7916cfd3b080df59bcd79aa7cf9b342998df1b0a 100644 --- a/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb +++ b/tutorials/1.Introduction/1.6.Read_Write_Providentia.ipynb @@ -13,8 +13,6 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", - "from netCDF4 import Dataset\n", "from nes import *" ] }, @@ -31,6320 +29,774 @@ "metadata": {}, "outputs": [], "source": [ - "obs_path = '/gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/sconco3_201804.nc'" + "# Original path: /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/sconco3_201804.nc\n", + "# Points grid from EBAS network\n", + "obs_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/obs_sconco3_201804.nc'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 1.1. Read dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Open with xarray" + "### 1.1 Read dataset" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:                                                           (station: 168, time: 720, N_flag_codes: 186, N_qa_codes: 77)\n",
-       "Coordinates:\n",
-       "  * time                                                              (time) datetime64[ns] ...\n",
-       "Dimensions without coordinates: station, N_flag_codes, N_qa_codes\n",
-       "Data variables: (12/179)\n",
-       "    ASTER_v3_altitude                                                 (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_BC_emissions                          (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_CO_emissions                          (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_NH3_emissions                         (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_NMVOC_emissions                       (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_NOx_emissions                         (station) float32 ...\n",
-       "    ...                                                                ...\n",
-       "    station_timezone                                                  (station) object ...\n",
-       "    street_type                                                       (station) object ...\n",
-       "    street_width                                                      (station) float32 ...\n",
-       "    terrain                                                           (station) object ...\n",
-       "    vertical_datum                                                    (station) object ...\n",
-       "    weekday_weekend_code                                              (station, time) uint8 ...\n",
-       "Attributes:\n",
-       "    title:                     Surface ozone data in the EBAS network in 2018...\n",
-       "    institution:               Barcelona Supercomputing Center\n",
-       "    source:                    Surface observations\n",
-       "    creator_name:              Dene R. Bowdalo\n",
-       "    creator_email:             dene.bowdalo@bsc.es\n",
-       "    conventions:               CF-1.7\n",
-       "    data_version:              1.3.3\n",
-       "    history:                   Tue Mar 30 12:38:43 2021: ncks -O --fix_rec_dm...\n",
-       "    NCO:                       4.7.2\n",
-       "    nco_openmp_thread_number:  1
" - ], "text/plain": [ - "\n", - "Dimensions: (station: 168, time: 720, N_flag_codes: 186, N_qa_codes: 77)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] ...\n", - "Dimensions without coordinates: station, N_flag_codes, N_qa_codes\n", - "Data variables: (12/179)\n", - " ASTER_v3_altitude (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_NMVOC_emissions (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_NOx_emissions (station) float32 ...\n", - " ... ...\n", - " station_timezone (station) object ...\n", - " street_type (station) object ...\n", - " street_width (station) float32 ...\n", - " terrain (station) object ...\n", - " vertical_datum (station) object ...\n", - " weekday_weekend_code (station, time) uint8 ...\n", - "Attributes:\n", - " title: Surface ozone data in the EBAS network in 2018...\n", - " institution: Barcelona Supercomputing Center\n", - " source: Surface observations\n", - " creator_name: Dene R. Bowdalo\n", - " creator_email: dene.bowdalo@bsc.es\n", - " conventions: CF-1.7\n", - " data_version: 1.3.3\n", - " history: Tue Mar 30 12:38:43 2021: ncks -O --fix_rec_dm...\n", - " NCO: 4.7.2\n", - " nco_openmp_thread_number: 1" + "" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "xr.open_dataset(obs_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Open with NES" + "obs_nes = open_netcdf(path=obs_path, info=True, parallel_method='X')\n", + "obs_nes" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "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", + " datetime.datetime(2018, 4, 1, 18, 0),\n", + " datetime.datetime(2018, 4, 1, 19, 0),\n", + " datetime.datetime(2018, 4, 1, 20, 0),\n", + " datetime.datetime(2018, 4, 1, 21, 0),\n", + " datetime.datetime(2018, 4, 1, 22, 0),\n", + " datetime.datetime(2018, 4, 1, 23, 0),\n", + " datetime.datetime(2018, 4, 2, 0, 0),\n", + " datetime.datetime(2018, 4, 2, 1, 0),\n", + " datetime.datetime(2018, 4, 2, 2, 0),\n", + " datetime.datetime(2018, 4, 2, 3, 0),\n", + " datetime.datetime(2018, 4, 2, 4, 0),\n", + " datetime.datetime(2018, 4, 2, 5, 0),\n", + " datetime.datetime(2018, 4, 2, 6, 0),\n", + " datetime.datetime(2018, 4, 2, 7, 0),\n", + " datetime.datetime(2018, 4, 2, 8, 0),\n", + " datetime.datetime(2018, 4, 2, 9, 0),\n", + " datetime.datetime(2018, 4, 2, 10, 0),\n", + " datetime.datetime(2018, 4, 2, 11, 0),\n", + " datetime.datetime(2018, 4, 2, 12, 0),\n", + " datetime.datetime(2018, 4, 2, 13, 0),\n", + " datetime.datetime(2018, 4, 2, 14, 0),\n", + " datetime.datetime(2018, 4, 2, 15, 0),\n", + " datetime.datetime(2018, 4, 2, 16, 0),\n", + " datetime.datetime(2018, 4, 2, 17, 0),\n", + " datetime.datetime(2018, 4, 2, 18, 0),\n", + " datetime.datetime(2018, 4, 2, 19, 0),\n", + " datetime.datetime(2018, 4, 2, 20, 0),\n", + " datetime.datetime(2018, 4, 2, 21, 0),\n", + " datetime.datetime(2018, 4, 2, 22, 0),\n", + " datetime.datetime(2018, 4, 2, 23, 0),\n", + " datetime.datetime(2018, 4, 3, 0, 0),\n", + " datetime.datetime(2018, 4, 3, 1, 0),\n", + " datetime.datetime(2018, 4, 3, 2, 0),\n", + " datetime.datetime(2018, 4, 3, 3, 0),\n", + " datetime.datetime(2018, 4, 3, 4, 0),\n", + " datetime.datetime(2018, 4, 3, 5, 0),\n", + " datetime.datetime(2018, 4, 3, 6, 0),\n", + " datetime.datetime(2018, 4, 3, 7, 0),\n", + " datetime.datetime(2018, 4, 3, 8, 0),\n", + " datetime.datetime(2018, 4, 3, 9, 0),\n", + " datetime.datetime(2018, 4, 3, 10, 0),\n", + " datetime.datetime(2018, 4, 3, 11, 0),\n", + " datetime.datetime(2018, 4, 3, 12, 0),\n", + " datetime.datetime(2018, 4, 3, 13, 0),\n", + " datetime.datetime(2018, 4, 3, 14, 0),\n", + " datetime.datetime(2018, 4, 3, 15, 0),\n", + " datetime.datetime(2018, 4, 3, 16, 0),\n", + " datetime.datetime(2018, 4, 3, 17, 0),\n", + " datetime.datetime(2018, 4, 3, 18, 0),\n", + " datetime.datetime(2018, 4, 3, 19, 0),\n", + " datetime.datetime(2018, 4, 3, 20, 0),\n", + " datetime.datetime(2018, 4, 3, 21, 0),\n", + " datetime.datetime(2018, 4, 3, 22, 0),\n", + " datetime.datetime(2018, 4, 3, 23, 0),\n", + " datetime.datetime(2018, 4, 4, 0, 0),\n", + " datetime.datetime(2018, 4, 4, 1, 0),\n", + " datetime.datetime(2018, 4, 4, 2, 0),\n", + " datetime.datetime(2018, 4, 4, 3, 0),\n", + " datetime.datetime(2018, 4, 4, 4, 0),\n", + " datetime.datetime(2018, 4, 4, 5, 0),\n", + " datetime.datetime(2018, 4, 4, 6, 0),\n", + " datetime.datetime(2018, 4, 4, 7, 0),\n", + " datetime.datetime(2018, 4, 4, 8, 0),\n", + " datetime.datetime(2018, 4, 4, 9, 0),\n", + " datetime.datetime(2018, 4, 4, 10, 0),\n", + " datetime.datetime(2018, 4, 4, 11, 0),\n", + " datetime.datetime(2018, 4, 4, 12, 0),\n", + " datetime.datetime(2018, 4, 4, 13, 0),\n", + " datetime.datetime(2018, 4, 4, 14, 0),\n", + " datetime.datetime(2018, 4, 4, 15, 0),\n", + " datetime.datetime(2018, 4, 4, 16, 0),\n", + " datetime.datetime(2018, 4, 4, 17, 0),\n", + " datetime.datetime(2018, 4, 4, 18, 0),\n", + " datetime.datetime(2018, 4, 4, 19, 0),\n", + " datetime.datetime(2018, 4, 4, 20, 0),\n", + " datetime.datetime(2018, 4, 4, 21, 0),\n", + " datetime.datetime(2018, 4, 4, 22, 0),\n", + " datetime.datetime(2018, 4, 4, 23, 0),\n", + " datetime.datetime(2018, 4, 5, 0, 0),\n", + " datetime.datetime(2018, 4, 5, 1, 0),\n", + " datetime.datetime(2018, 4, 5, 2, 0),\n", + " datetime.datetime(2018, 4, 5, 3, 0),\n", + " datetime.datetime(2018, 4, 5, 4, 0),\n", + " datetime.datetime(2018, 4, 5, 5, 0),\n", + " datetime.datetime(2018, 4, 5, 6, 0),\n", + " datetime.datetime(2018, 4, 5, 7, 0),\n", + " datetime.datetime(2018, 4, 5, 8, 0),\n", + " datetime.datetime(2018, 4, 5, 9, 0),\n", + " datetime.datetime(2018, 4, 5, 10, 0),\n", + " datetime.datetime(2018, 4, 5, 11, 0),\n", + " datetime.datetime(2018, 4, 5, 12, 0),\n", + " datetime.datetime(2018, 4, 5, 13, 0),\n", + " datetime.datetime(2018, 4, 5, 14, 0),\n", + " datetime.datetime(2018, 4, 5, 15, 0),\n", + " datetime.datetime(2018, 4, 5, 16, 0),\n", + " datetime.datetime(2018, 4, 5, 17, 0),\n", + " datetime.datetime(2018, 4, 5, 18, 0),\n", + " datetime.datetime(2018, 4, 5, 19, 0),\n", + " datetime.datetime(2018, 4, 5, 20, 0),\n", + " datetime.datetime(2018, 4, 5, 21, 0),\n", + " datetime.datetime(2018, 4, 5, 22, 0),\n", + " datetime.datetime(2018, 4, 5, 23, 0),\n", + " datetime.datetime(2018, 4, 6, 0, 0),\n", + " datetime.datetime(2018, 4, 6, 1, 0),\n", + " datetime.datetime(2018, 4, 6, 2, 0),\n", + " datetime.datetime(2018, 4, 6, 3, 0),\n", + " datetime.datetime(2018, 4, 6, 4, 0),\n", + " datetime.datetime(2018, 4, 6, 5, 0),\n", + " datetime.datetime(2018, 4, 6, 6, 0),\n", + " datetime.datetime(2018, 4, 6, 7, 0),\n", + " datetime.datetime(2018, 4, 6, 8, 0),\n", + " datetime.datetime(2018, 4, 6, 9, 0),\n", + " datetime.datetime(2018, 4, 6, 10, 0),\n", + " datetime.datetime(2018, 4, 6, 11, 0),\n", + " datetime.datetime(2018, 4, 6, 12, 0),\n", + " datetime.datetime(2018, 4, 6, 13, 0),\n", + " datetime.datetime(2018, 4, 6, 14, 0),\n", + " datetime.datetime(2018, 4, 6, 15, 0),\n", + " datetime.datetime(2018, 4, 6, 16, 0),\n", + " datetime.datetime(2018, 4, 6, 17, 0),\n", + " datetime.datetime(2018, 4, 6, 18, 0),\n", + " datetime.datetime(2018, 4, 6, 19, 0),\n", + " datetime.datetime(2018, 4, 6, 20, 0),\n", + " datetime.datetime(2018, 4, 6, 21, 0),\n", + " datetime.datetime(2018, 4, 6, 22, 0),\n", + " datetime.datetime(2018, 4, 6, 23, 0),\n", + " datetime.datetime(2018, 4, 7, 0, 0),\n", + " datetime.datetime(2018, 4, 7, 1, 0),\n", + " datetime.datetime(2018, 4, 7, 2, 0),\n", + " datetime.datetime(2018, 4, 7, 3, 0),\n", + " datetime.datetime(2018, 4, 7, 4, 0),\n", + " datetime.datetime(2018, 4, 7, 5, 0),\n", + " datetime.datetime(2018, 4, 7, 6, 0),\n", + " datetime.datetime(2018, 4, 7, 7, 0),\n", + " datetime.datetime(2018, 4, 7, 8, 0),\n", + " datetime.datetime(2018, 4, 7, 9, 0),\n", + " datetime.datetime(2018, 4, 7, 10, 0),\n", + " datetime.datetime(2018, 4, 7, 11, 0),\n", + " datetime.datetime(2018, 4, 7, 12, 0),\n", + " datetime.datetime(2018, 4, 7, 13, 0),\n", + " datetime.datetime(2018, 4, 7, 14, 0),\n", + " datetime.datetime(2018, 4, 7, 15, 0),\n", + " datetime.datetime(2018, 4, 7, 16, 0),\n", + " datetime.datetime(2018, 4, 7, 17, 0),\n", + " datetime.datetime(2018, 4, 7, 18, 0),\n", + " datetime.datetime(2018, 4, 7, 19, 0),\n", + " datetime.datetime(2018, 4, 7, 20, 0),\n", + " datetime.datetime(2018, 4, 7, 21, 0),\n", + " datetime.datetime(2018, 4, 7, 22, 0),\n", + " datetime.datetime(2018, 4, 7, 23, 0),\n", + " datetime.datetime(2018, 4, 8, 0, 0),\n", + " datetime.datetime(2018, 4, 8, 1, 0),\n", + " datetime.datetime(2018, 4, 8, 2, 0),\n", + " datetime.datetime(2018, 4, 8, 3, 0),\n", + " datetime.datetime(2018, 4, 8, 4, 0),\n", + " datetime.datetime(2018, 4, 8, 5, 0),\n", + " datetime.datetime(2018, 4, 8, 6, 0),\n", + " datetime.datetime(2018, 4, 8, 7, 0),\n", + " datetime.datetime(2018, 4, 8, 8, 0),\n", + " datetime.datetime(2018, 4, 8, 9, 0),\n", + " datetime.datetime(2018, 4, 8, 10, 0),\n", + " datetime.datetime(2018, 4, 8, 11, 0),\n", + " datetime.datetime(2018, 4, 8, 12, 0),\n", + " datetime.datetime(2018, 4, 8, 13, 0),\n", + " datetime.datetime(2018, 4, 8, 14, 0),\n", + " datetime.datetime(2018, 4, 8, 15, 0),\n", + " datetime.datetime(2018, 4, 8, 16, 0),\n", + " datetime.datetime(2018, 4, 8, 17, 0),\n", + " datetime.datetime(2018, 4, 8, 18, 0),\n", + " datetime.datetime(2018, 4, 8, 19, 0),\n", + " datetime.datetime(2018, 4, 8, 20, 0),\n", + " datetime.datetime(2018, 4, 8, 21, 0),\n", + " datetime.datetime(2018, 4, 8, 22, 0),\n", + " datetime.datetime(2018, 4, 8, 23, 0),\n", + " datetime.datetime(2018, 4, 9, 0, 0),\n", + " datetime.datetime(2018, 4, 9, 1, 0),\n", + " datetime.datetime(2018, 4, 9, 2, 0),\n", + " datetime.datetime(2018, 4, 9, 3, 0),\n", + " datetime.datetime(2018, 4, 9, 4, 0),\n", + " datetime.datetime(2018, 4, 9, 5, 0),\n", + " datetime.datetime(2018, 4, 9, 6, 0),\n", + " datetime.datetime(2018, 4, 9, 7, 0),\n", + " datetime.datetime(2018, 4, 9, 8, 0),\n", + " datetime.datetime(2018, 4, 9, 9, 0),\n", + " datetime.datetime(2018, 4, 9, 10, 0),\n", + " datetime.datetime(2018, 4, 9, 11, 0),\n", + " datetime.datetime(2018, 4, 9, 12, 0),\n", + " datetime.datetime(2018, 4, 9, 13, 0),\n", + " datetime.datetime(2018, 4, 9, 14, 0),\n", + " datetime.datetime(2018, 4, 9, 15, 0),\n", + " datetime.datetime(2018, 4, 9, 16, 0),\n", + " datetime.datetime(2018, 4, 9, 17, 0),\n", + " datetime.datetime(2018, 4, 9, 18, 0),\n", + " datetime.datetime(2018, 4, 9, 19, 0),\n", + " datetime.datetime(2018, 4, 9, 20, 0),\n", + " datetime.datetime(2018, 4, 9, 21, 0),\n", + " datetime.datetime(2018, 4, 9, 22, 0),\n", + " datetime.datetime(2018, 4, 9, 23, 0),\n", + " datetime.datetime(2018, 4, 10, 0, 0),\n", + " datetime.datetime(2018, 4, 10, 1, 0),\n", + " datetime.datetime(2018, 4, 10, 2, 0),\n", + " datetime.datetime(2018, 4, 10, 3, 0),\n", + " datetime.datetime(2018, 4, 10, 4, 0),\n", + " datetime.datetime(2018, 4, 10, 5, 0),\n", + " datetime.datetime(2018, 4, 10, 6, 0),\n", + " datetime.datetime(2018, 4, 10, 7, 0),\n", + " datetime.datetime(2018, 4, 10, 8, 0),\n", + " datetime.datetime(2018, 4, 10, 9, 0),\n", + " datetime.datetime(2018, 4, 10, 10, 0),\n", + " datetime.datetime(2018, 4, 10, 11, 0),\n", + " datetime.datetime(2018, 4, 10, 12, 0),\n", + " datetime.datetime(2018, 4, 10, 13, 0),\n", + " datetime.datetime(2018, 4, 10, 14, 0),\n", + " datetime.datetime(2018, 4, 10, 15, 0),\n", + " datetime.datetime(2018, 4, 10, 16, 0),\n", + " datetime.datetime(2018, 4, 10, 17, 0),\n", + " datetime.datetime(2018, 4, 10, 18, 0),\n", + " datetime.datetime(2018, 4, 10, 19, 0),\n", + " datetime.datetime(2018, 4, 10, 20, 0),\n", + " datetime.datetime(2018, 4, 10, 21, 0),\n", + " datetime.datetime(2018, 4, 10, 22, 0),\n", + " datetime.datetime(2018, 4, 10, 23, 0),\n", + " datetime.datetime(2018, 4, 11, 0, 0),\n", + " datetime.datetime(2018, 4, 11, 1, 0),\n", + " datetime.datetime(2018, 4, 11, 2, 0),\n", + " datetime.datetime(2018, 4, 11, 3, 0),\n", + " datetime.datetime(2018, 4, 11, 4, 0),\n", + " datetime.datetime(2018, 4, 11, 5, 0),\n", + " datetime.datetime(2018, 4, 11, 6, 0),\n", + " datetime.datetime(2018, 4, 11, 7, 0),\n", + " datetime.datetime(2018, 4, 11, 8, 0),\n", + " datetime.datetime(2018, 4, 11, 9, 0),\n", + " datetime.datetime(2018, 4, 11, 10, 0),\n", + " datetime.datetime(2018, 4, 11, 11, 0),\n", + " datetime.datetime(2018, 4, 11, 12, 0),\n", + " datetime.datetime(2018, 4, 11, 13, 0),\n", + " datetime.datetime(2018, 4, 11, 14, 0),\n", + " datetime.datetime(2018, 4, 11, 15, 0),\n", + " datetime.datetime(2018, 4, 11, 16, 0),\n", + " datetime.datetime(2018, 4, 11, 17, 0),\n", + " datetime.datetime(2018, 4, 11, 18, 0),\n", + " datetime.datetime(2018, 4, 11, 19, 0),\n", + " datetime.datetime(2018, 4, 11, 20, 0),\n", + " datetime.datetime(2018, 4, 11, 21, 0),\n", + " datetime.datetime(2018, 4, 11, 22, 0),\n", + " datetime.datetime(2018, 4, 11, 23, 0),\n", + " datetime.datetime(2018, 4, 12, 0, 0),\n", + " datetime.datetime(2018, 4, 12, 1, 0),\n", + " datetime.datetime(2018, 4, 12, 2, 0),\n", + " datetime.datetime(2018, 4, 12, 3, 0),\n", + " datetime.datetime(2018, 4, 12, 4, 0),\n", + " datetime.datetime(2018, 4, 12, 5, 0),\n", + " datetime.datetime(2018, 4, 12, 6, 0),\n", + " datetime.datetime(2018, 4, 12, 7, 0),\n", + " datetime.datetime(2018, 4, 12, 8, 0),\n", + " datetime.datetime(2018, 4, 12, 9, 0),\n", + " datetime.datetime(2018, 4, 12, 10, 0),\n", + " datetime.datetime(2018, 4, 12, 11, 0),\n", + " datetime.datetime(2018, 4, 12, 12, 0),\n", + " datetime.datetime(2018, 4, 12, 13, 0),\n", + " datetime.datetime(2018, 4, 12, 14, 0),\n", + " datetime.datetime(2018, 4, 12, 15, 0),\n", + " datetime.datetime(2018, 4, 12, 16, 0),\n", + " datetime.datetime(2018, 4, 12, 17, 0),\n", + " datetime.datetime(2018, 4, 12, 18, 0),\n", + " datetime.datetime(2018, 4, 12, 19, 0),\n", + " datetime.datetime(2018, 4, 12, 20, 0),\n", + " datetime.datetime(2018, 4, 12, 21, 0),\n", + " datetime.datetime(2018, 4, 12, 22, 0),\n", + " datetime.datetime(2018, 4, 12, 23, 0),\n", + " datetime.datetime(2018, 4, 13, 0, 0),\n", + " datetime.datetime(2018, 4, 13, 1, 0),\n", + " datetime.datetime(2018, 4, 13, 2, 0),\n", + " datetime.datetime(2018, 4, 13, 3, 0),\n", + " datetime.datetime(2018, 4, 13, 4, 0),\n", + " datetime.datetime(2018, 4, 13, 5, 0),\n", + " datetime.datetime(2018, 4, 13, 6, 0),\n", + " datetime.datetime(2018, 4, 13, 7, 0),\n", + " datetime.datetime(2018, 4, 13, 8, 0),\n", + " datetime.datetime(2018, 4, 13, 9, 0),\n", + " datetime.datetime(2018, 4, 13, 10, 0),\n", + " datetime.datetime(2018, 4, 13, 11, 0),\n", + " datetime.datetime(2018, 4, 13, 12, 0),\n", + " datetime.datetime(2018, 4, 13, 13, 0),\n", + " datetime.datetime(2018, 4, 13, 14, 0),\n", + " datetime.datetime(2018, 4, 13, 15, 0),\n", + " datetime.datetime(2018, 4, 13, 16, 0),\n", + " datetime.datetime(2018, 4, 13, 17, 0),\n", + " datetime.datetime(2018, 4, 13, 18, 0),\n", + " datetime.datetime(2018, 4, 13, 19, 0),\n", + " datetime.datetime(2018, 4, 13, 20, 0),\n", + " datetime.datetime(2018, 4, 13, 21, 0),\n", + " datetime.datetime(2018, 4, 13, 22, 0),\n", + " datetime.datetime(2018, 4, 13, 23, 0),\n", + " datetime.datetime(2018, 4, 14, 0, 0),\n", + " datetime.datetime(2018, 4, 14, 1, 0),\n", + " datetime.datetime(2018, 4, 14, 2, 0),\n", + " datetime.datetime(2018, 4, 14, 3, 0),\n", + " datetime.datetime(2018, 4, 14, 4, 0),\n", + " datetime.datetime(2018, 4, 14, 5, 0),\n", + " datetime.datetime(2018, 4, 14, 6, 0),\n", + " datetime.datetime(2018, 4, 14, 7, 0),\n", + " datetime.datetime(2018, 4, 14, 8, 0),\n", + " datetime.datetime(2018, 4, 14, 9, 0),\n", + " datetime.datetime(2018, 4, 14, 10, 0),\n", + " datetime.datetime(2018, 4, 14, 11, 0),\n", + " datetime.datetime(2018, 4, 14, 12, 0),\n", + " datetime.datetime(2018, 4, 14, 13, 0),\n", + " datetime.datetime(2018, 4, 14, 14, 0),\n", + " datetime.datetime(2018, 4, 14, 15, 0),\n", + " datetime.datetime(2018, 4, 14, 16, 0),\n", + " datetime.datetime(2018, 4, 14, 17, 0),\n", + " datetime.datetime(2018, 4, 14, 18, 0),\n", + " datetime.datetime(2018, 4, 14, 19, 0),\n", + " datetime.datetime(2018, 4, 14, 20, 0),\n", + " datetime.datetime(2018, 4, 14, 21, 0),\n", + " datetime.datetime(2018, 4, 14, 22, 0),\n", + " datetime.datetime(2018, 4, 14, 23, 0),\n", + " datetime.datetime(2018, 4, 15, 0, 0),\n", + " datetime.datetime(2018, 4, 15, 1, 0),\n", + " datetime.datetime(2018, 4, 15, 2, 0),\n", + " datetime.datetime(2018, 4, 15, 3, 0),\n", + " datetime.datetime(2018, 4, 15, 4, 0),\n", + " datetime.datetime(2018, 4, 15, 5, 0),\n", + " datetime.datetime(2018, 4, 15, 6, 0),\n", + " datetime.datetime(2018, 4, 15, 7, 0),\n", + " datetime.datetime(2018, 4, 15, 8, 0),\n", + " datetime.datetime(2018, 4, 15, 9, 0),\n", + " datetime.datetime(2018, 4, 15, 10, 0),\n", + " datetime.datetime(2018, 4, 15, 11, 0),\n", + " datetime.datetime(2018, 4, 15, 12, 0),\n", + " datetime.datetime(2018, 4, 15, 13, 0),\n", + " datetime.datetime(2018, 4, 15, 14, 0),\n", + " datetime.datetime(2018, 4, 15, 15, 0),\n", + " datetime.datetime(2018, 4, 15, 16, 0),\n", + " datetime.datetime(2018, 4, 15, 17, 0),\n", + " datetime.datetime(2018, 4, 15, 18, 0),\n", + " datetime.datetime(2018, 4, 15, 19, 0),\n", + " datetime.datetime(2018, 4, 15, 20, 0),\n", + " datetime.datetime(2018, 4, 15, 21, 0),\n", + " datetime.datetime(2018, 4, 15, 22, 0),\n", + " datetime.datetime(2018, 4, 15, 23, 0),\n", + " datetime.datetime(2018, 4, 16, 0, 0),\n", + " datetime.datetime(2018, 4, 16, 1, 0),\n", + " datetime.datetime(2018, 4, 16, 2, 0),\n", + " datetime.datetime(2018, 4, 16, 3, 0),\n", + " datetime.datetime(2018, 4, 16, 4, 0),\n", + " datetime.datetime(2018, 4, 16, 5, 0),\n", + " datetime.datetime(2018, 4, 16, 6, 0),\n", + " datetime.datetime(2018, 4, 16, 7, 0),\n", + " datetime.datetime(2018, 4, 16, 8, 0),\n", + " datetime.datetime(2018, 4, 16, 9, 0),\n", + " datetime.datetime(2018, 4, 16, 10, 0),\n", + " datetime.datetime(2018, 4, 16, 11, 0),\n", + " datetime.datetime(2018, 4, 16, 12, 0),\n", + " datetime.datetime(2018, 4, 16, 13, 0),\n", + " datetime.datetime(2018, 4, 16, 14, 0),\n", + " datetime.datetime(2018, 4, 16, 15, 0),\n", + " datetime.datetime(2018, 4, 16, 16, 0),\n", + " datetime.datetime(2018, 4, 16, 17, 0),\n", + " datetime.datetime(2018, 4, 16, 18, 0),\n", + " datetime.datetime(2018, 4, 16, 19, 0),\n", + " datetime.datetime(2018, 4, 16, 20, 0),\n", + " datetime.datetime(2018, 4, 16, 21, 0),\n", + " datetime.datetime(2018, 4, 16, 22, 0),\n", + " datetime.datetime(2018, 4, 16, 23, 0),\n", + " datetime.datetime(2018, 4, 17, 0, 0),\n", + " datetime.datetime(2018, 4, 17, 1, 0),\n", + " datetime.datetime(2018, 4, 17, 2, 0),\n", + " datetime.datetime(2018, 4, 17, 3, 0),\n", + " datetime.datetime(2018, 4, 17, 4, 0),\n", + " datetime.datetime(2018, 4, 17, 5, 0),\n", + " datetime.datetime(2018, 4, 17, 6, 0),\n", + " datetime.datetime(2018, 4, 17, 7, 0),\n", + " datetime.datetime(2018, 4, 17, 8, 0),\n", + " datetime.datetime(2018, 4, 17, 9, 0),\n", + " datetime.datetime(2018, 4, 17, 10, 0),\n", + " datetime.datetime(2018, 4, 17, 11, 0),\n", + " datetime.datetime(2018, 4, 17, 12, 0),\n", + " datetime.datetime(2018, 4, 17, 13, 0),\n", + " datetime.datetime(2018, 4, 17, 14, 0),\n", + " datetime.datetime(2018, 4, 17, 15, 0),\n", + " datetime.datetime(2018, 4, 17, 16, 0),\n", + " datetime.datetime(2018, 4, 17, 17, 0),\n", + " datetime.datetime(2018, 4, 17, 18, 0),\n", + " datetime.datetime(2018, 4, 17, 19, 0),\n", + " datetime.datetime(2018, 4, 17, 20, 0),\n", + " datetime.datetime(2018, 4, 17, 21, 0),\n", + " datetime.datetime(2018, 4, 17, 22, 0),\n", + " datetime.datetime(2018, 4, 17, 23, 0),\n", + " datetime.datetime(2018, 4, 18, 0, 0),\n", + " datetime.datetime(2018, 4, 18, 1, 0),\n", + " datetime.datetime(2018, 4, 18, 2, 0),\n", + " datetime.datetime(2018, 4, 18, 3, 0),\n", + " datetime.datetime(2018, 4, 18, 4, 0),\n", + " datetime.datetime(2018, 4, 18, 5, 0),\n", + " datetime.datetime(2018, 4, 18, 6, 0),\n", + " datetime.datetime(2018, 4, 18, 7, 0),\n", + " datetime.datetime(2018, 4, 18, 8, 0),\n", + " datetime.datetime(2018, 4, 18, 9, 0),\n", + " datetime.datetime(2018, 4, 18, 10, 0),\n", + " datetime.datetime(2018, 4, 18, 11, 0),\n", + " datetime.datetime(2018, 4, 18, 12, 0),\n", + " datetime.datetime(2018, 4, 18, 13, 0),\n", + " datetime.datetime(2018, 4, 18, 14, 0),\n", + " datetime.datetime(2018, 4, 18, 15, 0),\n", + " datetime.datetime(2018, 4, 18, 16, 0),\n", + " datetime.datetime(2018, 4, 18, 17, 0),\n", + " datetime.datetime(2018, 4, 18, 18, 0),\n", + " datetime.datetime(2018, 4, 18, 19, 0),\n", + " datetime.datetime(2018, 4, 18, 20, 0),\n", + " datetime.datetime(2018, 4, 18, 21, 0),\n", + " datetime.datetime(2018, 4, 18, 22, 0),\n", + " datetime.datetime(2018, 4, 18, 23, 0),\n", + " datetime.datetime(2018, 4, 19, 0, 0),\n", + " datetime.datetime(2018, 4, 19, 1, 0),\n", + " datetime.datetime(2018, 4, 19, 2, 0),\n", + " datetime.datetime(2018, 4, 19, 3, 0),\n", + " datetime.datetime(2018, 4, 19, 4, 0),\n", + " datetime.datetime(2018, 4, 19, 5, 0),\n", + " datetime.datetime(2018, 4, 19, 6, 0),\n", + " datetime.datetime(2018, 4, 19, 7, 0),\n", + " datetime.datetime(2018, 4, 19, 8, 0),\n", + " datetime.datetime(2018, 4, 19, 9, 0),\n", + " datetime.datetime(2018, 4, 19, 10, 0),\n", + " datetime.datetime(2018, 4, 19, 11, 0),\n", + " datetime.datetime(2018, 4, 19, 12, 0),\n", + " datetime.datetime(2018, 4, 19, 13, 0),\n", + " datetime.datetime(2018, 4, 19, 14, 0),\n", + " datetime.datetime(2018, 4, 19, 15, 0),\n", + " datetime.datetime(2018, 4, 19, 16, 0),\n", + " datetime.datetime(2018, 4, 19, 17, 0),\n", + " datetime.datetime(2018, 4, 19, 18, 0),\n", + " datetime.datetime(2018, 4, 19, 19, 0),\n", + " datetime.datetime(2018, 4, 19, 20, 0),\n", + " datetime.datetime(2018, 4, 19, 21, 0),\n", + " datetime.datetime(2018, 4, 19, 22, 0),\n", + " datetime.datetime(2018, 4, 19, 23, 0),\n", + " datetime.datetime(2018, 4, 20, 0, 0),\n", + " datetime.datetime(2018, 4, 20, 1, 0),\n", + " datetime.datetime(2018, 4, 20, 2, 0),\n", + " datetime.datetime(2018, 4, 20, 3, 0),\n", + " datetime.datetime(2018, 4, 20, 4, 0),\n", + " datetime.datetime(2018, 4, 20, 5, 0),\n", + " datetime.datetime(2018, 4, 20, 6, 0),\n", + " datetime.datetime(2018, 4, 20, 7, 0),\n", + " datetime.datetime(2018, 4, 20, 8, 0),\n", + " datetime.datetime(2018, 4, 20, 9, 0),\n", + " datetime.datetime(2018, 4, 20, 10, 0),\n", + " datetime.datetime(2018, 4, 20, 11, 0),\n", + " datetime.datetime(2018, 4, 20, 12, 0),\n", + " datetime.datetime(2018, 4, 20, 13, 0),\n", + " datetime.datetime(2018, 4, 20, 14, 0),\n", + " datetime.datetime(2018, 4, 20, 15, 0),\n", + " datetime.datetime(2018, 4, 20, 16, 0),\n", + " datetime.datetime(2018, 4, 20, 17, 0),\n", + " datetime.datetime(2018, 4, 20, 18, 0),\n", + " datetime.datetime(2018, 4, 20, 19, 0),\n", + " datetime.datetime(2018, 4, 20, 20, 0),\n", + " datetime.datetime(2018, 4, 20, 21, 0),\n", + " datetime.datetime(2018, 4, 20, 22, 0),\n", + " datetime.datetime(2018, 4, 20, 23, 0),\n", + " datetime.datetime(2018, 4, 21, 0, 0),\n", + " datetime.datetime(2018, 4, 21, 1, 0),\n", + " datetime.datetime(2018, 4, 21, 2, 0),\n", + " datetime.datetime(2018, 4, 21, 3, 0),\n", + " datetime.datetime(2018, 4, 21, 4, 0),\n", + " datetime.datetime(2018, 4, 21, 5, 0),\n", + " datetime.datetime(2018, 4, 21, 6, 0),\n", + " datetime.datetime(2018, 4, 21, 7, 0),\n", + " datetime.datetime(2018, 4, 21, 8, 0),\n", + " datetime.datetime(2018, 4, 21, 9, 0),\n", + " datetime.datetime(2018, 4, 21, 10, 0),\n", + " datetime.datetime(2018, 4, 21, 11, 0),\n", + " datetime.datetime(2018, 4, 21, 12, 0),\n", + " datetime.datetime(2018, 4, 21, 13, 0),\n", + " datetime.datetime(2018, 4, 21, 14, 0),\n", + " datetime.datetime(2018, 4, 21, 15, 0),\n", + " datetime.datetime(2018, 4, 21, 16, 0),\n", + " datetime.datetime(2018, 4, 21, 17, 0),\n", + " datetime.datetime(2018, 4, 21, 18, 0),\n", + " datetime.datetime(2018, 4, 21, 19, 0),\n", + " datetime.datetime(2018, 4, 21, 20, 0),\n", + " datetime.datetime(2018, 4, 21, 21, 0),\n", + " datetime.datetime(2018, 4, 21, 22, 0),\n", + " datetime.datetime(2018, 4, 21, 23, 0),\n", + " datetime.datetime(2018, 4, 22, 0, 0),\n", + " datetime.datetime(2018, 4, 22, 1, 0),\n", + " datetime.datetime(2018, 4, 22, 2, 0),\n", + " datetime.datetime(2018, 4, 22, 3, 0),\n", + " datetime.datetime(2018, 4, 22, 4, 0),\n", + " datetime.datetime(2018, 4, 22, 5, 0),\n", + " datetime.datetime(2018, 4, 22, 6, 0),\n", + " datetime.datetime(2018, 4, 22, 7, 0),\n", + " datetime.datetime(2018, 4, 22, 8, 0),\n", + " datetime.datetime(2018, 4, 22, 9, 0),\n", + " datetime.datetime(2018, 4, 22, 10, 0),\n", + " datetime.datetime(2018, 4, 22, 11, 0),\n", + " datetime.datetime(2018, 4, 22, 12, 0),\n", + " datetime.datetime(2018, 4, 22, 13, 0),\n", + " datetime.datetime(2018, 4, 22, 14, 0),\n", + " datetime.datetime(2018, 4, 22, 15, 0),\n", + " datetime.datetime(2018, 4, 22, 16, 0),\n", + " datetime.datetime(2018, 4, 22, 17, 0),\n", + " datetime.datetime(2018, 4, 22, 18, 0),\n", + " datetime.datetime(2018, 4, 22, 19, 0),\n", + " datetime.datetime(2018, 4, 22, 20, 0),\n", + " datetime.datetime(2018, 4, 22, 21, 0),\n", + " datetime.datetime(2018, 4, 22, 22, 0),\n", + " datetime.datetime(2018, 4, 22, 23, 0),\n", + " datetime.datetime(2018, 4, 23, 0, 0),\n", + " datetime.datetime(2018, 4, 23, 1, 0),\n", + " datetime.datetime(2018, 4, 23, 2, 0),\n", + " datetime.datetime(2018, 4, 23, 3, 0),\n", + " datetime.datetime(2018, 4, 23, 4, 0),\n", + " datetime.datetime(2018, 4, 23, 5, 0),\n", + " datetime.datetime(2018, 4, 23, 6, 0),\n", + " datetime.datetime(2018, 4, 23, 7, 0),\n", + " datetime.datetime(2018, 4, 23, 8, 0),\n", + " datetime.datetime(2018, 4, 23, 9, 0),\n", + " datetime.datetime(2018, 4, 23, 10, 0),\n", + " datetime.datetime(2018, 4, 23, 11, 0),\n", + " datetime.datetime(2018, 4, 23, 12, 0),\n", + " datetime.datetime(2018, 4, 23, 13, 0),\n", + " datetime.datetime(2018, 4, 23, 14, 0),\n", + " datetime.datetime(2018, 4, 23, 15, 0),\n", + " datetime.datetime(2018, 4, 23, 16, 0),\n", + " datetime.datetime(2018, 4, 23, 17, 0),\n", + " datetime.datetime(2018, 4, 23, 18, 0),\n", + " datetime.datetime(2018, 4, 23, 19, 0),\n", + " datetime.datetime(2018, 4, 23, 20, 0),\n", + " datetime.datetime(2018, 4, 23, 21, 0),\n", + " datetime.datetime(2018, 4, 23, 22, 0),\n", + " datetime.datetime(2018, 4, 23, 23, 0),\n", + " datetime.datetime(2018, 4, 24, 0, 0),\n", + " datetime.datetime(2018, 4, 24, 1, 0),\n", + " datetime.datetime(2018, 4, 24, 2, 0),\n", + " datetime.datetime(2018, 4, 24, 3, 0),\n", + " datetime.datetime(2018, 4, 24, 4, 0),\n", + " datetime.datetime(2018, 4, 24, 5, 0),\n", + " datetime.datetime(2018, 4, 24, 6, 0),\n", + " datetime.datetime(2018, 4, 24, 7, 0),\n", + " datetime.datetime(2018, 4, 24, 8, 0),\n", + " datetime.datetime(2018, 4, 24, 9, 0),\n", + " datetime.datetime(2018, 4, 24, 10, 0),\n", + " datetime.datetime(2018, 4, 24, 11, 0),\n", + " datetime.datetime(2018, 4, 24, 12, 0),\n", + " datetime.datetime(2018, 4, 24, 13, 0),\n", + " datetime.datetime(2018, 4, 24, 14, 0),\n", + " datetime.datetime(2018, 4, 24, 15, 0),\n", + " datetime.datetime(2018, 4, 24, 16, 0),\n", + " datetime.datetime(2018, 4, 24, 17, 0),\n", + " datetime.datetime(2018, 4, 24, 18, 0),\n", + " datetime.datetime(2018, 4, 24, 19, 0),\n", + " datetime.datetime(2018, 4, 24, 20, 0),\n", + " datetime.datetime(2018, 4, 24, 21, 0),\n", + " datetime.datetime(2018, 4, 24, 22, 0),\n", + " datetime.datetime(2018, 4, 24, 23, 0),\n", + " datetime.datetime(2018, 4, 25, 0, 0),\n", + " datetime.datetime(2018, 4, 25, 1, 0),\n", + " datetime.datetime(2018, 4, 25, 2, 0),\n", + " datetime.datetime(2018, 4, 25, 3, 0),\n", + " datetime.datetime(2018, 4, 25, 4, 0),\n", + " datetime.datetime(2018, 4, 25, 5, 0),\n", + " datetime.datetime(2018, 4, 25, 6, 0),\n", + " datetime.datetime(2018, 4, 25, 7, 0),\n", + " datetime.datetime(2018, 4, 25, 8, 0),\n", + " datetime.datetime(2018, 4, 25, 9, 0),\n", + " datetime.datetime(2018, 4, 25, 10, 0),\n", + " datetime.datetime(2018, 4, 25, 11, 0),\n", + " datetime.datetime(2018, 4, 25, 12, 0),\n", + " datetime.datetime(2018, 4, 25, 13, 0),\n", + " datetime.datetime(2018, 4, 25, 14, 0),\n", + " datetime.datetime(2018, 4, 25, 15, 0),\n", + " datetime.datetime(2018, 4, 25, 16, 0),\n", + " datetime.datetime(2018, 4, 25, 17, 0),\n", + " datetime.datetime(2018, 4, 25, 18, 0),\n", + " datetime.datetime(2018, 4, 25, 19, 0),\n", + " datetime.datetime(2018, 4, 25, 20, 0),\n", + " datetime.datetime(2018, 4, 25, 21, 0),\n", + " datetime.datetime(2018, 4, 25, 22, 0),\n", + " datetime.datetime(2018, 4, 25, 23, 0),\n", + " datetime.datetime(2018, 4, 26, 0, 0),\n", + " datetime.datetime(2018, 4, 26, 1, 0),\n", + " datetime.datetime(2018, 4, 26, 2, 0),\n", + " datetime.datetime(2018, 4, 26, 3, 0),\n", + " datetime.datetime(2018, 4, 26, 4, 0),\n", + " datetime.datetime(2018, 4, 26, 5, 0),\n", + " datetime.datetime(2018, 4, 26, 6, 0),\n", + " datetime.datetime(2018, 4, 26, 7, 0),\n", + " datetime.datetime(2018, 4, 26, 8, 0),\n", + " datetime.datetime(2018, 4, 26, 9, 0),\n", + " datetime.datetime(2018, 4, 26, 10, 0),\n", + " datetime.datetime(2018, 4, 26, 11, 0),\n", + " datetime.datetime(2018, 4, 26, 12, 0),\n", + " datetime.datetime(2018, 4, 26, 13, 0),\n", + " datetime.datetime(2018, 4, 26, 14, 0),\n", + " datetime.datetime(2018, 4, 26, 15, 0),\n", + " datetime.datetime(2018, 4, 26, 16, 0),\n", + " datetime.datetime(2018, 4, 26, 17, 0),\n", + " datetime.datetime(2018, 4, 26, 18, 0),\n", + " datetime.datetime(2018, 4, 26, 19, 0),\n", + " datetime.datetime(2018, 4, 26, 20, 0),\n", + " datetime.datetime(2018, 4, 26, 21, 0),\n", + " datetime.datetime(2018, 4, 26, 22, 0),\n", + " datetime.datetime(2018, 4, 26, 23, 0),\n", + " datetime.datetime(2018, 4, 27, 0, 0),\n", + " datetime.datetime(2018, 4, 27, 1, 0),\n", + " datetime.datetime(2018, 4, 27, 2, 0),\n", + " datetime.datetime(2018, 4, 27, 3, 0),\n", + " datetime.datetime(2018, 4, 27, 4, 0),\n", + " datetime.datetime(2018, 4, 27, 5, 0),\n", + " datetime.datetime(2018, 4, 27, 6, 0),\n", + " datetime.datetime(2018, 4, 27, 7, 0),\n", + " datetime.datetime(2018, 4, 27, 8, 0),\n", + " datetime.datetime(2018, 4, 27, 9, 0),\n", + " datetime.datetime(2018, 4, 27, 10, 0),\n", + " datetime.datetime(2018, 4, 27, 11, 0),\n", + " datetime.datetime(2018, 4, 27, 12, 0),\n", + " datetime.datetime(2018, 4, 27, 13, 0),\n", + " datetime.datetime(2018, 4, 27, 14, 0),\n", + " datetime.datetime(2018, 4, 27, 15, 0),\n", + " datetime.datetime(2018, 4, 27, 16, 0),\n", + " datetime.datetime(2018, 4, 27, 17, 0),\n", + " datetime.datetime(2018, 4, 27, 18, 0),\n", + " datetime.datetime(2018, 4, 27, 19, 0),\n", + " datetime.datetime(2018, 4, 27, 20, 0),\n", + " datetime.datetime(2018, 4, 27, 21, 0),\n", + " datetime.datetime(2018, 4, 27, 22, 0),\n", + " datetime.datetime(2018, 4, 27, 23, 0),\n", + " datetime.datetime(2018, 4, 28, 0, 0),\n", + " datetime.datetime(2018, 4, 28, 1, 0),\n", + " datetime.datetime(2018, 4, 28, 2, 0),\n", + " datetime.datetime(2018, 4, 28, 3, 0),\n", + " datetime.datetime(2018, 4, 28, 4, 0),\n", + " datetime.datetime(2018, 4, 28, 5, 0),\n", + " datetime.datetime(2018, 4, 28, 6, 0),\n", + " datetime.datetime(2018, 4, 28, 7, 0),\n", + " 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)]" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "obs_nes = open_netcdf(path=obs_path, info=True, parallel_method='X')\n", - "obs_nes" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], "source": [ "obs_nes.time" ] @@ -6894,13 +1346,6 @@ "### 1.2. Write dataset" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Write with NES" - ] - }, { "cell_type": "code", "execution_count": 10, @@ -7237,15 +1682,14 @@ "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)\n", - "Rank 000: Var daily_native_max_gap_percent created (82/175)\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:570: UserWarning: WARNING!!! GHOST datasets cannot be written in parallel yet. Changing to serial mode.\n", + "/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" ] }, @@ -7253,6 +1697,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "\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", "Rank 000: Writing daily_native_representativity_percent var (83/175)\n", @@ -7634,6294 +2080,6 @@ "obs_nes.to_netcdf('prv_obs_file.nc', info=True)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reopen with xarray" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:                                                           (time: 720, station: 168, N_flag_codes: 186, N_qa_codes: 77)\n",
-       "Coordinates:\n",
-       "  * time                                                              (time) datetime64[ns] ...\n",
-       "  * station                                                           (station) float64 ...\n",
-       "Dimensions without coordinates: N_flag_codes, N_qa_codes\n",
-       "Data variables: (12/179)\n",
-       "    latitude                                                          (station) float64 ...\n",
-       "    longitude                                                         (station) float64 ...\n",
-       "    ASTER_v3_altitude                                                 (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_BC_emissions                          (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_CO_emissions                          (station) float32 ...\n",
-       "    EDGAR_v4.3.2_annual_average_NH3_emissions                         (station) float32 ...\n",
-       "    ...                                                                ...\n",
-       "    street_width                                                      (station) float32 ...\n",
-       "    terrain                                                           (station) object ...\n",
-       "    vertical_datum                                                    (station) object ...\n",
-       "    weekday_weekend_code                                              (station, time) uint8 ...\n",
-       "    flag                                                              (station, time, N_flag_codes) int64 ...\n",
-       "    qa                                                                (station, time, N_qa_codes) int64 ...\n",
-       "Attributes:\n",
-       "    title:                     Surface ozone data in the EBAS network in 2018...\n",
-       "    institution:               Barcelona Supercomputing Center\n",
-       "    source:                    Surface observations\n",
-       "    creator_name:              Dene R. Bowdalo\n",
-       "    creator_email:             dene.bowdalo@bsc.es\n",
-       "    conventions:               CF-1.7\n",
-       "    data_version:              1.3.3\n",
-       "    history:                   Tue Mar 30 12:38:43 2021: ncks -O --fix_rec_dm...\n",
-       "    NCO:                       4.7.2\n",
-       "    nco_openmp_thread_number:  1\n",
-       "    Conventions:               CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 720, station: 168, N_flag_codes: 186, N_qa_codes: 77)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] ...\n", - " * station (station) float64 ...\n", - "Dimensions without coordinates: N_flag_codes, N_qa_codes\n", - "Data variables: (12/179)\n", - " latitude (station) float64 ...\n", - " longitude (station) float64 ...\n", - " ASTER_v3_altitude (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_BC_emissions (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_CO_emissions (station) float32 ...\n", - " EDGAR_v4.3.2_annual_average_NH3_emissions (station) float32 ...\n", - " ... ...\n", - " street_width (station) float32 ...\n", - " terrain (station) object ...\n", - " vertical_datum (station) object ...\n", - " weekday_weekend_code (station, time) uint8 ...\n", - " flag (station, time, N_flag_codes) int64 ...\n", - " qa (station, time, N_qa_codes) int64 ...\n", - "Attributes:\n", - " title: Surface ozone data in the EBAS network in 2018...\n", - " institution: Barcelona Supercomputing Center\n", - " source: Surface observations\n", - " creator_name: Dene R. Bowdalo\n", - " creator_email: dene.bowdalo@bsc.es\n", - " conventions: CF-1.7\n", - " data_version: 1.3.3\n", - " history: Tue Mar 30 12:38:43 2021: ncks -O --fix_rec_dm...\n", - " NCO: 4.7.2\n", - " nco_openmp_thread_number: 1\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('prv_obs_file.nc')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -13931,11 +2089,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "exp_path = '/gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.3.3/cams61_chimere_ph2-eu-000/hourly/sconco3/EBAS/sconco3_201804.nc'" + "# Original path: /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.3.3/cams61_chimere_ph2-eu-000/hourly/sconco3/EBAS/sconco3_201804.nc\n", + "# Interpolated experiment to EBAS network from CAMS\n", + "exp_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/exp_interp_sconco3_201804.nc'" ] }, { @@ -13947,610 +2107,769 @@ "### 2.1. Read dataset" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Open with xarray" - ] - }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:                 (grid_edge: 1125, station: 175, model_latitude: 211, model_longitude: 351, time: 720)\n",
-       "Coordinates:\n",
-       "  * time                    (time) datetime64[ns] 2018-04-01 ... 2018-04-30T2...\n",
-       "Dimensions without coordinates: grid_edge, station, model_latitude, model_longitude\n",
-       "Data variables:\n",
-       "    grid_edge_latitude      (grid_edge) float64 29.9 30.1 30.3 ... 29.9 29.9\n",
-       "    grid_edge_longitude     (grid_edge) float64 -25.1 -25.1 ... -24.9 -25.1\n",
-       "    latitude                (station) float64 -64.24 -54.85 ... 21.57 -34.35\n",
-       "    longitude               (station) float64 -56.62 -68.31 ... 103.5 18.49\n",
-       "    model_centre_latitude   (model_latitude, model_longitude) float64 30.0 .....\n",
-       "    model_centre_longitude  (model_latitude, model_longitude) float64 -25.0 ....\n",
-       "    sconco3                 (station, time) float32 ...\n",
-       "    station_reference       (station) object 'AR0001R_UVP' ... 'ZA0001G_UVP'\n",
-       "Attributes:\n",
-       "    title:          Inverse distance weighting (4 neighbours) interpolated ca...\n",
-       "    institution:    Barcelona Supercomputing Center\n",
-       "    source:         Experiment cams61_chimere_ph2\n",
-       "    creator_name:   Dene R. Bowdalo\n",
-       "    creator_email:  dene.bowdalo@bsc.es\n",
-       "    conventions:    CF-1.7\n",
-       "    data_version:   1.0\n",
-       "    history:        Thu Feb 11 10:19:01 2021: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
-       "    NCO:            4.7.2
" - ], "text/plain": [ - "\n", - "Dimensions: (grid_edge: 1125, station: 175, model_latitude: 211, model_longitude: 351, time: 720)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2018-04-01 ... 2018-04-30T2...\n", - "Dimensions without coordinates: grid_edge, station, model_latitude, model_longitude\n", - "Data variables:\n", - " grid_edge_latitude (grid_edge) float64 ...\n", - " grid_edge_longitude (grid_edge) float64 ...\n", - " latitude (station) float64 ...\n", - " longitude (station) float64 ...\n", - " model_centre_latitude (model_latitude, model_longitude) float64 ...\n", - " model_centre_longitude (model_latitude, model_longitude) float64 ...\n", - " sconco3 (station, time) float32 ...\n", - " station_reference (station) object ...\n", - "Attributes:\n", - " title: Inverse distance weighting (4 neighbours) interpolated ca...\n", - " institution: Barcelona Supercomputing Center\n", - " source: Experiment cams61_chimere_ph2\n", - " creator_name: Dene R. Bowdalo\n", - " creator_email: dene.bowdalo@bsc.es\n", - " conventions: CF-1.7\n", - " data_version: 1.0\n", - " history: Thu Feb 11 10:19:01 2021: ncks -O --dfl_lvl 1 /gpfs/proje...\n", - " NCO: 4.7.2" + "" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "xr.open_dataset(exp_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Open with NES" + "exp_interp_nes = open_netcdf(path=exp_path, info=True, parallel_method='X')\n", + "exp_interp_nes" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "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", + " datetime.datetime(2018, 4, 1, 18, 0),\n", + " datetime.datetime(2018, 4, 1, 19, 0),\n", + " datetime.datetime(2018, 4, 1, 20, 0),\n", + " datetime.datetime(2018, 4, 1, 21, 0),\n", + " datetime.datetime(2018, 4, 1, 22, 0),\n", + " datetime.datetime(2018, 4, 1, 23, 0),\n", + " datetime.datetime(2018, 4, 2, 0, 0),\n", + " datetime.datetime(2018, 4, 2, 1, 0),\n", + " datetime.datetime(2018, 4, 2, 2, 0),\n", + " datetime.datetime(2018, 4, 2, 3, 0),\n", + " datetime.datetime(2018, 4, 2, 4, 0),\n", + " datetime.datetime(2018, 4, 2, 5, 0),\n", + " datetime.datetime(2018, 4, 2, 6, 0),\n", + " datetime.datetime(2018, 4, 2, 7, 0),\n", + " datetime.datetime(2018, 4, 2, 8, 0),\n", + " datetime.datetime(2018, 4, 2, 9, 0),\n", + " datetime.datetime(2018, 4, 2, 10, 0),\n", + " datetime.datetime(2018, 4, 2, 11, 0),\n", + " datetime.datetime(2018, 4, 2, 12, 0),\n", + " datetime.datetime(2018, 4, 2, 13, 0),\n", + " datetime.datetime(2018, 4, 2, 14, 0),\n", + " datetime.datetime(2018, 4, 2, 15, 0),\n", + " datetime.datetime(2018, 4, 2, 16, 0),\n", + " datetime.datetime(2018, 4, 2, 17, 0),\n", + " datetime.datetime(2018, 4, 2, 18, 0),\n", + " datetime.datetime(2018, 4, 2, 19, 0),\n", + " datetime.datetime(2018, 4, 2, 20, 0),\n", + " datetime.datetime(2018, 4, 2, 21, 0),\n", + " datetime.datetime(2018, 4, 2, 22, 0),\n", + " datetime.datetime(2018, 4, 2, 23, 0),\n", + " datetime.datetime(2018, 4, 3, 0, 0),\n", + " datetime.datetime(2018, 4, 3, 1, 0),\n", + " datetime.datetime(2018, 4, 3, 2, 0),\n", + " datetime.datetime(2018, 4, 3, 3, 0),\n", + " datetime.datetime(2018, 4, 3, 4, 0),\n", + " datetime.datetime(2018, 4, 3, 5, 0),\n", + " datetime.datetime(2018, 4, 3, 6, 0),\n", + " datetime.datetime(2018, 4, 3, 7, 0),\n", + " datetime.datetime(2018, 4, 3, 8, 0),\n", + " datetime.datetime(2018, 4, 3, 9, 0),\n", + " datetime.datetime(2018, 4, 3, 10, 0),\n", + " datetime.datetime(2018, 4, 3, 11, 0),\n", + " datetime.datetime(2018, 4, 3, 12, 0),\n", + " datetime.datetime(2018, 4, 3, 13, 0),\n", + " datetime.datetime(2018, 4, 3, 14, 0),\n", + " datetime.datetime(2018, 4, 3, 15, 0),\n", + " datetime.datetime(2018, 4, 3, 16, 0),\n", + " datetime.datetime(2018, 4, 3, 17, 0),\n", + " datetime.datetime(2018, 4, 3, 18, 0),\n", + " datetime.datetime(2018, 4, 3, 19, 0),\n", + " datetime.datetime(2018, 4, 3, 20, 0),\n", + " datetime.datetime(2018, 4, 3, 21, 0),\n", + " datetime.datetime(2018, 4, 3, 22, 0),\n", + " datetime.datetime(2018, 4, 3, 23, 0),\n", + " datetime.datetime(2018, 4, 4, 0, 0),\n", + " datetime.datetime(2018, 4, 4, 1, 0),\n", + " datetime.datetime(2018, 4, 4, 2, 0),\n", + " datetime.datetime(2018, 4, 4, 3, 0),\n", + " datetime.datetime(2018, 4, 4, 4, 0),\n", + " datetime.datetime(2018, 4, 4, 5, 0),\n", + " datetime.datetime(2018, 4, 4, 6, 0),\n", + " datetime.datetime(2018, 4, 4, 7, 0),\n", + " datetime.datetime(2018, 4, 4, 8, 0),\n", + " datetime.datetime(2018, 4, 4, 9, 0),\n", + " datetime.datetime(2018, 4, 4, 10, 0),\n", + " datetime.datetime(2018, 4, 4, 11, 0),\n", + " datetime.datetime(2018, 4, 4, 12, 0),\n", + " datetime.datetime(2018, 4, 4, 13, 0),\n", + " datetime.datetime(2018, 4, 4, 14, 0),\n", + " datetime.datetime(2018, 4, 4, 15, 0),\n", + " datetime.datetime(2018, 4, 4, 16, 0),\n", + " datetime.datetime(2018, 4, 4, 17, 0),\n", + " datetime.datetime(2018, 4, 4, 18, 0),\n", + " datetime.datetime(2018, 4, 4, 19, 0),\n", + " datetime.datetime(2018, 4, 4, 20, 0),\n", + " datetime.datetime(2018, 4, 4, 21, 0),\n", + " datetime.datetime(2018, 4, 4, 22, 0),\n", + " datetime.datetime(2018, 4, 4, 23, 0),\n", + " datetime.datetime(2018, 4, 5, 0, 0),\n", + " datetime.datetime(2018, 4, 5, 1, 0),\n", + " datetime.datetime(2018, 4, 5, 2, 0),\n", + " datetime.datetime(2018, 4, 5, 3, 0),\n", + " datetime.datetime(2018, 4, 5, 4, 0),\n", + " datetime.datetime(2018, 4, 5, 5, 0),\n", + " datetime.datetime(2018, 4, 5, 6, 0),\n", + " datetime.datetime(2018, 4, 5, 7, 0),\n", + " datetime.datetime(2018, 4, 5, 8, 0),\n", + " datetime.datetime(2018, 4, 5, 9, 0),\n", + " datetime.datetime(2018, 4, 5, 10, 0),\n", + " datetime.datetime(2018, 4, 5, 11, 0),\n", + " datetime.datetime(2018, 4, 5, 12, 0),\n", + " datetime.datetime(2018, 4, 5, 13, 0),\n", + " datetime.datetime(2018, 4, 5, 14, 0),\n", + " datetime.datetime(2018, 4, 5, 15, 0),\n", + " datetime.datetime(2018, 4, 5, 16, 0),\n", + " datetime.datetime(2018, 4, 5, 17, 0),\n", + " datetime.datetime(2018, 4, 5, 18, 0),\n", + " datetime.datetime(2018, 4, 5, 19, 0),\n", + " datetime.datetime(2018, 4, 5, 20, 0),\n", + " datetime.datetime(2018, 4, 5, 21, 0),\n", + " datetime.datetime(2018, 4, 5, 22, 0),\n", + " datetime.datetime(2018, 4, 5, 23, 0),\n", + " datetime.datetime(2018, 4, 6, 0, 0),\n", + " datetime.datetime(2018, 4, 6, 1, 0),\n", + " datetime.datetime(2018, 4, 6, 2, 0),\n", + " datetime.datetime(2018, 4, 6, 3, 0),\n", + " datetime.datetime(2018, 4, 6, 4, 0),\n", + " datetime.datetime(2018, 4, 6, 5, 0),\n", + " datetime.datetime(2018, 4, 6, 6, 0),\n", + " datetime.datetime(2018, 4, 6, 7, 0),\n", + " datetime.datetime(2018, 4, 6, 8, 0),\n", + " datetime.datetime(2018, 4, 6, 9, 0),\n", + " datetime.datetime(2018, 4, 6, 10, 0),\n", + " datetime.datetime(2018, 4, 6, 11, 0),\n", + " datetime.datetime(2018, 4, 6, 12, 0),\n", + " datetime.datetime(2018, 4, 6, 13, 0),\n", + " datetime.datetime(2018, 4, 6, 14, 0),\n", + " datetime.datetime(2018, 4, 6, 15, 0),\n", + " datetime.datetime(2018, 4, 6, 16, 0),\n", + " datetime.datetime(2018, 4, 6, 17, 0),\n", + " datetime.datetime(2018, 4, 6, 18, 0),\n", + " datetime.datetime(2018, 4, 6, 19, 0),\n", + " datetime.datetime(2018, 4, 6, 20, 0),\n", + " datetime.datetime(2018, 4, 6, 21, 0),\n", + " datetime.datetime(2018, 4, 6, 22, 0),\n", + " datetime.datetime(2018, 4, 6, 23, 0),\n", + " datetime.datetime(2018, 4, 7, 0, 0),\n", + " datetime.datetime(2018, 4, 7, 1, 0),\n", + " datetime.datetime(2018, 4, 7, 2, 0),\n", + " datetime.datetime(2018, 4, 7, 3, 0),\n", + " datetime.datetime(2018, 4, 7, 4, 0),\n", + " datetime.datetime(2018, 4, 7, 5, 0),\n", + " datetime.datetime(2018, 4, 7, 6, 0),\n", + " datetime.datetime(2018, 4, 7, 7, 0),\n", + " datetime.datetime(2018, 4, 7, 8, 0),\n", + " datetime.datetime(2018, 4, 7, 9, 0),\n", + " datetime.datetime(2018, 4, 7, 10, 0),\n", + " datetime.datetime(2018, 4, 7, 11, 0),\n", + " datetime.datetime(2018, 4, 7, 12, 0),\n", + " datetime.datetime(2018, 4, 7, 13, 0),\n", + " datetime.datetime(2018, 4, 7, 14, 0),\n", + " datetime.datetime(2018, 4, 7, 15, 0),\n", + " datetime.datetime(2018, 4, 7, 16, 0),\n", + " datetime.datetime(2018, 4, 7, 17, 0),\n", + " datetime.datetime(2018, 4, 7, 18, 0),\n", + " datetime.datetime(2018, 4, 7, 19, 0),\n", + " datetime.datetime(2018, 4, 7, 20, 0),\n", + " datetime.datetime(2018, 4, 7, 21, 0),\n", + " datetime.datetime(2018, 4, 7, 22, 0),\n", + " datetime.datetime(2018, 4, 7, 23, 0),\n", + " datetime.datetime(2018, 4, 8, 0, 0),\n", + " datetime.datetime(2018, 4, 8, 1, 0),\n", + " datetime.datetime(2018, 4, 8, 2, 0),\n", + " datetime.datetime(2018, 4, 8, 3, 0),\n", + " datetime.datetime(2018, 4, 8, 4, 0),\n", + " datetime.datetime(2018, 4, 8, 5, 0),\n", + " datetime.datetime(2018, 4, 8, 6, 0),\n", + " datetime.datetime(2018, 4, 8, 7, 0),\n", + " datetime.datetime(2018, 4, 8, 8, 0),\n", + " datetime.datetime(2018, 4, 8, 9, 0),\n", + " datetime.datetime(2018, 4, 8, 10, 0),\n", + " datetime.datetime(2018, 4, 8, 11, 0),\n", + " datetime.datetime(2018, 4, 8, 12, 0),\n", + " datetime.datetime(2018, 4, 8, 13, 0),\n", + " datetime.datetime(2018, 4, 8, 14, 0),\n", + " datetime.datetime(2018, 4, 8, 15, 0),\n", + " datetime.datetime(2018, 4, 8, 16, 0),\n", + " datetime.datetime(2018, 4, 8, 17, 0),\n", + " datetime.datetime(2018, 4, 8, 18, 0),\n", + " datetime.datetime(2018, 4, 8, 19, 0),\n", + " datetime.datetime(2018, 4, 8, 20, 0),\n", + " datetime.datetime(2018, 4, 8, 21, 0),\n", + " datetime.datetime(2018, 4, 8, 22, 0),\n", + " datetime.datetime(2018, 4, 8, 23, 0),\n", + " datetime.datetime(2018, 4, 9, 0, 0),\n", + " datetime.datetime(2018, 4, 9, 1, 0),\n", + " datetime.datetime(2018, 4, 9, 2, 0),\n", + " datetime.datetime(2018, 4, 9, 3, 0),\n", + " datetime.datetime(2018, 4, 9, 4, 0),\n", + " datetime.datetime(2018, 4, 9, 5, 0),\n", + " datetime.datetime(2018, 4, 9, 6, 0),\n", + " datetime.datetime(2018, 4, 9, 7, 0),\n", + " datetime.datetime(2018, 4, 9, 8, 0),\n", + " datetime.datetime(2018, 4, 9, 9, 0),\n", + " datetime.datetime(2018, 4, 9, 10, 0),\n", + " datetime.datetime(2018, 4, 9, 11, 0),\n", + " datetime.datetime(2018, 4, 9, 12, 0),\n", + " datetime.datetime(2018, 4, 9, 13, 0),\n", + " datetime.datetime(2018, 4, 9, 14, 0),\n", + " datetime.datetime(2018, 4, 9, 15, 0),\n", + " datetime.datetime(2018, 4, 9, 16, 0),\n", + " datetime.datetime(2018, 4, 9, 17, 0),\n", + " datetime.datetime(2018, 4, 9, 18, 0),\n", + " datetime.datetime(2018, 4, 9, 19, 0),\n", + " datetime.datetime(2018, 4, 9, 20, 0),\n", + " datetime.datetime(2018, 4, 9, 21, 0),\n", + " datetime.datetime(2018, 4, 9, 22, 0),\n", + " datetime.datetime(2018, 4, 9, 23, 0),\n", + " datetime.datetime(2018, 4, 10, 0, 0),\n", + " datetime.datetime(2018, 4, 10, 1, 0),\n", + " datetime.datetime(2018, 4, 10, 2, 0),\n", + " datetime.datetime(2018, 4, 10, 3, 0),\n", + " datetime.datetime(2018, 4, 10, 4, 0),\n", + " datetime.datetime(2018, 4, 10, 5, 0),\n", + " datetime.datetime(2018, 4, 10, 6, 0),\n", + " datetime.datetime(2018, 4, 10, 7, 0),\n", + " datetime.datetime(2018, 4, 10, 8, 0),\n", + " datetime.datetime(2018, 4, 10, 9, 0),\n", + " datetime.datetime(2018, 4, 10, 10, 0),\n", + " datetime.datetime(2018, 4, 10, 11, 0),\n", + " datetime.datetime(2018, 4, 10, 12, 0),\n", + " datetime.datetime(2018, 4, 10, 13, 0),\n", + " datetime.datetime(2018, 4, 10, 14, 0),\n", + " datetime.datetime(2018, 4, 10, 15, 0),\n", + " datetime.datetime(2018, 4, 10, 16, 0),\n", + " datetime.datetime(2018, 4, 10, 17, 0),\n", + " datetime.datetime(2018, 4, 10, 18, 0),\n", + " datetime.datetime(2018, 4, 10, 19, 0),\n", + " datetime.datetime(2018, 4, 10, 20, 0),\n", + " datetime.datetime(2018, 4, 10, 21, 0),\n", + " datetime.datetime(2018, 4, 10, 22, 0),\n", + " datetime.datetime(2018, 4, 10, 23, 0),\n", + " datetime.datetime(2018, 4, 11, 0, 0),\n", + " datetime.datetime(2018, 4, 11, 1, 0),\n", + " datetime.datetime(2018, 4, 11, 2, 0),\n", + " datetime.datetime(2018, 4, 11, 3, 0),\n", + " datetime.datetime(2018, 4, 11, 4, 0),\n", + " datetime.datetime(2018, 4, 11, 5, 0),\n", + " datetime.datetime(2018, 4, 11, 6, 0),\n", + " datetime.datetime(2018, 4, 11, 7, 0),\n", + " datetime.datetime(2018, 4, 11, 8, 0),\n", + " datetime.datetime(2018, 4, 11, 9, 0),\n", + " datetime.datetime(2018, 4, 11, 10, 0),\n", + " datetime.datetime(2018, 4, 11, 11, 0),\n", + " datetime.datetime(2018, 4, 11, 12, 0),\n", + " datetime.datetime(2018, 4, 11, 13, 0),\n", + " datetime.datetime(2018, 4, 11, 14, 0),\n", + " datetime.datetime(2018, 4, 11, 15, 0),\n", + " datetime.datetime(2018, 4, 11, 16, 0),\n", + " datetime.datetime(2018, 4, 11, 17, 0),\n", + " datetime.datetime(2018, 4, 11, 18, 0),\n", + " datetime.datetime(2018, 4, 11, 19, 0),\n", + " datetime.datetime(2018, 4, 11, 20, 0),\n", + " datetime.datetime(2018, 4, 11, 21, 0),\n", + " datetime.datetime(2018, 4, 11, 22, 0),\n", + " datetime.datetime(2018, 4, 11, 23, 0),\n", + " datetime.datetime(2018, 4, 12, 0, 0),\n", + " datetime.datetime(2018, 4, 12, 1, 0),\n", + " datetime.datetime(2018, 4, 12, 2, 0),\n", + " datetime.datetime(2018, 4, 12, 3, 0),\n", + " datetime.datetime(2018, 4, 12, 4, 0),\n", + " datetime.datetime(2018, 4, 12, 5, 0),\n", + " datetime.datetime(2018, 4, 12, 6, 0),\n", + " datetime.datetime(2018, 4, 12, 7, 0),\n", + " datetime.datetime(2018, 4, 12, 8, 0),\n", + " datetime.datetime(2018, 4, 12, 9, 0),\n", + " datetime.datetime(2018, 4, 12, 10, 0),\n", + " datetime.datetime(2018, 4, 12, 11, 0),\n", + " datetime.datetime(2018, 4, 12, 12, 0),\n", + " datetime.datetime(2018, 4, 12, 13, 0),\n", + " datetime.datetime(2018, 4, 12, 14, 0),\n", + " datetime.datetime(2018, 4, 12, 15, 0),\n", + " datetime.datetime(2018, 4, 12, 16, 0),\n", + " datetime.datetime(2018, 4, 12, 17, 0),\n", + " datetime.datetime(2018, 4, 12, 18, 0),\n", + " datetime.datetime(2018, 4, 12, 19, 0),\n", + " datetime.datetime(2018, 4, 12, 20, 0),\n", + " datetime.datetime(2018, 4, 12, 21, 0),\n", + " datetime.datetime(2018, 4, 12, 22, 0),\n", + " datetime.datetime(2018, 4, 12, 23, 0),\n", + " datetime.datetime(2018, 4, 13, 0, 0),\n", + " datetime.datetime(2018, 4, 13, 1, 0),\n", + " datetime.datetime(2018, 4, 13, 2, 0),\n", + " datetime.datetime(2018, 4, 13, 3, 0),\n", + " datetime.datetime(2018, 4, 13, 4, 0),\n", + " datetime.datetime(2018, 4, 13, 5, 0),\n", + " datetime.datetime(2018, 4, 13, 6, 0),\n", + " datetime.datetime(2018, 4, 13, 7, 0),\n", + " datetime.datetime(2018, 4, 13, 8, 0),\n", + " datetime.datetime(2018, 4, 13, 9, 0),\n", + " datetime.datetime(2018, 4, 13, 10, 0),\n", + " datetime.datetime(2018, 4, 13, 11, 0),\n", + " datetime.datetime(2018, 4, 13, 12, 0),\n", + " datetime.datetime(2018, 4, 13, 13, 0),\n", + " datetime.datetime(2018, 4, 13, 14, 0),\n", + " datetime.datetime(2018, 4, 13, 15, 0),\n", + " datetime.datetime(2018, 4, 13, 16, 0),\n", + " datetime.datetime(2018, 4, 13, 17, 0),\n", + " datetime.datetime(2018, 4, 13, 18, 0),\n", + " datetime.datetime(2018, 4, 13, 19, 0),\n", + " datetime.datetime(2018, 4, 13, 20, 0),\n", + " datetime.datetime(2018, 4, 13, 21, 0),\n", + " datetime.datetime(2018, 4, 13, 22, 0),\n", + " datetime.datetime(2018, 4, 13, 23, 0),\n", + " datetime.datetime(2018, 4, 14, 0, 0),\n", + " datetime.datetime(2018, 4, 14, 1, 0),\n", + " datetime.datetime(2018, 4, 14, 2, 0),\n", + " datetime.datetime(2018, 4, 14, 3, 0),\n", + " datetime.datetime(2018, 4, 14, 4, 0),\n", + " datetime.datetime(2018, 4, 14, 5, 0),\n", + " datetime.datetime(2018, 4, 14, 6, 0),\n", + " datetime.datetime(2018, 4, 14, 7, 0),\n", + " datetime.datetime(2018, 4, 14, 8, 0),\n", + " datetime.datetime(2018, 4, 14, 9, 0),\n", + " datetime.datetime(2018, 4, 14, 10, 0),\n", + " datetime.datetime(2018, 4, 14, 11, 0),\n", + " datetime.datetime(2018, 4, 14, 12, 0),\n", + " datetime.datetime(2018, 4, 14, 13, 0),\n", + " datetime.datetime(2018, 4, 14, 14, 0),\n", + " datetime.datetime(2018, 4, 14, 15, 0),\n", + " datetime.datetime(2018, 4, 14, 16, 0),\n", + " datetime.datetime(2018, 4, 14, 17, 0),\n", + " datetime.datetime(2018, 4, 14, 18, 0),\n", + " datetime.datetime(2018, 4, 14, 19, 0),\n", + " datetime.datetime(2018, 4, 14, 20, 0),\n", + " datetime.datetime(2018, 4, 14, 21, 0),\n", + " datetime.datetime(2018, 4, 14, 22, 0),\n", + " datetime.datetime(2018, 4, 14, 23, 0),\n", + " datetime.datetime(2018, 4, 15, 0, 0),\n", + " datetime.datetime(2018, 4, 15, 1, 0),\n", + " datetime.datetime(2018, 4, 15, 2, 0),\n", + " datetime.datetime(2018, 4, 15, 3, 0),\n", + " datetime.datetime(2018, 4, 15, 4, 0),\n", + " datetime.datetime(2018, 4, 15, 5, 0),\n", + " datetime.datetime(2018, 4, 15, 6, 0),\n", + " datetime.datetime(2018, 4, 15, 7, 0),\n", + " datetime.datetime(2018, 4, 15, 8, 0),\n", + " datetime.datetime(2018, 4, 15, 9, 0),\n", + " datetime.datetime(2018, 4, 15, 10, 0),\n", + " datetime.datetime(2018, 4, 15, 11, 0),\n", + " datetime.datetime(2018, 4, 15, 12, 0),\n", + " datetime.datetime(2018, 4, 15, 13, 0),\n", + " datetime.datetime(2018, 4, 15, 14, 0),\n", + " datetime.datetime(2018, 4, 15, 15, 0),\n", + " datetime.datetime(2018, 4, 15, 16, 0),\n", + " datetime.datetime(2018, 4, 15, 17, 0),\n", + " datetime.datetime(2018, 4, 15, 18, 0),\n", + " datetime.datetime(2018, 4, 15, 19, 0),\n", + " datetime.datetime(2018, 4, 15, 20, 0),\n", + " datetime.datetime(2018, 4, 15, 21, 0),\n", + " datetime.datetime(2018, 4, 15, 22, 0),\n", + " datetime.datetime(2018, 4, 15, 23, 0),\n", + " datetime.datetime(2018, 4, 16, 0, 0),\n", + " datetime.datetime(2018, 4, 16, 1, 0),\n", + " datetime.datetime(2018, 4, 16, 2, 0),\n", + " datetime.datetime(2018, 4, 16, 3, 0),\n", + " datetime.datetime(2018, 4, 16, 4, 0),\n", + " datetime.datetime(2018, 4, 16, 5, 0),\n", + " datetime.datetime(2018, 4, 16, 6, 0),\n", + " datetime.datetime(2018, 4, 16, 7, 0),\n", + " datetime.datetime(2018, 4, 16, 8, 0),\n", + " datetime.datetime(2018, 4, 16, 9, 0),\n", + " datetime.datetime(2018, 4, 16, 10, 0),\n", + " datetime.datetime(2018, 4, 16, 11, 0),\n", + " datetime.datetime(2018, 4, 16, 12, 0),\n", + " datetime.datetime(2018, 4, 16, 13, 0),\n", + " datetime.datetime(2018, 4, 16, 14, 0),\n", + " datetime.datetime(2018, 4, 16, 15, 0),\n", + " datetime.datetime(2018, 4, 16, 16, 0),\n", + " datetime.datetime(2018, 4, 16, 17, 0),\n", + " datetime.datetime(2018, 4, 16, 18, 0),\n", + " datetime.datetime(2018, 4, 16, 19, 0),\n", + " datetime.datetime(2018, 4, 16, 20, 0),\n", + " datetime.datetime(2018, 4, 16, 21, 0),\n", + " datetime.datetime(2018, 4, 16, 22, 0),\n", + " datetime.datetime(2018, 4, 16, 23, 0),\n", + " datetime.datetime(2018, 4, 17, 0, 0),\n", + " datetime.datetime(2018, 4, 17, 1, 0),\n", + " datetime.datetime(2018, 4, 17, 2, 0),\n", + " datetime.datetime(2018, 4, 17, 3, 0),\n", + " datetime.datetime(2018, 4, 17, 4, 0),\n", + " datetime.datetime(2018, 4, 17, 5, 0),\n", + " datetime.datetime(2018, 4, 17, 6, 0),\n", + " datetime.datetime(2018, 4, 17, 7, 0),\n", + " datetime.datetime(2018, 4, 17, 8, 0),\n", + " datetime.datetime(2018, 4, 17, 9, 0),\n", + " datetime.datetime(2018, 4, 17, 10, 0),\n", + " datetime.datetime(2018, 4, 17, 11, 0),\n", + " datetime.datetime(2018, 4, 17, 12, 0),\n", + " datetime.datetime(2018, 4, 17, 13, 0),\n", + " datetime.datetime(2018, 4, 17, 14, 0),\n", + " datetime.datetime(2018, 4, 17, 15, 0),\n", + " datetime.datetime(2018, 4, 17, 16, 0),\n", + " datetime.datetime(2018, 4, 17, 17, 0),\n", + " datetime.datetime(2018, 4, 17, 18, 0),\n", + " datetime.datetime(2018, 4, 17, 19, 0),\n", + " datetime.datetime(2018, 4, 17, 20, 0),\n", + " datetime.datetime(2018, 4, 17, 21, 0),\n", + " datetime.datetime(2018, 4, 17, 22, 0),\n", + " datetime.datetime(2018, 4, 17, 23, 0),\n", + " datetime.datetime(2018, 4, 18, 0, 0),\n", + " datetime.datetime(2018, 4, 18, 1, 0),\n", + " datetime.datetime(2018, 4, 18, 2, 0),\n", + " datetime.datetime(2018, 4, 18, 3, 0),\n", + " datetime.datetime(2018, 4, 18, 4, 0),\n", + " datetime.datetime(2018, 4, 18, 5, 0),\n", + " datetime.datetime(2018, 4, 18, 6, 0),\n", + " datetime.datetime(2018, 4, 18, 7, 0),\n", + " datetime.datetime(2018, 4, 18, 8, 0),\n", + " datetime.datetime(2018, 4, 18, 9, 0),\n", + " datetime.datetime(2018, 4, 18, 10, 0),\n", + " datetime.datetime(2018, 4, 18, 11, 0),\n", + " datetime.datetime(2018, 4, 18, 12, 0),\n", + " datetime.datetime(2018, 4, 18, 13, 0),\n", + " datetime.datetime(2018, 4, 18, 14, 0),\n", + " datetime.datetime(2018, 4, 18, 15, 0),\n", + " datetime.datetime(2018, 4, 18, 16, 0),\n", + " datetime.datetime(2018, 4, 18, 17, 0),\n", + " datetime.datetime(2018, 4, 18, 18, 0),\n", + " datetime.datetime(2018, 4, 18, 19, 0),\n", + " datetime.datetime(2018, 4, 18, 20, 0),\n", + " datetime.datetime(2018, 4, 18, 21, 0),\n", + " datetime.datetime(2018, 4, 18, 22, 0),\n", + " datetime.datetime(2018, 4, 18, 23, 0),\n", + " datetime.datetime(2018, 4, 19, 0, 0),\n", + " datetime.datetime(2018, 4, 19, 1, 0),\n", + " datetime.datetime(2018, 4, 19, 2, 0),\n", + " datetime.datetime(2018, 4, 19, 3, 0),\n", + " datetime.datetime(2018, 4, 19, 4, 0),\n", + " datetime.datetime(2018, 4, 19, 5, 0),\n", + " datetime.datetime(2018, 4, 19, 6, 0),\n", + " datetime.datetime(2018, 4, 19, 7, 0),\n", + " datetime.datetime(2018, 4, 19, 8, 0),\n", + " datetime.datetime(2018, 4, 19, 9, 0),\n", + " datetime.datetime(2018, 4, 19, 10, 0),\n", + " datetime.datetime(2018, 4, 19, 11, 0),\n", + " datetime.datetime(2018, 4, 19, 12, 0),\n", + " datetime.datetime(2018, 4, 19, 13, 0),\n", + " datetime.datetime(2018, 4, 19, 14, 0),\n", + " datetime.datetime(2018, 4, 19, 15, 0),\n", + " datetime.datetime(2018, 4, 19, 16, 0),\n", + " datetime.datetime(2018, 4, 19, 17, 0),\n", + " datetime.datetime(2018, 4, 19, 18, 0),\n", + " datetime.datetime(2018, 4, 19, 19, 0),\n", + " datetime.datetime(2018, 4, 19, 20, 0),\n", + " datetime.datetime(2018, 4, 19, 21, 0),\n", + " datetime.datetime(2018, 4, 19, 22, 0),\n", + " datetime.datetime(2018, 4, 19, 23, 0),\n", + " datetime.datetime(2018, 4, 20, 0, 0),\n", + " datetime.datetime(2018, 4, 20, 1, 0),\n", + " datetime.datetime(2018, 4, 20, 2, 0),\n", + " datetime.datetime(2018, 4, 20, 3, 0),\n", + " datetime.datetime(2018, 4, 20, 4, 0),\n", + " datetime.datetime(2018, 4, 20, 5, 0),\n", + " datetime.datetime(2018, 4, 20, 6, 0),\n", + " datetime.datetime(2018, 4, 20, 7, 0),\n", + " datetime.datetime(2018, 4, 20, 8, 0),\n", + " datetime.datetime(2018, 4, 20, 9, 0),\n", + " datetime.datetime(2018, 4, 20, 10, 0),\n", + " datetime.datetime(2018, 4, 20, 11, 0),\n", + " datetime.datetime(2018, 4, 20, 12, 0),\n", + " datetime.datetime(2018, 4, 20, 13, 0),\n", + " datetime.datetime(2018, 4, 20, 14, 0),\n", + " datetime.datetime(2018, 4, 20, 15, 0),\n", + " datetime.datetime(2018, 4, 20, 16, 0),\n", + " datetime.datetime(2018, 4, 20, 17, 0),\n", + " datetime.datetime(2018, 4, 20, 18, 0),\n", + " datetime.datetime(2018, 4, 20, 19, 0),\n", + " datetime.datetime(2018, 4, 20, 20, 0),\n", + " datetime.datetime(2018, 4, 20, 21, 0),\n", + " datetime.datetime(2018, 4, 20, 22, 0),\n", + " datetime.datetime(2018, 4, 20, 23, 0),\n", + " datetime.datetime(2018, 4, 21, 0, 0),\n", + " datetime.datetime(2018, 4, 21, 1, 0),\n", + " datetime.datetime(2018, 4, 21, 2, 0),\n", + " datetime.datetime(2018, 4, 21, 3, 0),\n", + " datetime.datetime(2018, 4, 21, 4, 0),\n", + " datetime.datetime(2018, 4, 21, 5, 0),\n", + " datetime.datetime(2018, 4, 21, 6, 0),\n", + " datetime.datetime(2018, 4, 21, 7, 0),\n", + " datetime.datetime(2018, 4, 21, 8, 0),\n", + " datetime.datetime(2018, 4, 21, 9, 0),\n", + " datetime.datetime(2018, 4, 21, 10, 0),\n", + " datetime.datetime(2018, 4, 21, 11, 0),\n", + " datetime.datetime(2018, 4, 21, 12, 0),\n", + " datetime.datetime(2018, 4, 21, 13, 0),\n", + " datetime.datetime(2018, 4, 21, 14, 0),\n", + " datetime.datetime(2018, 4, 21, 15, 0),\n", + " datetime.datetime(2018, 4, 21, 16, 0),\n", + " datetime.datetime(2018, 4, 21, 17, 0),\n", + " datetime.datetime(2018, 4, 21, 18, 0),\n", + " datetime.datetime(2018, 4, 21, 19, 0),\n", + " datetime.datetime(2018, 4, 21, 20, 0),\n", + " datetime.datetime(2018, 4, 21, 21, 0),\n", + " datetime.datetime(2018, 4, 21, 22, 0),\n", + " datetime.datetime(2018, 4, 21, 23, 0),\n", + " datetime.datetime(2018, 4, 22, 0, 0),\n", + " datetime.datetime(2018, 4, 22, 1, 0),\n", + " datetime.datetime(2018, 4, 22, 2, 0),\n", + " datetime.datetime(2018, 4, 22, 3, 0),\n", + " datetime.datetime(2018, 4, 22, 4, 0),\n", + " datetime.datetime(2018, 4, 22, 5, 0),\n", + " datetime.datetime(2018, 4, 22, 6, 0),\n", + " datetime.datetime(2018, 4, 22, 7, 0),\n", + " datetime.datetime(2018, 4, 22, 8, 0),\n", + " datetime.datetime(2018, 4, 22, 9, 0),\n", + " datetime.datetime(2018, 4, 22, 10, 0),\n", + " datetime.datetime(2018, 4, 22, 11, 0),\n", + " datetime.datetime(2018, 4, 22, 12, 0),\n", + " datetime.datetime(2018, 4, 22, 13, 0),\n", + " datetime.datetime(2018, 4, 22, 14, 0),\n", + " datetime.datetime(2018, 4, 22, 15, 0),\n", + " datetime.datetime(2018, 4, 22, 16, 0),\n", + " datetime.datetime(2018, 4, 22, 17, 0),\n", + " datetime.datetime(2018, 4, 22, 18, 0),\n", + " datetime.datetime(2018, 4, 22, 19, 0),\n", + " datetime.datetime(2018, 4, 22, 20, 0),\n", + " datetime.datetime(2018, 4, 22, 21, 0),\n", + " datetime.datetime(2018, 4, 22, 22, 0),\n", + " datetime.datetime(2018, 4, 22, 23, 0),\n", + " datetime.datetime(2018, 4, 23, 0, 0),\n", + " datetime.datetime(2018, 4, 23, 1, 0),\n", + " datetime.datetime(2018, 4, 23, 2, 0),\n", + " datetime.datetime(2018, 4, 23, 3, 0),\n", + " datetime.datetime(2018, 4, 23, 4, 0),\n", + " datetime.datetime(2018, 4, 23, 5, 0),\n", + " datetime.datetime(2018, 4, 23, 6, 0),\n", + " datetime.datetime(2018, 4, 23, 7, 0),\n", + " datetime.datetime(2018, 4, 23, 8, 0),\n", + " datetime.datetime(2018, 4, 23, 9, 0),\n", + " datetime.datetime(2018, 4, 23, 10, 0),\n", + " datetime.datetime(2018, 4, 23, 11, 0),\n", + " datetime.datetime(2018, 4, 23, 12, 0),\n", + " datetime.datetime(2018, 4, 23, 13, 0),\n", + " datetime.datetime(2018, 4, 23, 14, 0),\n", + " datetime.datetime(2018, 4, 23, 15, 0),\n", + " datetime.datetime(2018, 4, 23, 16, 0),\n", + " datetime.datetime(2018, 4, 23, 17, 0),\n", + " datetime.datetime(2018, 4, 23, 18, 0),\n", + " datetime.datetime(2018, 4, 23, 19, 0),\n", + " datetime.datetime(2018, 4, 23, 20, 0),\n", + " datetime.datetime(2018, 4, 23, 21, 0),\n", + " datetime.datetime(2018, 4, 23, 22, 0),\n", + " datetime.datetime(2018, 4, 23, 23, 0),\n", + " datetime.datetime(2018, 4, 24, 0, 0),\n", + " datetime.datetime(2018, 4, 24, 1, 0),\n", + " datetime.datetime(2018, 4, 24, 2, 0),\n", + " datetime.datetime(2018, 4, 24, 3, 0),\n", + " datetime.datetime(2018, 4, 24, 4, 0),\n", + " datetime.datetime(2018, 4, 24, 5, 0),\n", + " datetime.datetime(2018, 4, 24, 6, 0),\n", + " datetime.datetime(2018, 4, 24, 7, 0),\n", + " datetime.datetime(2018, 4, 24, 8, 0),\n", + " datetime.datetime(2018, 4, 24, 9, 0),\n", + " datetime.datetime(2018, 4, 24, 10, 0),\n", + " datetime.datetime(2018, 4, 24, 11, 0),\n", + " datetime.datetime(2018, 4, 24, 12, 0),\n", + " datetime.datetime(2018, 4, 24, 13, 0),\n", + " datetime.datetime(2018, 4, 24, 14, 0),\n", + " datetime.datetime(2018, 4, 24, 15, 0),\n", + " datetime.datetime(2018, 4, 24, 16, 0),\n", + " datetime.datetime(2018, 4, 24, 17, 0),\n", + " datetime.datetime(2018, 4, 24, 18, 0),\n", + " datetime.datetime(2018, 4, 24, 19, 0),\n", + " datetime.datetime(2018, 4, 24, 20, 0),\n", + " datetime.datetime(2018, 4, 24, 21, 0),\n", + " datetime.datetime(2018, 4, 24, 22, 0),\n", + " datetime.datetime(2018, 4, 24, 23, 0),\n", + " datetime.datetime(2018, 4, 25, 0, 0),\n", + " datetime.datetime(2018, 4, 25, 1, 0),\n", + " datetime.datetime(2018, 4, 25, 2, 0),\n", + " datetime.datetime(2018, 4, 25, 3, 0),\n", + " datetime.datetime(2018, 4, 25, 4, 0),\n", + " datetime.datetime(2018, 4, 25, 5, 0),\n", + " datetime.datetime(2018, 4, 25, 6, 0),\n", + " datetime.datetime(2018, 4, 25, 7, 0),\n", + " datetime.datetime(2018, 4, 25, 8, 0),\n", + " datetime.datetime(2018, 4, 25, 9, 0),\n", + " datetime.datetime(2018, 4, 25, 10, 0),\n", + " datetime.datetime(2018, 4, 25, 11, 0),\n", + " datetime.datetime(2018, 4, 25, 12, 0),\n", + " datetime.datetime(2018, 4, 25, 13, 0),\n", + " datetime.datetime(2018, 4, 25, 14, 0),\n", + " datetime.datetime(2018, 4, 25, 15, 0),\n", + " datetime.datetime(2018, 4, 25, 16, 0),\n", + " datetime.datetime(2018, 4, 25, 17, 0),\n", + " datetime.datetime(2018, 4, 25, 18, 0),\n", + " datetime.datetime(2018, 4, 25, 19, 0),\n", + " datetime.datetime(2018, 4, 25, 20, 0),\n", + " datetime.datetime(2018, 4, 25, 21, 0),\n", + " datetime.datetime(2018, 4, 25, 22, 0),\n", + " datetime.datetime(2018, 4, 25, 23, 0),\n", + " datetime.datetime(2018, 4, 26, 0, 0),\n", + " datetime.datetime(2018, 4, 26, 1, 0),\n", + " datetime.datetime(2018, 4, 26, 2, 0),\n", + " datetime.datetime(2018, 4, 26, 3, 0),\n", + " datetime.datetime(2018, 4, 26, 4, 0),\n", + " datetime.datetime(2018, 4, 26, 5, 0),\n", + " datetime.datetime(2018, 4, 26, 6, 0),\n", + " datetime.datetime(2018, 4, 26, 7, 0),\n", + " datetime.datetime(2018, 4, 26, 8, 0),\n", + " datetime.datetime(2018, 4, 26, 9, 0),\n", + " datetime.datetime(2018, 4, 26, 10, 0),\n", + " datetime.datetime(2018, 4, 26, 11, 0),\n", + " datetime.datetime(2018, 4, 26, 12, 0),\n", + " datetime.datetime(2018, 4, 26, 13, 0),\n", + " datetime.datetime(2018, 4, 26, 14, 0),\n", + " datetime.datetime(2018, 4, 26, 15, 0),\n", + " datetime.datetime(2018, 4, 26, 16, 0),\n", + " datetime.datetime(2018, 4, 26, 17, 0),\n", + " datetime.datetime(2018, 4, 26, 18, 0),\n", + " datetime.datetime(2018, 4, 26, 19, 0),\n", + " datetime.datetime(2018, 4, 26, 20, 0),\n", + " datetime.datetime(2018, 4, 26, 21, 0),\n", + " datetime.datetime(2018, 4, 26, 22, 0),\n", + " datetime.datetime(2018, 4, 26, 23, 0),\n", + " datetime.datetime(2018, 4, 27, 0, 0),\n", + " datetime.datetime(2018, 4, 27, 1, 0),\n", + " datetime.datetime(2018, 4, 27, 2, 0),\n", + " datetime.datetime(2018, 4, 27, 3, 0),\n", + " datetime.datetime(2018, 4, 27, 4, 0),\n", + " datetime.datetime(2018, 4, 27, 5, 0),\n", + " datetime.datetime(2018, 4, 27, 6, 0),\n", + " datetime.datetime(2018, 4, 27, 7, 0),\n", + " datetime.datetime(2018, 4, 27, 8, 0),\n", + " datetime.datetime(2018, 4, 27, 9, 0),\n", + " datetime.datetime(2018, 4, 27, 10, 0),\n", + " datetime.datetime(2018, 4, 27, 11, 0),\n", + " datetime.datetime(2018, 4, 27, 12, 0),\n", + " datetime.datetime(2018, 4, 27, 13, 0),\n", + " datetime.datetime(2018, 4, 27, 14, 0),\n", + " datetime.datetime(2018, 4, 27, 15, 0),\n", + " datetime.datetime(2018, 4, 27, 16, 0),\n", + " datetime.datetime(2018, 4, 27, 17, 0),\n", + " datetime.datetime(2018, 4, 27, 18, 0),\n", + " datetime.datetime(2018, 4, 27, 19, 0),\n", + " datetime.datetime(2018, 4, 27, 20, 0),\n", + " datetime.datetime(2018, 4, 27, 21, 0),\n", + " datetime.datetime(2018, 4, 27, 22, 0),\n", + " datetime.datetime(2018, 4, 27, 23, 0),\n", + " datetime.datetime(2018, 4, 28, 0, 0),\n", + " datetime.datetime(2018, 4, 28, 1, 0),\n", + " datetime.datetime(2018, 4, 28, 2, 0),\n", + " datetime.datetime(2018, 4, 28, 3, 0),\n", + " datetime.datetime(2018, 4, 28, 4, 0),\n", + " datetime.datetime(2018, 4, 28, 5, 0),\n", + " datetime.datetime(2018, 4, 28, 6, 0),\n", + " datetime.datetime(2018, 4, 28, 7, 0),\n", + " 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)]" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "exp_interp_nes = open_netcdf(path=exp_path, info=True, parallel_method='X')\n", - "exp_interp_nes" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], "source": [ "exp_interp_nes.time" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -14559,7 +2878,7 @@ "{'data': array([0]), 'units': ''}" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -14570,7 +2889,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -14630,7 +2949,7 @@ " 'axis': 'Y'}" ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -14641,7 +2960,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -14716,7 +3035,7 @@ " 'axis': 'X'}" ] }, - "execution_count": 20, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -14727,24 +3046,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Rank 000: Loading grid_edge_latitude var (1/6)\n", - "Rank 000: Loaded grid_edge_latitude var ((1125,))\n", - "Rank 000: Loading grid_edge_longitude var (2/6)\n", - "Rank 000: Loaded grid_edge_longitude var ((1125,))\n", - "Rank 000: Loading model_centre_latitude var (3/6)\n", - "Rank 000: Loaded model_centre_latitude var ((211, 351))\n", - "Rank 000: Loading model_centre_longitude var (4/6)\n", - "Rank 000: Loaded model_centre_longitude var ((211, 351))\n", - "Rank 000: Loading sconco3 var (5/6)\n", + "Rank 000: Loading sconco3 var (1/2)\n", "Rank 000: Loaded sconco3 var ((175, 720))\n", - "Rank 000: Loading station_reference var (6/6)\n", + "Rank 000: Loading station_reference var (2/2)\n", "Rank 000: Loaded station_reference var ((175,))\n" ] } @@ -14760,16 +3071,9 @@ "### 2.2. Write dataset" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Write with NES" - ] - }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -14793,7 +3097,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_providentia.py:587: UserWarning: WARNING!!! Providentia datasets cannot be written in parallel yet. Changing to serial mode.\n", + "/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" ] } @@ -14801,588 +3105,6 @@ "source": [ "exp_interp_nes.to_netcdf('prv_exp_file.nc', info=True)" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Reopen with xarray" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:                 (time: 720, station: 175, model_latitude: 211, model_longitude: 351, grid_edge: 1125)\n",
-       "Coordinates:\n",
-       "  * time                    (time) datetime64[ns] 2018-04-01 ... 2018-04-30T2...\n",
-       "  * station                 (station) float64 0.0 1.0 2.0 ... 172.0 173.0 174.0\n",
-       "Dimensions without coordinates: model_latitude, model_longitude, grid_edge\n",
-       "Data variables:\n",
-       "    lat                     (station) float64 -64.24 -54.85 ... 21.57 -34.35\n",
-       "    lon                     (station) float64 -56.62 -68.31 ... 103.5 18.49\n",
-       "    model_centre_longitude  (model_latitude, model_longitude) float64 -25.0 ....\n",
-       "    model_centre_latitude   (model_latitude, model_longitude) float64 30.0 .....\n",
-       "    grid_edge_longitude     (grid_edge) float64 -25.1 -25.1 ... -24.9 -25.1\n",
-       "    grid_edge_latitude      (grid_edge) float64 29.9 30.1 30.3 ... 29.9 29.9\n",
-       "    sconco3                 (station, time) float32 ...\n",
-       "    station_reference       (station) object 'AR0001R_UVP' ... 'ZA0001G_UVP'\n",
-       "Attributes:\n",
-       "    title:          Inverse distance weighting (4 neighbours) interpolated ca...\n",
-       "    institution:    Barcelona Supercomputing Center\n",
-       "    source:         Experiment cams61_chimere_ph2\n",
-       "    creator_name:   Dene R. Bowdalo\n",
-       "    creator_email:  dene.bowdalo@bsc.es\n",
-       "    conventions:    CF-1.7\n",
-       "    data_version:   1.0\n",
-       "    history:        Thu Feb 11 10:19:01 2021: ncks -O --dfl_lvl 1 /gpfs/proje...\n",
-       "    NCO:            4.7.2\n",
-       "    Conventions:    CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 720, station: 175, model_latitude: 211, model_longitude: 351, grid_edge: 1125)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2018-04-01 ... 2018-04-30T2...\n", - " * station (station) float64 0.0 1.0 2.0 ... 172.0 173.0 174.0\n", - "Dimensions without coordinates: model_latitude, model_longitude, grid_edge\n", - "Data variables:\n", - " lat (station) float64 ...\n", - " lon (station) float64 ...\n", - " model_centre_longitude (model_latitude, model_longitude) float64 ...\n", - " model_centre_latitude (model_latitude, model_longitude) float64 ...\n", - " grid_edge_longitude (grid_edge) float64 ...\n", - " grid_edge_latitude (grid_edge) float64 ...\n", - " sconco3 (station, time) float32 ...\n", - " station_reference (station) object ...\n", - "Attributes:\n", - " title: Inverse distance weighting (4 neighbours) interpolated ca...\n", - " institution: Barcelona Supercomputing Center\n", - " source: Experiment cams61_chimere_ph2\n", - " creator_name: Dene R. Bowdalo\n", - " creator_email: dene.bowdalo@bsc.es\n", - " conventions: CF-1.7\n", - " data_version: 1.0\n", - " history: Thu Feb 11 10:19:01 2021: ncks -O --dfl_lvl 1 /gpfs/proje...\n", - " NCO: 4.7.2\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('prv_exp_file.nc')" - ] } ], "metadata": { diff --git a/tutorials/2.Creation/2.1.Create_Regular.ipynb b/tutorials/2.Creation/2.1.Create_Regular.ipynb index f2d3455856d85d686d6f9af1d17ae7136f5dfa16..8c0c6e0ae13f3b276e00e5c923300ec65792f0b4 100644 --- a/tutorials/2.Creation/2.1.Create_Regular.ipynb +++ b/tutorials/2.Creation/2.1.Create_Regular.ipynb @@ -13,7 +13,6 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", "from nes import *" ] }, @@ -45,7 +44,8 @@ "text": [ "Rank 000: Creating regular_grid.nc\n", "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n" + "Rank 000: Dimensions done\n", + "Rank 000: Cell measures done\n" ] } ], @@ -60,382 +60,9 @@ "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:  (time: 1, lev: 1, lat: 10, lon: 10)\n",
-       "Coordinates:\n",
-       "  * time     (time) datetime64[ns] 1996-12-31\n",
-       "  * lev      (lev) float64 0.0\n",
-       "  * lat      (lat) float64 41.15 41.25 41.35 41.45 ... 41.75 41.85 41.95 42.05\n",
-       "  * lon      (lon) float64 1.85 1.95 2.05 2.15 2.25 2.35 2.45 2.55 2.65 2.75\n",
-       "Data variables:\n",
-       "    crs      |S1 b''\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], "text/plain": [ - "\n", - "Dimensions: (time: 1, lev: 1, lat: 10, lon: 10)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 1996-12-31\n", - " * lev (lev) float64 0.0\n", - " * lat (lat) float64 41.15 41.25 41.35 41.45 ... 41.75 41.85 41.95 42.05\n", - " * lon (lon) float64 1.85 1.95 2.05 2.15 2.25 2.35 2.45 2.55 2.65 2.75\n", - "Data variables:\n", - " crs |S1 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" + "{'data': array([41.15, 41.25, 41.35, 41.45, 41.55, 41.65, 41.75, 41.85, 41.95,\n", + " 42.05])}" ] }, "execution_count": 4, @@ -444,7 +71,27 @@ } ], "source": [ - "xr.open_dataset('regular_grid.nc')" + "regular_grid.lat" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'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, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regular_grid.lon" ] } ], diff --git a/tutorials/2.Creation/2.2.Create_Rotated.ipynb b/tutorials/2.Creation/2.2.Create_Rotated.ipynb index 5493bd5cd55484a20f4a78cd6f9c038515d2fb4d..63a85b645eb3831a5b1dc523116d8797a76d7968 100644 --- a/tutorials/2.Creation/2.2.Create_Rotated.ipynb +++ b/tutorials/2.Creation/2.2.Create_Rotated.ipynb @@ -13,7 +13,6 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", "from nes import *" ] }, @@ -46,7 +45,8 @@ "text": [ "Rank 000: Creating rotated_grid.nc\n", "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n" + "Rank 000: Dimensions done\n", + "Rank 000: Cell measures done\n" ] } ], @@ -61,404 +61,38 @@ "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:       (time: 1, lev: 1, rlat: 271, rlon: 351)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 1996-12-31\n",
-       "  * lev           (lev) float64 0.0\n",
-       "  * rlat          (rlat) float64 -27.0 -26.8 -26.6 -26.4 ... 26.4 26.6 26.8 27.0\n",
-       "  * rlon          (rlon) float64 -35.0 -34.8 -34.6 -34.4 ... 34.4 34.6 34.8 35.0\n",
-       "Data variables:\n",
-       "    lat           (rlat, rlon) float64 16.35 16.43 16.52 ... 58.83 58.68 58.53\n",
-       "    lon           (rlat, rlon) float64 -22.18 -22.02 -21.85 ... 88.05 88.23\n",
-       "    rotated_pole  |S1 b''\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], "text/plain": [ - "\n", - "Dimensions: (time: 1, lev: 1, rlat: 271, rlon: 351)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 1996-12-31\n", - " * lev (lev) float64 0.0\n", - " * rlat (rlat) float64 -27.0 -26.8 -26.6 -26.4 ... 26.4 26.6 26.8 27.0\n", - " * rlon (rlon) float64 -35.0 -34.8 -34.6 -34.4 ... 34.4 34.6 34.8 35.0\n", - "Data variables:\n", - " lat (rlat, rlon) float64 ...\n", - " lon (rlat, rlon) float64 ...\n", - " rotated_pole |S1 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" + "{'data': array([-27. , -26.8, -26.6, -26.4, -26.2, -26. , -25.8, -25.6, -25.4,\n", + " -25.2, -25. , -24.8, -24.6, -24.4, -24.2, -24. , -23.8, -23.6,\n", + " -23.4, -23.2, -23. , -22.8, -22.6, -22.4, -22.2, -22. , -21.8,\n", + " -21.6, -21.4, -21.2, -21. , -20.8, -20.6, -20.4, -20.2, -20. ,\n", + " -19.8, -19.6, -19.4, -19.2, -19. , -18.8, -18.6, -18.4, -18.2,\n", + " -18. , -17.8, -17.6, -17.4, -17.2, -17. , -16.8, -16.6, -16.4,\n", + " -16.2, -16. , -15.8, -15.6, -15.4, -15.2, -15. , -14.8, -14.6,\n", + " -14.4, -14.2, -14. , -13.8, -13.6, -13.4, -13.2, -13. , -12.8,\n", + " -12.6, -12.4, -12.2, -12. , -11.8, -11.6, -11.4, -11.2, -11. ,\n", + " -10.8, -10.6, -10.4, -10.2, -10. , -9.8, -9.6, -9.4, -9.2,\n", + " -9. , -8.8, -8.6, -8.4, -8.2, -8. , -7.8, -7.6, -7.4,\n", + " -7.2, -7. , -6.8, -6.6, -6.4, -6.2, -6. , -5.8, -5.6,\n", + " -5.4, -5.2, -5. , -4.8, -4.6, -4.4, -4.2, -4. , -3.8,\n", + " -3.6, -3.4, -3.2, -3. , -2.8, -2.6, -2.4, -2.2, -2. ,\n", + " -1.8, -1.6, -1.4, -1.2, -1. , -0.8, -0.6, -0.4, -0.2,\n", + " 0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6,\n", + " 1.8, 2. , 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4,\n", + " 3.6, 3.8, 4. , 4.2, 4.4, 4.6, 4.8, 5. , 5.2,\n", + " 5.4, 5.6, 5.8, 6. , 6.2, 6.4, 6.6, 6.8, 7. ,\n", + " 7.2, 7.4, 7.6, 7.8, 8. , 8.2, 8.4, 8.6, 8.8,\n", + " 9. , 9.2, 9.4, 9.6, 9.8, 10. , 10.2, 10.4, 10.6,\n", + " 10.8, 11. , 11.2, 11.4, 11.6, 11.8, 12. , 12.2, 12.4,\n", + " 12.6, 12.8, 13. , 13.2, 13.4, 13.6, 13.8, 14. , 14.2,\n", + " 14.4, 14.6, 14.8, 15. , 15.2, 15.4, 15.6, 15.8, 16. ,\n", + " 16.2, 16.4, 16.6, 16.8, 17. , 17.2, 17.4, 17.6, 17.8,\n", + " 18. , 18.2, 18.4, 18.6, 18.8, 19. , 19.2, 19.4, 19.6,\n", + " 19.8, 20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2, 21.4,\n", + " 21.6, 21.8, 22. , 22.2, 22.4, 22.6, 22.8, 23. , 23.2,\n", + " 23.4, 23.6, 23.8, 24. , 24.2, 24.4, 24.6, 24.8, 25. ,\n", + " 25.2, 25.4, 25.6, 25.8, 26. , 26.2, 26.4, 26.6, 26.8,\n", + " 27. ])}" ] }, "execution_count": 4, @@ -467,7 +101,65 @@ } ], "source": [ - "xr.open_dataset('rotated_grid.nc')" + "rotated_grid.rlat" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([-35. , -34.8, -34.6, -34.4, -34.2, -34. , -33.8, -33.6, -33.4,\n", + " -33.2, -33. , -32.8, -32.6, -32.4, -32.2, -32. , -31.8, -31.6,\n", + " -31.4, -31.2, -31. , -30.8, -30.6, -30.4, -30.2, -30. , -29.8,\n", + " -29.6, -29.4, -29.2, -29. , -28.8, -28.6, -28.4, -28.2, -28. ,\n", + " -27.8, -27.6, -27.4, -27.2, -27. , -26.8, -26.6, -26.4, -26.2,\n", + " -26. , -25.8, -25.6, -25.4, -25.2, -25. , -24.8, -24.6, -24.4,\n", + " -24.2, -24. , -23.8, -23.6, -23.4, -23.2, -23. , -22.8, -22.6,\n", + " -22.4, -22.2, -22. , -21.8, -21.6, -21.4, -21.2, -21. , -20.8,\n", + " -20.6, -20.4, -20.2, -20. , -19.8, -19.6, -19.4, -19.2, -19. ,\n", + " -18.8, -18.6, -18.4, -18.2, -18. , -17.8, -17.6, -17.4, -17.2,\n", + " -17. , -16.8, -16.6, -16.4, -16.2, -16. , -15.8, -15.6, -15.4,\n", + " -15.2, -15. , -14.8, -14.6, -14.4, -14.2, -14. , -13.8, -13.6,\n", + " -13.4, -13.2, -13. , -12.8, -12.6, -12.4, -12.2, -12. , -11.8,\n", + " -11.6, -11.4, -11.2, -11. , -10.8, -10.6, -10.4, -10.2, -10. ,\n", + " -9.8, -9.6, -9.4, -9.2, -9. , -8.8, -8.6, -8.4, -8.2,\n", + " -8. , -7.8, -7.6, -7.4, -7.2, -7. , -6.8, -6.6, -6.4,\n", + " -6.2, -6. , -5.8, -5.6, -5.4, -5.2, -5. , -4.8, -4.6,\n", + " -4.4, -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. , -2.8,\n", + " -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2, -1. ,\n", + " -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4, 0.6, 0.8,\n", + " 1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6,\n", + " 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. , 4.2, 4.4,\n", + " 4.6, 4.8, 5. , 5.2, 5.4, 5.6, 5.8, 6. , 6.2,\n", + " 6.4, 6.6, 6.8, 7. , 7.2, 7.4, 7.6, 7.8, 8. ,\n", + " 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6, 9.8,\n", + " 10. , 10.2, 10.4, 10.6, 10.8, 11. , 11.2, 11.4, 11.6,\n", + " 11.8, 12. , 12.2, 12.4, 12.6, 12.8, 13. , 13.2, 13.4,\n", + " 13.6, 13.8, 14. , 14.2, 14.4, 14.6, 14.8, 15. , 15.2,\n", + " 15.4, 15.6, 15.8, 16. , 16.2, 16.4, 16.6, 16.8, 17. ,\n", + " 17.2, 17.4, 17.6, 17.8, 18. , 18.2, 18.4, 18.6, 18.8,\n", + " 19. , 19.2, 19.4, 19.6, 19.8, 20. , 20.2, 20.4, 20.6,\n", + " 20.8, 21. , 21.2, 21.4, 21.6, 21.8, 22. , 22.2, 22.4,\n", + " 22.6, 22.8, 23. , 23.2, 23.4, 23.6, 23.8, 24. , 24.2,\n", + " 24.4, 24.6, 24.8, 25. , 25.2, 25.4, 25.6, 25.8, 26. ,\n", + " 26.2, 26.4, 26.6, 26.8, 27. , 27.2, 27.4, 27.6, 27.8,\n", + " 28. , 28.2, 28.4, 28.6, 28.8, 29. , 29.2, 29.4, 29.6,\n", + " 29.8, 30. , 30.2, 30.4, 30.6, 30.8, 31. , 31.2, 31.4,\n", + " 31.6, 31.8, 32. , 32.2, 32.4, 32.6, 32.8, 33. , 33.2,\n", + " 33.4, 33.6, 33.8, 34. , 34.2, 34.4, 34.6, 34.8, 35. ])}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rotated_grid.rlon" ] } ], diff --git a/tutorials/2.Creation/2.3.Create-Points.ipynb b/tutorials/2.Creation/2.3.Create-Points.ipynb index e55ef7abbc0c5a0723abbddb4f500e1c2acce92f..b06aed5d9053be4ca57293a7f2bcd437e4d20dd0 100644 --- a/tutorials/2.Creation/2.3.Create-Points.ipynb +++ b/tutorials/2.Creation/2.3.Create-Points.ipynb @@ -13,7 +13,6 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", "import pandas as pd\n", "import numpy as np\n", "from nes import *" @@ -177,7 +176,7 @@ } ], "source": [ - "file_path = '/esarchive/scratch/avilanova/software/NES/tutorials/data/XVPCA_info.csv'\n", + "file_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/XVPCA_info.csv'\n", "df = pd.read_csv(file_path)\n", "df" ] @@ -237,6 +236,7 @@ "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", @@ -251,9 +251,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "/gpfs/projects/bsc32/software/suselinux/11/software/NES/0.9.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/nes/nc_projections/points_nes.py:405: UserWarning: WARNING!!! Different data types for variable station_codeInput 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", - "/gpfs/projects/bsc32/software/suselinux/11/software/NES/0.9.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/nes/nc_projections/points_nes.py:405: UserWarning: WARNING!!! Different data types for variable area_classificationInput 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" ] } @@ -262,508 +262,6 @@ "points_grid.to_netcdf('points_grid_1.nc', info=True)" ] }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:              (time: 1, station: 134)\n",
-       "Coordinates:\n",
-       "  * time                 (time) datetime64[ns] 1996-12-31\n",
-       "  * station              (station) float64 0.0 1.0 2.0 3.0 ... 131.0 132.0 133.0\n",
-       "Data variables:\n",
-       "    station_code         (station) object 'ES0266A' 'ES0392A' ... 'ES9994A'\n",
-       "    area_classification  (station) object 'urban-centre' ... 'nan'\n",
-       "    lat                  (station) float64 41.38 41.73 41.57 ... 41.24 42.36\n",
-       "    lon                  (station) float64 2.086 1.839 2.015 ... 1.857 1.459\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 1, station: 134)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 1996-12-31\n", - " * station (station) float64 0.0 1.0 2.0 3.0 ... 131.0 132.0 133.0\n", - "Data variables:\n", - " station_code (station) object ...\n", - " area_classification (station) object ...\n", - " lat (station) float64 ...\n", - " lon (station) float64 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('points_grid_1.nc')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -773,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1237,20 +735,20 @@ "[367 rows x 84 columns]" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "file_path_2 = '/esarchive/scratch/avilanova/software/NES/tutorials/data/Dades_2017.xlsx'\n", + "file_path_2 = '/gpfs/projects/bsc32/models/NES_tutorial_data/Dades_2017.xlsx'\n", "df_2 = pd.read_excel(file_path_2)\n", "df_2" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -1261,7 +759,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -1271,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1288,7 +786,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -1297,7 +795,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1307,6 +805,7 @@ "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", @@ -1325,11 +824,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/gpfs/projects/bsc32/software/suselinux/11/software/NES/0.9.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/nes/nc_projections/points_nes.py:405: UserWarning: WARNING!!! Different data types for variable station_nameInput 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", - "/gpfs/projects/bsc32/software/suselinux/11/software/NES/0.9.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/nes/nc_projections/points_nes.py:405: UserWarning: WARNING!!! Different data types for variable station_codeInput 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", - "/gpfs/projects/bsc32/software/suselinux/11/software/NES/0.9.0-foss-2019b-Python-3.7.4/lib/python3.7/site-packages/nes/nc_projections/points_nes.py:405: UserWarning: WARNING!!! Different data types for variable pm10Input 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" ] } @@ -1337,488 +836,6 @@ "source": [ "points_grid.to_netcdf('points_grid_2.nc', info=True)" ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:       (time: 365, station: 83)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2017-01-01 2017-01-02 ... 2017-12-31\n",
-       "  * station       (station) float64 0.0 1.0 2.0 3.0 4.0 ... 79.0 80.0 81.0 82.0\n",
-       "Data variables:\n",
-       "    station_name  (station) object 'Barcelona (Eixample)' ... 'Vic (Centre Cí...\n",
-       "    station_code  (station) object 'ES1438A' 'ES1928A' ... 'ES9994A' 'ES1874A'\n",
-       "    pm10          (station, time) float64 19.6 27.2 35.7 30.9 ... nan nan nan\n",
-       "    lat           (station) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
-       "    lon           (station) float64 nan nan nan nan nan ... nan nan nan nan nan\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 365, station: 83)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2017-01-01 2017-01-02 ... 2017-12-31\n", - " * station (station) float64 0.0 1.0 2.0 3.0 4.0 ... 79.0 80.0 81.0 82.0\n", - "Data variables:\n", - " station_name (station) object ...\n", - " station_code (station) object ...\n", - " pm10 (station, time) float64 ...\n", - " lat (station) float64 ...\n", - " lon (station) float64 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('points_grid_2.nc')" - ] } ], "metadata": { @@ -1838,6 +855,11 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } } }, "nbformat": 4, 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 2e7020e0ed430fca8de3de6a809a289ec3e8286d..b27f816904df62598613c2fc2e2b26508364a6a9 100644 --- a/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb +++ b/tutorials/2.Creation/2.4.Create_Points_Port_Barcelona.ipynb @@ -13,7 +13,6 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", "import pandas as pd\n", "import numpy as np\n", "from datetime import datetime, timedelta\n", @@ -167,7 +166,7 @@ } ], "source": [ - "file_path = '/esarchive/scratch/avilanova/software/NES/tutorials/data/Dades_Port_Barcelona_2017-2021_corr.xlsx'\n", + "file_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/Dades_Port_Barcelona_2017-2021_corr.xlsx'\n", "df_data = pd.read_excel(file_path, header=3, index_col='Horario: UTC')\n", "df_data" ] @@ -280,7 +279,7 @@ } ], "source": [ - "file_path = '/esarchive/scratch/avilanova/software/NES/tutorials/data/estaciones.csv'\n", + "file_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/estaciones.csv'\n", "df_stations = pd.read_csv(file_path)\n", "df_stations" ] @@ -405,14 +404,11 @@ "Rank 000: Creating points_grid_no2.nc\n", "Rank 000: NetCDF ready to write\n", "Rank 000: Dimensions done\n", - "|S1\n", - "S75\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", - "float64\n", - "\n", "Rank 000: Writing sconcno2 var (2/2)\n", "Rank 000: Var sconcno2 created (2/2)\n" ] @@ -421,7 +417,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:336: UserWarning: WARNING!!! Different data types for variable station_nameInput 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" ] }, @@ -439,422 +435,6 @@ "del points_grid" ] }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:       (time: 43818, station: 2, strlen: 75)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2017-01-01 ... 2021-12-31T17:00:00\n",
-       "  * station       (station) float64 0.0 1.0\n",
-       "Dimensions without coordinates: strlen\n",
-       "Data variables:\n",
-       "    lat           (station) float64 41.37 41.32\n",
-       "    lon           (station) float64 2.185 2.135\n",
-       "    station_name  (station, strlen) object 'N' 'O' '2' '-' 'U' ... '' '' '' ''\n",
-       "    sconcno2      (time, station) float64 64.64 49.08 68.16 ... 12.76 28.66\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 43818, station: 2, strlen: 75)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2017-01-01 ... 2021-12-31T17:00:00\n", - " * station (station) float64 0.0 1.0\n", - "Dimensions without coordinates: strlen\n", - "Data variables:\n", - " lat (station) float64 ...\n", - " lon (station) float64 ...\n", - " station_name (station, strlen) object ...\n", - " sconcno2 (time, station) float64 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('points_grid_no2.nc')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -864,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -874,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1028,7 +608,7 @@ "[43818 rows x 6 columns]" ] }, - "execution_count": 17, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1039,22 +619,73 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 14, "metadata": {}, "outputs": [ { - "ename": "PermissionError", - "evalue": "[Errno 13] Permission denied: b'/esarchive/obs/port_barcelona/port-barcelona/hourly/sconcno2/sconcno2_201701.nc'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mPermissionError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;31m# Save files\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0mpoints_grid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_netcdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnetcdf_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'/sconcno2_{0}{1}.nc'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmonth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzfill\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\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[0m\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mpoints_grid\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;36mto_netcdf\u001b[0;34m(self, path, compression_level, serial, info, chunking)\u001b[0m\n\u001b[1;32m 1946\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1947\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[0;32m-> 1948\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__to_netcdf_py\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchunking\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 1949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1950\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mold_info\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__to_netcdf_py\u001b[0;34m(self, path, chunking)\u001b[0m\n\u001b[1;32m 1882\u001b[0m \u001b[0mnetcdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"NETCDF4\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparallel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcomm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mMPI\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInfo\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[1;32m 1883\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[0;32m-> 1884\u001b[0;31m \u001b[0mnetcdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"NETCDF4\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparallel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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 1885\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1886\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Rank {0:03d}: NetCDF ready to write\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrank\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;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32mnetCDF4/_netCDF4.pyx\u001b[0m in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mPermissionError\u001b[0m: [Errno 13] Permission denied: b'/esarchive/obs/port_barcelona/port-barcelona/hourly/sconcno2/sconcno2_201701.nc'" + "name": "stdout", + "output_type": "stream", + "text": [ + "Done sconcno2_201701.nc\n", + "Done sconcno2_201702.nc\n", + "Done sconcno2_201703.nc\n", + "Done sconcno2_201704.nc\n", + "Done sconcno2_201705.nc\n", + "Done sconcno2_201706.nc\n", + "Done sconcno2_201707.nc\n", + "Done sconcno2_201708.nc\n", + "Done sconcno2_201709.nc\n", + "Done sconcno2_201710.nc\n", + "Done sconcno2_201711.nc\n", + "Done sconcno2_201712.nc\n", + "Done sconcno2_201801.nc\n", + "Done sconcno2_201802.nc\n", + "Done sconcno2_201803.nc\n", + "Done sconcno2_201804.nc\n", + "Done sconcno2_201805.nc\n", + "Done sconcno2_201806.nc\n", + "Done sconcno2_201807.nc\n", + "Done sconcno2_201808.nc\n", + "Done sconcno2_201809.nc\n", + "Done sconcno2_201810.nc\n", + "Done sconcno2_201811.nc\n", + "Done sconcno2_201812.nc\n", + "Done sconcno2_201901.nc\n", + "Done sconcno2_201902.nc\n", + "Done sconcno2_201903.nc\n", + "Done sconcno2_201904.nc\n", + "Done sconcno2_201905.nc\n", + "Done sconcno2_201906.nc\n", + "Done sconcno2_201907.nc\n", + "Done sconcno2_201908.nc\n", + "Done sconcno2_201909.nc\n", + "Done sconcno2_201910.nc\n", + "Done sconcno2_201911.nc\n", + "Done sconcno2_201912.nc\n", + "Done sconcno2_202001.nc\n", + "Done sconcno2_202002.nc\n", + "Done sconcno2_202003.nc\n", + "Done sconcno2_202004.nc\n", + "Done sconcno2_202005.nc\n", + "Done sconcno2_202006.nc\n", + "Done sconcno2_202007.nc\n", + "Done sconcno2_202008.nc\n", + "Done sconcno2_202009.nc\n", + "Done sconcno2_202010.nc\n", + "Done sconcno2_202011.nc\n", + "Done sconcno2_202012.nc\n", + "Done sconcno2_202101.nc\n", + "Done sconcno2_202102.nc\n", + "Done sconcno2_202103.nc\n", + "Done sconcno2_202104.nc\n", + "Done sconcno2_202105.nc\n", + "Done sconcno2_202106.nc\n", + "Done sconcno2_202107.nc\n", + "Done sconcno2_202108.nc\n", + "Done sconcno2_202109.nc\n", + "Done sconcno2_202110.nc\n", + "Done sconcno2_202111.nc\n", + "Done sconcno2_202112.nc\n" ] } ], @@ -1088,839 +719,20 @@ " # Assign metadata\n", " points_grid.variables = metadata\n", " \n", - "\n", " # Making directory\n", - " netcdf_path = '/esarchive/obs/port_barcelona/port-barcelona/hourly/sconcno2'\n", + " netcdf_path = 'port_barcelona/port-barcelona/hourly/sconcno2/'\n", " if not os.path.exists(os.path.dirname(netcdf_path)):\n", " os.makedirs(os.path.dirname(netcdf_path))\n", " \n", + " # To run Providentia, this folder should be moved to:\n", + " # '/esarchive/obs/' as in '/esarchive/obs/port_barcelona/port-barcelona/hourly/sconcno2'\n", + " \n", " # Save files\n", - " points_grid.to_netcdf(netcdf_path + '/sconcno2_{0}{1}.nc'.format(year, str(month).zfill(2)))\n", + " points_grid.to_netcdf(netcdf_path + 'sconcno2_{0}{1}.nc'.format(year, str(month).zfill(2)))\n", " \n", " del points_grid\n", " print('Done sconcno2_{0}{1}.nc'.format(year, str(month).zfill(2)))" ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:       (time: 744, station: 2)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2017-05-01 ... 2017-05-31T23:00:00\n",
-       "  * station       (station) float64 0.0 1.0\n",
-       "Data variables:\n",
-       "    station_name  (station) |S75 b'NO2-UM' b'NO2-ZAL Prat'\n",
-       "    altitude      (station) float64 nan nan\n",
-       "    sconcno2      (time, station) float64 8.77 23.49 1.76 ... 27.11 13.87 30.07\n",
-       "    lat           (station) float64 41.37 41.32\n",
-       "    lon           (station) float64 2.185 2.135\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 744, station: 2)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2017-05-01 ... 2017-05-31T23:00:00\n", - " * station (station) float64 0.0 1.0\n", - "Data variables:\n", - " station_name (station) |S75 ...\n", - " altitude (station) float64 ...\n", - " sconcno2 (time, station) float64 ...\n", - " lat (station) float64 ...\n", - " lon (station) float64 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('/esarchive/obs/port_barcelona/port-barcelona/hourly/sconcno2/sconcno2_201705.nc')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:       (time: 738, station: 2)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2021-12-01 ... 2021-12-31T17:00:00\n",
-       "  * station       (station) float64 0.0 1.0\n",
-       "Data variables:\n",
-       "    station_name  (station) |S75 b'NO2-UM' b'NO2-ZAL Prat'\n",
-       "    altitude      (station) float64 nan nan\n",
-       "    sconcno2      (time, station) float64 13.33 nan 13.42 ... 29.82 12.76 28.66\n",
-       "    lat           (station) float64 41.37 41.32\n",
-       "    lon           (station) float64 2.185 2.135\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 738, station: 2)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2021-12-01 ... 2021-12-31T17:00:00\n", - " * station (station) float64 0.0 1.0\n", - "Data variables:\n", - " station_name (station) |S75 ...\n", - " altitude (station) float64 ...\n", - " sconcno2 (time, station) float64 ...\n", - " lat (station) float64 ...\n", - " lon (station) float64 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('/esarchive/obs/port_barcelona/port-barcelona/hourly/sconcno2/sconcno2_202112.nc')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb b/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb index 65d623e8c48565ecd1279c7d57168bbcac3e44c2..4484f438b7f11ec2c5f219c52347047ef2593807 100644 --- a/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb +++ b/tutorials/2.Creation/2.5.Create_Points_CSIC.ipynb @@ -13,7 +13,6 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", "import pandas as pd\n", "import numpy as np\n", "from datetime import datetime, timedelta\n", @@ -144,7 +143,7 @@ } ], "source": [ - "file_path = '/esarchive/scratch/avilanova/software/NES/tutorials/data/NH3_barcelona_2019_csic.csv'\n", + "file_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/NH3_barcelona_2019_csic.csv'\n", "df_data = pd.read_csv(file_path, index_col='Date-hour in', parse_dates=True)\n", "df_data" ] @@ -209,7 +208,7 @@ } ], "source": [ - "file_path = '/esarchive/scratch/avilanova/software/NES/tutorials/data/NH3_stations_CSIC.csv'\n", + "file_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/NH3_stations_CSIC.csv'\n", "df_stations = pd.read_csv(file_path)\n", "df_stations" ] @@ -371,6 +370,7 @@ "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", @@ -385,7 +385,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes.py:336: UserWarning: WARNING!!! Different data types for variable station_nameInput 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" ] } @@ -395,430 +395,6 @@ "del points_grid" ] }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:       (time: 12, station: 2, strlen: 75)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2019-01-01 2019-02-01 ... 2019-12-01\n",
-       "  * station       (station) float64 0.0 1.0\n",
-       "Dimensions without coordinates: strlen\n",
-       "Data variables:\n",
-       "    lat           (station) float64 41.39 41.4\n",
-       "    lon           (station) float64 2.115 2.153\n",
-       "    station_name  (station, strlen) object 't' 'r' 'a' 'f' 'f' ... '' '' '' ''\n",
-       "    sconcnh3      (time, station) float64 4.989 2.553 3.423 ... 2.511 0.0 0.0\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 12, station: 2, strlen: 75)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2019-01-01 2019-02-01 ... 2019-12-01\n", - " * station (station) float64 0.0 1.0\n", - "Dimensions without coordinates: strlen\n", - "Data variables:\n", - " lat (station) float64 ...\n", - " lon (station) float64 ...\n", - " station_name (station, strlen) object ...\n", - " sconcnh3 (time, station) float64 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('points_grid_nh3.nc')" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -828,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -838,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -981,7 +557,7 @@ "2019-12-01 0.000000 0.000000 12 2019" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -992,7 +568,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1045,812 +621,19 @@ " points_grid.variables = metadata\n", " \n", " # Making directory\n", - " netcdf_path = '/esarchive/obs/csic/csic/monthly/sconcnh3/'\n", + " netcdf_path = 'csic/csic/monthly/sconcnh3/'\n", " if not os.path.exists(os.path.dirname(netcdf_path)):\n", " os.makedirs(os.path.dirname(netcdf_path))\n", " \n", + " # To run Providentia, this folder should be moved to:\n", + " # '/esarchive/obs/' as in '/esarchive/obs/csic/csic/monthly/sconcnh3/'\n", + " \n", " # Save files\n", " points_grid.to_netcdf(netcdf_path + '/sconcnh3_{0}{1}.nc'.format(year, str(month).zfill(2)))\n", " \n", " del points_grid\n", " print('Done sconcnh3_{0}{1}.nc'.format(year, str(month).zfill(2)))" ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:       (time: 1, station: 2)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2019-05-01\n",
-       "  * station       (station) float64 0.0 1.0\n",
-       "Data variables:\n",
-       "    lat           (station) float64 41.39 41.4\n",
-       "    lon           (station) float64 2.115 2.153\n",
-       "    station_name  (station) object 'traffic_site' 'urban_site'\n",
-       "    altitude      (station) float64 nan nan\n",
-       "    sconcnh3      (time, station) float64 5.315 1.119\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 1, station: 2)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2019-05-01\n", - " * station (station) float64 0.0 1.0\n", - "Data variables:\n", - " lat (station) float64 ...\n", - " lon (station) float64 ...\n", - " station_name (station) object ...\n", - " altitude (station) float64 ...\n", - " sconcnh3 (time, station) float64 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('/esarchive/obs/csic/csic/monthly/sconcnh3/sconcnh3_201905.nc')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:       (time: 1, station: 2)\n",
-       "Coordinates:\n",
-       "  * time          (time) datetime64[ns] 2019-01-01\n",
-       "  * station       (station) float64 0.0 1.0\n",
-       "Data variables:\n",
-       "    lat           (station) float64 41.39 41.4\n",
-       "    lon           (station) float64 2.115 2.153\n",
-       "    station_name  (station) object 'traffic_site' 'urban_site'\n",
-       "    altitude      (station) float64 nan nan\n",
-       "    sconcnh3      (time, station) float64 4.989 2.553\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 1, station: 2)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2019-01-01\n", - " * station (station) float64 0.0 1.0\n", - "Data variables:\n", - " lat (station) float64 ...\n", - " lon (station) float64 ...\n", - " station_name (station) object ...\n", - " altitude (station) float64 ...\n", - " sconcnh3 (time, station) float64 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('/esarchive/obs/csic/csic/monthly/sconcnh3/sconcnh3_201901.nc')" - ] } ], "metadata": { @@ -1869,7 +652,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.7.4" }, "vscode": { "interpreter": { diff --git a/tutorials/2.Creation/2.6.Create-LCC.ipynb b/tutorials/2.Creation/2.6.Create-LCC.ipynb index e64d5200e52dfe5aca0aa830b50fbdfddd211399..3c1e675bf69b08f75994321b11a11682f9648199 100644 --- a/tutorials/2.Creation/2.6.Create-LCC.ipynb +++ b/tutorials/2.Creation/2.6.Create-LCC.ipynb @@ -13,7 +13,6 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", "from nes import *" ] }, @@ -49,7 +48,8 @@ "text": [ "Rank 000: Creating lcc_grid.nc\n", "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n" + "Rank 000: Dimensions done\n", + "Rank 000: Cell measures done\n" ] } ], @@ -64,388 +64,87 @@ "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:            (time: 1, lev: 1, y: 397, x: 397)\n",
-       "Coordinates:\n",
-       "  * time               (time) datetime64[ns] 1996-12-31\n",
-       "  * lev                (lev) float64 0.0\n",
-       "  * y                  (y) float64 -7.951e+05 -7.911e+05 ... 7.849e+05 7.889e+05\n",
-       "  * x                  (x) float64 -8.058e+05 -8.018e+05 ... 7.742e+05 7.782e+05\n",
-       "Data variables:\n",
-       "    lat                (y, x) float64 ...\n",
-       "    lon                (y, x) float64 ...\n",
-       "    Lambert_conformal  |S1 b''\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], "text/plain": [ - "\n", - "Dimensions: (time: 1, lev: 1, y: 397, x: 397)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 1996-12-31\n", - " * lev (lev) float64 0.0\n", - " * y (y) float64 -7.951e+05 -7.911e+05 ... 7.849e+05 7.889e+05\n", - " * x (x) float64 -8.058e+05 -8.018e+05 ... 7.742e+05 7.782e+05\n", - "Data variables:\n", - " lat (y, x) float64 ...\n", - " lon (y, x) float64 ...\n", - " Lambert_conformal |S1 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" + "{'data': array([-795137.125, -791137.125, -787137.125, -783137.125, -779137.125,\n", + " -775137.125, -771137.125, -767137.125, -763137.125, -759137.125,\n", + " -755137.125, -751137.125, -747137.125, -743137.125, -739137.125,\n", + " -735137.125, -731137.125, -727137.125, -723137.125, -719137.125,\n", + " -715137.125, -711137.125, -707137.125, -703137.125, -699137.125,\n", + " -695137.125, -691137.125, -687137.125, -683137.125, -679137.125,\n", + " -675137.125, -671137.125, -667137.125, -663137.125, -659137.125,\n", + " -655137.125, -651137.125, -647137.125, -643137.125, -639137.125,\n", + " -635137.125, -631137.125, -627137.125, -623137.125, -619137.125,\n", + " -615137.125, -611137.125, -607137.125, -603137.125, -599137.125,\n", + " -595137.125, -591137.125, -587137.125, -583137.125, -579137.125,\n", + " -575137.125, -571137.125, -567137.125, -563137.125, -559137.125,\n", + " -555137.125, -551137.125, -547137.125, -543137.125, -539137.125,\n", + " -535137.125, -531137.125, -527137.125, -523137.125, -519137.125,\n", + " -515137.125, -511137.125, -507137.125, -503137.125, -499137.125,\n", + " -495137.125, -491137.125, -487137.125, -483137.125, -479137.125,\n", + " -475137.125, -471137.125, -467137.125, -463137.125, -459137.125,\n", + " -455137.125, -451137.125, -447137.125, -443137.125, -439137.125,\n", + " -435137.125, -431137.125, -427137.125, -423137.125, -419137.125,\n", + " -415137.125, -411137.125, -407137.125, -403137.125, -399137.125,\n", + " -395137.125, -391137.125, -387137.125, -383137.125, -379137.125,\n", + " -375137.125, -371137.125, -367137.125, -363137.125, -359137.125,\n", + " -355137.125, -351137.125, -347137.125, -343137.125, -339137.125,\n", + " -335137.125, -331137.125, -327137.125, -323137.125, -319137.125,\n", + " -315137.125, -311137.125, -307137.125, -303137.125, -299137.125,\n", + " -295137.125, -291137.125, -287137.125, -283137.125, -279137.125,\n", + " -275137.125, -271137.125, -267137.125, -263137.125, -259137.125,\n", + " -255137.125, -251137.125, -247137.125, -243137.125, -239137.125,\n", + " -235137.125, -231137.125, -227137.125, -223137.125, -219137.125,\n", + " -215137.125, -211137.125, -207137.125, -203137.125, -199137.125,\n", + " -195137.125, -191137.125, -187137.125, -183137.125, -179137.125,\n", + " -175137.125, -171137.125, -167137.125, -163137.125, -159137.125,\n", + " -155137.125, -151137.125, -147137.125, -143137.125, -139137.125,\n", + " -135137.125, -131137.125, -127137.125, -123137.125, -119137.125,\n", + " -115137.125, -111137.125, -107137.125, -103137.125, -99137.125,\n", + " -95137.125, -91137.125, -87137.125, -83137.125, -79137.125,\n", + " -75137.125, -71137.125, -67137.125, -63137.125, -59137.125,\n", + " -55137.125, -51137.125, -47137.125, -43137.125, -39137.125,\n", + " -35137.125, -31137.125, -27137.125, -23137.125, -19137.125,\n", + " -15137.125, -11137.125, -7137.125, -3137.125, 862.875,\n", + " 4862.875, 8862.875, 12862.875, 16862.875, 20862.875,\n", + " 24862.875, 28862.875, 32862.875, 36862.875, 40862.875,\n", + " 44862.875, 48862.875, 52862.875, 56862.875, 60862.875,\n", + " 64862.875, 68862.875, 72862.875, 76862.875, 80862.875,\n", + " 84862.875, 88862.875, 92862.875, 96862.875, 100862.875,\n", + " 104862.875, 108862.875, 112862.875, 116862.875, 120862.875,\n", + " 124862.875, 128862.875, 132862.875, 136862.875, 140862.875,\n", + " 144862.875, 148862.875, 152862.875, 156862.875, 160862.875,\n", + " 164862.875, 168862.875, 172862.875, 176862.875, 180862.875,\n", + " 184862.875, 188862.875, 192862.875, 196862.875, 200862.875,\n", + " 204862.875, 208862.875, 212862.875, 216862.875, 220862.875,\n", + " 224862.875, 228862.875, 232862.875, 236862.875, 240862.875,\n", + " 244862.875, 248862.875, 252862.875, 256862.875, 260862.875,\n", + " 264862.875, 268862.875, 272862.875, 276862.875, 280862.875,\n", + " 284862.875, 288862.875, 292862.875, 296862.875, 300862.875,\n", + " 304862.875, 308862.875, 312862.875, 316862.875, 320862.875,\n", + " 324862.875, 328862.875, 332862.875, 336862.875, 340862.875,\n", + " 344862.875, 348862.875, 352862.875, 356862.875, 360862.875,\n", + " 364862.875, 368862.875, 372862.875, 376862.875, 380862.875,\n", + " 384862.875, 388862.875, 392862.875, 396862.875, 400862.875,\n", + " 404862.875, 408862.875, 412862.875, 416862.875, 420862.875,\n", + " 424862.875, 428862.875, 432862.875, 436862.875, 440862.875,\n", + " 444862.875, 448862.875, 452862.875, 456862.875, 460862.875,\n", + " 464862.875, 468862.875, 472862.875, 476862.875, 480862.875,\n", + " 484862.875, 488862.875, 492862.875, 496862.875, 500862.875,\n", + " 504862.875, 508862.875, 512862.875, 516862.875, 520862.875,\n", + " 524862.875, 528862.875, 532862.875, 536862.875, 540862.875,\n", + " 544862.875, 548862.875, 552862.875, 556862.875, 560862.875,\n", + " 564862.875, 568862.875, 572862.875, 576862.875, 580862.875,\n", + " 584862.875, 588862.875, 592862.875, 596862.875, 600862.875,\n", + " 604862.875, 608862.875, 612862.875, 616862.875, 620862.875,\n", + " 624862.875, 628862.875, 632862.875, 636862.875, 640862.875,\n", + " 644862.875, 648862.875, 652862.875, 656862.875, 660862.875,\n", + " 664862.875, 668862.875, 672862.875, 676862.875, 680862.875,\n", + " 684862.875, 688862.875, 692862.875, 696862.875, 700862.875,\n", + " 704862.875, 708862.875, 712862.875, 716862.875, 720862.875,\n", + " 724862.875, 728862.875, 732862.875, 736862.875, 740862.875,\n", + " 744862.875, 748862.875, 752862.875, 756862.875, 760862.875,\n", + " 764862.875, 768862.875, 772862.875, 776862.875, 780862.875,\n", + " 784862.875, 788862.875])}" ] }, "execution_count": 4, @@ -454,7 +153,106 @@ } ], "source": [ - "xr.open_dataset('lcc_grid.nc')" + "lcc_grid.y" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([-805847.688, -801847.688, -797847.688, -793847.688, -789847.688,\n", + " -785847.688, -781847.688, -777847.688, -773847.688, -769847.688,\n", + " -765847.688, -761847.688, -757847.688, -753847.688, -749847.688,\n", + " -745847.688, -741847.688, -737847.688, -733847.688, -729847.688,\n", + " -725847.688, -721847.688, -717847.688, -713847.688, -709847.688,\n", + " -705847.688, -701847.688, -697847.688, -693847.688, -689847.688,\n", + " -685847.688, -681847.688, -677847.688, -673847.688, -669847.688,\n", + " -665847.688, -661847.688, -657847.688, -653847.688, -649847.688,\n", + " -645847.688, -641847.688, -637847.688, -633847.688, -629847.688,\n", + " -625847.688, -621847.688, -617847.688, -613847.688, -609847.688,\n", + " -605847.688, -601847.688, -597847.688, -593847.688, -589847.688,\n", + " -585847.688, -581847.688, -577847.688, -573847.688, -569847.688,\n", + " -565847.688, -561847.688, -557847.688, -553847.688, -549847.688,\n", + " -545847.688, -541847.688, -537847.688, -533847.688, -529847.688,\n", + " -525847.688, -521847.688, -517847.688, -513847.688, -509847.688,\n", + " -505847.688, -501847.688, -497847.688, -493847.688, -489847.688,\n", + " -485847.688, -481847.688, -477847.688, -473847.688, -469847.688,\n", + " -465847.688, -461847.688, -457847.688, -453847.688, -449847.688,\n", + " -445847.688, -441847.688, -437847.688, -433847.688, -429847.688,\n", + " -425847.688, -421847.688, -417847.688, -413847.688, -409847.688,\n", + " -405847.688, -401847.688, -397847.688, -393847.688, -389847.688,\n", + " -385847.688, -381847.688, -377847.688, -373847.688, -369847.688,\n", + " -365847.688, -361847.688, -357847.688, -353847.688, -349847.688,\n", + " -345847.688, -341847.688, -337847.688, -333847.688, -329847.688,\n", + " -325847.688, -321847.688, -317847.688, -313847.688, -309847.688,\n", + " -305847.688, -301847.688, -297847.688, -293847.688, -289847.688,\n", + " -285847.688, -281847.688, -277847.688, -273847.688, -269847.688,\n", + " -265847.688, -261847.688, -257847.688, -253847.688, -249847.688,\n", + " -245847.688, -241847.688, -237847.688, -233847.688, -229847.688,\n", + " -225847.688, -221847.688, -217847.688, -213847.688, -209847.688,\n", + " -205847.688, -201847.688, -197847.688, -193847.688, -189847.688,\n", + " -185847.688, -181847.688, -177847.688, -173847.688, -169847.688,\n", + " -165847.688, -161847.688, -157847.688, -153847.688, -149847.688,\n", + " -145847.688, -141847.688, -137847.688, -133847.688, -129847.688,\n", + " -125847.688, -121847.688, -117847.688, -113847.688, -109847.688,\n", + " -105847.688, -101847.688, -97847.688, -93847.688, -89847.688,\n", + " -85847.688, -81847.688, -77847.688, -73847.688, -69847.688,\n", + " -65847.688, -61847.688, -57847.688, -53847.688, -49847.688,\n", + " -45847.688, -41847.688, -37847.688, -33847.688, -29847.688,\n", + " -25847.688, -21847.688, -17847.688, -13847.688, -9847.688,\n", + " -5847.688, -1847.688, 2152.312, 6152.312, 10152.312,\n", + " 14152.312, 18152.312, 22152.312, 26152.312, 30152.312,\n", + " 34152.312, 38152.312, 42152.312, 46152.312, 50152.312,\n", + " 54152.312, 58152.312, 62152.312, 66152.312, 70152.312,\n", + " 74152.312, 78152.312, 82152.312, 86152.312, 90152.312,\n", + " 94152.312, 98152.312, 102152.312, 106152.312, 110152.312,\n", + " 114152.312, 118152.312, 122152.312, 126152.312, 130152.312,\n", + " 134152.312, 138152.312, 142152.312, 146152.312, 150152.312,\n", + " 154152.312, 158152.312, 162152.312, 166152.312, 170152.312,\n", + " 174152.312, 178152.312, 182152.312, 186152.312, 190152.312,\n", + " 194152.312, 198152.312, 202152.312, 206152.312, 210152.312,\n", + " 214152.312, 218152.312, 222152.312, 226152.312, 230152.312,\n", + " 234152.312, 238152.312, 242152.312, 246152.312, 250152.312,\n", + " 254152.312, 258152.312, 262152.312, 266152.312, 270152.312,\n", + " 274152.312, 278152.312, 282152.312, 286152.312, 290152.312,\n", + " 294152.312, 298152.312, 302152.312, 306152.312, 310152.312,\n", + " 314152.312, 318152.312, 322152.312, 326152.312, 330152.312,\n", + " 334152.312, 338152.312, 342152.312, 346152.312, 350152.312,\n", + " 354152.312, 358152.312, 362152.312, 366152.312, 370152.312,\n", + " 374152.312, 378152.312, 382152.312, 386152.312, 390152.312,\n", + " 394152.312, 398152.312, 402152.312, 406152.312, 410152.312,\n", + " 414152.312, 418152.312, 422152.312, 426152.312, 430152.312,\n", + " 434152.312, 438152.312, 442152.312, 446152.312, 450152.312,\n", + " 454152.312, 458152.312, 462152.312, 466152.312, 470152.312,\n", + " 474152.312, 478152.312, 482152.312, 486152.312, 490152.312,\n", + " 494152.312, 498152.312, 502152.312, 506152.312, 510152.312,\n", + " 514152.312, 518152.312, 522152.312, 526152.312, 530152.312,\n", + " 534152.312, 538152.312, 542152.312, 546152.312, 550152.312,\n", + " 554152.312, 558152.312, 562152.312, 566152.312, 570152.312,\n", + " 574152.312, 578152.312, 582152.312, 586152.312, 590152.312,\n", + " 594152.312, 598152.312, 602152.312, 606152.312, 610152.312,\n", + " 614152.312, 618152.312, 622152.312, 626152.312, 630152.312,\n", + " 634152.312, 638152.312, 642152.312, 646152.312, 650152.312,\n", + " 654152.312, 658152.312, 662152.312, 666152.312, 670152.312,\n", + " 674152.312, 678152.312, 682152.312, 686152.312, 690152.312,\n", + " 694152.312, 698152.312, 702152.312, 706152.312, 710152.312,\n", + " 714152.312, 718152.312, 722152.312, 726152.312, 730152.312,\n", + " 734152.312, 738152.312, 742152.312, 746152.312, 750152.312,\n", + " 754152.312, 758152.312, 762152.312, 766152.312, 770152.312,\n", + " 774152.312, 778152.312])}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lcc_grid.x" ] } ], diff --git a/tutorials/2.Creation/2.7.Create_Mercator.ipynb b/tutorials/2.Creation/2.7.Create_Mercator.ipynb index e2c7399beb4618d86ff29389eb6e05ceeedef64d..a69ee6a405c0e9a2e993a5ab12e1a7d477b4d631 100644 --- a/tutorials/2.Creation/2.7.Create_Mercator.ipynb +++ b/tutorials/2.Creation/2.7.Create_Mercator.ipynb @@ -13,7 +13,6 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", "from nes import *" ] }, @@ -47,7 +46,8 @@ "text": [ "Rank 000: Creating mercator_grid.nc\n", "Rank 000: NetCDF ready to write\n", - "Rank 000: Dimensions done\n" + "Rank 000: Dimensions done\n", + "Rank 000: Cell measures done\n" ] } ], @@ -62,411 +62,47 @@ "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:   (time: 1, lev: 1, y: 236, x: 210)\n",
-       "Coordinates:\n",
-       "  * time      (time) datetime64[ns] 1996-12-31\n",
-       "  * lev       (lev) float64 0.0\n",
-       "  * y         (y) float64 -5.382e+06 -5.332e+06 ... 6.318e+06 6.368e+06\n",
-       "  * x         (x) float64 -1.01e+05 -5.102e+04 -1.018e+03 ... 1.03e+07 1.035e+07\n",
-       "Data variables:\n",
-       "    lat       (y, x) float64 -43.67 -43.67 -43.67 -43.67 ... 49.75 49.75 49.75\n",
-       "    lon       (y, x) float64 -18.91 -18.46 -18.01 -17.56 ... 74.1 74.55 75.0\n",
-       "    mercator  |S1 b''\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], "text/plain": [ - "\n", - "Dimensions: (time: 1, lev: 1, y: 236, x: 210)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 1996-12-31\n", - " * lev (lev) float64 0.0\n", - " * y (y) float64 -5.382e+06 -5.332e+06 ... 6.318e+06 6.368e+06\n", - " * x (x) float64 -1.01e+05 -5.102e+04 -1.018e+03 ... 1.03e+07 1.035e+07\n", - "Data variables:\n", - " lat (y, x) float64 ...\n", - " lon (y, x) float64 ...\n", - " mercator |S1 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" + "{'data': array([-5382460., -5332460., -5282460., -5232460., -5182460., -5132460.,\n", + " -5082460., -5032460., -4982460., -4932460., -4882460., -4832460.,\n", + " -4782460., -4732460., -4682460., -4632460., -4582460., -4532460.,\n", + " -4482460., -4432460., -4382460., -4332460., -4282460., -4232460.,\n", + " -4182460., -4132460., -4082460., -4032460., -3982460., -3932460.,\n", + " -3882460., -3832460., -3782460., -3732460., -3682460., -3632460.,\n", + " -3582460., -3532460., -3482460., -3432460., -3382460., -3332460.,\n", + " -3282460., -3232460., -3182460., -3132460., -3082460., -3032460.,\n", + " -2982460., -2932460., -2882460., -2832460., -2782460., -2732460.,\n", + " -2682460., -2632460., -2582460., -2532460., -2482460., -2432460.,\n", + " -2382460., -2332460., -2282460., -2232460., -2182460., -2132460.,\n", + " -2082460., -2032460., -1982460., -1932460., -1882460., -1832460.,\n", + " -1782460., -1732460., -1682460., -1632460., -1582460., -1532460.,\n", + " -1482460., -1432460., -1382460., -1332460., -1282460., -1232460.,\n", + " -1182460., -1132460., -1082460., -1032460., -982460., -932460.,\n", + " -882460., -832460., -782460., -732460., -682460., -632460.,\n", + " -582460., -532460., -482460., -432460., -382460., -332460.,\n", + " -282460., -232460., -182460., -132460., -82460., -32460.,\n", + " 17540., 67540., 117540., 167540., 217540., 267540.,\n", + " 317540., 367540., 417540., 467540., 517540., 567540.,\n", + " 617540., 667540., 717540., 767540., 817540., 867540.,\n", + " 917540., 967540., 1017540., 1067540., 1117540., 1167540.,\n", + " 1217540., 1267540., 1317540., 1367540., 1417540., 1467540.,\n", + " 1517540., 1567540., 1617540., 1667540., 1717540., 1767540.,\n", + " 1817540., 1867540., 1917540., 1967540., 2017540., 2067540.,\n", + " 2117540., 2167540., 2217540., 2267540., 2317540., 2367540.,\n", + " 2417540., 2467540., 2517540., 2567540., 2617540., 2667540.,\n", + " 2717540., 2767540., 2817540., 2867540., 2917540., 2967540.,\n", + " 3017540., 3067540., 3117540., 3167540., 3217540., 3267540.,\n", + " 3317540., 3367540., 3417540., 3467540., 3517540., 3567540.,\n", + " 3617540., 3667540., 3717540., 3767540., 3817540., 3867540.,\n", + " 3917540., 3967540., 4017540., 4067540., 4117540., 4167540.,\n", + " 4217540., 4267540., 4317540., 4367540., 4417540., 4467540.,\n", + " 4517540., 4567540., 4617540., 4667540., 4717540., 4767540.,\n", + " 4817540., 4867540., 4917540., 4967540., 5017540., 5067540.,\n", + " 5117540., 5167540., 5217540., 5267540., 5317540., 5367540.,\n", + " 5417540., 5467540., 5517540., 5567540., 5617540., 5667540.,\n", + " 5717540., 5767540., 5817540., 5867540., 5917540., 5967540.,\n", + " 6017540., 6067540., 6117540., 6167540., 6217540., 6267540.,\n", + " 6317540., 6367540.])}" ] }, "execution_count": 4, @@ -475,7 +111,79 @@ } ], "source": [ - "xr.open_dataset('mercator_grid.nc')" + "mercator_grid.y" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([-1.01017500e+05, -5.10175000e+04, -1.01750000e+03, 4.89825000e+04,\n", + " 9.89825000e+04, 1.48982500e+05, 1.98982500e+05, 2.48982500e+05,\n", + " 2.98982500e+05, 3.48982500e+05, 3.98982500e+05, 4.48982500e+05,\n", + " 4.98982500e+05, 5.48982500e+05, 5.98982500e+05, 6.48982500e+05,\n", + " 6.98982500e+05, 7.48982500e+05, 7.98982500e+05, 8.48982500e+05,\n", + " 8.98982500e+05, 9.48982500e+05, 9.98982500e+05, 1.04898250e+06,\n", + " 1.09898250e+06, 1.14898250e+06, 1.19898250e+06, 1.24898250e+06,\n", + " 1.29898250e+06, 1.34898250e+06, 1.39898250e+06, 1.44898250e+06,\n", + " 1.49898250e+06, 1.54898250e+06, 1.59898250e+06, 1.64898250e+06,\n", + " 1.69898250e+06, 1.74898250e+06, 1.79898250e+06, 1.84898250e+06,\n", + " 1.89898250e+06, 1.94898250e+06, 1.99898250e+06, 2.04898250e+06,\n", + " 2.09898250e+06, 2.14898250e+06, 2.19898250e+06, 2.24898250e+06,\n", + " 2.29898250e+06, 2.34898250e+06, 2.39898250e+06, 2.44898250e+06,\n", + " 2.49898250e+06, 2.54898250e+06, 2.59898250e+06, 2.64898250e+06,\n", + " 2.69898250e+06, 2.74898250e+06, 2.79898250e+06, 2.84898250e+06,\n", + " 2.89898250e+06, 2.94898250e+06, 2.99898250e+06, 3.04898250e+06,\n", + " 3.09898250e+06, 3.14898250e+06, 3.19898250e+06, 3.24898250e+06,\n", + " 3.29898250e+06, 3.34898250e+06, 3.39898250e+06, 3.44898250e+06,\n", + " 3.49898250e+06, 3.54898250e+06, 3.59898250e+06, 3.64898250e+06,\n", + " 3.69898250e+06, 3.74898250e+06, 3.79898250e+06, 3.84898250e+06,\n", + " 3.89898250e+06, 3.94898250e+06, 3.99898250e+06, 4.04898250e+06,\n", + " 4.09898250e+06, 4.14898250e+06, 4.19898250e+06, 4.24898250e+06,\n", + " 4.29898250e+06, 4.34898250e+06, 4.39898250e+06, 4.44898250e+06,\n", + " 4.49898250e+06, 4.54898250e+06, 4.59898250e+06, 4.64898250e+06,\n", + " 4.69898250e+06, 4.74898250e+06, 4.79898250e+06, 4.84898250e+06,\n", + " 4.89898250e+06, 4.94898250e+06, 4.99898250e+06, 5.04898250e+06,\n", + " 5.09898250e+06, 5.14898250e+06, 5.19898250e+06, 5.24898250e+06,\n", + " 5.29898250e+06, 5.34898250e+06, 5.39898250e+06, 5.44898250e+06,\n", + " 5.49898250e+06, 5.54898250e+06, 5.59898250e+06, 5.64898250e+06,\n", + " 5.69898250e+06, 5.74898250e+06, 5.79898250e+06, 5.84898250e+06,\n", + " 5.89898250e+06, 5.94898250e+06, 5.99898250e+06, 6.04898250e+06,\n", + " 6.09898250e+06, 6.14898250e+06, 6.19898250e+06, 6.24898250e+06,\n", + " 6.29898250e+06, 6.34898250e+06, 6.39898250e+06, 6.44898250e+06,\n", + " 6.49898250e+06, 6.54898250e+06, 6.59898250e+06, 6.64898250e+06,\n", + " 6.69898250e+06, 6.74898250e+06, 6.79898250e+06, 6.84898250e+06,\n", + " 6.89898250e+06, 6.94898250e+06, 6.99898250e+06, 7.04898250e+06,\n", + " 7.09898250e+06, 7.14898250e+06, 7.19898250e+06, 7.24898250e+06,\n", + " 7.29898250e+06, 7.34898250e+06, 7.39898250e+06, 7.44898250e+06,\n", + " 7.49898250e+06, 7.54898250e+06, 7.59898250e+06, 7.64898250e+06,\n", + " 7.69898250e+06, 7.74898250e+06, 7.79898250e+06, 7.84898250e+06,\n", + " 7.89898250e+06, 7.94898250e+06, 7.99898250e+06, 8.04898250e+06,\n", + " 8.09898250e+06, 8.14898250e+06, 8.19898250e+06, 8.24898250e+06,\n", + " 8.29898250e+06, 8.34898250e+06, 8.39898250e+06, 8.44898250e+06,\n", + " 8.49898250e+06, 8.54898250e+06, 8.59898250e+06, 8.64898250e+06,\n", + " 8.69898250e+06, 8.74898250e+06, 8.79898250e+06, 8.84898250e+06,\n", + " 8.89898250e+06, 8.94898250e+06, 8.99898250e+06, 9.04898250e+06,\n", + " 9.09898250e+06, 9.14898250e+06, 9.19898250e+06, 9.24898250e+06,\n", + " 9.29898250e+06, 9.34898250e+06, 9.39898250e+06, 9.44898250e+06,\n", + " 9.49898250e+06, 9.54898250e+06, 9.59898250e+06, 9.64898250e+06,\n", + " 9.69898250e+06, 9.74898250e+06, 9.79898250e+06, 9.84898250e+06,\n", + " 9.89898250e+06, 9.94898250e+06, 9.99898250e+06, 1.00489825e+07,\n", + " 1.00989825e+07, 1.01489825e+07, 1.01989825e+07, 1.02489825e+07,\n", + " 1.02989825e+07, 1.03489825e+07])}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mercator_grid.x" ] } ], diff --git a/tutorials/2.Creation/2.8.Create_Global.ipynb b/tutorials/2.Creation/2.8.Create_Global.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..107f0ddac3984805be9cf0e1b1c8642c9c5ead16 --- /dev/null +++ b/tutorials/2.Creation/2.8.Create_Global.ipynb @@ -0,0 +1,113 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to create global grids" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from nes import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "inc_lat = 0.1\n", + "inc_lon = 0.1\n", + "global_grid = create_nes(projection='global', \n", + " inc_lat=inc_lat, inc_lon=inc_lon)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "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" + ] + } + ], + "source": [ + "global_grid.to_netcdf('global_grid.nc', info=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([-89.95, -89.85, -89.75, ..., 89.65, 89.75, 89.85])}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_grid.lat" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([-179.95, -179.85, -179.75, ..., 179.65, 179.75, 179.85])}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "global_grid.lon" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/3.Statistics/3.1.Statistics.ipynb b/tutorials/3.Statistics/3.1.Statistics.ipynb index e65883867faddd8ea4e1b7cd4b81a5d35ff5a417..f3566e4c8d598a6fd1a33734a7b0107cac546534 100644 --- a/tutorials/3.Statistics/3.1.Statistics.ipynb +++ b/tutorials/3.Statistics/3.1.Statistics.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Calculate simple statistics" + "# Calculate daily statistics" ] }, { @@ -20,7 +20,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Open NetCDF" + "## 1. Read dataset" ] }, { @@ -32,13 +32,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 156 ms, sys: 52.8 ms, total: 209 ms\n", - "Wall time: 15.2 s\n" + "CPU times: user 571 ms, sys: 93.9 ms, total: 665 ms\n", + "Wall time: 10.5 s\n" ] } ], "source": [ - "cams_file = \"/gpfs/scratch/bsc32/bsc32538/a4mg/nmmb-monarch/ARCHIVE/000/2022050312/MONARCH_d01_2022050312.nc\"\n", + "# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc\n", + "# Rotated grid from MONARCH\n", + "cams_file = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'\n", "%time nessy = open_netcdf(path=cams_file, info=True)" ] }, @@ -50,7 +52,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -66,8 +68,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Time\n", - "NES.time : list of time steps (datetime)" + "### 1.1. Time" ] }, { @@ -78,115 +79,43 @@ { "data": { "text/plain": [ - "[datetime.datetime(2022, 5, 3, 12, 0),\n", - " datetime.datetime(2022, 5, 3, 13, 0),\n", - " datetime.datetime(2022, 5, 3, 14, 0),\n", - " datetime.datetime(2022, 5, 3, 15, 0),\n", - " datetime.datetime(2022, 5, 3, 16, 0),\n", - " datetime.datetime(2022, 5, 3, 17, 0),\n", - " datetime.datetime(2022, 5, 3, 18, 0),\n", - " datetime.datetime(2022, 5, 3, 19, 0),\n", - " datetime.datetime(2022, 5, 3, 20, 0),\n", - " datetime.datetime(2022, 5, 3, 21, 0),\n", - " datetime.datetime(2022, 5, 3, 22, 0),\n", - " datetime.datetime(2022, 5, 3, 23, 0),\n", - " datetime.datetime(2022, 5, 4, 0, 0),\n", - " datetime.datetime(2022, 5, 4, 1, 0),\n", - " datetime.datetime(2022, 5, 4, 2, 0),\n", - " datetime.datetime(2022, 5, 4, 3, 0),\n", - " datetime.datetime(2022, 5, 4, 4, 0),\n", - " datetime.datetime(2022, 5, 4, 5, 0),\n", - " datetime.datetime(2022, 5, 4, 6, 0),\n", - " datetime.datetime(2022, 5, 4, 7, 0),\n", - " datetime.datetime(2022, 5, 4, 8, 0),\n", - " datetime.datetime(2022, 5, 4, 9, 0),\n", - " datetime.datetime(2022, 5, 4, 10, 0),\n", - " datetime.datetime(2022, 5, 4, 11, 0),\n", - " datetime.datetime(2022, 5, 4, 12, 0),\n", - " datetime.datetime(2022, 5, 4, 13, 0),\n", - " datetime.datetime(2022, 5, 4, 14, 0),\n", - " datetime.datetime(2022, 5, 4, 15, 0),\n", - " datetime.datetime(2022, 5, 4, 16, 0),\n", - " datetime.datetime(2022, 5, 4, 17, 0),\n", - " datetime.datetime(2022, 5, 4, 18, 0),\n", - " datetime.datetime(2022, 5, 4, 19, 0),\n", - " datetime.datetime(2022, 5, 4, 20, 0),\n", - " datetime.datetime(2022, 5, 4, 21, 0),\n", - " datetime.datetime(2022, 5, 4, 22, 0),\n", - " datetime.datetime(2022, 5, 4, 23, 0),\n", - " datetime.datetime(2022, 5, 5, 0, 0),\n", - " datetime.datetime(2022, 5, 5, 1, 0),\n", - " datetime.datetime(2022, 5, 5, 2, 0),\n", - " datetime.datetime(2022, 5, 5, 3, 0),\n", - " datetime.datetime(2022, 5, 5, 4, 0),\n", - " datetime.datetime(2022, 5, 5, 5, 0),\n", - " datetime.datetime(2022, 5, 5, 6, 0),\n", - " datetime.datetime(2022, 5, 5, 7, 0),\n", - " datetime.datetime(2022, 5, 5, 8, 0),\n", - " datetime.datetime(2022, 5, 5, 9, 0),\n", - " datetime.datetime(2022, 5, 5, 10, 0),\n", - " datetime.datetime(2022, 5, 5, 11, 0),\n", - " datetime.datetime(2022, 5, 5, 12, 0),\n", - " datetime.datetime(2022, 5, 5, 13, 0),\n", - " datetime.datetime(2022, 5, 5, 14, 0),\n", - " datetime.datetime(2022, 5, 5, 15, 0),\n", - " datetime.datetime(2022, 5, 5, 16, 0),\n", - " datetime.datetime(2022, 5, 5, 17, 0),\n", - " datetime.datetime(2022, 5, 5, 18, 0),\n", - " datetime.datetime(2022, 5, 5, 19, 0),\n", - " datetime.datetime(2022, 5, 5, 20, 0),\n", - " datetime.datetime(2022, 5, 5, 21, 0),\n", - " datetime.datetime(2022, 5, 5, 22, 0),\n", - " datetime.datetime(2022, 5, 5, 23, 0),\n", - " datetime.datetime(2022, 5, 6, 0, 0),\n", - " datetime.datetime(2022, 5, 6, 1, 0),\n", - " datetime.datetime(2022, 5, 6, 2, 0),\n", - " datetime.datetime(2022, 5, 6, 3, 0),\n", - " datetime.datetime(2022, 5, 6, 4, 0),\n", - " datetime.datetime(2022, 5, 6, 5, 0),\n", - " datetime.datetime(2022, 5, 6, 6, 0),\n", - " datetime.datetime(2022, 5, 6, 7, 0),\n", - " datetime.datetime(2022, 5, 6, 8, 0),\n", - " datetime.datetime(2022, 5, 6, 9, 0),\n", - " datetime.datetime(2022, 5, 6, 10, 0),\n", - " datetime.datetime(2022, 5, 6, 11, 0),\n", - " datetime.datetime(2022, 5, 6, 12, 0),\n", - " datetime.datetime(2022, 5, 6, 13, 0),\n", - " datetime.datetime(2022, 5, 6, 14, 0),\n", - " datetime.datetime(2022, 5, 6, 15, 0),\n", - " datetime.datetime(2022, 5, 6, 16, 0),\n", - " datetime.datetime(2022, 5, 6, 17, 0),\n", - " datetime.datetime(2022, 5, 6, 18, 0),\n", - " datetime.datetime(2022, 5, 6, 19, 0),\n", - " datetime.datetime(2022, 5, 6, 20, 0),\n", - " datetime.datetime(2022, 5, 6, 21, 0),\n", - " datetime.datetime(2022, 5, 6, 22, 0),\n", - " datetime.datetime(2022, 5, 6, 23, 0),\n", - " datetime.datetime(2022, 5, 7, 0, 0),\n", - " datetime.datetime(2022, 5, 7, 1, 0),\n", - " datetime.datetime(2022, 5, 7, 2, 0),\n", - " datetime.datetime(2022, 5, 7, 3, 0),\n", - " datetime.datetime(2022, 5, 7, 4, 0),\n", - " datetime.datetime(2022, 5, 7, 5, 0),\n", - " datetime.datetime(2022, 5, 7, 6, 0),\n", - " datetime.datetime(2022, 5, 7, 7, 0),\n", - " datetime.datetime(2022, 5, 7, 8, 0),\n", - " datetime.datetime(2022, 5, 7, 9, 0),\n", - " datetime.datetime(2022, 5, 7, 10, 0),\n", - " datetime.datetime(2022, 5, 7, 11, 0),\n", - " datetime.datetime(2022, 5, 7, 12, 0),\n", - " datetime.datetime(2022, 5, 7, 13, 0),\n", - " datetime.datetime(2022, 5, 7, 14, 0),\n", - " datetime.datetime(2022, 5, 7, 15, 0),\n", - " datetime.datetime(2022, 5, 7, 16, 0),\n", - " datetime.datetime(2022, 5, 7, 17, 0),\n", - " datetime.datetime(2022, 5, 7, 18, 0),\n", - " datetime.datetime(2022, 5, 7, 19, 0),\n", - " datetime.datetime(2022, 5, 7, 20, 0),\n", - " datetime.datetime(2022, 5, 7, 21, 0),\n", - " datetime.datetime(2022, 5, 7, 22, 0),\n", - " datetime.datetime(2022, 5, 7, 23, 0),\n", - " datetime.datetime(2022, 5, 8, 0, 0)]" + "[datetime.datetime(2022, 11, 15, 12, 0),\n", + " datetime.datetime(2022, 11, 15, 13, 0),\n", + " datetime.datetime(2022, 11, 15, 14, 0),\n", + " datetime.datetime(2022, 11, 15, 15, 0),\n", + " datetime.datetime(2022, 11, 15, 16, 0),\n", + " datetime.datetime(2022, 11, 15, 17, 0),\n", + " datetime.datetime(2022, 11, 15, 18, 0),\n", + " datetime.datetime(2022, 11, 15, 19, 0),\n", + " datetime.datetime(2022, 11, 15, 20, 0),\n", + " datetime.datetime(2022, 11, 15, 21, 0),\n", + " datetime.datetime(2022, 11, 15, 22, 0),\n", + " datetime.datetime(2022, 11, 15, 23, 0),\n", + " datetime.datetime(2022, 11, 16, 0, 0),\n", + " datetime.datetime(2022, 11, 16, 1, 0),\n", + " datetime.datetime(2022, 11, 16, 2, 0),\n", + " datetime.datetime(2022, 11, 16, 3, 0),\n", + " datetime.datetime(2022, 11, 16, 4, 0),\n", + " datetime.datetime(2022, 11, 16, 5, 0),\n", + " datetime.datetime(2022, 11, 16, 6, 0),\n", + " datetime.datetime(2022, 11, 16, 7, 0),\n", + " datetime.datetime(2022, 11, 16, 8, 0),\n", + " datetime.datetime(2022, 11, 16, 9, 0),\n", + " datetime.datetime(2022, 11, 16, 10, 0),\n", + " datetime.datetime(2022, 11, 16, 11, 0),\n", + " datetime.datetime(2022, 11, 16, 12, 0),\n", + " datetime.datetime(2022, 11, 16, 13, 0),\n", + " datetime.datetime(2022, 11, 16, 14, 0),\n", + " datetime.datetime(2022, 11, 16, 15, 0),\n", + " datetime.datetime(2022, 11, 16, 16, 0),\n", + " datetime.datetime(2022, 11, 16, 17, 0),\n", + " datetime.datetime(2022, 11, 16, 18, 0),\n", + " datetime.datetime(2022, 11, 16, 19, 0),\n", + " datetime.datetime(2022, 11, 16, 20, 0),\n", + " datetime.datetime(2022, 11, 16, 21, 0),\n", + " datetime.datetime(2022, 11, 16, 22, 0),\n", + " datetime.datetime(2022, 11, 16, 23, 0),\n", + " datetime.datetime(2022, 11, 17, 0, 0)]" ] }, "execution_count": 4, @@ -202,7 +131,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Level, Latitude, Longitude" + "### 1.2. Level, Latitude, Longitude" ] }, { @@ -241,19 +170,19 @@ "data": { "text/plain": [ "{'data': masked_array(\n", - " data=[[16.371021, 16.43293 , 16.494629, ..., 16.494629, 16.43293 ,\n", - " 16.371021],\n", - " [16.503883, 16.565914, 16.627739, ..., 16.627739, 16.565918,\n", - " 16.503883],\n", - " [16.636723, 16.69888 , 16.760828, ..., 16.760828, 16.698881,\n", - " 16.636723],\n", + " data=[[16.350338, 16.43293 , 16.515146, ..., 16.515146, 16.43293 ,\n", + " 16.350338],\n", + " [16.527426, 16.610239, 16.692677, ..., 16.692677, 16.610243,\n", + " 16.527426],\n", + " [16.704472, 16.787508, 16.870167, ..., 16.870167, 16.78751 ,\n", + " 16.704472],\n", " ...,\n", - " [58.41168 , 58.525536, 58.63936 , ..., 58.63936 , 58.525547,\n", - " 58.41168 ],\n", - " [58.49049 , 58.604454, 58.718372, ..., 58.718372, 58.604454,\n", - " 58.49049 ],\n", - " [58.56883 , 58.6829 , 58.796925, ..., 58.796925, 58.682903,\n", - " 58.56883 ]],\n", + " [58.32095 , 58.472683, 58.62431 , ..., 58.62431 , 58.472683,\n", + " 58.32095 ],\n", + " [58.426285, 58.5782 , 58.730026, ..., 58.730026, 58.5782 ,\n", + " 58.426285],\n", + " [58.530792, 58.6829 , 58.83492 , ..., 58.83492 , 58.682903,\n", + " 58.530792]],\n", " mask=False,\n", " fill_value=1e+20,\n", " dtype=float32),\n", @@ -277,9 +206,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Variables\n", - "\n", - "- List of variables in lazy mode: No data" + "### 1.3. Variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### In lazy mode" ] }, { @@ -290,7 +224,7 @@ { "data": { "text/plain": [ - "dict_keys(['lmp', 'IM', 'JM', 'LM', 'IHRST', 'I_PAR_STA', 'J_PAR_STA', 'NPHS', 'NCLOD', 'NHEAT', 'NPREC', 'NRDLW', 'NRDSW', 'NSRFC', 'AVGMAXLEN', 'MDRMINout', 'MDRMAXout', 'MDIMINout', 'MDIMAXout', 'IDAT', 'DXH', 'SG1', 'SG2', 'DSG1', 'DSG2', 'SGML1', 'SGML2', 'SLDPTH', 'ISLTYP', 'IVGTYP', 'NCFRCV', 'NCFRST', 'FIS', 'GLAT', 'GLON', 'PD', 'VLAT', 'VLON', 'ACPREC', 'CUPREC', 'MIXHT', 'PBLH', 'RLWTOA', 'RSWIN', 'U10', 'USTAR', 'V10', 'RMOL', 'T2', 'relative_humidity_2m', 'T', 'U', 'V', 'SH2O', 'SMC', 'STC', 'AERO_ACPREC', 'AERO_CUPREC', 'AERO_DEPDRY', 'AERO_OPT_R', 'DRE_SW_TOA', 'DRE_SW_SFC', 'DRE_LW_TOA', 'DRE_LW_SFC', 'ENG_SW_SFC', 'ADRYDEP', 'WETDEP', 'PH_NO2', 'HSUM', 'POLR', 'aerosol_optical_depth_dim', 'aerosol_optical_depth', 'satellite_AOD_dim', 'satellite_AOD', 'aerosol_loading_dim', 'aerosol_loading', 'clear_sky_AOD_dim', 'clear_sky_AOD', 'layer_thickness', 'mid_layer_pressure', 'interface_pressure', 'relative_humidity', 'mid_layer_height', 'mid_layer_height_agl', 'air_density', 'dry_pm10_mass', 'dry_pm2p5_mass', 'QC', 'QR', 'QS', 'QG', 'aero_dust_001', 'aero_dust_002', 'aero_dust_003', 'aero_dust_004', 'aero_dust_005', 'aero_dust_006', 'aero_dust_007', 'aero_dust_008', 'aero_ssa_001', 'aero_ssa_002', 'aero_ssa_003', 'aero_ssa_004', 'aero_ssa_005', 'aero_ssa_006', 'aero_ssa_007', 'aero_ssa_008', 'aero_om_001', 'aero_om_002', 'aero_om_003', 'aero_om_004', 'aero_om_005', 'aero_om_006', 'aero_bc_001', 'aero_bc_002', 'aero_so4_001', 'aero_no3_001', 'aero_no3_002', 'aero_no3_003', 'aero_nh4_001', 'aero_unsp_001', 'aero_unsp_002', 'aero_unsp_003', 'aero_unsp_004', 'aero_unsp_005', 'aero_pol_001', 'aero_pol_002', 'aero_pol_003', 'aero_pol_004', 'aero_pol_005', 'aero_pol_006', 'aero_pol_007', 'aero_pol_008', 'aero_pol_009', 'aero_pol_010', 'NO2', 'NO', 'O3', 'NO3', 'N2O5', 'HNO3', 'HONO', 'PNA', 'H2O2', 'NTR', 'ROOH', 'FORM', 'ALD2', 'ALDX', 'PAR', 'CO', 'MEPX', 'MEOH', 'FACD', 'PAN', 'PACD', 'AACD', 'PANX', 'OLE', 'ETH', 'IOLE', 'TOL', 'CRES', 'OPEN', 'MGLY', 'XYL', 'ISOP', 'ISPD', 'TERP', 'SO2', 'SULF', 'ETOH', 'ETHA', 'CL2', 'HOCL', 'FMCL', 'HCL', 'BENZENE', 'SESQ', 'NH3', 'DMS', 'SOAP_I', 'SOAP_T', 'SOAP_F', 'SOAP_A', 'O', 'O1D', 'OH', 'HO2', 'XO2', 'XO2N', 'MEO2', 'HCO3', 'C2O3', 'CXO3', 'ROR', 'TO2', 'TOLRO2', 'CRO', 'XYLRO2', 'ISOPRXN', 'TRPRXN', 'SULRXN', 'CL', 'CLO', 'TOLNRXN', 'TOLHRXN', 'XYLNRXN', 'XYLHRXN', 'BENZRO2', 'BNZNRXN', 'BNZHRXN', 'SESQRXN', 'aerosol_extinction_dim', 'aerosol_extinction_DUST_1', 'aerosol_extinction_DUST_2', 'aerosol_extinction_DUST_3', 'aerosol_extinction_DUST_4', 'aerosol_extinction_DUST_5', 'aerosol_extinction_DUST_6', 'aerosol_extinction_DUST_7', 'aerosol_extinction_DUST_8', 'aerosol_extinction_SALT_total', 'aerosol_extinction_OM_total', 'aerosol_extinction_BC_total', 'aerosol_extinction_SO4_total', 'aerosol_extinction_NO3_total', 'aerosol_extinction_NH4_total', 'aerosol_extinction_UNSPC_1', 'aerosol_extinction_UNSPC_2', 'aerosol_extinction_UNSPC_3', 'aerosol_extinction_UNSPC_4', 'aerosol_extinction_UNSPC_5', 'aerosol_extinction_POLLEN_total'])" + "dict_keys(['lmp', 'IM', 'JM', 'LM', 'IHRST', 'I_PAR_STA', 'J_PAR_STA', 'NPHS', 'NCLOD', 'NHEAT', 'NPREC', 'NRDLW', 'NRDSW', 'NSRFC', 'AVGMAXLEN', 'MDRMINout', 'MDRMAXout', 'MDIMINout', 'MDIMAXout', 'IDAT', 'DXH', 'SG1', 'SG2', 'DSG1', 'DSG2', 'SGML1', 'SGML2', 'SLDPTH', 'ISLTYP', 'IVGTYP', 'NCFRCV', 'NCFRST', 'FIS', 'GLAT', 'GLON', 'PD', 'VLAT', 'VLON', 'ACPREC', 'CUPREC', 'MIXHT', 'PBLH', 'RLWTOA', 'RSWIN', 'U10', 'USTAR', 'V10', 'RMOL', 'T2', 'relative_humidity_2m', 'T', 'U', 'V', 'SH2O', 'SMC', 'STC', 'AERO_ACPREC', 'AERO_CUPREC', 'AERO_DEPDRY', 'AERO_OPT_R', 'DRE_SW_TOA', 'DRE_SW_SFC', 'DRE_LW_TOA', 'DRE_LW_SFC', 'ENG_SW_SFC', 'ADRYDEP', 'WETDEP', 'PH_NO2', 'HSUM', 'POLR', 'aerosol_optical_depth_dim', 'aerosol_optical_depth', 'satellite_AOD_dim', 'satellite_AOD', 'aerosol_loading_dim', 'aerosol_loading', 'clear_sky_AOD_dim', 'clear_sky_AOD', 'layer_thickness', 'mid_layer_pressure', 'interface_pressure', 'relative_humidity', 'mid_layer_height', 'mid_layer_height_agl', 'air_density', 'dry_pm10_mass', 'dry_pm2p5_mass', 'QC', 'QR', 'QS', 'QG', 'aero_dust_001', 'aero_dust_002', 'aero_dust_003', 'aero_dust_004', 'aero_dust_005', 'aero_dust_006', 'aero_dust_007', 'aero_dust_008', 'aero_ssa_001', 'aero_ssa_002', 'aero_ssa_003', 'aero_ssa_004', 'aero_ssa_005', 'aero_ssa_006', 'aero_ssa_007', 'aero_ssa_008', 'aero_om_001', 'aero_om_002', 'aero_om_003', 'aero_om_004', 'aero_om_005', 'aero_om_006', 'aero_bc_001', 'aero_bc_002', 'aero_so4_001', 'aero_no3_001', 'aero_no3_002', 'aero_no3_003', 'aero_nh4_001', 'aero_unsp_001', 'aero_unsp_002', 'aero_unsp_003', 'aero_unsp_004', 'aero_unsp_005', 'NO2', 'NO', 'O3', 'NO3', 'N2O5', 'HNO3', 'HONO', 'PNA', 'H2O2', 'NTR', 'ROOH', 'FORM', 'ALD2', 'ALDX', 'PAR', 'CO', 'MEPX', 'MEOH', 'FACD', 'PAN', 'PACD', 'AACD', 'PANX', 'OLE', 'ETH', 'IOLE', 'TOL', 'CRES', 'OPEN', 'MGLY', 'XYL', 'ISOP', 'ISPD', 'TERP', 'SO2', 'SULF', 'ETOH', 'ETHA', 'CL2', 'HOCL', 'FMCL', 'HCL', 'BENZENE', 'SESQ', 'NH3', 'DMS', 'SOAP_I', 'SOAP_T', 'SOAP_F', 'SOAP_A', 'O', 'O1D', 'OH', 'HO2', 'XO2', 'XO2N', 'MEO2', 'HCO3', 'C2O3', 'CXO3', 'ROR', 'TO2', 'TOLRO2', 'CRO', 'XYLRO2', 'ISOPRXN', 'TRPRXN', 'SULRXN', 'CL', 'CLO', 'TOLNRXN', 'TOLHRXN', 'XYLNRXN', 'XYLHRXN', 'BENZRO2', 'BNZNRXN', 'BNZHRXN', 'SESQRXN', 'aerosol_extinction_dim', 'aerosol_extinction_DUST_1', 'aerosol_extinction_DUST_2', 'aerosol_extinction_DUST_3', 'aerosol_extinction_DUST_4', 'aerosol_extinction_DUST_5', 'aerosol_extinction_DUST_6', 'aerosol_extinction_DUST_7', 'aerosol_extinction_DUST_8', 'aerosol_extinction_SALT_total', 'aerosol_extinction_OM_total', 'aerosol_extinction_BC_total', 'aerosol_extinction_SO4_total', 'aerosol_extinction_NO3_total', 'aerosol_extinction_NH4_total', 'aerosol_extinction_UNSPC_1', 'aerosol_extinction_UNSPC_2', 'aerosol_extinction_UNSPC_3', 'aerosol_extinction_UNSPC_4', 'aerosol_extinction_UNSPC_5'])" ] }, "execution_count": 7, @@ -312,9 +246,9 @@ "text/plain": [ "{'O3': {'data': None,\n", " 'dimensions': ('time', 'lm', 'rlat', 'rlon'),\n", - " 'long_name': 'TRACERS_054',\n", + " 'long_name': 'TRACERS_044',\n", " 'units': 'unknown',\n", - " 'standard_name': 'TRACERS_054',\n", + " 'standard_name': 'TRACERS_044',\n", " 'coordinates': 'lon lat',\n", " 'grid_mapping': 'rotated_pole'}}" ] @@ -330,6 +264,13 @@ "nessy.variables" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### After load" + ] + }, { "cell_type": "code", "execution_count": 9, @@ -340,9 +281,9 @@ "output_type": "stream", "text": [ "Rank 000: Loading O3 var (1/1)\n", - "Rank 000: Loaded O3 var ((109, 24, 361, 467))\n", - "CPU times: user 1.15 s, sys: 6.44 s, total: 7.6 s\n", - "Wall time: 39.5 s\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" ] } ], @@ -357,30 +298,51 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "(109, 24, 361, 467)\n", - "('time', 'lm', 'rlat', 'rlon')\n" - ] + "data": { + "text/plain": [ + "(37, 24, 271, 351)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(nessy.variables['O3']['data'].shape)\n", - "print(nessy.variables['O3']['dimensions'])" + "nessy.variables['O3']['data'].shape" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('time', 'lm', 'rlat', 'rlon')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy.variables['O3']['dimensions']" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 708 ms, sys: 497 ms, total: 1.2 s\n", - "Wall time: 14.5 s\n" + "CPU times: user 106 ms, sys: 104 ms, total: 210 ms\n", + "Wall time: 3.03 s\n" ] } ], @@ -393,20 +355,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Calculate daily statistics" + "## 2. Calculate daily statistics" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 340 ms, sys: 482 ms, total: 822 ms\n", - "Wall time: 823 ms\n" + "CPU times: user 74.8 ms, sys: 21 ms, total: 95.8 ms\n", + "Wall time: 260 ms\n" ] } ], @@ -416,34 +378,55 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "(5, 24, 361, 467)\n", - "('time', 'lm', 'rlat', 'rlon')\n" - ] + "data": { + "text/plain": [ + "(2, 24, 271, 351)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(nessy.variables['O3']['data'].shape)\n", - "print(nessy.variables['O3']['dimensions'])" + "nessy.variables['O3']['data'].shape" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('time', 'lm', 'rlat', 'rlon')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy.variables['O3']['dimensions']" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 39.4 ms, sys: 29.8 ms, total: 69.2 ms\n", - "Wall time: 715 ms\n" + "CPU times: user 18.2 ms, sys: 8.24 ms, total: 26.4 ms\n", + "Wall time: 402 ms\n" ] } ], @@ -453,27 +436,68 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Metadata 'cell_methods': time: mean (interval: 1hr)\n", - "Time: [datetime.datetime(2022, 5, 3, 0, 0), datetime.datetime(2022, 5, 4, 0, 0), datetime.datetime(2022, 5, 5, 0, 0), datetime.datetime(2022, 5, 6, 0, 0), datetime.datetime(2022, 5, 7, 0, 0)]\n", - "Time bounds: 5\n", - "[datetime.datetime(2022, 5, 3, 12, 0), datetime.datetime(2022, 5, 3, 23, 0)]\n" - ] + "data": { + "text/plain": [ + "'time: mean (interval: 1hr)'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# See metadata\n", + "nessy.variables['O3']['cell_methods']" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[datetime.datetime(2022, 11, 15, 0, 0), datetime.datetime(2022, 11, 16, 0, 0)]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# See time\n", + "nessy.time" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[datetime.datetime(2022, 11, 15, 12, 0),\n", + " datetime.datetime(2022, 11, 15, 23, 0)],\n", + " [datetime.datetime(2022, 11, 16, 0, 0),\n", + " datetime.datetime(2022, 11, 16, 23, 0)]]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "print(\"Metadata 'cell_methods':\", nessy.variables['O3']['cell_methods'])\n", - "\n", - "print(\"Time:\", nessy.time)\n", - "print(\"Time bounds:\", len(nessy.time_bnds))\n", - "\n", - "print(nessy.time_bnds[0])" + "# See bounds\n", + "nessy.time_bnds" ] } ], diff --git a/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb b/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb index 435f3463ff05277fbf3f4b7b146a622f225136c2..0fce0381e7f94c5a876685d748a252831a2aad37 100644 --- a/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.1.Vertical_Interpolation.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# How to interpolate vertically" + "# Vertical interpolation" ] }, { @@ -13,7 +13,8 @@ "metadata": {}, "outputs": [], "source": [ - "from nes import *" + "from nes import *\n", + "from datetime import datetime" ] }, { @@ -36,7 +37,9 @@ "metadata": {}, "outputs": [], "source": [ - "source_path = '/gpfs/scratch/bsc32/bsc32538/original_files/CAMS_MONARCH_d01_2022070412.nc'" + "# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc\n", + "# Rotated grid from MONARCH\n", + "source_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'" ] }, { @@ -47,7 +50,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -56,7 +59,7 @@ } ], "source": [ - "source_grid = open_netcdf(path=source_path, info=True, avoid_first_hours=12, avoid_last_hours=73)\n", + "source_grid = open_netcdf(path=source_path, info=True)\n", "source_grid" ] }, @@ -64,6 +67,16 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [], + "source": [ + "source_grid.sel(time_min=datetime(year=2022, month=11, day=16, hour=0), \n", + " time_max=datetime(year=2022, month=11, day=16, hour=0))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { @@ -78,7 +91,7 @@ " 'long_name': 'layer id'}" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -89,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -106,9 +119,9 @@ "output_type": "stream", "text": [ "Rank 000: Loading mid_layer_height_agl var (1/2)\n", - "Rank 000: Loaded mid_layer_height_agl var ((24, 24, 361, 467))\n", + "Rank 000: Loaded mid_layer_height_agl var ((1, 24, 271, 351))\n", "Rank 000: Loading O3 var (2/2)\n", - "Rank 000: Loaded O3 var ((24, 24, 361, 467))\n" + "Rank 000: Loaded O3 var ((1, 24, 271, 351))\n" ] } ], @@ -125,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -143,57 +156,35 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\tO3 vertical interpolation\n", - "\t\tO3 time step 2022-07-05 00:00:00 (1/24))\n", - "\t\tO3 time step 2022-07-05 01:00:00 (2/24))\n", - "\t\tO3 time step 2022-07-05 02:00:00 (3/24))\n", - "\t\tO3 time step 2022-07-05 03:00:00 (4/24))\n", - "\t\tO3 time step 2022-07-05 04:00:00 (5/24))\n", - "\t\tO3 time step 2022-07-05 05:00:00 (6/24))\n", - "\t\tO3 time step 2022-07-05 06:00:00 (7/24))\n", - "\t\tO3 time step 2022-07-05 07:00:00 (8/24))\n", - "\t\tO3 time step 2022-07-05 08:00:00 (9/24))\n", - "\t\tO3 time step 2022-07-05 09:00:00 (10/24))\n", - "\t\tO3 time step 2022-07-05 10:00:00 (11/24))\n", - "\t\tO3 time step 2022-07-05 11:00:00 (12/24))\n", - "\t\tO3 time step 2022-07-05 12:00:00 (13/24))\n", - "\t\tO3 time step 2022-07-05 13:00:00 (14/24))\n", - "\t\tO3 time step 2022-07-05 14:00:00 (15/24))\n", - "\t\tO3 time step 2022-07-05 15:00:00 (16/24))\n", - "\t\tO3 time step 2022-07-05 16:00:00 (17/24))\n", - "\t\tO3 time step 2022-07-05 17:00:00 (18/24))\n", - "\t\tO3 time step 2022-07-05 18:00:00 (19/24))\n", - "\t\tO3 time step 2022-07-05 19:00:00 (20/24))\n", - "\t\tO3 time step 2022-07-05 20:00:00 (21/24))\n", - "\t\tO3 time step 2022-07-05 21:00:00 (22/24))\n", - "\t\tO3 time step 2022-07-05 22:00:00 (23/24))\n", - "\t\tO3 time step 2022-07-05 23:00:00 (24/24))\n" + "\tO3 vertical methods\n", + "\t\tO3 time step 2022-11-16 00:00:00 (1/1))\n" ] } ], "source": [ - "interpolated_source_grid = source_grid.interpolate_vertical(level_list, info=True, kind='linear', extrapolate=None)" + "interpolated_source_grid = source_grid.interpolate_vertical(level_list, info=True, \n", + " kind='linear', extrapolate=None)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -204,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -226,7 +217,7 @@ " 'coordinates': 'lon lat'}" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -251,37 +242,47 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "source_data = open_netcdf(path=source_path, info=True, avoid_first_hours=12, avoid_last_hours=73)\n", + "source_data = open_netcdf(path=source_path, info=True)\n", "source_data" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "source_data.keep_vars(['O3'])" + "source_data.sel(time_min=datetime(year=2022, month=11, day=16, hour=0), \n", + " time_max=datetime(year=2022, month=11, day=16, hour=0))" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "source_data.keep_vars('O3')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -289,7 +290,7 @@ "output_type": "stream", "text": [ "Rank 000: Loading O3 var (1/1)\n", - "Rank 000: Loaded O3 var ((24, 24, 361, 467))\n" + "Rank 000: Loaded O3 var ((1, 24, 271, 351))\n" ] } ], @@ -306,28 +307,38 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "source_levels = open_netcdf(path=source_path, info=True, avoid_first_hours=12, avoid_last_hours=73)\n", + "source_levels = open_netcdf(path=source_path, info=True)\n", "source_levels" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "source_levels.sel(time_min=datetime(year=2022, month=11, day=16, hour=0), \n", + " time_max=datetime(year=2022, month=11, day=16, hour=0))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -336,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -344,7 +355,7 @@ "output_type": "stream", "text": [ "Rank 000: Loading mid_layer_height_agl var (1/1)\n", - "Rank 000: Loaded mid_layer_height_agl var ((24, 24, 361, 467))\n" + "Rank 000: Loaded mid_layer_height_agl var ((1, 24, 271, 351))\n" ] } ], @@ -361,38 +372,15 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\tO3 vertical interpolation\n", - "\t\tO3 time step 2022-07-05 00:00:00 (1/24))\n", - "\t\tO3 time step 2022-07-05 01:00:00 (2/24))\n", - "\t\tO3 time step 2022-07-05 02:00:00 (3/24))\n", - "\t\tO3 time step 2022-07-05 03:00:00 (4/24))\n", - "\t\tO3 time step 2022-07-05 04:00:00 (5/24))\n", - "\t\tO3 time step 2022-07-05 05:00:00 (6/24))\n", - "\t\tO3 time step 2022-07-05 06:00:00 (7/24))\n", - "\t\tO3 time step 2022-07-05 07:00:00 (8/24))\n", - "\t\tO3 time step 2022-07-05 08:00:00 (9/24))\n", - "\t\tO3 time step 2022-07-05 09:00:00 (10/24))\n", - "\t\tO3 time step 2022-07-05 10:00:00 (11/24))\n", - "\t\tO3 time step 2022-07-05 11:00:00 (12/24))\n", - "\t\tO3 time step 2022-07-05 12:00:00 (13/24))\n", - "\t\tO3 time step 2022-07-05 13:00:00 (14/24))\n", - "\t\tO3 time step 2022-07-05 14:00:00 (15/24))\n", - "\t\tO3 time step 2022-07-05 15:00:00 (16/24))\n", - "\t\tO3 time step 2022-07-05 16:00:00 (17/24))\n", - "\t\tO3 time step 2022-07-05 17:00:00 (18/24))\n", - "\t\tO3 time step 2022-07-05 18:00:00 (19/24))\n", - "\t\tO3 time step 2022-07-05 19:00:00 (20/24))\n", - "\t\tO3 time step 2022-07-05 20:00:00 (21/24))\n", - "\t\tO3 time step 2022-07-05 21:00:00 (22/24))\n", - "\t\tO3 time step 2022-07-05 22:00:00 (23/24))\n", - "\t\tO3 time step 2022-07-05 23:00:00 (24/24))\n" + "\tO3 vertical methods\n", + "\t\tO3 time step 2022-11-16 00:00:00 (1/1))\n" ] } ], @@ -403,16 +391,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -423,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -445,7 +433,7 @@ " 'coordinates': 'lon lat'}" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -472,6 +460,11 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } } }, "nbformat": 4, diff --git a/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb b/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb index f1f44986341b3109b57225eaaf72c6405737c46d..59189fa9bbdda796a72bcfac9d8f874b74faab20 100644 --- a/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.2.Horizontal_Interpolation.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# How to interpolate horizontally" + "# Horizontal interpolation" ] }, { @@ -29,7 +29,9 @@ "metadata": {}, "outputs": [], "source": [ - "source_path = '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc'" + "# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc\n", + "# Rotated grid from MONARCH\n", + "source_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'" ] }, { @@ -40,7 +42,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -62,26 +64,27 @@ "data": { "text/plain": [ "{'data': masked_array(\n", - " data=[[16.35033798, 16.43292999, 16.51514626, ..., 16.51514626,\n", - " 16.43292999, 16.35033798],\n", - " [16.52742577, 16.61023903, 16.69267654, ..., 16.69267654,\n", - " 16.61024284, 16.52742577],\n", - " [16.70447159, 16.78750801, 16.87016678, ..., 16.87016678,\n", - " 16.78750992, 16.70447159],\n", + " data=[[16.350338, 16.43293 , 16.515146, ..., 16.515146, 16.43293 ,\n", + " 16.350338],\n", + " [16.527426, 16.610239, 16.692677, ..., 16.692677, 16.610243,\n", + " 16.527426],\n", + " [16.704472, 16.787508, 16.870167, ..., 16.870167, 16.78751 ,\n", + " 16.704472],\n", " ...,\n", - " [58.32094955, 58.47268295, 58.62430954, ..., 58.62430954,\n", - " 58.47268295, 58.32094955],\n", - " [58.42628479, 58.57820129, 58.73002625, ..., 58.73002625,\n", - " 58.57820129, 58.42628479],\n", - " [58.53079224, 58.68289948, 58.83491898, ..., 58.83491898,\n", - " 58.68290329, 58.53079224]],\n", + " [58.32095 , 58.472683, 58.62431 , ..., 58.62431 , 58.472683,\n", + " 58.32095 ],\n", + " [58.426285, 58.5782 , 58.730026, ..., 58.730026, 58.5782 ,\n", + " 58.426285],\n", + " [58.530792, 58.6829 , 58.83492 , ..., 58.83492 , 58.682903,\n", + " 58.530792]],\n", " mask=False,\n", - " fill_value=1e+20),\n", + " fill_value=1e+20,\n", + " dtype=float32),\n", " 'dimensions': ('rlat', 'rlon'),\n", + " 'long_name': 'latitude',\n", " 'units': 'degrees_north',\n", - " 'axis': 'Y',\n", - " 'long_name': 'latitude coordinate',\n", - " 'standard_name': 'latitude'}" + " 'standard_name': 'latitude',\n", + " 'coordinates': 'lon lat'}" ] }, "execution_count": 4, @@ -102,26 +105,27 @@ "data": { "text/plain": [ "{'data': masked_array(\n", - " data=[[-22.18126488, -22.01667213, -21.85179901, ..., 41.8517952 ,\n", - " 42.01666641, 42.18125916],\n", - " [-22.27817917, -22.11318588, -21.94790459, ..., 41.94789886,\n", - " 42.11317444, 42.27817154],\n", - " [-22.37526703, -22.2098732 , -22.04418945, ..., 42.04418564,\n", - " 42.2098732 , 42.37526321],\n", + " data=[[-22.181265, -22.016672, -21.851799, ..., 41.851795, 42.016666,\n", + " 42.18126 ],\n", + " [-22.27818 , -22.113186, -21.947905, ..., 41.9479 , 42.113174,\n", + " 42.27817 ],\n", + " [-22.375267, -22.209873, -22.04419 , ..., 42.044186, 42.209873,\n", + " 42.375263],\n", " ...,\n", - " [-67.57766724, -67.39706421, -67.21534729, ..., 87.21533966,\n", - " 87.39705658, 87.57765961],\n", - " [-67.90187836, -67.72247314, -67.54193878, ..., 87.54193878,\n", - " 87.72245789, 87.90187073],\n", - " [-68.22803497, -68.04981995, -67.87051392, ..., 87.87050629,\n", - " 88.04981995, 88.22803497]],\n", + " [-67.57767 , -67.397064, -67.21535 , ..., 87.21534 , 87.39706 ,\n", + " 87.57766 ],\n", + " [-67.90188 , -67.72247 , -67.54194 , ..., 87.54194 , 87.72246 ,\n", + " 87.90187 ],\n", + " [-68.228035, -68.04982 , -67.870514, ..., 87.87051 , 88.04982 ,\n", + " 88.228035]],\n", " mask=False,\n", - " fill_value=1e+20),\n", + " fill_value=1e+20,\n", + " dtype=float32),\n", " 'dimensions': ('rlat', 'rlon'),\n", + " 'long_name': 'longitude',\n", " 'units': 'degrees_east',\n", - " 'axis': 'X',\n", - " 'long_name': 'longitude coordinate',\n", - " 'standard_name': 'longitude'}" + " 'standard_name': 'longitude',\n", + " 'coordinates': 'lon lat'}" ] }, "execution_count": 5, @@ -137,13 +141,22 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, + "outputs": [], + "source": [ + "source_grid.keep_vars('O3')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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" + "Rank 000: Loading O3 var (1/1)\n", + "Rank 000: Loaded O3 var ((37, 24, 271, 351))\n" ] } ], @@ -174,22 +187,24 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dst_grid_path = '/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/original_file/MONARCH_d01_2008123100.nc'\n", + "# Original path: /esarchive/exp/snes/a5g1/ip/daily_max/sconco3/sconco3_2022111500.nc\n", + "# LCC grid with a coverage over the Iberian Peninsula (4x4km)\n", + "dst_grid_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/sconco3_2022111500.nc'\n", "dst_grid = open_netcdf(path=dst_grid_path, info=True)\n", "dst_grid" ] @@ -202,34 +217,27 @@ { "data": { "text/plain": [ - "{'data': masked_array(data=[-90., -89., -88., -87., -86., -85., -84., -83., -82.,\n", - " -81., -80., -79., -78., -77., -76., -75., -74., -73.,\n", - " -72., -71., -70., -69., -68., -67., -66., -65., -64.,\n", - " -63., -62., -61., -60., -59., -58., -57., -56., -55.,\n", - " -54., -53., -52., -51., -50., -49., -48., -47., -46.,\n", - " -45., -44., -43., -42., -41., -40., -39., -38., -37.,\n", - " -36., -35., -34., -33., -32., -31., -30., -29., -28.,\n", - " -27., -26., -25., -24., -23., -22., -21., -20., -19.,\n", - " -18., -17., -16., -15., -14., -13., -12., -11., -10.,\n", - " -9., -8., -7., -6., -5., -4., -3., -2., -1.,\n", - " 0., 1., 2., 3., 4., 5., 6., 7., 8.,\n", - " 9., 10., 11., 12., 13., 14., 15., 16., 17.,\n", - " 18., 19., 20., 21., 22., 23., 24., 25., 26.,\n", - " 27., 28., 29., 30., 31., 32., 33., 34., 35.,\n", - " 36., 37., 38., 39., 40., 41., 42., 43., 44.,\n", - " 45., 46., 47., 48., 49., 50., 51., 52., 53.,\n", - " 54., 55., 56., 57., 58., 59., 60., 61., 62.,\n", - " 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n", - " 72., 73., 74., 75., 76., 77., 78., 79., 80.,\n", - " 81., 82., 83., 84., 85., 86., 87., 88., 89.,\n", - " 90.],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", - " 'dimensions': ('lat',),\n", - " 'long_name': 'latitude',\n", + "{'data': masked_array(\n", + " data=[[32.51078415, 32.51420212, 32.51760101, ..., 32.54065323,\n", + " 32.53738403, 32.53408432],\n", + " [32.54635239, 32.54976654, 32.55315399, ..., 32.57624817,\n", + " 32.57295609, 32.56965637],\n", + " [32.58191681, 32.58533096, 32.58874512, ..., 32.61181641,\n", + " 32.60853195, 32.60523224],\n", + " ...,\n", + " [46.58963013, 46.59383011, 46.59801102, ..., 46.62640381,\n", + " 46.62236404, 46.61830521],\n", + " [46.62516785, 46.62936783, 46.63354874, ..., 46.66195679,\n", + " 46.65791702, 46.65385818],\n", + " [46.66069794, 46.66490173, 46.66908264, ..., 46.69750214,\n", + " 46.69345856, 46.6893959 ]],\n", + " mask=False,\n", + " fill_value=1e+20),\n", + " 'dimensions': ('y', 'x'),\n", " 'units': 'degrees_north',\n", - " 'standard_name': 'grid_latitude'}" + " 'axis': 'Y',\n", + " 'long_name': 'latitude coordinate',\n", + " 'standard_name': 'latitude'}" ] }, "execution_count": 9, @@ -249,77 +257,26 @@ { "data": { "text/plain": [ - "{'data': masked_array(data=[-180. , -178.59375, -177.1875 , -175.78125,\n", - " -174.375 , -172.96875, -171.5625 , -170.15625,\n", - " -168.75 , -167.34375, -165.9375 , -164.53125,\n", - " -163.125 , -161.71875, -160.3125 , -158.90625,\n", - " -157.5 , -156.09375, -154.6875 , -153.28125,\n", - " -151.875 , -150.46875, -149.0625 , -147.65625,\n", - " -146.25 , -144.84375, -143.4375 , -142.03125,\n", - " -140.625 , -139.21875, -137.8125 , -136.40625,\n", - " -135. , -133.59375, -132.1875 , -130.78125,\n", - " -129.375 , -127.96875, -126.5625 , -125.15625,\n", - " -123.75 , -122.34375, -120.9375 , -119.53125,\n", - " -118.125 , -116.71875, -115.3125 , -113.90625,\n", - " -112.5 , -111.09375, -109.6875 , -108.28125,\n", - " -106.875 , -105.46875, -104.0625 , -102.65625,\n", - " -101.25 , -99.84375, -98.4375 , -97.03125,\n", - " -95.625 , -94.21875, -92.8125 , -91.40625,\n", - " -90. , -88.59375, -87.1875 , -85.78125,\n", - " -84.375 , -82.96875, -81.5625 , -80.15625,\n", - " -78.75 , -77.34375, -75.9375 , -74.53125,\n", - " -73.125 , -71.71875, -70.3125 , -68.90625,\n", - " -67.5 , -66.09375, -64.6875 , -63.28125,\n", - " -61.875 , -60.46875, -59.0625 , -57.65625,\n", - " -56.25 , -54.84375, -53.4375 , -52.03125,\n", - " -50.625 , -49.21875, -47.8125 , -46.40625,\n", - " -45. , -43.59375, -42.1875 , -40.78125,\n", - " -39.375 , -37.96875, -36.5625 , -35.15625,\n", - " -33.75 , -32.34375, -30.9375 , -29.53125,\n", - " -28.125 , -26.71875, -25.3125 , -23.90625,\n", - " -22.5 , -21.09375, -19.6875 , -18.28125,\n", - " -16.875 , -15.46875, -14.0625 , -12.65625,\n", - " -11.25 , -9.84375, -8.4375 , -7.03125,\n", - " -5.625 , -4.21875, -2.8125 , -1.40625,\n", - " 0. , 1.40625, 2.8125 , 4.21875,\n", - " 5.625 , 7.03125, 8.4375 , 9.84375,\n", - " 11.25 , 12.65625, 14.0625 , 15.46875,\n", - " 16.875 , 18.28125, 19.6875 , 21.09375,\n", - " 22.5 , 23.90625, 25.3125 , 26.71875,\n", - " 28.125 , 29.53125, 30.9375 , 32.34375,\n", - " 33.75 , 35.15625, 36.5625 , 37.96875,\n", - " 39.375 , 40.78125, 42.1875 , 43.59375,\n", - " 45. , 46.40625, 47.8125 , 49.21875,\n", - " 50.625 , 52.03125, 53.4375 , 54.84375,\n", - " 56.25 , 57.65625, 59.0625 , 60.46875,\n", - " 61.875 , 63.28125, 64.6875 , 66.09375,\n", - " 67.5 , 68.90625, 70.3125 , 71.71875,\n", - " 73.125 , 74.53125, 75.9375 , 77.34375,\n", - " 78.75 , 80.15625, 81.5625 , 82.96875,\n", - " 84.375 , 85.78125, 87.1875 , 88.59375,\n", - " 90. , 91.40625, 92.8125 , 94.21875,\n", - " 95.625 , 97.03125, 98.4375 , 99.84375,\n", - " 101.25 , 102.65625, 104.0625 , 105.46875,\n", - " 106.875 , 108.28125, 109.6875 , 111.09375,\n", - " 112.5 , 113.90625, 115.3125 , 116.71875,\n", - " 118.125 , 119.53125, 120.9375 , 122.34375,\n", - " 123.75 , 125.15625, 126.5625 , 127.96875,\n", - " 129.375 , 130.78125, 132.1875 , 133.59375,\n", - " 135. , 136.40625, 137.8125 , 139.21875,\n", - " 140.625 , 142.03125, 143.4375 , 144.84375,\n", - " 146.25 , 147.65625, 149.0625 , 150.46875,\n", - " 151.875 , 153.28125, 154.6875 , 156.09375,\n", - " 157.5 , 158.90625, 160.3125 , 161.71875,\n", - " 163.125 , 164.53125, 165.9375 , 167.34375,\n", - " 168.75 , 170.15625, 171.5625 , 172.96875,\n", - " 174.375 , 175.78125, 177.1875 , 178.59375,\n", - " 180. ],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", - " 'dimensions': ('lon',),\n", - " 'long_name': 'longitude',\n", + "{'data': masked_array(\n", + " data=[[-11.54882812, -11.50665283, -11.46447754, ..., 5.17233276,\n", + " 5.21453857, 5.25671387],\n", + " [-11.55288696, -11.51068115, -11.46847534, ..., 5.1762085 ,\n", + " 5.21844482, 5.26065063],\n", + " [-11.5569458 , -11.51473999, -11.47250366, ..., 5.18008423,\n", + " 5.22235107, 5.2645874 ],\n", + " ...,\n", + " [-13.51443481, -13.46273804, -13.41101074, ..., 7.05273438,\n", + " 7.10449219, 7.15625 ],\n", + " [-13.52056885, -13.46884155, -13.41708374, ..., 7.05859375,\n", + " 7.1104126 , 7.16220093],\n", + " [-13.5267334 , -13.47494507, -13.42315674, ..., 7.06448364,\n", + " 7.11630249, 7.16812134]],\n", + " mask=False,\n", + " fill_value=1e+20),\n", + " 'dimensions': ('y', 'x'),\n", " 'units': 'degrees_east',\n", + " 'axis': 'X',\n", + " 'long_name': 'longitude coordinate',\n", " 'standard_name': 'longitude'}" ] }, @@ -348,7 +305,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "\tO3_all horizontal interpolation\n" + "Creating Weight Matrix\n", + "Weight Matrix done!\n", + "Applying weights\n", + "\tO3 horizontal methods\n", + "Formatting\n" ] } ], @@ -366,7 +327,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -386,34 +347,27 @@ { "data": { "text/plain": [ - "{'data': masked_array(data=[-90., -89., -88., -87., -86., -85., -84., -83., -82.,\n", - " -81., -80., -79., -78., -77., -76., -75., -74., -73.,\n", - " -72., -71., -70., -69., -68., -67., -66., -65., -64.,\n", - " -63., -62., -61., -60., -59., -58., -57., -56., -55.,\n", - " -54., -53., -52., -51., -50., -49., -48., -47., -46.,\n", - " -45., -44., -43., -42., -41., -40., -39., -38., -37.,\n", - " -36., -35., -34., -33., -32., -31., -30., -29., -28.,\n", - " -27., -26., -25., -24., -23., -22., -21., -20., -19.,\n", - " -18., -17., -16., -15., -14., -13., -12., -11., -10.,\n", - " -9., -8., -7., -6., -5., -4., -3., -2., -1.,\n", - " 0., 1., 2., 3., 4., 5., 6., 7., 8.,\n", - " 9., 10., 11., 12., 13., 14., 15., 16., 17.,\n", - " 18., 19., 20., 21., 22., 23., 24., 25., 26.,\n", - " 27., 28., 29., 30., 31., 32., 33., 34., 35.,\n", - " 36., 37., 38., 39., 40., 41., 42., 43., 44.,\n", - " 45., 46., 47., 48., 49., 50., 51., 52., 53.,\n", - " 54., 55., 56., 57., 58., 59., 60., 61., 62.,\n", - " 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n", - " 72., 73., 74., 75., 76., 77., 78., 79., 80.,\n", - " 81., 82., 83., 84., 85., 86., 87., 88., 89.,\n", - " 90.],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", - " 'dimensions': ('lat',),\n", - " 'long_name': 'latitude',\n", + "{'data': masked_array(\n", + " data=[[32.51078415, 32.51420212, 32.51760101, ..., 32.54065323,\n", + " 32.53738403, 32.53408432],\n", + " [32.54635239, 32.54976654, 32.55315399, ..., 32.57624817,\n", + " 32.57295609, 32.56965637],\n", + " [32.58191681, 32.58533096, 32.58874512, ..., 32.61181641,\n", + " 32.60853195, 32.60523224],\n", + " ...,\n", + " [46.58963013, 46.59383011, 46.59801102, ..., 46.62640381,\n", + " 46.62236404, 46.61830521],\n", + " [46.62516785, 46.62936783, 46.63354874, ..., 46.66195679,\n", + " 46.65791702, 46.65385818],\n", + " [46.66069794, 46.66490173, 46.66908264, ..., 46.69750214,\n", + " 46.69345856, 46.6893959 ]],\n", + " mask=False,\n", + " fill_value=1e+20),\n", + " 'dimensions': ('y', 'x'),\n", " 'units': 'degrees_north',\n", - " 'standard_name': 'grid_latitude'}" + " 'axis': 'Y',\n", + " 'long_name': 'latitude coordinate',\n", + " 'standard_name': 'latitude'}" ] }, "execution_count": 13, @@ -433,77 +387,26 @@ { "data": { "text/plain": [ - "{'data': masked_array(data=[-180. , -178.59375, -177.1875 , -175.78125,\n", - " -174.375 , -172.96875, -171.5625 , -170.15625,\n", - " -168.75 , -167.34375, -165.9375 , -164.53125,\n", - " -163.125 , -161.71875, -160.3125 , -158.90625,\n", - " -157.5 , -156.09375, -154.6875 , -153.28125,\n", - " -151.875 , -150.46875, -149.0625 , -147.65625,\n", - " -146.25 , -144.84375, -143.4375 , -142.03125,\n", - " -140.625 , -139.21875, -137.8125 , -136.40625,\n", - " -135. , -133.59375, -132.1875 , -130.78125,\n", - " -129.375 , -127.96875, -126.5625 , -125.15625,\n", - " -123.75 , -122.34375, -120.9375 , -119.53125,\n", - " -118.125 , -116.71875, -115.3125 , -113.90625,\n", - " -112.5 , -111.09375, -109.6875 , -108.28125,\n", - " -106.875 , -105.46875, -104.0625 , -102.65625,\n", - " -101.25 , -99.84375, -98.4375 , -97.03125,\n", - " -95.625 , -94.21875, -92.8125 , -91.40625,\n", - " -90. , -88.59375, -87.1875 , -85.78125,\n", - " -84.375 , -82.96875, -81.5625 , -80.15625,\n", - " -78.75 , -77.34375, -75.9375 , -74.53125,\n", - " -73.125 , -71.71875, -70.3125 , -68.90625,\n", - " -67.5 , -66.09375, -64.6875 , -63.28125,\n", - " -61.875 , -60.46875, -59.0625 , -57.65625,\n", - " -56.25 , -54.84375, -53.4375 , -52.03125,\n", - " -50.625 , -49.21875, -47.8125 , -46.40625,\n", - " -45. , -43.59375, -42.1875 , -40.78125,\n", - " -39.375 , -37.96875, -36.5625 , -35.15625,\n", - " -33.75 , -32.34375, -30.9375 , -29.53125,\n", - " -28.125 , -26.71875, -25.3125 , -23.90625,\n", - " -22.5 , -21.09375, -19.6875 , -18.28125,\n", - " -16.875 , -15.46875, -14.0625 , -12.65625,\n", - " -11.25 , -9.84375, -8.4375 , -7.03125,\n", - " -5.625 , -4.21875, -2.8125 , -1.40625,\n", - " 0. , 1.40625, 2.8125 , 4.21875,\n", - " 5.625 , 7.03125, 8.4375 , 9.84375,\n", - " 11.25 , 12.65625, 14.0625 , 15.46875,\n", - " 16.875 , 18.28125, 19.6875 , 21.09375,\n", - " 22.5 , 23.90625, 25.3125 , 26.71875,\n", - " 28.125 , 29.53125, 30.9375 , 32.34375,\n", - " 33.75 , 35.15625, 36.5625 , 37.96875,\n", - " 39.375 , 40.78125, 42.1875 , 43.59375,\n", - " 45. , 46.40625, 47.8125 , 49.21875,\n", - " 50.625 , 52.03125, 53.4375 , 54.84375,\n", - " 56.25 , 57.65625, 59.0625 , 60.46875,\n", - " 61.875 , 63.28125, 64.6875 , 66.09375,\n", - " 67.5 , 68.90625, 70.3125 , 71.71875,\n", - " 73.125 , 74.53125, 75.9375 , 77.34375,\n", - " 78.75 , 80.15625, 81.5625 , 82.96875,\n", - " 84.375 , 85.78125, 87.1875 , 88.59375,\n", - " 90. , 91.40625, 92.8125 , 94.21875,\n", - " 95.625 , 97.03125, 98.4375 , 99.84375,\n", - " 101.25 , 102.65625, 104.0625 , 105.46875,\n", - " 106.875 , 108.28125, 109.6875 , 111.09375,\n", - " 112.5 , 113.90625, 115.3125 , 116.71875,\n", - " 118.125 , 119.53125, 120.9375 , 122.34375,\n", - " 123.75 , 125.15625, 126.5625 , 127.96875,\n", - " 129.375 , 130.78125, 132.1875 , 133.59375,\n", - " 135. , 136.40625, 137.8125 , 139.21875,\n", - " 140.625 , 142.03125, 143.4375 , 144.84375,\n", - " 146.25 , 147.65625, 149.0625 , 150.46875,\n", - " 151.875 , 153.28125, 154.6875 , 156.09375,\n", - " 157.5 , 158.90625, 160.3125 , 161.71875,\n", - " 163.125 , 164.53125, 165.9375 , 167.34375,\n", - " 168.75 , 170.15625, 171.5625 , 172.96875,\n", - " 174.375 , 175.78125, 177.1875 , 178.59375,\n", - " 180. ],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", - " 'dimensions': ('lon',),\n", - " 'long_name': 'longitude',\n", + "{'data': masked_array(\n", + " data=[[-11.54882812, -11.50665283, -11.46447754, ..., 5.17233276,\n", + " 5.21453857, 5.25671387],\n", + " [-11.55288696, -11.51068115, -11.46847534, ..., 5.1762085 ,\n", + " 5.21844482, 5.26065063],\n", + " [-11.5569458 , -11.51473999, -11.47250366, ..., 5.18008423,\n", + " 5.22235107, 5.2645874 ],\n", + " ...,\n", + " [-13.51443481, -13.46273804, -13.41101074, ..., 7.05273438,\n", + " 7.10449219, 7.15625 ],\n", + " [-13.52056885, -13.46884155, -13.41708374, ..., 7.05859375,\n", + " 7.1104126 , 7.16220093],\n", + " [-13.5267334 , -13.47494507, -13.42315674, ..., 7.06448364,\n", + " 7.11630249, 7.16812134]],\n", + " mask=False,\n", + " fill_value=1e+20),\n", + " 'dimensions': ('y', 'x'),\n", " 'units': 'degrees_east',\n", + " 'axis': 'X',\n", + " 'long_name': 'longitude coordinate',\n", " 'standard_name': 'longitude'}" ] }, @@ -605,7 +508,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "\tO3_all horizontal interpolation\n" + "Creating Weight Matrix\n", + "Weight Matrix done!\n", + "Applying weights\n", + "\tO3 horizontal methods\n", + "Formatting\n" ] } ], @@ -623,7 +530,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 19, @@ -695,6 +602,11 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } } }, "nbformat": 4, diff --git a/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb b/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..48b02162d5e4e8167290f02a4975d774d920f514 --- /dev/null +++ b/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb @@ -0,0 +1,396 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Horizontal conservative interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from nes import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Read data to interpolate" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc\n", + "# Rotated grid from MONARCH (NAMEE)\n", + "source_path = \"/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "source_grid = open_netcdf(path=source_path, info=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(\n", + " data=[[16.350338, 16.43293 , 16.515146, ..., 16.515146, 16.43293 ,\n", + " 16.350338],\n", + " [16.527426, 16.610239, 16.692677, ..., 16.692677, 16.610243,\n", + " 16.527426],\n", + " [16.704472, 16.787508, 16.870167, ..., 16.870167, 16.78751 ,\n", + " 16.704472],\n", + " ...,\n", + " [58.32095 , 58.472683, 58.62431 , ..., 58.62431 , 58.472683,\n", + " 58.32095 ],\n", + " [58.426285, 58.5782 , 58.730026, ..., 58.730026, 58.5782 ,\n", + " 58.426285],\n", + " [58.530792, 58.6829 , 58.83492 , ..., 58.83492 , 58.682903,\n", + " 58.530792]],\n", + " mask=False,\n", + " fill_value=1e+20,\n", + " dtype=float32),\n", + " 'dimensions': ('rlat', 'rlon'),\n", + " 'long_name': 'latitude',\n", + " 'units': 'degrees_north',\n", + " 'standard_name': 'latitude',\n", + " 'coordinates': 'lon lat'}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source_grid.lat" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(\n", + " data=[[-22.181265, -22.016672, -21.851799, ..., 41.851795, 42.016666,\n", + " 42.18126 ],\n", + " [-22.27818 , -22.113186, -21.947905, ..., 41.9479 , 42.113174,\n", + " 42.27817 ],\n", + " [-22.375267, -22.209873, -22.04419 , ..., 42.044186, 42.209873,\n", + " 42.375263],\n", + " ...,\n", + " [-67.57767 , -67.397064, -67.21535 , ..., 87.21534 , 87.39706 ,\n", + " 87.57766 ],\n", + " [-67.90188 , -67.72247 , -67.54194 , ..., 87.54194 , 87.72246 ,\n", + " 87.90187 ],\n", + " [-68.228035, -68.04982 , -67.870514, ..., 87.87051 , 88.04982 ,\n", + " 88.228035]],\n", + " mask=False,\n", + " fill_value=1e+20,\n", + " dtype=float32),\n", + " 'dimensions': ('rlat', 'rlon'),\n", + " 'long_name': 'longitude',\n", + " 'units': 'degrees_east',\n", + " 'standard_name': 'longitude',\n", + " 'coordinates': 'lon lat'}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source_grid.lon" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank 000: Loading O3 var (1/1)\n", + "Rank 000: Loaded O3 var ((37, 24, 271, 351))\n" + ] + } + ], + "source": [ + "source_grid.keep_vars('O3')\n", + "source_grid.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Create destination grid" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "lat_1 = 37\n", + "lat_2 = 43\n", + "lon_0 = -3\n", + "lat_0 = 40\n", + "nx = 397\n", + "ny = 397\n", + "inc_x = 4000\n", + "inc_y = 4000\n", + "x_0 = -807847.688\n", + "y_0 = -797137.125\n", + "dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,\n", + " nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, times=source_grid.get_full_times())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Interpolation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1. Without creating weight matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([[32.49460816, 32.49803615, 32.50144726, ..., 32.52460207,\n", + " 32.52130788, 32.51799681],\n", + " [32.53024662, 32.5336765 , 32.5370895 , ..., 32.5602571 ,\n", + " 32.55696109, 32.55364819],\n", + " [32.5658877 , 32.56931947, 32.57273435, ..., 32.59591474,\n", + " 32.59261692, 32.58930218],\n", + " ...,\n", + " [46.60231473, 46.60653579, 46.6107361 , ..., 46.63924881,\n", + " 46.63519229, 46.63111499],\n", + " [46.63791957, 46.64214277, 46.6463452 , ..., 46.67487234,\n", + " 46.67081377, 46.66673441],\n", + " [46.67352141, 46.67774674, 46.68195131, ..., 46.71049289,\n", + " 46.70643226, 46.70235083]])}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interp_nes.lat" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([[-11.56484687, -11.52259289, -11.48033507, ..., 5.18764453,\n", + " 5.22992847, 5.27220869],\n", + " [-11.56892309, -11.52664925, -11.48437156, ..., 5.1915433 ,\n", + " 5.23384715, 5.27614727],\n", + " [-11.57300318, -11.53070946, -11.48841188, ..., 5.19544578,\n", + " 5.23776955, 5.2800896 ],\n", + " ...,\n", + " [-13.53865445, -13.48682654, -13.4349915 , ..., 7.07590089,\n", + " 7.12778439, 7.17966098],\n", + " [-13.54481681, -13.49295916, -13.44109437, ..., 7.08179741,\n", + " 7.13371074, 7.18561716],\n", + " [-13.55098635, -13.49909893, -13.44720434, ..., 7.0877008 ,\n", + " 7.139644 , 7.19158028]])}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interp_nes.lon" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2. Creating weight matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Weight Matrix\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2041: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", + " time_var.units, time_var.calendar)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Weight Matrix done!\n" + ] + } + ], + "source": [ + "wm_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', info=True,\n", + " weight_matrix_path=\"CONS_WM_NAMEE_to_IP.nc\", only_create_wm=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', wm=wm_nes)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([[32.49460816, 32.49803615, 32.50144726, ..., 32.52460207,\n", + " 32.52130788, 32.51799681],\n", + " [32.53024662, 32.5336765 , 32.5370895 , ..., 32.5602571 ,\n", + " 32.55696109, 32.55364819],\n", + " [32.5658877 , 32.56931947, 32.57273435, ..., 32.59591474,\n", + " 32.59261692, 32.58930218],\n", + " ...,\n", + " [46.60231473, 46.60653579, 46.6107361 , ..., 46.63924881,\n", + " 46.63519229, 46.63111499],\n", + " [46.63791957, 46.64214277, 46.6463452 , ..., 46.67487234,\n", + " 46.67081377, 46.66673441],\n", + " [46.67352141, 46.67774674, 46.68195131, ..., 46.71049289,\n", + " 46.70643226, 46.70235083]])}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interp_nes.lat" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([[-11.56484687, -11.52259289, -11.48033507, ..., 5.18764453,\n", + " 5.22992847, 5.27220869],\n", + " [-11.56892309, -11.52664925, -11.48437156, ..., 5.1915433 ,\n", + " 5.23384715, 5.27614727],\n", + " [-11.57300318, -11.53070946, -11.48841188, ..., 5.19544578,\n", + " 5.23776955, 5.2800896 ],\n", + " ...,\n", + " [-13.53865445, -13.48682654, -13.4349915 , ..., 7.07590089,\n", + " 7.12778439, 7.17966098],\n", + " [-13.54481681, -13.49295916, -13.44109437, ..., 7.08179741,\n", + " 7.13371074, 7.18561716],\n", + " [-13.55098635, -13.49909893, -13.44720434, ..., 7.0877008 ,\n", + " 7.139644 , 7.19158028]])}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interp_nes.lon" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/4.Interpolation/4.3.Providentia_Interpolation.ipynb b/tutorials/4.Interpolation/4.3.Providentia_Interpolation.ipynb deleted file mode 100644 index 6618c680438a8d8e215476926fc1e2505ec51e87..0000000000000000000000000000000000000000 --- a/tutorials/4.Interpolation/4.3.Providentia_Interpolation.ipynb +++ /dev/null @@ -1,1656 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to interpolate horizontally using Providentia format" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from nes import *\n", - "import xarray as xr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Read data to interpolate" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "source_path = '/esarchive/recon/ecmwf/cams61/cams61_chimere_ph2/eu/hourly/sconco3/sconco3_201804.nc'" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "source_grid = open_netcdf(path=source_path, info=True)\n", - "source_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'sconco3': {'data': None,\n", - " 'dimensions': ('time', 'lat', 'lon'),\n", - " 'coordinates': 'lat lon',\n", - " 'grid_mapping': 'crs',\n", - " 'units': 'ppb'}}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "source_grid.variables" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': masked_array(data=[30. , 30.2, 30.4, 30.6, 30.8, 31. , 31.2, 31.4, 31.6,\n", - " 31.8, 32. , 32.2, 32.4, 32.6, 32.8, 33. , 33.2, 33.4,\n", - " 33.6, 33.8, 34. , 34.2, 34.4, 34.6, 34.8, 35. , 35.2,\n", - " 35.4, 35.6, 35.8, 36. , 36.2, 36.4, 36.6, 36.8, 37. ,\n", - " 37.2, 37.4, 37.6, 37.8, 38. , 38.2, 38.4, 38.6, 38.8,\n", - " 39. , 39.2, 39.4, 39.6, 39.8, 40. , 40.2, 40.4, 40.6,\n", - " 40.8, 41. , 41.2, 41.4, 41.6, 41.8, 42. , 42.2, 42.4,\n", - " 42.6, 42.8, 43. , 43.2, 43.4, 43.6, 43.8, 44. , 44.2,\n", - " 44.4, 44.6, 44.8, 45. , 45.2, 45.4, 45.6, 45.8, 46. ,\n", - " 46.2, 46.4, 46.6, 46.8, 47. , 47.2, 47.4, 47.6, 47.8,\n", - " 48. , 48.2, 48.4, 48.6, 48.8, 49. , 49.2, 49.4, 49.6,\n", - " 49.8, 50. , 50.2, 50.4, 50.6, 50.8, 51. , 51.2, 51.4,\n", - " 51.6, 51.8, 52. , 52.2, 52.4, 52.6, 52.8, 53. , 53.2,\n", - " 53.4, 53.6, 53.8, 54. , 54.2, 54.4, 54.6, 54.8, 55. ,\n", - " 55.2, 55.4, 55.6, 55.8, 56. , 56.2, 56.4, 56.6, 56.8,\n", - " 57. , 57.2, 57.4, 57.6, 57.8, 58. , 58.2, 58.4, 58.6,\n", - " 58.8, 59. , 59.2, 59.4, 59.6, 59.8, 60. , 60.2, 60.4,\n", - " 60.6, 60.8, 61. , 61.2, 61.4, 61.6, 61.8, 62. , 62.2,\n", - " 62.4, 62.6, 62.8, 63. , 63.2, 63.4, 63.6, 63.8, 64. ,\n", - " 64.2, 64.4, 64.6, 64.8, 65. , 65.2, 65.4, 65.6, 65.8,\n", - " 66. , 66.2, 66.4, 66.6, 66.8, 67. , 67.2, 67.4, 67.6,\n", - " 67.8, 68. , 68.2, 68.4, 68.6, 68.8, 69. , 69.2, 69.4,\n", - " 69.6, 69.8, 70. , 70.2, 70.4, 70.6, 70.8, 71. , 71.2,\n", - " 71.4, 71.6, 71.8, 72. ],\n", - " mask=False,\n", - " fill_value=1e+20),\n", - " 'dimensions': ('lat',),\n", - " 'standard_name': 'latitude',\n", - " 'long_name': 'Latitude',\n", - " 'units': 'degrees_north',\n", - " 'axis': 'Y'}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "source_grid.lat" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': masked_array(data=[-25. , -24.8, -24.6, -24.4, -24.2, -24. , -23.8, -23.6,\n", - " -23.4, -23.2, -23. , -22.8, -22.6, -22.4, -22.2, -22. ,\n", - " -21.8, -21.6, -21.4, -21.2, -21. , -20.8, -20.6, -20.4,\n", - " -20.2, -20. , -19.8, -19.6, -19.4, -19.2, -19. , -18.8,\n", - " -18.6, -18.4, -18.2, -18. , -17.8, -17.6, -17.4, -17.2,\n", - " -17. , -16.8, -16.6, -16.4, -16.2, -16. , -15.8, -15.6,\n", - " -15.4, -15.2, -15. , -14.8, -14.6, -14.4, -14.2, -14. ,\n", - " -13.8, -13.6, -13.4, -13.2, -13. , -12.8, -12.6, -12.4,\n", - " -12.2, -12. , -11.8, -11.6, -11.4, -11.2, -11. , -10.8,\n", - " -10.6, -10.4, -10.2, -10. , -9.8, -9.6, -9.4, -9.2,\n", - " -9. , -8.8, -8.6, -8.4, -8.2, -8. , -7.8, -7.6,\n", - " -7.4, -7.2, -7. , -6.8, -6.6, -6.4, -6.2, -6. ,\n", - " -5.8, -5.6, -5.4, -5.2, -5. , -4.8, -4.6, -4.4,\n", - " -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. , -2.8,\n", - " -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", - " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4,\n", - " 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ,\n", - " 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6,\n", - " 3.8, 4. , 4.2, 4.4, 4.6, 4.8, 5. , 5.2,\n", - " 5.4, 5.6, 5.8, 6. , 6.2, 6.4, 6.6, 6.8,\n", - " 7. , 7.2, 7.4, 7.6, 7.8, 8. , 8.2, 8.4,\n", - " 8.6, 8.8, 9. , 9.2, 9.4, 9.6, 9.8, 10. ,\n", - " 10.2, 10.4, 10.6, 10.8, 11. , 11.2, 11.4, 11.6,\n", - " 11.8, 12. , 12.2, 12.4, 12.6, 12.8, 13. , 13.2,\n", - " 13.4, 13.6, 13.8, 14. , 14.2, 14.4, 14.6, 14.8,\n", - " 15. , 15.2, 15.4, 15.6, 15.8, 16. , 16.2, 16.4,\n", - " 16.6, 16.8, 17. , 17.2, 17.4, 17.6, 17.8, 18. ,\n", - " 18.2, 18.4, 18.6, 18.8, 19. , 19.2, 19.4, 19.6,\n", - " 19.8, 20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2,\n", - " 21.4, 21.6, 21.8, 22. , 22.2, 22.4, 22.6, 22.8,\n", - " 23. , 23.2, 23.4, 23.6, 23.8, 24. , 24.2, 24.4,\n", - " 24.6, 24.8, 25. , 25.2, 25.4, 25.6, 25.8, 26. ,\n", - " 26.2, 26.4, 26.6, 26.8, 27. , 27.2, 27.4, 27.6,\n", - " 27.8, 28. , 28.2, 28.4, 28.6, 28.8, 29. , 29.2,\n", - " 29.4, 29.6, 29.8, 30. , 30.2, 30.4, 30.6, 30.8,\n", - " 31. , 31.2, 31.4, 31.6, 31.8, 32. , 32.2, 32.4,\n", - " 32.6, 32.8, 33. , 33.2, 33.4, 33.6, 33.8, 34. ,\n", - " 34.2, 34.4, 34.6, 34.8, 35. , 35.2, 35.4, 35.6,\n", - " 35.8, 36. , 36.2, 36.4, 36.6, 36.8, 37. , 37.2,\n", - " 37.4, 37.6, 37.8, 38. , 38.2, 38.4, 38.6, 38.8,\n", - " 39. , 39.2, 39.4, 39.6, 39.8, 40. , 40.2, 40.4,\n", - " 40.6, 40.8, 41. , 41.2, 41.4, 41.6, 41.8, 42. ,\n", - " 42.2, 42.4, 42.6, 42.8, 43. , 43.2, 43.4, 43.6,\n", - " 43.8, 44. , 44.2, 44.4, 44.6, 44.8, 45. ],\n", - " mask=False,\n", - " fill_value=1e+20),\n", - " 'dimensions': ('lon',),\n", - " 'standard_name': 'longitude',\n", - " 'long_name': 'Longitude',\n", - " 'units': 'degrees_east',\n", - " 'axis': 'X'}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "source_grid.lon" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rank 000: Loading sconco3 var (1/1)\n", - "Rank 000: Loaded sconco3 var ((720, 1, 211, 351))\n" - ] - } - ], - "source": [ - "source_grid.load()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometry
FID
0POLYGON ((-25.10000 29.90000, -24.90000 29.900...
1POLYGON ((-24.90000 29.90000, -24.70000 29.900...
2POLYGON ((-24.70000 29.90000, -24.50000 29.900...
3POLYGON ((-24.50000 29.90000, -24.30000 29.900...
4POLYGON ((-24.30000 29.90000, -24.10000 29.900...
......
74056POLYGON ((44.10000 71.90000, 44.30000 71.90000...
74057POLYGON ((44.30000 71.90000, 44.50000 71.90000...
74058POLYGON ((44.50000 71.90000, 44.70000 71.90000...
74059POLYGON ((44.70000 71.90000, 44.90000 71.90000...
74060POLYGON ((44.90000 71.90000, 45.10000 71.90000...
\n", - "

74061 rows × 1 columns

\n", - "
" - ], - "text/plain": [ - " geometry\n", - "FID \n", - "0 POLYGON ((-25.10000 29.90000, -24.90000 29.900...\n", - "1 POLYGON ((-24.90000 29.90000, -24.70000 29.900...\n", - "2 POLYGON ((-24.70000 29.90000, -24.50000 29.900...\n", - "3 POLYGON ((-24.50000 29.90000, -24.30000 29.900...\n", - "4 POLYGON ((-24.30000 29.90000, -24.10000 29.900...\n", - "... ...\n", - "74056 POLYGON ((44.10000 71.90000, 44.30000 71.90000...\n", - "74057 POLYGON ((44.30000 71.90000, 44.50000 71.90000...\n", - "74058 POLYGON ((44.50000 71.90000, 44.70000 71.90000...\n", - "74059 POLYGON ((44.70000 71.90000, 44.90000 71.90000...\n", - "74060 POLYGON ((44.90000 71.90000, 45.10000 71.90000...\n", - "\n", - "[74061 rows x 1 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "source_grid.create_shapefile()\n", - "source_grid.shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "source_grid.shapefile['sconco3'] = source_grid.variables['sconco3']['data'][0, 0, :, :].ravel()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "source_grid.write_shapefile('model')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Interpolation" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:69: UserWarning: Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\n", - " warnings.warn(\"Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\")\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dst_grid_path = '/gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.4/hourly/sconco3/sconco3_201804.nc'\n", - "dst_grid = open_netcdf(path=dst_grid_path, info=True)\n", - "dst_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': masked_array(data=[-64.24006 , -54.84846497, 47.76666641, 46.677778 ,\n", - " 48.721111 , 47.529167 , 47.05407 , 47.348056 ,\n", - " 47.973056 , 48.878611 , 48.106111 , 48.371111 ,\n", - " 48.334722 , 48.050833 , 47.838611 , 47.040277 ,\n", - " 47.06694444, 49.877778 , 50.629421 , 50.503333 ,\n", - " 41.695833 , 32.27000046, 80.05000305, 46.5475 ,\n", - " 46.813056 , 47.479722 , 47.049722 , 47.0675 ,\n", - " 47.18961391, -30.17254 , 16.86403 , 35.0381 ,\n", - " 49.73508444, 49.573394 , 49.066667 , 54.925556 ,\n", - " 52.802222 , 47.914722 , 53.166667 , 50.65 ,\n", - " 54.4368 , 47.80149841, 47.4165 , -70.666 ,\n", - " 54.746495 , 81.6 , 55.693588 , 72.58000183,\n", - " 56.290424 , 59.5 , 58.383333 , 39.54694 ,\n", - " 42.72056 , 39.87528 , 37.23722 , 43.43917 ,\n", - " 41.27417 , 42.31917 , 38.47278 , 39.08278 ,\n", - " 41.23889 , 41.39389 , 42.63472 , 37.05194 ,\n", - " 28.309 , 59.779167 , 60.53002 , 66.320278 ,\n", - " 67.97333333, 48.5 , 49.9 , 47.266667 ,\n", - " 43.616667 , 47.3 , 46.65 , 45. ,\n", - " 45.8 , 48.633333 , 42.936667 , 44.56944444,\n", - " 46.81472778, 45.772223 , 55.313056 , 54.443056 ,\n", - " 50.596389 , 54.334444 , 57.734444 , 52.503889 ,\n", - " 55.858611 , 53.398889 , 50.792778 , 52.293889 ,\n", - " 51.781784 , 52.298333 , 55.79216 , 52.950556 ,\n", - " 51.778056 , 60.13922 , 51.149617 , 38.366667 ,\n", - " 35.316667 , 46.966667 , 46.91 , -0.20194 ,\n", - " 51.939722 , 53.32583 , 45.8 , 44.183333 ,\n", - " 37.571111 , 42.805462 , -69.005 , 39.0319 ,\n", - " 24.2883 , 24.466941 , 36.53833389, 33.293917 ,\n", - " 55.37611111, 56.161944 , 57.135278 , 36.0722 ,\n", - " 52.083333 , 53.333889 , 51.541111 , 52.3 ,\n", - " 51.974444 , 58.38853 , 65.833333 , 62.783333 ,\n", - " 78.90715 , 59. , 69.45 , 59.2 ,\n", - " 60.372386 , -72.0117 , 59.2 , -41.40819168,\n", - " -77.83200073, -45.0379982 , 51.814408 , 50.736444 ,\n", - " 54.753894 , 54.15 , 43.4 , 71.58616638,\n", - " 63.85 , 67.883333 , 57.394 , 57.1645 ,\n", - " 57.9525 , 56.0429 , 60.0858 , 57.816667 ,\n", - " 64.25 , 59.728 , 45.566667 , 46.428611 ,\n", - " 46.299444 , 48.933333 , 49.15 , 49.05 ,\n", - " 47.96 , 71.32301331, 40.12498 , 19.53623009,\n", - " -89.99694824, 41.05410004, 21.5731 , -34.35348 ],\n", - " mask=False,\n", - " fill_value=1e+20),\n", - " 'dimensions': ('station',),\n", - " 'standard_name': 'latitude',\n", - " 'long_name': 'latitude',\n", - " 'units': 'decimal degrees North',\n", - " 'description': 'Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum.',\n", - " 'axis': 'Y'}" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dst_grid.lat" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': masked_array(data=[-5.66247800e+01, -6.83106918e+01, 1.67666664e+01,\n", - " 1.29722220e+01, 1.59422220e+01, 9.92666700e+00,\n", - " 1.29579400e+01, 1.58822220e+01, 1.30161110e+01,\n", - " 1.50466670e+01, 1.59194440e+01, 1.55466670e+01,\n", - " 1.67305560e+01, 1.66766670e+01, 1.44413890e+01,\n", - " 1.43300000e+01, 1.54936111e+01, 5.20361100e+00,\n", - " 6.00101900e+00, 4.98944400e+00, 2.47386110e+01,\n", - " -6.48799973e+01, -8.64166565e+01, 7.98500000e+00,\n", - " 6.94472200e+00, 8.90472200e+00, 6.97944400e+00,\n", - " 8.46388900e+00, 8.17543368e+00, -7.07992300e+01,\n", - " -2.48675200e+01, 3.30578000e+01, 1.60341969e+01,\n", - " 1.50802780e+01, 1.36000000e+01, 8.30972200e+00,\n", - " 1.07594440e+01, 7.90861100e+00, 1.30333330e+01,\n", - " 1.07666670e+01, 1.27249000e+01, 1.10096197e+01,\n", - " 1.09796400e+01, -8.26600000e+00, 1.07361600e+01,\n", - " -1.66700000e+01, 1.20857970e+01, -3.84799995e+01,\n", - " 8.42748600e+00, 2.59000000e+01, 2.18166670e+01,\n", - " -4.35056000e+00, -8.92361000e+00, 4.31639000e+00,\n", - " -3.53417000e+00, -4.85000000e+00, -3.14250000e+00,\n", - " 3.31583000e+00, -6.92361000e+00, -1.10111000e+00,\n", - " -5.89750000e+00, 7.34720000e-01, -7.70472000e+00,\n", - " -6.55528000e+00, -1.64994000e+01, 2.13772220e+01,\n", - " 2.76675400e+01, 2.94016670e+01, 2.41161111e+01,\n", - " 7.13333300e+00, 4.63333300e+00, 4.08333300e+00,\n", - " 1.83333000e-01, 6.83333300e+00, -7.50000000e-01,\n", - " 6.46666700e+00, 2.06666700e+00, -4.50000000e-01,\n", - " 1.41944000e-01, 5.27897222e+00, 2.61000833e+00,\n", - " 2.96488600e+00, -3.20416700e+00, -7.87000000e+00,\n", - " -3.71305600e+00, -8.07500000e-01, -4.77444400e+00,\n", - " -3.03305600e+00, -3.20500000e+00, -1.75333300e+00,\n", - " 1.79444000e-01, 1.46305600e+00, -4.69146200e+00,\n", - " 2.92778000e-01, -3.24290000e+00, 1.12194400e+00,\n", - " 1.08223000e+00, -1.18531900e+00, -1.43822800e+00,\n", - " 2.30833330e+01, 2.56666670e+01, 1.95833330e+01,\n", - " 1.63200000e+01, 1.00318100e+02, -1.02444440e+01,\n", - " -9.89944000e+00, 8.63333300e+00, 1.07000000e+01,\n", - " 1.26597220e+01, 1.25656450e+01, 3.95905556e+01,\n", - " 1.41822200e+02, 1.53983300e+02, 1.23010872e+02,\n", - " 1.26330002e+02, 1.26163111e+02, 2.10305556e+01,\n", - " 2.11730560e+01, 2.59055560e+01, 1.42184000e+01,\n", - " 6.56666700e+00, 6.27722200e+00, 5.85361100e+00,\n", - " 4.50000000e+00, 4.92361100e+00, 8.25200000e+00,\n", - " 1.39166670e+01, 8.88333300e+00, 1.18866800e+01,\n", - " 1.15333330e+01, 3.00333330e+01, 5.20000000e+00,\n", - " 1.10781420e+01, 2.53510000e+00, 9.51666700e+00,\n", - " 1.74870804e+02, 1.66660004e+02, 1.69684006e+02,\n", - " 2.19724190e+01, 1.57395000e+01, 1.75342640e+01,\n", - " 2.20666670e+01, 2.19500000e+01, 1.28918823e+02,\n", - " 1.53333330e+01, 2.10666670e+01, 1.19140000e+01,\n", - " 1.47825000e+01, 1.24030000e+01, 1.31480000e+01,\n", - " 1.75052800e+01, 1.55666670e+01, 1.97666670e+01,\n", - " 1.54720000e+01, 1.48666670e+01, 1.50033330e+01,\n", - " 1.45386110e+01, 1.95833330e+01, 2.02833330e+01,\n", - " 2.22666670e+01, 1.78605560e+01, -1.56611465e+02,\n", - " -1.05236800e+02, -1.55576157e+02, -2.47999992e+01,\n", - " -1.24151001e+02, 1.03515700e+02, 1.84896800e+01],\n", - " mask=False,\n", - " fill_value=1e+20),\n", - " 'dimensions': ('station',),\n", - " 'standard_name': 'longitude',\n", - " 'long_name': 'longitude',\n", - " 'units': 'decimal degrees East',\n", - " 'description': 'Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum.',\n", - " 'axis': 'X'}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dst_grid.lon" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\tsconco3 horizontal interpolation\n", - "sconco3\n" - ] - } - ], - "source": [ - "interpolated_source_grid = source_grid.interpolate_horizontal(dst_grid, weight_matrix_path=None, \n", - " kind='NearestNeighbour', n_neighbours=4,\n", - " info=True, to_providentia=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': masked_array(data=[-64.24006 , -54.84846497, 47.76666641, 46.677778 ,\n", - " 48.721111 , 47.529167 , 47.05407 , 47.348056 ,\n", - " 47.973056 , 48.878611 , 48.106111 , 48.371111 ,\n", - " 48.334722 , 48.050833 , 47.838611 , 47.040277 ,\n", - " 47.06694444, 49.877778 , 50.629421 , 50.503333 ,\n", - " 41.695833 , 32.27000046, 80.05000305, 46.5475 ,\n", - " 46.813056 , 47.479722 , 47.049722 , 47.0675 ,\n", - " 47.18961391, -30.17254 , 16.86403 , 35.0381 ,\n", - " 49.73508444, 49.573394 , 49.066667 , 54.925556 ,\n", - " 52.802222 , 47.914722 , 53.166667 , 50.65 ,\n", - " 54.4368 , 47.80149841, 47.4165 , -70.666 ,\n", - " 54.746495 , 81.6 , 55.693588 , 72.58000183,\n", - " 56.290424 , 59.5 , 58.383333 , 39.54694 ,\n", - " 42.72056 , 39.87528 , 37.23722 , 43.43917 ,\n", - " 41.27417 , 42.31917 , 38.47278 , 39.08278 ,\n", - " 41.23889 , 41.39389 , 42.63472 , 37.05194 ,\n", - " 28.309 , 59.779167 , 60.53002 , 66.320278 ,\n", - " 67.97333333, 48.5 , 49.9 , 47.266667 ,\n", - " 43.616667 , 47.3 , 46.65 , 45. ,\n", - " 45.8 , 48.633333 , 42.936667 , 44.56944444,\n", - " 46.81472778, 45.772223 , 55.313056 , 54.443056 ,\n", - " 50.596389 , 54.334444 , 57.734444 , 52.503889 ,\n", - " 55.858611 , 53.398889 , 50.792778 , 52.293889 ,\n", - " 51.781784 , 52.298333 , 55.79216 , 52.950556 ,\n", - " 51.778056 , 60.13922 , 51.149617 , 38.366667 ,\n", - " 35.316667 , 46.966667 , 46.91 , -0.20194 ,\n", - " 51.939722 , 53.32583 , 45.8 , 44.183333 ,\n", - " 37.571111 , 42.805462 , -69.005 , 39.0319 ,\n", - " 24.2883 , 24.466941 , 36.53833389, 33.293917 ,\n", - " 55.37611111, 56.161944 , 57.135278 , 36.0722 ,\n", - " 52.083333 , 53.333889 , 51.541111 , 52.3 ,\n", - " 51.974444 , 58.38853 , 65.833333 , 62.783333 ,\n", - " 78.90715 , 59. , 69.45 , 59.2 ,\n", - " 60.372386 , -72.0117 , 59.2 , -41.40819168,\n", - " -77.83200073, -45.0379982 , 51.814408 , 50.736444 ,\n", - " 54.753894 , 54.15 , 43.4 , 71.58616638,\n", - " 63.85 , 67.883333 , 57.394 , 57.1645 ,\n", - " 57.9525 , 56.0429 , 60.0858 , 57.816667 ,\n", - " 64.25 , 59.728 , 45.566667 , 46.428611 ,\n", - " 46.299444 , 48.933333 , 49.15 , 49.05 ,\n", - " 47.96 , 71.32301331, 40.12498 , 19.53623009,\n", - " -89.99694824, 41.05410004, 21.5731 , -34.35348 ],\n", - " mask=False,\n", - " fill_value=1e+20)}" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "interpolated_source_grid.lat" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'data': masked_array(data=[-5.66247800e+01, -6.83106918e+01, 1.67666664e+01,\n", - " 1.29722220e+01, 1.59422220e+01, 9.92666700e+00,\n", - " 1.29579400e+01, 1.58822220e+01, 1.30161110e+01,\n", - " 1.50466670e+01, 1.59194440e+01, 1.55466670e+01,\n", - " 1.67305560e+01, 1.66766670e+01, 1.44413890e+01,\n", - " 1.43300000e+01, 1.54936111e+01, 5.20361100e+00,\n", - " 6.00101900e+00, 4.98944400e+00, 2.47386110e+01,\n", - " -6.48799973e+01, -8.64166565e+01, 7.98500000e+00,\n", - " 6.94472200e+00, 8.90472200e+00, 6.97944400e+00,\n", - " 8.46388900e+00, 8.17543368e+00, -7.07992300e+01,\n", - " -2.48675200e+01, 3.30578000e+01, 1.60341969e+01,\n", - " 1.50802780e+01, 1.36000000e+01, 8.30972200e+00,\n", - " 1.07594440e+01, 7.90861100e+00, 1.30333330e+01,\n", - " 1.07666670e+01, 1.27249000e+01, 1.10096197e+01,\n", - " 1.09796400e+01, -8.26600000e+00, 1.07361600e+01,\n", - " -1.66700000e+01, 1.20857970e+01, -3.84799995e+01,\n", - " 8.42748600e+00, 2.59000000e+01, 2.18166670e+01,\n", - " -4.35056000e+00, -8.92361000e+00, 4.31639000e+00,\n", - " -3.53417000e+00, -4.85000000e+00, -3.14250000e+00,\n", - " 3.31583000e+00, -6.92361000e+00, -1.10111000e+00,\n", - " -5.89750000e+00, 7.34720000e-01, -7.70472000e+00,\n", - " -6.55528000e+00, -1.64994000e+01, 2.13772220e+01,\n", - " 2.76675400e+01, 2.94016670e+01, 2.41161111e+01,\n", - " 7.13333300e+00, 4.63333300e+00, 4.08333300e+00,\n", - " 1.83333000e-01, 6.83333300e+00, -7.50000000e-01,\n", - " 6.46666700e+00, 2.06666700e+00, -4.50000000e-01,\n", - " 1.41944000e-01, 5.27897222e+00, 2.61000833e+00,\n", - " 2.96488600e+00, -3.20416700e+00, -7.87000000e+00,\n", - " -3.71305600e+00, -8.07500000e-01, -4.77444400e+00,\n", - " -3.03305600e+00, -3.20500000e+00, -1.75333300e+00,\n", - " 1.79444000e-01, 1.46305600e+00, -4.69146200e+00,\n", - " 2.92778000e-01, -3.24290000e+00, 1.12194400e+00,\n", - " 1.08223000e+00, -1.18531900e+00, -1.43822800e+00,\n", - " 2.30833330e+01, 2.56666670e+01, 1.95833330e+01,\n", - " 1.63200000e+01, 1.00318100e+02, -1.02444440e+01,\n", - " -9.89944000e+00, 8.63333300e+00, 1.07000000e+01,\n", - " 1.26597220e+01, 1.25656450e+01, 3.95905556e+01,\n", - " 1.41822200e+02, 1.53983300e+02, 1.23010872e+02,\n", - " 1.26330002e+02, 1.26163111e+02, 2.10305556e+01,\n", - " 2.11730560e+01, 2.59055560e+01, 1.42184000e+01,\n", - " 6.56666700e+00, 6.27722200e+00, 5.85361100e+00,\n", - " 4.50000000e+00, 4.92361100e+00, 8.25200000e+00,\n", - " 1.39166670e+01, 8.88333300e+00, 1.18866800e+01,\n", - " 1.15333330e+01, 3.00333330e+01, 5.20000000e+00,\n", - " 1.10781420e+01, 2.53510000e+00, 9.51666700e+00,\n", - " 1.74870804e+02, 1.66660004e+02, 1.69684006e+02,\n", - " 2.19724190e+01, 1.57395000e+01, 1.75342640e+01,\n", - " 2.20666670e+01, 2.19500000e+01, 1.28918823e+02,\n", - " 1.53333330e+01, 2.10666670e+01, 1.19140000e+01,\n", - " 1.47825000e+01, 1.24030000e+01, 1.31480000e+01,\n", - " 1.75052800e+01, 1.55666670e+01, 1.97666670e+01,\n", - " 1.54720000e+01, 1.48666670e+01, 1.50033330e+01,\n", - " 1.45386110e+01, 1.95833330e+01, 2.02833330e+01,\n", - " 2.22666670e+01, 1.78605560e+01, -1.56611465e+02,\n", - " -1.05236800e+02, -1.55576157e+02, -2.47999992e+01,\n", - " -1.24151001e+02, 1.03515700e+02, 1.84896800e+01],\n", - " mask=False,\n", - " fill_value=1e+20)}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "interpolated_source_grid.lon" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometry
FID
0POINT (-56.62478 -64.24006)
1POINT (-68.31069 -54.84846)
2POINT (16.76667 47.76667)
3POINT (12.97222 46.67778)
4POINT (15.94222 48.72111)
......
163POINT (-155.57616 19.53623)
164POINT (-24.80000 -89.99695)
165POINT (-124.15100 41.05410)
166POINT (103.51570 21.57310)
167POINT (18.48968 -34.35348)
\n", - "

168 rows × 1 columns

\n", - "
" - ], - "text/plain": [ - " geometry\n", - "FID \n", - "0 POINT (-56.62478 -64.24006)\n", - "1 POINT (-68.31069 -54.84846)\n", - "2 POINT (16.76667 47.76667)\n", - "3 POINT (12.97222 46.67778)\n", - "4 POINT (15.94222 48.72111)\n", - ".. ...\n", - "163 POINT (-155.57616 19.53623)\n", - "164 POINT (-24.80000 -89.99695)\n", - "165 POINT (-124.15100 41.05410)\n", - "166 POINT (103.51570 21.57310)\n", - "167 POINT (18.48968 -34.35348)\n", - "\n", - "[168 rows x 1 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "interpolated_source_grid.create_shapefile()\n", - "interpolated_source_grid.shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "interpolated_source_grid.shapefile['sconco3'] = interpolated_source_grid.variables['sconco3']['data'][:, 0].ravel()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "interpolated_source_grid.write_shapefile('interpolated_points')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Compare outputs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### From NES" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "interpolated_source_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[53.66018016, 53.83203477, 53.63669104, ..., 53.2461802 ,\n", - " 53.21874343, 53.10930358],\n", - " [53.72655496, 53.84765007, 53.58983273, ..., 53.39846363,\n", - " 53.19921385, 52.96872593],\n", - " [39.71929631, 40.01980784, 39.58707525, ..., 54.41383702,\n", - " 53.30565688, 51.58627296],\n", - " ...,\n", - " [50.31659288, 50.36346616, 50.3400049 , ..., 50.5349958 ,\n", - " 50.66003373, 50.60927108],\n", - " [38.61718492, 39.64453277, 40.28125328, ..., 47.66795862,\n", - " 46.42186248, 45.68748656],\n", - " [44.8632812 , 44.78906232, 44.64843684, ..., 45.16406056,\n", - " 45.16796676, 45.30077914]])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "interpolated_source_grid.variables['sconco3']['data']" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/points_nes_providentia.py:585: UserWarning: WARNING!!! Providentia datasets cannot be written in parallel yet. Changing to serial mode.\n", - " warnings.warn(msg)\n" - ] - } - ], - "source": [ - "interpolated_source_grid.to_netcdf('interpolated_source_grid.nc')" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset>\n",
-       "Dimensions:                 (time: 720, station: 168, model_latitude: 211, model_longitude: 351, grid_edge: 1125)\n",
-       "Coordinates:\n",
-       "  * time                    (time) datetime64[ns] 2018-04-01 ... 2018-04-30T2...\n",
-       "  * station                 (station) float64 0.0 1.0 2.0 ... 165.0 166.0 167.0\n",
-       "Dimensions without coordinates: model_latitude, model_longitude, grid_edge\n",
-       "Data variables:\n",
-       "    lat                     (station) float64 -64.24 -54.85 ... 21.57 -34.35\n",
-       "    lon                     (station) float64 -56.62 -68.31 ... 103.5 18.49\n",
-       "    model_centre_longitude  (model_latitude, model_longitude) float64 -25.0 ....\n",
-       "    model_centre_latitude   (model_latitude, model_longitude) float64 30.0 .....\n",
-       "    grid_edge_longitude     (grid_edge) float64 -25.1 -25.1 ... -24.9 -25.1\n",
-       "    grid_edge_latitude      (grid_edge) float64 29.9 30.1 30.3 ... 29.9 29.9\n",
-       "    sconco3                 (station, time) float64 ...\n",
-       "Attributes:\n",
-       "    Conventions:  CF-1.7
" - ], - "text/plain": [ - "\n", - "Dimensions: (time: 720, station: 168, model_latitude: 211, model_longitude: 351, grid_edge: 1125)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 2018-04-01 ... 2018-04-30T2...\n", - " * station (station) float64 0.0 1.0 2.0 ... 165.0 166.0 167.0\n", - "Dimensions without coordinates: model_latitude, model_longitude, grid_edge\n", - "Data variables:\n", - " lat (station) float64 ...\n", - " lon (station) float64 ...\n", - " model_centre_longitude (model_latitude, model_longitude) float64 ...\n", - " model_centre_latitude (model_latitude, model_longitude) float64 ...\n", - " grid_edge_longitude (grid_edge) float64 ...\n", - " grid_edge_latitude (grid_edge) float64 ...\n", - " sconco3 (station, time) float64 ...\n", - "Attributes:\n", - " Conventions: CF-1.7" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "xr.open_dataset('interpolated_source_grid.nc')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### From Providentia IT" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "tool_interpolated_grid_path = '/gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.3.3/cams61_chimere_ph2-eu-000/hourly/sconco3/EBAS/sconco3_201804.nc'" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:69: UserWarning: Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\n", - " warnings.warn(\"Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\")\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tool_interpolated_grid = open_netcdf(path=tool_interpolated_grid_path, info=True)\n", - "tool_interpolated_grid" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rank 000: Loading sconco3 var (1/2)\n", - "Rank 000: Loaded sconco3 var ((175, 720))\n", - "Rank 000: Loading station_reference var (2/2)\n", - "Rank 000: Loaded station_reference var ((175,))\n" - ] - } - ], - "source": [ - "tool_interpolated_grid.load()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "masked_array(\n", - " data=[[nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " ...,\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan],\n", - " [nan, nan, nan, ..., nan, nan, nan]],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32)" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tool_interpolated_grid.variables['sconco3']['data']" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometry
FID
0POINT (-56.62478 -64.24006)
1POINT (-68.31069 -54.84846)
2POINT (-65.60083 -22.10333)
3POINT (-63.88194 -31.66861)
4POINT (16.76667 47.76667)
......
170POINT (-155.57616 19.53623)
171POINT (-24.80000 -89.99695)
172POINT (-124.15100 41.05410)
173POINT (103.51570 21.57310)
174POINT (18.48968 -34.35348)
\n", - "

175 rows × 1 columns

\n", - "
" - ], - "text/plain": [ - " geometry\n", - "FID \n", - "0 POINT (-56.62478 -64.24006)\n", - "1 POINT (-68.31069 -54.84846)\n", - "2 POINT (-65.60083 -22.10333)\n", - "3 POINT (-63.88194 -31.66861)\n", - "4 POINT (16.76667 47.76667)\n", - ".. ...\n", - "170 POINT (-155.57616 19.53623)\n", - "171 POINT (-24.80000 -89.99695)\n", - "172 POINT (-124.15100 41.05410)\n", - "173 POINT (103.51570 21.57310)\n", - "174 POINT (18.48968 -34.35348)\n", - "\n", - "[175 rows x 1 columns]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tool_interpolated_grid.create_shapefile()\n", - "tool_interpolated_grid.shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "tool_interpolated_grid.shapefile['sconco3'] = tool_interpolated_grid.variables['sconco3']['data'][:, 0].ravel()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "tool_interpolated_grid.write_shapefile('providentia_it_points')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb b/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..3b60ef0036d9502820be7c26e1f4deba4c9f5571 --- /dev/null +++ b/tutorials/4.Interpolation/4.4.Providentia_Interpolation.ipynb @@ -0,0 +1,1005 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to interpolate horizontally using Providentia format" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from nes import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Read data to interpolate" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Original path: /esarchive/recon/ecmwf/cams61/cams61_chimere_ph2/eu/hourly/sconco3/sconco3_201804.nc\n", + "# Regular lat-lon grid from CAMS \n", + "source_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/exp_sconco3_201804.nc'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source_grid = open_netcdf(path=source_path, info=True)\n", + "source_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sconco3': {'data': None,\n", + " 'dimensions': ('time', 'lat', 'lon'),\n", + " 'coordinates': 'lat lon',\n", + " 'grid_mapping': 'crs',\n", + " 'units': 'ppb'}}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source_grid.variables" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(data=[30. , 30.2, 30.4, 30.6, 30.8, 31. , 31.2, 31.4, 31.6,\n", + " 31.8, 32. , 32.2, 32.4, 32.6, 32.8, 33. , 33.2, 33.4,\n", + " 33.6, 33.8, 34. , 34.2, 34.4, 34.6, 34.8, 35. , 35.2,\n", + " 35.4, 35.6, 35.8, 36. , 36.2, 36.4, 36.6, 36.8, 37. ,\n", + " 37.2, 37.4, 37.6, 37.8, 38. , 38.2, 38.4, 38.6, 38.8,\n", + " 39. , 39.2, 39.4, 39.6, 39.8, 40. , 40.2, 40.4, 40.6,\n", + " 40.8, 41. , 41.2, 41.4, 41.6, 41.8, 42. , 42.2, 42.4,\n", + " 42.6, 42.8, 43. , 43.2, 43.4, 43.6, 43.8, 44. , 44.2,\n", + " 44.4, 44.6, 44.8, 45. , 45.2, 45.4, 45.6, 45.8, 46. ,\n", + " 46.2, 46.4, 46.6, 46.8, 47. , 47.2, 47.4, 47.6, 47.8,\n", + " 48. , 48.2, 48.4, 48.6, 48.8, 49. , 49.2, 49.4, 49.6,\n", + " 49.8, 50. , 50.2, 50.4, 50.6, 50.8, 51. , 51.2, 51.4,\n", + " 51.6, 51.8, 52. , 52.2, 52.4, 52.6, 52.8, 53. , 53.2,\n", + " 53.4, 53.6, 53.8, 54. , 54.2, 54.4, 54.6, 54.8, 55. ,\n", + " 55.2, 55.4, 55.6, 55.8, 56. , 56.2, 56.4, 56.6, 56.8,\n", + " 57. , 57.2, 57.4, 57.6, 57.8, 58. , 58.2, 58.4, 58.6,\n", + " 58.8, 59. , 59.2, 59.4, 59.6, 59.8, 60. , 60.2, 60.4,\n", + " 60.6, 60.8, 61. , 61.2, 61.4, 61.6, 61.8, 62. , 62.2,\n", + " 62.4, 62.6, 62.8, 63. , 63.2, 63.4, 63.6, 63.8, 64. ,\n", + " 64.2, 64.4, 64.6, 64.8, 65. , 65.2, 65.4, 65.6, 65.8,\n", + " 66. , 66.2, 66.4, 66.6, 66.8, 67. , 67.2, 67.4, 67.6,\n", + " 67.8, 68. , 68.2, 68.4, 68.6, 68.8, 69. , 69.2, 69.4,\n", + " 69.6, 69.8, 70. , 70.2, 70.4, 70.6, 70.8, 71. , 71.2,\n", + " 71.4, 71.6, 71.8, 72. ],\n", + " mask=False,\n", + " fill_value=1e+20),\n", + " 'dimensions': ('lat',),\n", + " 'standard_name': 'latitude',\n", + " 'long_name': 'Latitude',\n", + " 'units': 'degrees_north',\n", + " 'axis': 'Y'}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source_grid.lat" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(data=[-25. , -24.8, -24.6, -24.4, -24.2, -24. , -23.8, -23.6,\n", + " -23.4, -23.2, -23. , -22.8, -22.6, -22.4, -22.2, -22. ,\n", + " -21.8, -21.6, -21.4, -21.2, -21. , -20.8, -20.6, -20.4,\n", + " -20.2, -20. , -19.8, -19.6, -19.4, -19.2, -19. , -18.8,\n", + " -18.6, -18.4, -18.2, -18. , -17.8, -17.6, -17.4, -17.2,\n", + " -17. , -16.8, -16.6, -16.4, -16.2, -16. , -15.8, -15.6,\n", + " -15.4, -15.2, -15. , -14.8, -14.6, -14.4, -14.2, -14. ,\n", + " -13.8, -13.6, -13.4, -13.2, -13. , -12.8, -12.6, -12.4,\n", + " -12.2, -12. , -11.8, -11.6, -11.4, -11.2, -11. , -10.8,\n", + " -10.6, -10.4, -10.2, -10. , -9.8, -9.6, -9.4, -9.2,\n", + " -9. , -8.8, -8.6, -8.4, -8.2, -8. , -7.8, -7.6,\n", + " -7.4, -7.2, -7. , -6.8, -6.6, -6.4, -6.2, -6. ,\n", + " -5.8, -5.6, -5.4, -5.2, -5. , -4.8, -4.6, -4.4,\n", + " -4.2, -4. , -3.8, -3.6, -3.4, -3.2, -3. , -2.8,\n", + " -2.6, -2.4, -2.2, -2. , -1.8, -1.6, -1.4, -1.2,\n", + " -1. , -0.8, -0.6, -0.4, -0.2, 0. , 0.2, 0.4,\n", + " 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ,\n", + " 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6,\n", + " 3.8, 4. , 4.2, 4.4, 4.6, 4.8, 5. , 5.2,\n", + " 5.4, 5.6, 5.8, 6. , 6.2, 6.4, 6.6, 6.8,\n", + " 7. , 7.2, 7.4, 7.6, 7.8, 8. , 8.2, 8.4,\n", + " 8.6, 8.8, 9. , 9.2, 9.4, 9.6, 9.8, 10. ,\n", + " 10.2, 10.4, 10.6, 10.8, 11. , 11.2, 11.4, 11.6,\n", + " 11.8, 12. , 12.2, 12.4, 12.6, 12.8, 13. , 13.2,\n", + " 13.4, 13.6, 13.8, 14. , 14.2, 14.4, 14.6, 14.8,\n", + " 15. , 15.2, 15.4, 15.6, 15.8, 16. , 16.2, 16.4,\n", + " 16.6, 16.8, 17. , 17.2, 17.4, 17.6, 17.8, 18. ,\n", + " 18.2, 18.4, 18.6, 18.8, 19. , 19.2, 19.4, 19.6,\n", + " 19.8, 20. , 20.2, 20.4, 20.6, 20.8, 21. , 21.2,\n", + " 21.4, 21.6, 21.8, 22. , 22.2, 22.4, 22.6, 22.8,\n", + " 23. , 23.2, 23.4, 23.6, 23.8, 24. , 24.2, 24.4,\n", + " 24.6, 24.8, 25. , 25.2, 25.4, 25.6, 25.8, 26. ,\n", + " 26.2, 26.4, 26.6, 26.8, 27. , 27.2, 27.4, 27.6,\n", + " 27.8, 28. , 28.2, 28.4, 28.6, 28.8, 29. , 29.2,\n", + " 29.4, 29.6, 29.8, 30. , 30.2, 30.4, 30.6, 30.8,\n", + " 31. , 31.2, 31.4, 31.6, 31.8, 32. , 32.2, 32.4,\n", + " 32.6, 32.8, 33. , 33.2, 33.4, 33.6, 33.8, 34. ,\n", + " 34.2, 34.4, 34.6, 34.8, 35. , 35.2, 35.4, 35.6,\n", + " 35.8, 36. , 36.2, 36.4, 36.6, 36.8, 37. , 37.2,\n", + " 37.4, 37.6, 37.8, 38. , 38.2, 38.4, 38.6, 38.8,\n", + " 39. , 39.2, 39.4, 39.6, 39.8, 40. , 40.2, 40.4,\n", + " 40.6, 40.8, 41. , 41.2, 41.4, 41.6, 41.8, 42. ,\n", + " 42.2, 42.4, 42.6, 42.8, 43. , 43.2, 43.4, 43.6,\n", + " 43.8, 44. , 44.2, 44.4, 44.6, 44.8, 45. ],\n", + " mask=False,\n", + " fill_value=1e+20),\n", + " 'dimensions': ('lon',),\n", + " 'standard_name': 'longitude',\n", + " 'long_name': 'Longitude',\n", + " 'units': 'degrees_east',\n", + " 'axis': 'X'}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source_grid.lon" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank 000: Loading sconco3 var (1/1)\n", + "Rank 000: Loaded sconco3 var ((720, 1, 211, 351))\n" + ] + } + ], + "source": [ + "source_grid.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "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", + " 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", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "source_grid.to_shapefile(path='model_shp', var_list=['sconco3'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
geometrysconco3
FID
0POLYGON ((-25.10000 29.90000, -24.90000 29.900...53.734375
1POLYGON ((-24.90000 29.90000, -24.70000 29.900...53.718746
2POLYGON ((-24.70000 29.90000, -24.50000 29.900...53.656250
3POLYGON ((-24.50000 29.90000, -24.30000 29.900...53.531246
4POLYGON ((-24.30000 29.90000, -24.10000 29.900...53.281250
.........
74056POLYGON ((44.10000 71.90000, 44.30000 71.90000...42.718750
74057POLYGON ((44.30000 71.90000, 44.50000 71.90000...42.499996
74058POLYGON ((44.50000 71.90000, 44.70000 71.90000...42.203125
74059POLYGON ((44.70000 71.90000, 44.90000 71.90000...41.843746
74060POLYGON ((44.90000 71.90000, 45.10000 71.90000...41.453125
\n", + "

74061 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " geometry sconco3\n", + "FID \n", + "0 POLYGON ((-25.10000 29.90000, -24.90000 29.900... 53.734375\n", + "1 POLYGON ((-24.90000 29.90000, -24.70000 29.900... 53.718746\n", + "2 POLYGON ((-24.70000 29.90000, -24.50000 29.900... 53.656250\n", + "3 POLYGON ((-24.50000 29.90000, -24.30000 29.900... 53.531246\n", + "4 POLYGON ((-24.30000 29.90000, -24.10000 29.900... 53.281250\n", + "... ... ...\n", + "74056 POLYGON ((44.10000 71.90000, 44.30000 71.90000... 42.718750\n", + "74057 POLYGON ((44.30000 71.90000, 44.50000 71.90000... 42.499996\n", + "74058 POLYGON ((44.50000 71.90000, 44.70000 71.90000... 42.203125\n", + "74059 POLYGON ((44.70000 71.90000, 44.90000 71.90000... 41.843746\n", + "74060 POLYGON ((44.90000 71.90000, 45.10000 71.90000... 41.453125\n", + "\n", + "[74061 rows x 2 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source_grid.shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:69: UserWarning: Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\n", + " warnings.warn(\"Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\")\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Original path: /gpfs/projects/bsc32/AC_cache/obs/ghost/EBAS/1.3.3/hourly/sconco3/sconco3_201804.nc\n", + "# Points grid from EBAS network\n", + "dst_grid_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/obs_sconco3_201804.nc'\n", + "dst_grid = open_netcdf(path=dst_grid_path, info=True)\n", + "dst_grid" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(data=[-64.24006 , -54.84846497, 47.76666641, 46.677778 ,\n", + " 48.721111 , 47.529167 , 47.05407 , 47.348056 ,\n", + " 47.973056 , 48.878611 , 48.106111 , 48.371111 ,\n", + " 48.334722 , 48.050833 , 47.838611 , 47.040277 ,\n", + " 47.06694444, 49.877778 , 50.629421 , 50.503333 ,\n", + " 41.695833 , 32.27000046, 80.05000305, 46.5475 ,\n", + " 46.813056 , 47.479722 , 47.049722 , 47.0675 ,\n", + " 47.18961391, -30.17254 , 16.86403 , 35.0381 ,\n", + " 49.73508444, 49.573394 , 49.066667 , 54.925556 ,\n", + " 52.802222 , 47.914722 , 53.166667 , 50.65 ,\n", + " 54.4368 , 47.80149841, 47.4165 , -70.666 ,\n", + " 54.746495 , 81.6 , 55.693588 , 72.58000183,\n", + " 56.290424 , 59.5 , 58.383333 , 39.54694 ,\n", + " 42.72056 , 39.87528 , 37.23722 , 43.43917 ,\n", + " 41.27417 , 42.31917 , 38.47278 , 39.08278 ,\n", + " 41.23889 , 41.39389 , 42.63472 , 37.05194 ,\n", + " 28.309 , 59.779167 , 60.53002 , 66.320278 ,\n", + " 67.97333333, 48.5 , 49.9 , 47.266667 ,\n", + " 43.616667 , 47.3 , 46.65 , 45. ,\n", + " 45.8 , 48.633333 , 42.936667 , 44.56944444,\n", + " 46.81472778, 45.772223 , 55.313056 , 54.443056 ,\n", + " 50.596389 , 54.334444 , 57.734444 , 52.503889 ,\n", + " 55.858611 , 53.398889 , 50.792778 , 52.293889 ,\n", + " 51.781784 , 52.298333 , 55.79216 , 52.950556 ,\n", + " 51.778056 , 60.13922 , 51.149617 , 38.366667 ,\n", + " 35.316667 , 46.966667 , 46.91 , -0.20194 ,\n", + " 51.939722 , 53.32583 , 45.8 , 44.183333 ,\n", + " 37.571111 , 42.805462 , -69.005 , 39.0319 ,\n", + " 24.2883 , 24.466941 , 36.53833389, 33.293917 ,\n", + " 55.37611111, 56.161944 , 57.135278 , 36.0722 ,\n", + " 52.083333 , 53.333889 , 51.541111 , 52.3 ,\n", + " 51.974444 , 58.38853 , 65.833333 , 62.783333 ,\n", + " 78.90715 , 59. , 69.45 , 59.2 ,\n", + " 60.372386 , -72.0117 , 59.2 , -41.40819168,\n", + " -77.83200073, -45.0379982 , 51.814408 , 50.736444 ,\n", + " 54.753894 , 54.15 , 43.4 , 71.58616638,\n", + " 63.85 , 67.883333 , 57.394 , 57.1645 ,\n", + " 57.9525 , 56.0429 , 60.0858 , 57.816667 ,\n", + " 64.25 , 59.728 , 45.566667 , 46.428611 ,\n", + " 46.299444 , 48.933333 , 49.15 , 49.05 ,\n", + " 47.96 , 71.32301331, 40.12498 , 19.53623009,\n", + " -89.99694824, 41.05410004, 21.5731 , -34.35348 ],\n", + " mask=False,\n", + " fill_value=1e+20),\n", + " 'dimensions': ('station',),\n", + " 'standard_name': 'latitude',\n", + " 'long_name': 'latitude',\n", + " 'units': 'decimal degrees North',\n", + " 'description': 'Geodetic latitude of measuring instrument, in decimal degrees North, following the stated horizontal datum.',\n", + " 'axis': 'Y'}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dst_grid.lat" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(data=[-5.66247800e+01, -6.83106918e+01, 1.67666664e+01,\n", + " 1.29722220e+01, 1.59422220e+01, 9.92666700e+00,\n", + " 1.29579400e+01, 1.58822220e+01, 1.30161110e+01,\n", + " 1.50466670e+01, 1.59194440e+01, 1.55466670e+01,\n", + " 1.67305560e+01, 1.66766670e+01, 1.44413890e+01,\n", + " 1.43300000e+01, 1.54936111e+01, 5.20361100e+00,\n", + " 6.00101900e+00, 4.98944400e+00, 2.47386110e+01,\n", + " -6.48799973e+01, -8.64166565e+01, 7.98500000e+00,\n", + " 6.94472200e+00, 8.90472200e+00, 6.97944400e+00,\n", + " 8.46388900e+00, 8.17543368e+00, -7.07992300e+01,\n", + " -2.48675200e+01, 3.30578000e+01, 1.60341969e+01,\n", + " 1.50802780e+01, 1.36000000e+01, 8.30972200e+00,\n", + " 1.07594440e+01, 7.90861100e+00, 1.30333330e+01,\n", + " 1.07666670e+01, 1.27249000e+01, 1.10096197e+01,\n", + " 1.09796400e+01, -8.26600000e+00, 1.07361600e+01,\n", + " -1.66700000e+01, 1.20857970e+01, -3.84799995e+01,\n", + " 8.42748600e+00, 2.59000000e+01, 2.18166670e+01,\n", + " -4.35056000e+00, -8.92361000e+00, 4.31639000e+00,\n", + " -3.53417000e+00, -4.85000000e+00, -3.14250000e+00,\n", + " 3.31583000e+00, -6.92361000e+00, -1.10111000e+00,\n", + " -5.89750000e+00, 7.34720000e-01, -7.70472000e+00,\n", + " -6.55528000e+00, -1.64994000e+01, 2.13772220e+01,\n", + " 2.76675400e+01, 2.94016670e+01, 2.41161111e+01,\n", + " 7.13333300e+00, 4.63333300e+00, 4.08333300e+00,\n", + " 1.83333000e-01, 6.83333300e+00, -7.50000000e-01,\n", + " 6.46666700e+00, 2.06666700e+00, -4.50000000e-01,\n", + " 1.41944000e-01, 5.27897222e+00, 2.61000833e+00,\n", + " 2.96488600e+00, -3.20416700e+00, -7.87000000e+00,\n", + " -3.71305600e+00, -8.07500000e-01, -4.77444400e+00,\n", + " -3.03305600e+00, -3.20500000e+00, -1.75333300e+00,\n", + " 1.79444000e-01, 1.46305600e+00, -4.69146200e+00,\n", + " 2.92778000e-01, -3.24290000e+00, 1.12194400e+00,\n", + " 1.08223000e+00, -1.18531900e+00, -1.43822800e+00,\n", + " 2.30833330e+01, 2.56666670e+01, 1.95833330e+01,\n", + " 1.63200000e+01, 1.00318100e+02, -1.02444440e+01,\n", + " -9.89944000e+00, 8.63333300e+00, 1.07000000e+01,\n", + " 1.26597220e+01, 1.25656450e+01, 3.95905556e+01,\n", + " 1.41822200e+02, 1.53983300e+02, 1.23010872e+02,\n", + " 1.26330002e+02, 1.26163111e+02, 2.10305556e+01,\n", + " 2.11730560e+01, 2.59055560e+01, 1.42184000e+01,\n", + " 6.56666700e+00, 6.27722200e+00, 5.85361100e+00,\n", + " 4.50000000e+00, 4.92361100e+00, 8.25200000e+00,\n", + " 1.39166670e+01, 8.88333300e+00, 1.18866800e+01,\n", + " 1.15333330e+01, 3.00333330e+01, 5.20000000e+00,\n", + " 1.10781420e+01, 2.53510000e+00, 9.51666700e+00,\n", + " 1.74870804e+02, 1.66660004e+02, 1.69684006e+02,\n", + " 2.19724190e+01, 1.57395000e+01, 1.75342640e+01,\n", + " 2.20666670e+01, 2.19500000e+01, 1.28918823e+02,\n", + " 1.53333330e+01, 2.10666670e+01, 1.19140000e+01,\n", + " 1.47825000e+01, 1.24030000e+01, 1.31480000e+01,\n", + " 1.75052800e+01, 1.55666670e+01, 1.97666670e+01,\n", + " 1.54720000e+01, 1.48666670e+01, 1.50033330e+01,\n", + " 1.45386110e+01, 1.95833330e+01, 2.02833330e+01,\n", + " 2.22666670e+01, 1.78605560e+01, -1.56611465e+02,\n", + " -1.05236800e+02, -1.55576157e+02, -2.47999992e+01,\n", + " -1.24151001e+02, 1.03515700e+02, 1.84896800e+01],\n", + " mask=False,\n", + " fill_value=1e+20),\n", + " 'dimensions': ('station',),\n", + " 'standard_name': 'longitude',\n", + " 'long_name': 'longitude',\n", + " 'units': 'decimal degrees East',\n", + " 'description': 'Geodetic longitude of measuring instrument, in decimal degrees East, following the stated horizontal datum.',\n", + " 'axis': 'X'}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dst_grid.lon" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "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", + " \n", + " \n", + " [[[53.812496, 53.875 , 53.874996, ..., 56.187496, 55.421875,\n", + " 54.843746],\n", + " [53.828125, 53.92187 , 53.875 , ..., 54.921875, 54.499996,\n", + " 53.765625],\n", + " [53.843746, 53.90625 , 53.89062 , ..., 52.85937 , 53.34375 ,\n", + " 53.062496],\n", + " ...,\n", + " [49.26562 , 49.859375, 50.60937 , ..., 41.781246, 41.6875 ,\n", + " 41.656246],\n", + " [48.765625, 49.60937 , 51.015625, ..., 42.03125 , 41.843746,\n", + " 41.671875],\n", + " [50.95312 , 50.9375 , 50.79687 , ..., 42.562496, 42.140625,\n", + " 41.781246]]],\n", + " \n", + " \n", + " [[[53.546875, 53.593746, 53.65625 , ..., 55.140625, 54.499996,\n", + " 53.96875 ],\n", + " [53.562496, 53.625 , 53.76562 , ..., 53.156246, 52.9375 ,\n", + " 52.57812 ],\n", + " [53.546875, 53.624996, 53.734375, ..., 50.96875 , 51.79687 ,\n", + " 51.828125],\n", + " ...,\n", + " [49.3125 , 49.843746, 50.46875 , ..., 42.25 , 42.14062 ,\n", + " 42.03125 ],\n", + " [48.874996, 49.578125, 50.92187 , ..., 42.17187 , 42.0625 ,\n", + " 42.031246],\n", + " [50.859375, 50.874996, 50.75 , ..., 42.84375 , 42.48437 ,\n", + " 42.125 ]]],\n", + " \n", + " \n", + " ...,\n", + " \n", + " \n", + " [[[53.374996, 53.5625 , 53.374996, ..., 54.67187 , 54.734375,\n", + " 54.687496],\n", + " [53.28125 , 53.593746, 53.59375 , ..., 54.5625 , 54.749996,\n", + " 54.71875 ],\n", + " [53.29687 , 53.625 , 53.687496, ..., 54.73437 , 55.140625,\n", + " 55.01562 ],\n", + " ...,\n", + " [51.35937 , 50.96875 , 50.76562 , ..., 40.781246, 40.671875,\n", + " 40.624996],\n", + " [51.015625, 50.70312 , 50.625 , ..., 40.515625, 40.48437 ,\n", + " 40.40625 ],\n", + " [50.20312 , 50.25 , 50.67187 , ..., 40.39062 , 40.34375 ,\n", + " 40.343746]]],\n", + " \n", + " \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", + " 'dimensions': ('time', 'lat', 'lon'),\n", + " 'coordinates': 'lat lon',\n", + " 'grid_mapping': 'crs',\n", + " 'units': 'ppb'}}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source_grid.variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Horizontal interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Weight Matrix\n", + "Weight Matrix done!\n", + "Applying weights\n", + "\tsconco3 horizontal methods\n", + "Formatting\n" + ] + } + ], + "source": [ + "interpolated_source_grid = source_grid.interpolate_horizontal(dst_grid, weight_matrix_path=None, \n", + " kind='NearestNeighbour', n_neighbours=4,\n", + " info=True, to_providentia=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(data=[-64.24006 , -54.84846497, 47.76666641, 46.677778 ,\n", + " 48.721111 , 47.529167 , 47.05407 , 47.348056 ,\n", + " 47.973056 , 48.878611 , 48.106111 , 48.371111 ,\n", + " 48.334722 , 48.050833 , 47.838611 , 47.040277 ,\n", + " 47.06694444, 49.877778 , 50.629421 , 50.503333 ,\n", + " 41.695833 , 32.27000046, 80.05000305, 46.5475 ,\n", + " 46.813056 , 47.479722 , 47.049722 , 47.0675 ,\n", + " 47.18961391, -30.17254 , 16.86403 , 35.0381 ,\n", + " 49.73508444, 49.573394 , 49.066667 , 54.925556 ,\n", + " 52.802222 , 47.914722 , 53.166667 , 50.65 ,\n", + " 54.4368 , 47.80149841, 47.4165 , -70.666 ,\n", + " 54.746495 , 81.6 , 55.693588 , 72.58000183,\n", + " 56.290424 , 59.5 , 58.383333 , 39.54694 ,\n", + " 42.72056 , 39.87528 , 37.23722 , 43.43917 ,\n", + " 41.27417 , 42.31917 , 38.47278 , 39.08278 ,\n", + " 41.23889 , 41.39389 , 42.63472 , 37.05194 ,\n", + " 28.309 , 59.779167 , 60.53002 , 66.320278 ,\n", + " 67.97333333, 48.5 , 49.9 , 47.266667 ,\n", + " 43.616667 , 47.3 , 46.65 , 45. ,\n", + " 45.8 , 48.633333 , 42.936667 , 44.56944444,\n", + " 46.81472778, 45.772223 , 55.313056 , 54.443056 ,\n", + " 50.596389 , 54.334444 , 57.734444 , 52.503889 ,\n", + " 55.858611 , 53.398889 , 50.792778 , 52.293889 ,\n", + " 51.781784 , 52.298333 , 55.79216 , 52.950556 ,\n", + " 51.778056 , 60.13922 , 51.149617 , 38.366667 ,\n", + " 35.316667 , 46.966667 , 46.91 , -0.20194 ,\n", + " 51.939722 , 53.32583 , 45.8 , 44.183333 ,\n", + " 37.571111 , 42.805462 , -69.005 , 39.0319 ,\n", + " 24.2883 , 24.466941 , 36.53833389, 33.293917 ,\n", + " 55.37611111, 56.161944 , 57.135278 , 36.0722 ,\n", + " 52.083333 , 53.333889 , 51.541111 , 52.3 ,\n", + " 51.974444 , 58.38853 , 65.833333 , 62.783333 ,\n", + " 78.90715 , 59. , 69.45 , 59.2 ,\n", + " 60.372386 , -72.0117 , 59.2 , -41.40819168,\n", + " -77.83200073, -45.0379982 , 51.814408 , 50.736444 ,\n", + " 54.753894 , 54.15 , 43.4 , 71.58616638,\n", + " 63.85 , 67.883333 , 57.394 , 57.1645 ,\n", + " 57.9525 , 56.0429 , 60.0858 , 57.816667 ,\n", + " 64.25 , 59.728 , 45.566667 , 46.428611 ,\n", + " 46.299444 , 48.933333 , 49.15 , 49.05 ,\n", + " 47.96 , 71.32301331, 40.12498 , 19.53623009,\n", + " -89.99694824, 41.05410004, 21.5731 , -34.35348 ],\n", + " mask=False,\n", + " fill_value=1e+20)}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interpolated_source_grid.lat" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(data=[-5.66247800e+01, -6.83106918e+01, 1.67666664e+01,\n", + " 1.29722220e+01, 1.59422220e+01, 9.92666700e+00,\n", + " 1.29579400e+01, 1.58822220e+01, 1.30161110e+01,\n", + " 1.50466670e+01, 1.59194440e+01, 1.55466670e+01,\n", + " 1.67305560e+01, 1.66766670e+01, 1.44413890e+01,\n", + " 1.43300000e+01, 1.54936111e+01, 5.20361100e+00,\n", + " 6.00101900e+00, 4.98944400e+00, 2.47386110e+01,\n", + " -6.48799973e+01, -8.64166565e+01, 7.98500000e+00,\n", + " 6.94472200e+00, 8.90472200e+00, 6.97944400e+00,\n", + " 8.46388900e+00, 8.17543368e+00, -7.07992300e+01,\n", + " -2.48675200e+01, 3.30578000e+01, 1.60341969e+01,\n", + " 1.50802780e+01, 1.36000000e+01, 8.30972200e+00,\n", + " 1.07594440e+01, 7.90861100e+00, 1.30333330e+01,\n", + " 1.07666670e+01, 1.27249000e+01, 1.10096197e+01,\n", + " 1.09796400e+01, -8.26600000e+00, 1.07361600e+01,\n", + " -1.66700000e+01, 1.20857970e+01, -3.84799995e+01,\n", + " 8.42748600e+00, 2.59000000e+01, 2.18166670e+01,\n", + " -4.35056000e+00, -8.92361000e+00, 4.31639000e+00,\n", + " -3.53417000e+00, -4.85000000e+00, -3.14250000e+00,\n", + " 3.31583000e+00, -6.92361000e+00, -1.10111000e+00,\n", + " -5.89750000e+00, 7.34720000e-01, -7.70472000e+00,\n", + " -6.55528000e+00, -1.64994000e+01, 2.13772220e+01,\n", + " 2.76675400e+01, 2.94016670e+01, 2.41161111e+01,\n", + " 7.13333300e+00, 4.63333300e+00, 4.08333300e+00,\n", + " 1.83333000e-01, 6.83333300e+00, -7.50000000e-01,\n", + " 6.46666700e+00, 2.06666700e+00, -4.50000000e-01,\n", + " 1.41944000e-01, 5.27897222e+00, 2.61000833e+00,\n", + " 2.96488600e+00, -3.20416700e+00, -7.87000000e+00,\n", + " -3.71305600e+00, -8.07500000e-01, -4.77444400e+00,\n", + " -3.03305600e+00, -3.20500000e+00, -1.75333300e+00,\n", + " 1.79444000e-01, 1.46305600e+00, -4.69146200e+00,\n", + " 2.92778000e-01, -3.24290000e+00, 1.12194400e+00,\n", + " 1.08223000e+00, -1.18531900e+00, -1.43822800e+00,\n", + " 2.30833330e+01, 2.56666670e+01, 1.95833330e+01,\n", + " 1.63200000e+01, 1.00318100e+02, -1.02444440e+01,\n", + " -9.89944000e+00, 8.63333300e+00, 1.07000000e+01,\n", + " 1.26597220e+01, 1.25656450e+01, 3.95905556e+01,\n", + " 1.41822200e+02, 1.53983300e+02, 1.23010872e+02,\n", + " 1.26330002e+02, 1.26163111e+02, 2.10305556e+01,\n", + " 2.11730560e+01, 2.59055560e+01, 1.42184000e+01,\n", + " 6.56666700e+00, 6.27722200e+00, 5.85361100e+00,\n", + " 4.50000000e+00, 4.92361100e+00, 8.25200000e+00,\n", + " 1.39166670e+01, 8.88333300e+00, 1.18866800e+01,\n", + " 1.15333330e+01, 3.00333330e+01, 5.20000000e+00,\n", + " 1.10781420e+01, 2.53510000e+00, 9.51666700e+00,\n", + " 1.74870804e+02, 1.66660004e+02, 1.69684006e+02,\n", + " 2.19724190e+01, 1.57395000e+01, 1.75342640e+01,\n", + " 2.20666670e+01, 2.19500000e+01, 1.28918823e+02,\n", + " 1.53333330e+01, 2.10666670e+01, 1.19140000e+01,\n", + " 1.47825000e+01, 1.24030000e+01, 1.31480000e+01,\n", + " 1.75052800e+01, 1.55666670e+01, 1.97666670e+01,\n", + " 1.54720000e+01, 1.48666670e+01, 1.50033330e+01,\n", + " 1.45386110e+01, 1.95833330e+01, 2.02833330e+01,\n", + " 2.22666670e+01, 1.78605560e+01, -1.56611465e+02,\n", + " -1.05236800e+02, -1.55576157e+02, -2.47999992e+01,\n", + " -1.24151001e+02, 1.03515700e+02, 1.84896800e+01],\n", + " mask=False,\n", + " fill_value=1e+20)}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interpolated_source_grid.lon" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "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", + " 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", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "interpolated_source_grid.to_shapefile(path='interpolated_points_shp', var_list=['sconco3'])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
geometrysconco3
FID
0POINT (-56.62478 -64.24006)53.660180
1POINT (-68.31069 -54.84846)53.726555
2POINT (16.76667 47.76667)39.719934
3POINT (12.97222 46.67778)33.805122
4POINT (15.94222 48.72111)35.401011
.........
163POINT (-155.57616 19.53623)50.547116
164POINT (-24.80000 -89.99695)53.660154
165POINT (-124.15100 41.05410)50.316594
166POINT (103.51570 21.57310)36.253923
167POINT (18.48968 -34.35348)44.863281
\n", + "

168 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " geometry sconco3\n", + "FID \n", + "0 POINT (-56.62478 -64.24006) 53.660180\n", + "1 POINT (-68.31069 -54.84846) 53.726555\n", + "2 POINT (16.76667 47.76667) 39.719934\n", + "3 POINT (12.97222 46.67778) 33.805122\n", + "4 POINT (15.94222 48.72111) 35.401011\n", + ".. ... ...\n", + "163 POINT (-155.57616 19.53623) 50.547116\n", + "164 POINT (-24.80000 -89.99695) 53.660154\n", + "165 POINT (-124.15100 41.05410) 50.316594\n", + "166 POINT (103.51570 21.57310) 36.253923\n", + "167 POINT (18.48968 -34.35348) 44.863281\n", + "\n", + "[168 rows x 2 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interpolated_source_grid.shapefile" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb b/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e3fdd56f56adb63ef546294476cb086bf15f8260 --- /dev/null +++ b/tutorials/4.Interpolation/4.5.NES_vs_Providentia_Interpolation.ipynb @@ -0,0 +1,1966 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing Providentia Interpolation and NES Interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from nes import *\n", + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "import copy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Original model (a4s2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1. Read data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Original path: /esarchive/exp/monarch/a4s2/global/hourly/od550du/od550du-000_2020060100.nc\n", + "# Regular lat-lon grid from MONARCH\n", + "path_0 = '/gpfs/projects/bsc32/models/NES_tutorial_data/exp_od550du-000_2020060100.nc'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_model = open_netcdf(path=path_0, info=True)\n", + "data_model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank 000: Loading od550du var (1/1)\n", + "Rank 000: Loaded od550du var ((25, 1, 181, 257))\n" + ] + } + ], + "source": [ + "data_model.keep_vars(['od550du'])\n", + "data_model.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2. Create shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
geometryod550du
FID
0POLYGON ((-180.70312 -90.50000, -179.29688 -90...0.000144
1POLYGON ((-179.29688 -90.50000, -177.89062 -90...0.000144
2POLYGON ((-177.89062 -90.50000, -176.48438 -90...0.000144
3POLYGON ((-176.48438 -90.50000, -175.07812 -90...0.000144
4POLYGON ((-175.07812 -90.50000, -173.67188 -90...0.000144
.........
46512POLYGON ((173.67188 89.50000, 175.07812 89.500...0.023041
46513POLYGON ((175.07812 89.50000, 176.48438 89.500...0.023041
46514POLYGON ((176.48438 89.50000, 177.89062 89.500...0.023041
46515POLYGON ((177.89062 89.50000, 179.29688 89.500...0.023041
46516POLYGON ((179.29688 89.50000, 180.70312 89.500...0.023041
\n", + "

46517 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " geometry od550du\n", + "FID \n", + "0 POLYGON ((-180.70312 -90.50000, -179.29688 -90... 0.000144\n", + "1 POLYGON ((-179.29688 -90.50000, -177.89062 -90... 0.000144\n", + "2 POLYGON ((-177.89062 -90.50000, -176.48438 -90... 0.000144\n", + "3 POLYGON ((-176.48438 -90.50000, -175.07812 -90... 0.000144\n", + "4 POLYGON ((-175.07812 -90.50000, -173.67188 -90... 0.000144\n", + "... ... ...\n", + "46512 POLYGON ((173.67188 89.50000, 175.07812 89.500... 0.023041\n", + "46513 POLYGON ((175.07812 89.50000, 176.48438 89.500... 0.023041\n", + "46514 POLYGON ((176.48438 89.50000, 177.89062 89.500... 0.023041\n", + "46515 POLYGON ((177.89062 89.50000, 179.29688 89.500... 0.023041\n", + "46516 POLYGON ((179.29688 89.50000, 180.70312 89.500... 0.023041\n", + "\n", + "[46517 rows x 2 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_model.create_shapefile()\n", + "data_model.shapefile['od550du'] = data_model.variables['od550du']['data'][0, 0, :].ravel()\n", + "data_model.shapefile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3. Plot data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, figsize=(20, 7))\n", + "\n", + "gdf_model = data_model.shapefile\n", + "gdf_model.plot(ax=ax, column='od550du', cmap='viridis', legend=True)\n", + "\n", + "gdf_earth = gpd.read_file('/gpfs/projects/bsc32/models/NES_tutorial_data/timezones_2021c/timezones_2021c.shp')\n", + "gdf_earth.plot(ax=ax, facecolor=\"none\", edgecolor='white', lw=0.5)\n", + "\n", + "ax.margins(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Observations (AERONET_v3_lev1.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1. Read data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Original path: /esarchive/obs/ghost/AERONET_v3_lev1.5/1.4/hourly/od550aero/od550aero_202006.nc\n", + "# Points grid from AERONET network\n", + "path_obs = '/gpfs/projects/bsc32/models/NES_tutorial_data/obs_od550aero_202006.nc'" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:69: UserWarning: Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\n", + " warnings.warn(\"Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\")\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_obs = open_netcdf(path=path_obs, info=True)\n", + "data_obs" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank 000: Loading od550aero var (1/1)\n", + "Rank 000: Loaded od550aero var ((366, 720))\n" + ] + } + ], + "source": [ + "data_obs.keep_vars(['od550aero'])\n", + "data_obs.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2. Create shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
geometryod550aero
FID
0POINT (109.62880 40.85170)NaN
1POINT (-28.02917 39.09109)NaN
2POINT (-149.88000 70.49950)NaN
3POINT (-97.48562 36.60518)NaN
4POINT (40.19450 -2.99600)NaN
.........
361POINT (-114.37625 62.45130)NaN
362POINT (126.93479 37.56443)0.152831
363POINT (-0.88235 41.63340)NaN
364POINT (8.99023 13.77668)NaN
365POINT (36.77500 55.69500)NaN
\n", + "

366 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " geometry od550aero\n", + "FID \n", + "0 POINT (109.62880 40.85170) NaN\n", + "1 POINT (-28.02917 39.09109) NaN\n", + "2 POINT (-149.88000 70.49950) NaN\n", + "3 POINT (-97.48562 36.60518) NaN\n", + "4 POINT (40.19450 -2.99600) NaN\n", + ".. ... ...\n", + "361 POINT (-114.37625 62.45130) NaN\n", + "362 POINT (126.93479 37.56443) 0.152831\n", + "363 POINT (-0.88235 41.63340) NaN\n", + "364 POINT (8.99023 13.77668) NaN\n", + "365 POINT (36.77500 55.69500) NaN\n", + "\n", + "[366 rows x 2 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_obs.create_shapefile()\n", + "data_obs.shapefile['od550aero'] = data_obs.variables['od550aero']['data'][:, 0].ravel()\n", + "data_obs.shapefile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Providentia Interpolation (before error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1. Read data" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Original path: /gpfs/projects/bsc32/AC_cache/recon/exp_interp/bug_1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc\n", + "# Interpolated experiment to AERONET network from MONARCH (with bug)\n", + "path_prv_before = '/gpfs/projects/bsc32/models/NES_tutorial_data/exp_interp_bug_od550aero_202006.nc'" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:69: UserWarning: Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\n", + " warnings.warn(\"Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\")\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_prv_before = open_netcdf(path=path_prv_before, info=True)\n", + "data_prv_before" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank 000: Loading od550aero var (1/1)\n", + "Rank 000: Loaded od550aero var ((366, 720))\n" + ] + } + ], + "source": [ + "data_prv_before.keep_vars(['od550aero'])\n", + "data_prv_before.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2. Create shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
geometryod550aero
FID
0POINT (109.62880 40.85170)0.227307
1POINT (-28.02917 39.09109)0.004195
2POINT (-149.88000 70.49950)0.006696
3POINT (-97.48562 36.60518)0.005329
4POINT (40.19450 -2.99600)0.059810
.........
361POINT (-114.37625 62.45130)0.007441
362POINT (126.93479 37.56443)0.082251
363POINT (-0.88235 41.63340)0.008346
364POINT (8.99023 13.77668)0.446823
365POINT (36.77500 55.69500)0.006547
\n", + "

366 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " geometry od550aero\n", + "FID \n", + "0 POINT (109.62880 40.85170) 0.227307\n", + "1 POINT (-28.02917 39.09109) 0.004195\n", + "2 POINT (-149.88000 70.49950) 0.006696\n", + "3 POINT (-97.48562 36.60518) 0.005329\n", + "4 POINT (40.19450 -2.99600) 0.059810\n", + ".. ... ...\n", + "361 POINT (-114.37625 62.45130) 0.007441\n", + "362 POINT (126.93479 37.56443) 0.082251\n", + "363 POINT (-0.88235 41.63340) 0.008346\n", + "364 POINT (8.99023 13.77668) 0.446823\n", + "365 POINT (36.77500 55.69500) 0.006547\n", + "\n", + "[366 rows x 2 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_prv_before.create_shapefile()\n", + "data_prv_before.shapefile['od550aero'] = data_prv_before.variables['od550aero']['data'][:, 0].ravel()\n", + "data_prv_before.shapefile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3. Plot data" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig_1, ax_1 = plt.subplots(1, figsize=(20, 7))\n", + "\n", + "gdf_model.plot(ax=ax_1, column='od550du', cmap='viridis', legend=True, vmin=0, vmax=3)\n", + "\n", + "gdf_earth.plot(ax=ax_1, facecolor=\"none\", edgecolor='white', lw=0.5)\n", + "\n", + "gdf_prv_before = data_prv_before.shapefile\n", + "gdf_prv_before.plot(ax=ax_1, column='od550aero', cmap='viridis', edgecolor='black', lw=0.5, vmin=0, vmax=3)\n", + "\n", + "ax_1.margins(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Providentia Interpolation (after error)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1. Read data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# Original path: /gpfs/projects/bsc32/AC_cache/recon/exp_interp/1.4/a4s2-global-000/hourly/od550aero/AERONET_v3_lev1.5/od550aero_202006.nc\n", + "# Interpolated experiment to AERONET network from MONARCH (without bug)\n", + "path_prv_after = '/gpfs/projects/bsc32/models/NES_tutorial_data/exp_interp_od550aero_202006.nc'" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/load_nes.py:69: UserWarning: Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\n", + " warnings.warn(\"Parallel method cannot be 'Y' to create points NES. Setting it to 'X'\")\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_prv_after = open_netcdf(path=path_prv_after, info=True)\n", + "data_prv_after" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank 000: Loading od550aero var (1/1)\n", + "Rank 000: Loaded od550aero var ((366, 720))\n" + ] + } + ], + "source": [ + "data_prv_after.keep_vars(['od550aero'])\n", + "data_prv_after.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2. Create shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
geometryod550aero
FID
0POINT (109.62880 40.85170)0.504757
1POINT (-28.02917 39.09109)0.007563
2POINT (-149.88000 70.49950)0.013467
3POINT (-97.48562 36.60518)0.011034
4POINT (40.19450 -2.99600)0.002979
.........
361POINT (-114.37625 62.45130)0.014462
362POINT (126.93479 37.56443)0.184404
363POINT (-0.88235 41.63340)0.018486
364POINT (8.99023 13.77668)0.900909
365POINT (36.77500 55.69500)0.013143
\n", + "

366 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " geometry od550aero\n", + "FID \n", + "0 POINT (109.62880 40.85170) 0.504757\n", + "1 POINT (-28.02917 39.09109) 0.007563\n", + "2 POINT (-149.88000 70.49950) 0.013467\n", + "3 POINT (-97.48562 36.60518) 0.011034\n", + "4 POINT (40.19450 -2.99600) 0.002979\n", + ".. ... ...\n", + "361 POINT (-114.37625 62.45130) 0.014462\n", + "362 POINT (126.93479 37.56443) 0.184404\n", + "363 POINT (-0.88235 41.63340) 0.018486\n", + "364 POINT (8.99023 13.77668) 0.900909\n", + "365 POINT (36.77500 55.69500) 0.013143\n", + "\n", + "[366 rows x 2 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_prv_after.create_shapefile()\n", + "data_prv_after.shapefile['od550aero'] = data_prv_after.variables['od550aero']['data'][:, 0].ravel()\n", + "data_prv_after.shapefile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3. Plot data" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig_2, ax_2 = plt.subplots(1, figsize=(20, 7))\n", + "\n", + "gdf_model.plot(ax=ax_2, column='od550du', cmap='viridis', legend=True, vmin=0, vmax=3)\n", + "\n", + "gdf_earth.plot(ax=ax_2, facecolor=\"none\", edgecolor='white', lw=0.5)\n", + "\n", + "gdf_prv_after = data_prv_after.shapefile\n", + "gdf_prv_after.plot(ax=ax_2, column='od550aero', cmap='viridis', edgecolor='black', lw=0.5, vmin=0, vmax=3)\n", + "\n", + "ax_2.margins(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "masked_array(\n", + " data=[[-0.27745032, -0.33557633, -0.39707237, ..., -0.02371369,\n", + " -0.02375146, -0.02365999],\n", + " [-0.00336807, -0.00303048, -0.00253099, ..., -0.00627814,\n", + " -0.00588607, -0.00548861],\n", + " [-0.00677101, -0.00678938, -0.00670995, ..., -0.00168319,\n", + " -0.0017733 , -0.00185721],\n", + " ...,\n", + " [-0.01014002, -0.01006637, -0.01026233, ..., -0.04439881,\n", + " -0.04458728, -0.04401928],\n", + " [-0.4540865 , -0.4427302 , -0.4316749 , ..., -0.5309974 ,\n", + " -0.52981824, -0.526042 ],\n", + " [-0.00659596, -0.0059174 , -0.0054107 , ..., -0.00239577,\n", + " -0.00239077, -0.00250021]],\n", + " mask=False,\n", + " fill_value=1e+20,\n", + " dtype=float32)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "diff = data_prv_before.variables['od550aero']['data'] - data_prv_after.variables['od550aero']['data']\n", + "diff" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. NES Interpolation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.1. Interpolate" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Weight Matrix\n", + "Weight Matrix done!\n", + "Applying weights\n", + "\tod550du horizontal methods\n", + "Formatting\n" + ] + } + ], + "source": [ + "data_nes = data_model.interpolate_horizontal(data_obs, weight_matrix_path=None, \n", + " kind='NearestNeighbour', n_neighbours=4,\n", + " info=True, to_providentia=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'od550du': {'data': array([[0.50475728, 0.63564274, 0.79752713, ..., 0.59597564, 0.5582657 ,\n", + " 0.54280552],\n", + " [0.00756297, 0.00732296, 0.00696342, ..., 0.01266858, 0.01230505,\n", + " 0.01215943],\n", + " [0.01346747, 0.01355799, 0.01349523, ..., 0.01335665, 0.01338057,\n", + " 0.01345622],\n", + " ...,\n", + " [0.01848554, 0.01946019, 0.02101676, ..., 0.02690396, 0.02741605,\n", + " 0.0283956 ],\n", + " [0.90090905, 0.88688139, 0.87311261, ..., 0.841753 , 0.86948918,\n", + " 0.89742322],\n", + " [0.0131431 , 0.01134514, 0.01024295, ..., 0.00430793, 0.00383704,\n", + " 0.00347976]]), 'dimensions': ('station', 'time')}}" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_nes.variables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2. Create shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
geometryod550du
FID
0POINT (109.62880 40.85170)0.504757
1POINT (-28.02917 39.09109)0.007563
2POINT (-149.88000 70.49950)0.013467
3POINT (-97.48562 36.60518)0.011034
4POINT (40.19450 -2.99600)0.002979
.........
361POINT (-114.37625 62.45130)0.014462
362POINT (126.93479 37.56443)0.184404
363POINT (-0.88235 41.63340)0.018486
364POINT (8.99023 13.77668)0.900909
365POINT (36.77500 55.69500)0.013143
\n", + "

366 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " geometry od550du\n", + "FID \n", + "0 POINT (109.62880 40.85170) 0.504757\n", + "1 POINT (-28.02917 39.09109) 0.007563\n", + "2 POINT (-149.88000 70.49950) 0.013467\n", + "3 POINT (-97.48562 36.60518) 0.011034\n", + "4 POINT (40.19450 -2.99600) 0.002979\n", + ".. ... ...\n", + "361 POINT (-114.37625 62.45130) 0.014462\n", + "362 POINT (126.93479 37.56443) 0.184404\n", + "363 POINT (-0.88235 41.63340) 0.018486\n", + "364 POINT (8.99023 13.77668) 0.900909\n", + "365 POINT (36.77500 55.69500) 0.013143\n", + "\n", + "[366 rows x 2 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_nes.create_shapefile()\n", + "data_nes.shapefile['od550du'] = data_nes.variables['od550du']['data'][:, 0].ravel()\n", + "data_nes.shapefile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.3. Plot data" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig_3, ax_3 = plt.subplots(1, figsize=(20, 7))\n", + "\n", + "gdf_model.plot(ax=ax_3, column='od550du', cmap='viridis', legend=True, vmin=0, vmax=3)\n", + "\n", + "gdf_earth.plot(ax=ax_3, facecolor=\"none\", edgecolor='white', lw=0.5)\n", + "\n", + "gdf_nes = data_nes.shapefile\n", + "gdf_nes.plot(ax=ax_3, column='od550du', cmap='viridis', edgecolor='black', lw=0.5, vmin=0, vmax=3)\n", + "\n", + "ax_3.margins(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Difference between Providentia Interpolation and NES" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.1. Visual comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_diff = gdf_prv_after.copy().rename(columns={'od550aero': 'providentia'})[['geometry', 'providentia']]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_diff['nes'] = gdf_nes['od550du']\n", + "gdf_diff['obs'] = data_obs.shapefile['od550aero']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.1.1. Absolute difference" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + " \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", + "
geometryprovidentianesobsabsolute_difference
FID
0POINT (109.62880 40.85170)0.5047570.504757NaN-6.329158e-09
1POINT (-28.02917 39.09109)0.0075630.007563NaN3.951382e-11
2POINT (-149.88000 70.49950)0.0134670.013467NaN-6.039905e-11
3POINT (-97.48562 36.60518)0.0110340.011034NaN2.067994e-10
4POINT (40.19450 -2.99600)0.0029790.002979NaN-3.650107e-11
..................
361POINT (-114.37625 62.45130)0.0144620.014462NaN1.836392e-10
362POINT (126.93479 37.56443)0.1844040.1844040.1528315.078100e-09
363POINT (-0.88235 41.63340)0.0184860.018486NaN-8.104343e-11
364POINT (8.99023 13.77668)0.9009090.900909NaN-2.038381e-08
365POINT (36.77500 55.69500)0.0131430.013143NaN1.533717e-10
\n", + "

366 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " geometry providentia nes obs \\\n", + "FID \n", + "0 POINT (109.62880 40.85170) 0.504757 0.504757 NaN \n", + "1 POINT (-28.02917 39.09109) 0.007563 0.007563 NaN \n", + "2 POINT (-149.88000 70.49950) 0.013467 0.013467 NaN \n", + "3 POINT (-97.48562 36.60518) 0.011034 0.011034 NaN \n", + "4 POINT (40.19450 -2.99600) 0.002979 0.002979 NaN \n", + ".. ... ... ... ... \n", + "361 POINT (-114.37625 62.45130) 0.014462 0.014462 NaN \n", + "362 POINT (126.93479 37.56443) 0.184404 0.184404 0.152831 \n", + "363 POINT (-0.88235 41.63340) 0.018486 0.018486 NaN \n", + "364 POINT (8.99023 13.77668) 0.900909 0.900909 NaN \n", + "365 POINT (36.77500 55.69500) 0.013143 0.013143 NaN \n", + "\n", + " absolute_difference \n", + "FID \n", + "0 -6.329158e-09 \n", + "1 3.951382e-11 \n", + "2 -6.039905e-11 \n", + "3 2.067994e-10 \n", + "4 -3.650107e-11 \n", + ".. ... \n", + "361 1.836392e-10 \n", + "362 5.078100e-09 \n", + "363 -8.104343e-11 \n", + "364 -2.038381e-08 \n", + "365 1.533717e-10 \n", + "\n", + "[366 rows x 5 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf_diff['absolute_difference'] = gdf_diff['nes'] - gdf_diff['providentia']\n", + "gdf_diff" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3cAAAGmCAYAAADfzn21AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydZ3hU1daA3zM9M+m9kUIPITTpSAdFLNgQFUVQvHav3atYsIti47N3UQFBUUCk9w6hJ6GT3nuZ1Jk534/BQEhPJskQ9vs8PGFO2Xvt09deTZJlGYFAIBAIBAKBQCAQXNoo2loAgUAgEAgEAoFAIBA0H6HcCQQCgUAgEAgEAkE7QCh3AoFAIBAIBAKBQNAOEMqdQCAQCAQCgUAgELQDhHInEAgEAoFAIBAIBO0AodwJBAKBQCAQCAQCQTtAKHcCgUAgEAgEAoGgXSBJ0veSJGVIkhRlo/bekyQpWpKkY5IkzZMkSbJFuy2FUO4EAoFAIBAIBAJBe+FHYIItGpIkaSgwDOgF9AQGACNt0XZLIZQ7gUAgEAgEAoFA0C6QZXkrkHPhMkmSOkmStFqSpP2SJG2TJKl7Q5sDdIAG0AJqIN2mAtsYodwJBAKBQCAQCASC9szXwGOyLF8BPAN83pCdZFneBWwCUs/9WyPL8rEWk9IGqNpaAIFAIBAIBAKBQCBoCSRJcgSGAksuCJfTnlt3M/B6Dbsly7J8tSRJnYEwIPDc8nWSJI04Zx20S4RyJxAIBAKBQCAQCNorCiBPluU+F6+QZXkpsLSOfW8CdsuyXAQgSdIqYDBgt8qdcMsUCAQCgUAgEAgE7RJZlguAWEmSJgNIVno3cPcEYKQkSSpJktRYk6nYtVumUO4EAoFAIBAIBAJBu0CSpIXALqCbJElJkiTdB0wF7pMk6TAQDUxqYHO/A2eAo8Bh4LAsyytaQGybIcmy3NYyCAQCgUAgEAgEAoGgmQjLnUAgEAgEAoFAIBC0A+wqoYpG0so6DG0thkAgEAgEAoFAcNlRSG6WLMtebS1HSzFBkuQsG7W131oWwSbF0m2JXSl3OgwMUoxvazEEAoFAIBAIBILLjvWWxfFtLUNLkgVE2qgtCTxt1JRNsSvlTiAQCAQCgUAgEAhaDIWNotIsFtu0Y2NEzJ1AIBAIBAKBQCAQtAOE5U4gEAgEAoFAgKSQ2loEAGSLyOQuaEHaueVOKHcCgUAgEAgEAoGg/SNJtlPu7JT2PTqBQCAQCAQCgUAguEwQljtB+0G2T/O44DJCEvNlgjZAPPvajnZ2zwt3SMFlQTu33AnlTiAQCAQCgUAgELR/LgO3TPtT7i71Gch2Not3SVHbsb/UrynBpUNrXWviOVM3jTkP7eFYtocxCASXC419T9j6/hbfRO0e+1PuBAKBQCAQCAQCgaAlEJY7QaNo6xmZywEx6yS43GmLe6Aln1VNHU9tMjVE1n/7bA/Pk/b2HrH1OWlvx0fQ8rT1c0Fcsy1LO1fu2vfoBAKBQCAQCAQCgeAyQVju2hpbzA5dbjM8F463rWfXWhJ7P6/t+dg3AEmSkBQSkiShUEhw7q8kWYsAl5dVYDG3o2N0KZ3vS0lWW9CS422L55C9P/tshbBQ2heXkufV5faMsyUioYpAIBBcuqjUKnQGLQ5ODhicHdA7O6B3OvfXWV/5W7pAKZPl86nAJUmq/H3h///dTrac+yfLWCwyyHLlNmqtGoVSUblvXRjzi0mPyyA2KpHUM+lYLOLFLRAIBAJBi9BKyp0kSd8D1wEZsiz3rGG9BHwCTASKgemyLB9obr9CuWsPNGQGpz3N6LX1jFUbHEuVWomLpzMuXs64ep/76+mMwUVfqUBcyIUKSXMJH9aNE/vOYCo3VbZtrjBTUW7CVG6irLQcY34xxrxiCnOLyEzIJCctr9n9XozBRY/eWY+Dow69kw5nDyfcfF1x93VFrVUjy3I1ZcxUYaKkqJSSwlKKC4opLijBWFBCVnIOxoISigtLKSkqrdvC1grXm8FFj2+oN90HdmbMnVeiuODFk59VQMqZdLKSskmLy6S4oLjF5blkaOtnweWAPWWAFee7Ou3p3d7WXErH8nLxYLr0+RH4FJhfy/prgC7n/g0Cvjj3t1kI5U4gaGPUGhVd+3ciODwQN28XlCol/p18UKqVJB5PAcBUbiI/u5D8rALyMgo4tf8s+ZkFGPOLrRajFuStv//H4veXUZR3XqlQqZWoNCrUGhUaBw0GFz1Obo64ervQpV8oHn5uIFkL4qacTuPotmOkx2dWaVeSJAwuehzdDDi5OeLm64qHnytuvq4oVcpqchjzrcpZSZFVISvIKiAuOpHc9HwqyiqaNjg7eZkb84s5cyiOM4fiqq1z9nDCv5MP/p196TcuAoOrAYCSwhKObj3Gicgz7cv9UyAQCASClqIV3TJlWd4qSVJIHZtMAubL1lnp3ZIkuUqS5CfLcmpz+hXKXW20N1/qmtq0kw/bRmPrGavmHId6+nfzcaVT72Dc/dxwcndE7+xQzZpmMVs4se80e1fuJzc9H7PJzMT7x+Hs7siiOX/ZVl4bYaowY6owU2osg1wj2Sm5VdYrFArCr+xGv7ER3PDIBB788B7W/rSFkqLSSuuabJExFpRQlFdEUa6RnNRcYo/Ek5uej6nC1DoDuQRmPAuyCynILoS9p6ssN7joiRgextSXbkGhVFS6hxZkFZKTlkdOWh655/6WGkvbSPqLsINrt9lcAteMLdE6aHDxckZn0KEzaFGqlFjMFqsrcXxm0ydWLubC49qcLKgCweWGsHo3Htspd56SJEVe8PtrWZa/bsT+AUDiBb+Tzi0Typ1A0Na4eDoT3COQoLAAPPzdrQslyEvP5/ShOI5siaEgp4iSwpIqcVvtBZVKwfhpIxkxeQg6vRaz2ULqmTSObj/Oa7d+UE35qxHx8mkUxvxidv+9n91/769cJklSFXfVHkO74u7rioOjQxV31YLsQg5vjubskfi2El9gJyhVStx9XTG4GnB01SNJEq7eLnTo5k+PId04uv0YeRn5lBrLKDWWYjZZUCgVBHTx5dF595Kbnk9qbHqNbUuShMFVz6J3/mwRV22BQCBoY7JkWe7fjP1rip1p9keiUO4uxF5mBRsrx+XwUVzbGG0xY9WQGeMa2vAJ9mL0HcPQO+vJSc0lPiaJXSv2k52SU79MFzcvyxS7peIdpEWjUrLn+FpGDLjWfq7Ji1AoFAyY0JsRk4cQ0NkX2SJzZNsx5tzzKXkZBU1rVMQQNBtZlsnPKiA/q4C4qIQat3H1ciZ8WHee+uZBVn23gZVfr29ZoezsGlZrVKi1aspLyzFVmBvfgC3G0wLXtyRJOLoa0Oo1Vqt4fjEqjYobH7sGpUpZLVFQqbGU6J0nePPvF/jqmfkU5RopyjOCLJNyJo1dyyPrtaBvXLC98v+v//U8Q27oj9lsQXlBHHBBdiEVZSbKS8oxFhSz8usNlBS1jCVZqVLi5uOCi6cTKo2KijIT+VkF5KbnN8t1ecStgwmNCEKuwQVekkCW645zliSQFArS4zOJXHOYzKTsJssisCPqu4/t7NknuAD7yZaZBHS44HcgkNLcRoVyJxA0ks59Qxl9x5Wkxabz95frrB9EzaTUJ4UPn5/C4B7dAUjLyeHxr35Blg02SYrSXIZOGsDQGwbgE+JFWXEZZpOZuOhEVn+3kaPbj7e1eIIGMGBCH/qMiSAzMYv4mCReuu4d8jKbqIhfIihVSjp086dj72BCegRisciUFZdRUWZC46BBrbHGdsoylR//FycokiQwFpQQF53I6QOxbX7MJEli9B3DCOzqX7lMoVRgNpkpyjVSWlx2TtHTYzFb2P33fk4fjK3WzosLnsAnxJs1P2xi1bcbmi3XKzfOQZIktHptFTdghVKBZ4A7Hbr5c+Utg+ncN5Sj2441u7+LeeD9u/EJ8eJk5Fkyk7KoKDdhcNYT2NWPPqN7suaHTSz/Ym2T2s7LyLdeJOfo3C+UK8b3YmrII9Z1DcS/kw8DJ/bFM8AdB0cdyz5bQ+rZmq2eAoGghbCvUgjLgUclSVqENZFKfnPj7QAke3IRc5bc5UHS2IbvIGZFqtPUGeFL6Vg2xIrXhOOgd9bToXsAnv5u+IR44XguccXFZKfk2NTaYZJNdB8HXzzzYJXlv27awqdzI3GU3WzWV2NwcNTxxJf/oVOfYA5ujOKv/1tN8qlmP3Oah7Do1UtoRBD9xvXC4KIHzlsSzh6JZ9sfu1tHiBZ4nkjOJiweWSgVMoWpCtwsAXTsFYJ/Z19rLJhSiUKpQKVWotVrcXTVU1xQQuLJFM4ejif5VGqdyYcUSgX8W9LiIvTODoT2DKLXiDC0ei1mkxlJkkiPz2TToh24erswbupw1FoVF79SZVlGoVSgVCr4/qVFTRv8Bde9UqVk5py7KD1n/TKbzRxYd4SYXSeb1nZTueAcB3Xzw6+TD6ERQXgFepCfVWgVW5axmC1kp+SSfCqVE/vOUF7WMvG0bj4u9LyyO+4+rmgcNIA1EVVhnpHE48mc2n+22cmnfEO8uOvlW9m7+iB7Vx6ktLisSe2Mvn0oz3z/ML++8Qdnj8ZzbPepymMmsBPaWw3CRoxnvfz7/ma6Gto1/TUaOdLLyyZtSSkpdR4rSZIWAqMATyAdeBVQA8iy/OW5UgifAhOwlkKYIctyZM2tNRxhuRNcVuid9XTo5k9gVz98Q71Rqc/fAsZ8I9fMHMfbd37M4c3RNrHINYQKyugeElpteZ/QYGTtDmjFXBgOjjrCh3aj96gelBSW4uRu4Jc3/2Dzop2tJ4Sg0YSEd2DE5CGo1Epijyaw9sfNFOYWtbVYzUKjU3P3K7diKjdxMjYKU+FJZt38IHqtlq3HjvPZ2l04OztxYt+Zylgws8mM2WSmrLgcs6lxLpd1ue0VF5QQvfME0TtP4ORmoOeV3ek2oDO9RoRxeEsMfqHeOLk7VnE3rDJxKssknGi2pw0AZpOZr57+qfK3UqVkyA39uee1KZUZauF8KZDy0gqKcoswFpRgrjBjqjCh1qhw93PDzdf1vMVSoUDjoKG8pBwkMFeYkWUZlVpFWmw6O5dFUphbhEqtwq+jN25+7hhc9Nz8+ETW/7yZuOgkVn61vv7rrpkfuZIkcc3MMXj+G9uM9dxlJmeTcjqdA+uPYsy3fbkQjU7N/+Y/xjt3/1+1zL+NZdOinexavh+Dqx4Pf3fGTh2OV6AHaXGZZCRkkh6fRVpcBsUFJTaSXiAQVKH1smXeUc96GXjE1v1eGpa7tp7xuJRob7NNNVHLGDU6NV6BHngHeeId5IlHgHsV5Q2guKCYxBMpJJ1MJS02o9oH4NyNs3lmzOyWkrxGLLIFr4EFLHr9mSrL3/v9L5Z/G4+DVLMFsVYacM70zg5EDA+jS99QlCpFpbWhpKiUUwfOEns0kYkzxxDQ1Q/fEC8UCgWyLJN4MoV9qw6x55+DlXXv7I7L0Lr3wi//Ra1VkZWSg6OLgazkbL6ftbB1Om+hZ4QkSTw6bwYL3vmTIqco/n7r6Srrf920nf/78jBOZo8W6R9AZ9DiE+xFUFgAHSOCUCgVFBeWknw6jaQTKdZyIFprSRCLRSYzMbvZH/5NopZrXqFUoNFpcHIzoHd2QKVWoVQrMZWbyE3PJze99iQnCoUCJKvi1LlvKBEjwnB0NWCuMJMen0lWco7VMnoipWHZWG10nShVSu6ZPRmz2UJFmYmC7AJkGTwD3Ok2oBMarZpnxr5uk74uZNC1/Zj0yNUc232K0wdj2bf6UNNiNuvAxdMJn2AvvIM98Qnysia4USgwVZg5ffAshzfHtFi84mVLW7wvWvK7ygbjafeWO61WjvT1tUlbUkKCXR4rYbkTXFLonR3oGNGBwK7+eAa6Vyn2POSG/hxYf4Q9fx/g8OYYspJzWi+lfjNQSAqSjlfw1s+LeWbKjahVKv7cuYfoxLzGK3YNZMKM0QSFBbD5t53W+nK+buidHdA76eg9KpzQnkFsW7qHpJPn3TAVCgW9RoYx+LoruPHRa1CqrMc+NTaDyDWH2f33fvHh0UbkpOWiVClJPZtOcUExR7baPqaptTG46HH3cyOwux+lNYQgXD+oH58t2Am5zVfu9M4OPPjBNLKSzidDkiTwDvbC4KwnNz2P3PQ8TBVmLGYLOoOWoB4BVJSZUCgk/Dr6ENjVj35jI/j4oW84sP5os2WyBRazhVJjaZNKYVgs5z8STx+MrTFury0wm8yV7q1qjQpnDyerS60ES+auoLiwZaxde1YeYM/KA0iSRI8hXZn60i0gy5UTYxaL1QXVYrZgqjBRXFhCUa6R/KxCspKyST6dVm8f+VmF5GcVcnL/2SrLlSolXft3ZNIjE9A76Ti4KZqDG+zjGhMIBPaHUO4Edotaq+aK8RF0H9il0nWouLCE2CNxHFh/hKzknCquVE4eTuxfd4QjW2PaSuQm41DoxcqFiaze9DYKJUT0GcTwPtfy29plNu1HpVZy/5y7CBvSld1/70dSKDh9IJY9qQfqdQGyWCwc2hTNoU3RVZb3GNKVIddfwYR7R6PWqECCrKQc9q87wo6/9lYpfi5oGSrKTXz1zPy2FsOmFOUZObQpmohhYaxfv6va+sOx8ViKrbFVFtmMUZ+OzqWcshIF2jwf1JK2wX2VFJYStf04gV38yM8qZN/qQySeSKFDN3/G3HnlueeMhCRZFUEHRx1gVQDLSipIOJ7EvtWH+Oy/P9qu7pugXirKTWSnNqDMig2RZbnSRfdfwod1o8/ocNRa6ydV8uEUYnafwsndERdPZ0ZOHkJcTCI7lzUtlMZsMnNs9ymO7T4FwPhpI7hn9mSQJCxmC/HHkpoVAygQXHbYT0KVFsH+lDt7dAO0d9qJG5okSbh4OuHX0YcrxvdCo1Oze+UBfnp18UXxKw0YbyNKGtgLBtkFUl0A6HRNWO1ZMht5j6i1akIjgug1PAw3XxccHHU8OfyVOmOSSuVizJ7ZKNUyFdkOOJZ71ipPzK6T1ZI5dOwVxLAbBzJr4RMEhQXw9bM/s2VJKyXzuAwxlZtw93Vtd7XEln22Gr2TA4aebvwdeYDr+vcDIN9o5PX5yzCUBmHGjLlDLJ/efwv9O4WSnJPDo18vJCvKFR0Ns3zLsszan7YA1lIR/cb3YtSUoYBVgdv+5177rgnYkOfdZf5uVSis9fskhURhThHlpU1XwrUOGnqPDsfd1xVTuYkrxvciMzEbU4UZSYIeQ7qxbeneyvqeR7fGMP6ekby+7DkiruzOfeFPNeteXTd/a5VxPfTRdEoKS9m3+lCT27xssdV90ZhvC1vfl835rqmpf/uJ1mo5hHInENgWSZLo3DeECTNGV8kQZrHIFGQVkJGYxe8f/i1mIZuBT7AX1/5nHGaTmYoyE7FRCaz/ZSt5mQV07d+Jmx67hmWfr6nRylCiziVsmJZ3H3wSN0dHlm7bxUc/rkWfHtzg/s8eSeDskQQemHs3xQUlQrFrQULCO+Di5WwXJTNaguLCEop3wdtHtvJl4BYMOjUFpTLXXz+TFR+vp1CfxDePTCGig7VUUIC7O4ue/g9XvfgppFoTFXkHeRISHojBRY/ZZCF654nKD++Lycu01kXr0M0fZBmzydIkt0ZB7Tg46tAZtBTlFbeKpdPgomfezjc5vucUhblG3H1dcfZ04n9Xv1XnfmqNil4je9BjSNcq99ewGwewceEO+owOJy0uk5DwDiyas4z4mKQqfQZ29aP7wC64+7pgMVtY//NWPpz5pU3LaVgsMiWFJcRFJdqsTUHjcfVyxq+TNY4r9Wx6o0pkCAS2Rih37YFLoPizWqum79ie9BjcFUmCUwdj6dw3lP9e+XLjG2vIeNvbjHUjx2Bw0WMqNzH/tSXV1un0GsKGdOHet+9Atlj48umfWfHl+fpPTp1K+frZpyo/Zm4dOYzcwiK+/yKqUWUZZi38L9kpubwx5aNGyS5oHDc+PpF5D31TJUbKZtjRvWMw+lJ2Av6d8on3TWHmO3eyev38SsXuX7RqNf7ejmSlQnCPQMZPG8n2pXtIPZuBSqNiwozRnDpwlr2rrJYOlVpJlys60nNYd1w8nYjacYL9647QoZs/siyjd9a38mhbgDZ6Jqo1KvpP6EOnXsEo1UqQZYoLSykxluLkakCtVVuzvEYlsKmFsvIa84v534S3CO3ZAb2zHrPJzNkjCfXu99JvT3D6YBzrf9mGLMuYyk24eDlTUVaBo6uev79ax4H1RykpKkXroGHopP50vaITkgQF2UUkn05ly5JdLf6h//Mbf3DbM9ej0qiqFFqXZZmctDzS4zLJzcgn4Vhy+3UbbkiJpFpQKCS69u9EaK8gHF0MaB00FOYUsWPZPrKSc+r9rurQzZ83VrzA0W0xKCQFA6/tx/pftlBSWEphbhGrvtlg+8nqxn7r2dGzvM2xrzp3LYJQ7gQtRtigLgy8pg8AZrPMwY1HmT97cWWtoRsfmdCW4rVb1Fo14UO7odGpa1x/1T2jyErKYfH7y9EZtHTqE8zDH93D8X1n2LJkJ506VHfBnDxqGD8s3A259St3KpWCd9e8xPa/9vHX/62yyZgENfPEVw+w9sdNLaPY2TlHtsRwZEsMpV4p5BYV4eboWLlOlmXyjWWEDerC+Gkj+Oy/P1ZxQ449msCYO69k2quTAWtM06mDsfzz7Qb0Tg48+OE01s3fQtT24/Qd05NpsyfzyqT3Wn2M9ojBRY+bjwveQZ74d/bF1csFhcL6vDBVmFEopEolTpatx/bA+qMsfPevWl3BXb2ceWTeDPasPNhiCVEyE7PJTMxu1D7vTP0/wod1I2J4dwCUaiWF2UWs+HIdxvxiPPzdGDVlKL4hXlSUmYhce7h6GEErUFFWwa9vLa1xnWeAOz7BXviGeDHkuitQqpWkxWWw5ofNrSqjLXF0NRAcHoibtwsOTjpcPJxxcNIiW2SyknNIi80g8UQK2Sk59TcGTLhvDBOmj+bgxqMU5hox5hfTtX9HMhKzrMod4OHnRlCPQNRaNdkpOZw5FFe5f+KJFB7u/zxege6ARF52AWVGqzLnE+yFVq8Vnkj2hlDu6kaSpG7Abxcs6gi8ArgC9wP/5oV+UZblf5rbn6AG7LT8wcT7x/LFkz+12MsaqC6rpKi+zE6tmZXUdLwl6dy/xp+L++dM5a//W0XKmfQaZ+vn3vfFuS6kKh8hXa/oyO3PTWL3od+rtXk8IQm5tGZl8UIcXfXMWfsyv7zxO7tW7G+07Dbh32Nm7+fdFshyFVewy3J2NsOTJ75fxPeP3ovy3Av7q41b6dlvONpiJ758en6NSsXGBdtrbK6suIwdf+2jU+8QgsMCObbnFMu/WFvjtnaPja4H7yBPRt8+DL2TjoLsInLS88hMzGbnX/vITc+vfI4oVcrKwuU1oVAqcPd1tWYX7eaHV4AHkkKi1FjGZ4//0CLvCrVGxV0v34LZVF0mjU7Ndy8urFEZ8w7yJLCrH5JCIictD68OHngHeuDdwZNOvYMxm2XyMvKIXHuEtNgMm8ttK7KScyoVlB1/7QPghoeuYsgN/dm1vNm1kluUzn1DGXqDNcu8xWxBrbWWHDHmFRMXnUjKmXSKC0soyC6kON+IQqHAM9Ad31Bvhlx/Bd5BnlgscuX9n5GQTWxUIrFH4jFVmFFrVZSXVvDPNxtYN38rTm7WGF2zycyfF0xMuvq4sSj5a3558w8Aeo/swQ8v/8bRbeezEhcXlhJ/zFrL8pvnfq0+mNZ6Nl+O7wBBNZqt3MmyfALoAyBJkhJIBv4EZgAfybI8t7l9CC49fEO8MLjo0Rm0Lavc2ZpGhC3p9FpeWvwkDo4OlR8HZSXl/PnJP0Suaf3Adq2DhjteuIldyyOtil09XPxBc3L/WU5GnqbYPY1NQ48wum8vAErKyvho+WpmPvkAapW6xtiuf9uacO9oNv+2E7PJTKc+IWQlZVeJqxTYhhG3Dua6B6/C0c2Rhz+ZwVdPz6cg+/I8zjpJz9lIE+NnzSPAy4nsgmIKknQ4lHoBjU/fb6ow16r4XS74BHsRGtGBrld0QqVWkp6Qxfqft9abmdJsMqNSKwno7I+bryuBXf0qi41LCglzhZncjHxSzqQTueZwoy1pTUKSGHPnlfiGeHOj+wzUWjVLUr/mlzd+J/l0WuWzKygsgL6je+LqY01qlZ2SQ2xUIhaTBSSIj04iM6kV5G0F9q4+xLi7Rti9cnfTYxOYe9+XyLKMTq/FwUlHbnrtLq4Wi4WMhCwyErI4sqVq1mxJkvDt6EtIeCADJ/RBoVRQVlKOTq+hSr42WbZa7JJyKMwpIi+zAP9OvsRFJ+Lh50b40G6s/WlzFcVOcIkh3DIbzVjgjCzL8e01uN9usGOrxBXje9FzWDfevvMTmxd5bRKNiUlsoDeNi6czi5K/Zs/K/Tw96tXK5T7BXsz+81nyMgs4feBs7Q3YenZNthA+tCtJJ5M5uOFIrdvU2P9Fx8Qh25eX3luOT8hqDDoNicl5mBM8WLTlz3rFWPj2UmvdPD9XfII86DWiBx5+rqTGZrBl8S6K8oxNGZ0ACB8Wxu3/uxFnD0dO7j/LW3d8Qn5WIR7+bjz44XT++HhlFVehywmdxRkSnUlLBPDAoa0FuoR59oeHObrtGPExSfzy5h+1WuEuxMPfjdFThuHkbqCizETymTRyUvPYt/pQ6yhwF2EtySJRUVaBzqDl8OYYPl/2E46uBrR6DRsXbmfhu39hcNEz4tbBdB/YmfiYJLb+sbtO5eFSxsPfjeCwQFy9nekxpCsL3/mrrUWql9MH47j7lVsB0DhoOLQxisNbYmqOG6znnSpjtWIGhQWg0qiIjUrgZORZUs9WnQhVKCS8OnjiGeCOh78bHXsHM+LWwXzy8Lec2n+W8tKKVne7PT+IpscVCi5CKHeN4nZg4QW/H5UkaRoQCTwty3K1aT9Jkv4D/AdARzsIWr+MMbjouXr6KIwFxfw0u3oij/ZEYU4R82f/Rq+RPfhwy+vIslz5wFepVTi6tO61fM3MsQR08WPrkur1wBqLJEno83wpPASFgBpH1A2cqzFVmMhMzCIzMetcY9YHqF9HH657YDyObgZ+enVx+w3qtzEduvlz96uT8QnxJvVMOr6NuioAACAASURBVF8+PZ/kU9aC3jq9ltG3D6VTn1CObI0h9mj9CSIuR/qMDqf3qHAsZgsKhYSxoITfP/y7rcWySwZd24+YXSdZ/f2mBm0/cGJfeg0PIyctrzIbb1sza+F/STqRQnlpBd0GdubPeav4YOaXdBvQiT5jwpFl2Pr7Hp7+9kFidp0iasdxtv7e/rL59h4VTv+retGpTyh6Jx2lxjJSzqTh7OHEwnf/Ijs1F7+OPgy8pi9aBw1xMYns/edgW4tdhdU/bCKgix/lJeWknEknYnh37n75FsxmC3kZBRzfe4oT+87UuK+rlzNDbuiPdwcPZNlqrDGbZQ5uOMqvb/5BQBc/eo0I46ppI1CqVVSUVVCUa6SstBy1WoXB1YBKrUSSYNvSPURtP97Koxe0KEK5axiSJGmAG4AXzi36AngD64TJG8AHwL0X7yfL8tfA1wDOkvvlUF2jfuzYKlcXkx6ZgFqrYukn7T+00mKxsPCdP1n4Tv3WrEpacHbNN8Sbb/+3oGnp8Fuhzk7q2XQWzfmL/lf3pvvAzsKlpSbOnQdXb2emvTqZTr1DyM8qYNGcZVXqCBpc9Nz61HVUlJnYs3J/i2UYbC9UlJtQqZVUnEvkpNM3vLj5JUkjs2J6+LkRPqwb3QZ04uT+s6z8en2d23fqHczQSQPQOmg4sOEo376woPFyNUK+xvLX/62m98geqDQqorYdJy4qgYjh3YkYHoZWr6Uwp4iM+EzmPfIdxvzi5stqh+/rmx6fyANzp1GUayQ9IYtHBlo/y9RaNU998wAdewVx9lAsU2fdhKncRH52IZMeuopTkWfITa+//p5CoeDmJyZelEVWwmKRST6VwrE9p5sfhyhbeHP586z5cRMV5SZG3z4UJIlF7y2jpLAUZw9HIoaHMWHGaBbNWUZ6fGblrn3H9OSN5c+z5IMVbP19d40TX3HRicRFny8fIUkSjq56NA4aTOUmjPnF9uF5BHZ5jQnsG1ta7q4BDsiynA7w718ASZK+AcRUaTtnwdtLeWvl//AJ9qryoL2UkBSXpjvxmSPxTHt1MhaLjCTJKJTWD5HkU6lsX7rXbup0Hd4UzQNz7ybhWJKIxbuIKc/dwLCbBlFSUMKyz9cw75Hvqm3T9YqOjLnzSn59aymFOUVtIOWlR/SOE0TvONHWYtglOr2Wr4/M5bnxb7Dtjz11upt5B3lyw0NXkXA8mQVv/1lr5sum4OHnxsCJffEK9Khclnw6lW1/7KlWbDwkvAPDbxlUOZElSVBRZiKwmz+ZidlV5NI7O3DDw1dzbPcplnywolmFyy8VFAoFmxbt4OGPZ+Di5czPb/xRuc5UbuLI1hj8O/owbfZtnD4YiwQYXA0c33ea6x+6qkpbkkIi7Ww6Gxdsp6LcVLnc4KKnQ/cAPvrPVxdujEIh0aF7AL1H9eCqaSORJCrj2WRZJmbXSQ5ujGqQqy/Ae9M/ZcydV6LV67CYLTgYtBic9RQXlJCfVcj2P/fSqXdwtev24MYorneahk+IF2GDOjP69mHkpuez7LPVtfYtyzKFuUbIFaED7R4Rc9co7uACl0xJkvxkWU499/MmIMqGfV16XAYzLz7BXpQUltq3glRP/F1+ZgEfbH6t8mVRmF3E6UOxRG0/TvTOE5gueME1GlvVmapB7q1LdrOV6q5FnXoHc9fLtxIblcCGX9sgScRFY64oN/HdiwuZ+e5UvntxAcUFrZRJ1Z7uv1rOvcVswZhnROug4ZYnr2XCjFEc3XaM7Uv3IikkJtw7hvT4TL58en7j+2zq+G1gWWn086CVz9uFdcHaBQ08ZwMm9KHfuAg+fey7OmM1A7v6MWHGaLJTcvnljT+antK9FrmG3TiAjr2CWfPjZjISsiqXh4R3YMpzk6p4I0gSZKflVVMuVWolL/zyOOt/2ULiiVSaRBveIzaR4xzDbxnMS789CcDMiKeJjz5vtZJlWPVN3ZbZiwkJ78CU529EqVKi1WspL63AYraQk5Zf7V61mCE+OqFKn/+iUCjoNbIH0169Fdkis/KbDZVZPGtEUpCekM3Cd5fVKZ+7nxud+4ZUuXasY5VJi80gLTaDTYt2MmLyYDr3CeHk/oti4e0pjq0x517E2TWPdq7cSbYIDJUkSQ8kAh1lWc4/t+xnrFk0ZSAOeOACZa9GnCV3eZBifLPlaQsq5HLK3TPQO0sY82R0ed6oJM35Dezp47I+mvDQUGvVzNvxBo8PfanKDF9dzN3wCs+Mfb3RfdXGI/NmsGvFfg6sqyWhyMU04Jz4hfrQZ3Q4XQd2JqCzL0qV0rqrLJMel8nJyDMc2hRNfExiPS1dRD3HeOL9Y3F2d2TRnBpebE1wbxp9xzA69Qpmx7J9HNt9qjGSNoyGXN8XyOjk7sj0127jx1cXt44Fyp7uvwbeXz7BXgyd1J+Rtw0lLz2ft+74uMH3VjWEclcr7U65qwetg4a7X53M4c3R7Ftdd1bf0XcMwy/Em0XvLWuwtaUx3PTYNWSn5tok5i1iRBiPfjKDr575mQMbjja+gfqutdb6mLbBNb8g/ku8OnjwwjVvE7nGRnF0kqJa+ZwqNEJunUHHTY9PZOG7zUvq4uHvxtg7h9NvfARvTvm4zoRdHv5u3P7cJAZO7MeGX7dhschkp+YQdzSBUmOptSZjhYnMxGzrBMZlrNyttyzeL8ty/xbroI3pr9fLkd262aQt6dAhuzxWNrHcybJcDHhctOxuW7TdcCFaudbbBf2VyaW49Mrjy2ceJMDTk4SMDB547yuKYzzRSHYW39ECDwS9kwMz372TN+/4pOkfny1JM66N1Nh0UmPTWfX9xirLFQoF3Qd1JnxYd6a+dDMe59J9I0FRrpF9qw+xdfFOCmpTXJozW1hTHbd6xrhpwTa2LNrBNTPHMPSG/pyMPMP2pXW7YTVYjov/3wALZWFOEd+9uJD73r6Db/73K6XGFijwak8KXQN4d/UsdAYt2Sm5rPhyLYc2RfPnvFWEhHfg4we/adi5agMlziaW+tr6b4RcTZZDav7Ham20qOLYBE8AF08npr06mQVv/1lvWYOJ948lIyGLTQt3NEfKWvEJ9kKhVDRdsbvo/BzdEs3sm95n/plPObwlmgPrjrB39aGGZy22F0uIDbw67gx+kDnrXuGdVS+Sn1nAncEPUV5a3jy5ZAvVHkFNPH6lxeVodOfrpqq1avqNi8CrgwcWs4VDG6PqLOej0akZc+eVePq7s+zzNSyeu7zWbT383fg88l3cvF1Ii83guxcWsGXJThQKBR7+boRGBOHh74YkSag0KoZOGoDOoEOhVJB4IpkNv2xDbkyNpIZgL1biyxXhltn69BsXQcSV3SsfIhaLTFxUAntWHqCi3IRfRx8COvuSGptRmTWurbH4ZvLLy8/gYrAWwAzy9mb+y49z4+OfoMkKrHPfYkMm7sEy7s56ktLyKE90QWd2ag2xm40kSYy9azhB3QOYP3uJXWRKay0sFgsxu05WSXTxL74h3oy+YxgvLPgvzu5OzHvkW07sO90GUlbFYrFUJksIH9ade16fgsVsQVJYU4afPhDLvtWHWi3Nc3FhCYvnruC6B8Zf9tkL+4wOpyjPyP8mvEXnPiFMenQC01+bgtlsRqlWtV3qbUG74sZHJzTYWu4V6MH+tQ30gmgEKrWSPqN7MnRSf7565mebtp0am87Mnk/x+OczmfHmHcx48w62LNnJm1M+smk/lwLPj7d6xby9ahYh4YHV3RHbEK2DhpCeQRhc9OidHLj9+Ums/3UbJyPPoFQp+S76Q96642O69u+MUilVUSolhYTZZGHXish6s7p+tOU1el7ZHbPJzH/6PEvc0fjKdRaLhcyk7DprF4YN6sJDH0+nINvIjr/2ttuMxFoHDb1H9aDXiB789v5yyKp/n0seody1LmGDuvDz679jOTfjqVAq6Nw3lHten0LMzhPc+eLNxOw6QfdBXfjq2Z/PB8q38oyGk4czPsFeaHRq0soLKxW7f/FycSF8UDAjB99GZlI2Zw7F4eCoo7y0gpy0PNLjMijSpTNjWl9mThwHgNlsZtq7/0fi7nK0kq5uAdrYKhEUFsB1D4xn08IdrP95a5vKUi+t7OaVFpdRmUlT76znjeXPc3hzNPNnL65/53/lk+Vz/+pwwWzINV/LeKN3HCd6x/nUzmqNirAhXXnoo+kkHEvi4IajJJ9Oa1LbtW5Tg7zp8ZlkJmUz7u4Rtr+O7Cnmrp5zNeON25l13TsAnD4UxwczvwRAo9Pw2P/dS4du/iSeSKl552bOAju5GRhwTV86dPOvtDQtmvNXncknbB5X2whLdpW+azuuF25Tj/VMUlhQKBWEhHege/+OeAS4W4/DxUOUqbKstKiM6F0nORl5xppVr7WvsSa889b8uJmps27mwIaj7FtV90TOgreWMm32ZBKOJ7Nl8a5mJyPROzkw9aWbKSks5ci2Y3z6+A/Nc/WsZfzxx5KpKLN6kJgqzPzw8uKW/T5oba+hRu57ZHM0H2x+jW1/7OG96Z82Q7C6+2nMcSgrLuX7WQt5/NP7uOKqXrw3/fMq4QIPD3gBNx8XFry9tMnXiKOrnp5XduehK57ndBPrfx7bc4pje06hVKu56p6RjJw8hD8+XIFSrcTd1xXvYC/cfFxxdndEq9ciy3JljGipsYzc9FzS4zI5cziOksJSgsMDkWWr5VGSJE4fjK09MVErfNO6ertw02MTMFWYObD+KHHRibh5u1weyl07x+6UO4VSQf8JfVBrVPiGeGNw0SNJYDGZOXs0gSeGv0xAFz9+e3852Sl1u5XYin/9zD383Bg1ZSiu3s7kpOWTFpdhrddVpKHCZEKtOn84S8vLOX4onth/fsMn2Ivg8EBKi0pxcNQRMbw7194/ltUbf6pU7ACUSiUfPTKdR6U/6dvxSmKPJhAfk9QisQ5NQaVW0m1gZ/qNjaAo18iXT8+3G9nsleKCYp4e9Sr/ef9u5qx7hZevf7f57jEtQEW5iSNbYjiyJQavQA/6jo1g3N0jUaoUGJz1LP9iDfExSS3S95bFu7h/ztS64znaMYMm9iUtLoOivOpp2ctLy/nuxQWMnTq8duWuGXgHefLiL4+z6vuNJJ5IIbCLH3pnh0s2Ds1oKcDkUIiyXI+j5F5tvcFFj1+oF76h3viGeOFg0CEpwGyyEBeVwN7Vh8hMqiPJw0Vt9RjSlSnP3oBKowJZ5vje0xzceNRuszKmxVkT8vQbF8GMN2/HmF/MpkU7qiWjACqTH4X07MCtT12PRqfGYraQeDKFE3tP1+k2VxN3zrqZ395bTkF2y2fJ/d+Et3B2d+Ta/4zjx+Mfs2XJLt68/eMW79cecHTV88gnMwjo6g+yjFKpIGbXSf7+am1bi1aFxBMp/N9j3xMUFsAn29/gzOE4Huz3PGDN8twcz6y3/3kRDz9XPnvihyYrdhdiNplZ9d1GXDydmHj/WIoLS8hNzycjMYczh+MpzC6qlmhIZ9Di7uOCT4gX18wcS1BYIIc3ReHVwYPslFyKco3c/95dlBSWErn2cJXJ1tYiuEdgZdZRzwB3OvYKrrVuYLujnVvubJJQxVY4S+7yKOfrCY0Ioqy4jLS4zNrr0LQCI28bQqfeIYycPIQNv24jLyOfHX/tqxarUKLIZ8DVTnzw0HQUCgUWi4VHPv6ao5sqcJDPuVjWMKvlN7iI+bMerbb82uc/xNscQac+IQR1D0BxbhbaYpHJSMxC76THyc1qKZQkSI3N4MD6o3VnnmoCnXoHM2hiP+uHy7n+T+w7TdT245QUNT+1fpsnVPmXVppx7z0qnIc/ns4nD31TozvnhUy8f9y5hCrNCzi3hfVqztpXOLgpCo1WzfIv1pKXkd/w9ho4+9hndDhqrbreBA+NojarYWtZWBo49tufn8Sgif2qzODKALJMUV4xRXlGtizeReTawzU30EzLXee+oXgGuJF6NqNOBb4x1rrGKoe1tV1TOzVZ7mRZpsQ3geuv6cf1Iwax41AMP/+5GYe0UMbfMQL/Tt5YLDLGvGJSYzNIjU0nPT6r+nNMqaxXVr2jjtKiMiwWS5VjLwFhg7sy8+3bmffod6ScTmuZWFIb4uTuyMjbhuDu41q5TJZlSo1llJdWUF5WTqmxjIRjyVaXNFkmsKs/4cO6EdQ9gBORZ9i/7kiD3DxvfHQCxoJi1s1veU8PnV7DA3OnMXbqcBwcdciyzFWq26tv2NYW/ZpohsXm1d+fZuDEvhTnFzPZ7z8N37GNj8MrS54mbHAXAE7sO8Psm99vUjt9Rvfk3rfvIPlUGnPu+az2DW317mrkcQvs6s/spc+yceE2VGqV1conSRzddowD62tPAiQpJJw9nOg+sDMGFz2psenkpubRb3wvPP3dMZWbyMsqICMhqzJjeVGuEWNBcaXiKUkSaq0KU7mp0jMOoOeV3Qns6kd2Si55GfmcOhDb/hOqODrKkRERNmlL2r3bLo+V3VnuSopK6/3wbWlUaiX3z7mLHX/t5fvFC+kxuAvzX1tS6/YOFhci1+dz1fG38XJ3JCO7iLJEZxxk5zr7SUzJq2bx233sOFlxFZSXpJJ0surMlVKlxDPAneKCYms9lnP4d/Jh6A39cfN1JSgsgJP7zqBQKlBprA+PtDhrKuCKsvOzyeHDutF3TARK5YUfShIWs8WqTEoScdGJLJ33T4t/oDg46nBw1KE1aHEwaPli/xwyE7NrLYLq4uVMSHgHAA5vjq5c7uhmYOlHK1tU1uZweHM0T1z5MnM3vcbn//2e6J2XRu0tU4WJv79cC0jc986dfPnUT5SV2Nb6eGTrMe566RbbKneXCIvmLKuWFVXv7MCoKUPpO7ona37cTNT2lpvVPX0wltMHY1us/dbAqE/n9aduY9xA6zu2R2go4wb1Zc6SdZw+FMva+Vuq7dNQZbVDdz+e+/oBALQGLca8Yly9nDm29zSbFm5n39ojdB/Ymd4jezDlmetZ/uU6Arv6Meiavmj1WswmM9uW7iEuqpEZdZuJWqsmKCwAB0cd6XGZNcYWFeYU8feX66oskyQJrV6DRqdBo1Pj4KgjODyQSQ9fjcViYcncFaz6zppgqku/UMZNHY6zh+M5T3KrW5okQUF2EXExSZyMPEOvEWF4BnpQerLllV2NTsOKwp/JTs3l7k6Ptut6msE9Apny3A0EdPHDYrbQc1h3bvKYUaMXgD3z+uQPAFia/QPDbhyITq9tUsmN3qPCcXQ14N/Zlz8yvmVK4IPNK19kY9LjMjh7JL4y67artzOObo7IFpmI4WHIFhmL2UJ+ViFms5miXCMF2YWED+2GxSJzaHMUqWfT8evoQ1D3AKK2HSfheDJqjQoXL2e8gzzROzlgNlsI6hGA3skBFw8ntHotFotMRVkFOr0GlUZVWeD+7OF4dsQkibqp7Qy7s9zd1nMGxYUlZCfnVJldaA0kScLRzcDV94wiLiaRyDXWmfJGWZgaYS0olYx49y3mk8dmEODhwY7oY7zw+WI0CUEomjh7N3fTa8y69h0sZktl5sqg7gE88/1DlePpPrAz/3y7gZ3LI6u4Vf5b+NpiquVhaCMf8H7je/HMtw9SkF1IUa6RinITFeUmTGUV1qQ5nXwxlVXw1KjZNumvVtpottLBUccnO97k0UEv1uqiaTPLnQ14a+ULvDN1HkV5Rtx9Xbn5yev49vlfqm5kg2vjzhduQlJILJ67ospERJOxx1l5qPVYOboaGHLDFXTo6k+psYytf+yuNsFTiS3G1sgMp/UpQ7Zy5bywn/ra/HdbVfcsVn4xu0pNNICbHn8d49ELEjlfMM7ug7py/YPj2LBgJwc3nZskUl54TKx9rzHO5+3pn7NlSdWsjtdMH8UTn91LzJ5ThPbsgLnCTHxMEk+Neb1SbtlsRuugYfgtgwjo7FuZGEKSIOVsOgfWH22R8IKrZ4zCJ8iLM4fjKC4sJaCzD94dPMlOyWXZ52uqbe/oasDV2xmFQoGxoJiC7KJq9+Cnu9/m0OZorp4+ClcvZ359ayk/vvJbrTI4uTsSGhHE0988wN9frWPJBzZImtTA6376G7czddYtWCwWjm47hpObI46uBspKy5l13bukNtKltNnYsMzCiFsH8+wPD3P6QCzZqbkknUxl/uwluPu7opAkMhJrTxDSKJkaS21jaKCVrPeocOZueIW9qw/x6qQ5mEwNzwZ9IQqFgqkv3cKds26mIKuQKQGNsGBeJFNT+m9M218dfA/PAA9u8b6vcpXOoEVn0KHWqnB0M+Dk5khCTJLNE9ZpHTT4dfQhNKIDrt6uuHo5YTZZWPz+cv4u+tkurVG2or+joxzZu7dN2pJ27rTLY2V3lrur7hlJRkI2bj4uSBKc3H+WPSsPWAPWbUy/cREMmNCHkkKra47FImPMLyZy7WHiolt+llUnG8jer2byk1+h1lsoz1VjKG66YgeALFezqiQcT6a8pLzS+jhnzUsc2hRVLV6uteLnnFz1rP9lK9/PWtQq/dkbJUWlxEYl4t/Zl7ioSyv7Vk5aHmVNLWJcDwve+ZOg7gGMu3s4q77dWP8O7QBnDycGX9ePwK7+FOUa2bUislXc1toLHv5uTHpkAn+v/wWLxYLyIrfKihomqsZOHc5zPzyMJElE7TjBDQ+P57anr+W3uSvISi/g+vutxeK3/L6Hz3a+QU56Hn8vWYlZVYZDhRtayYHuAzrxxGf3knwmjVk3zqW4oATMNb+jykrKWf/LtmrLAzr7MmzSADz83TBV1G3d8/B3I6CLHwFdfHH3cSUnPY/E4ykoFBIVZSZrkq74zMrtA7v48d2LCyt/H9xwFA9/N+576w4CuvgB0HtUD3yCPLFYZApzisjNyEe2yOidrbP9Gp0aWbYqogCnDpylMKeIyb73I0kSS7O+Y8/KAxzbU3PdzMKcIm567Br8O/nSZ0yEbZS7BvLjy4v48WXr+yWkZxD3vTOVPz5eycjbhjL/5DweHTzLLjIYN4Wtv++m79gItA5q3pv+eeXyrAbGitorcze8AsCsa99pVjtO7o7c/epkZt/8fpuG9TSE569+kyWp3+DgqKt0ES81llFqLENSSJWhNi0RA11WUk5cdGKVb93RdwzDO8gTYmzenX0hSiG0PsUFJaz4Yk2l1S58aDemzroZpUpJXIw1i19uen6z+7l6+ig69grm+1kLbe5i1hjUkgZ1XgDkgRaqZ2hrAYz5RpzcHC851412hR1ZzBvL8T2nmDnnLuKiEji262T9WTUbQcLxZCbcO7rGdRVyGWWumSgdzJhyHXAs9US6ROr+KJQKfEO8CezmT3CPDuidHZAkiYLsQvatPsTan6q7DQpqJygsgBG3DEZSSPz65h9kl+fz+ZJlPHb7zZXbrN29j5xECzOfvYFRU4ZQmGdEkiQiruzO3n8O8vJNc3l03gyuGBeBq5czr//xNCcPxLJpyW78Qr1ZcHoe2dnZTB5+I488czU9OgWxYOVGdm9LQKnswtmjCTw0+KUmjyH5dFrlvaPWqBhz55WMuX0YZpMZs8mawfNfxSo7NZfkU2lErj5MdmoungHudOjmj9lkxtHNQHB4oLVunELilzeXEn8smXteuw3ZIiPLMp6BHviFeHF83xl6jQyjvKScA+uOkBaXWYeEtSPLMjd53Fvvdq/d+gHeHTz4Ne5zJt4/ln++2dCk/ppDXFQCL98wB4C1P23h/fWvcPtzN/Da5A9bXRZb8clD3zB346ttLYZNGa+cwh8Z37HO/BvjlVOa1EaPIV2Zs/ZlVn6znt0r9lvjYu0An2AvulzREYOLHoUkET48DCdXAxXlJiwWi01yGNgCkSCv/WB3yt3FRO88URmbFNwjkJGTh+Dm40JZSQW7VkQ2ue7IicgzeAa4M/31KRjzi9m//kiVVLxNxgbFeBtEXW4Cta07t1ySpNrT7De1T2jwGPMyC7nqnlEoFBJLP1lFTlpe4+SwFTUVAxfUjaRg35oj7FtzhMCufoRfGcY1948n9Ww6G37dZpP4zJpeMCVSET79Svj4vw8R6OnJ2siDvPH1n+iSgmtX8GzlVtPIdryDPOk1sgf+nXytu0sSZrOZtNgMkk6ksPKrtRQXllRvuz6aULS6we3VQkPi0po6q9yQti/eRqVWMubOK3F0NbDkwxWUl1mtZY54snjhQfYcPsGAXl05kZCMR8dQvvnnK5xcDcy67l2rZUtSoHfWU1xQjKRU8tmT8ytdMXsND+P1359g75rDfDtrEd++9BulPrEs/3o2Lo6OAPTt1o0PvH5j6Q/7mHB4FJ9sfpWT+8/y1bO/UFFxwXFo5PVWUW5izY+bK38rlIo6P7QyErJqzHDpG+rNI59MJyc1D9kiI0nWWO19qw+y95+DbZLFMyMxG1mWmfzU9WgdNPw5b1X9OzWiNEZDuenxa3hw7jRKi8u4xWOGfT33m3BvS82dCG6OG6Ut9wM8/Fy47oGryM/K54unf2r0/jc/cS23PnU9x/ecQmfQ8dmj3zVfsWvm9REUFsB30R9zbM9JJIX1fj68KQpJoWDd/K3kpufRoZs/Xz97vtZjY1zSBc1EWO7sh/iYpMpsbg6OOgZf148xdwyjKK+YXSsiyc8soCC7qEHp1OOiEitdYNQaFQOu6cN9b99BfmYhv3/Uvgsqx+w6wfwznzJeMblN+j+8OZq7Oj7Kj8c/pmPvUPROOgpzjaz5cTPbl+5pE5kEjSfp5PmkPwFd/Lj1yevQ6NTkZRYQueYwCceTm9RuTfF26qBcfnvtJVTn3O6uHtAPpULBy2+uwrHMp+mDsBGhEUGMmjIMWZZJj8/kyJaYFqv/6OTmSNjQbiSdSGl0OvpLmaCwAG58dAIbF+wg6t+04Rd8oDoU+JK2y0xF3048/e5jPDvqLZ78fHa1j7Tigpo9Fo5sO0ZGYjZXTxvB4g+tiZmCg90rFbt/eeS2G1m6bBYfPPgtChlWGX+iILuozqRbjaWpM+hpsRl8/OA32daOWgAAIABJREFUNpPDVtzT7b/MPzmPPqPCG6bctQCrv9/Ewx9NR+/kgKm22mKXCJ37hVYm5biU6XpFR15f/j8KsguJ2naMmeFP1VeWsgori3+tnOj4/sVfKcwrJjstr9XzNdTEd9Efc3zfaR4fMqv6ynPPrYRjTXtHCmyAUO5aG7lBLmslRaVsWrSTTYt24uRmoP+EPji7O+HiaS094OCoY8uSXRzfW79ffUW5iZ3LItm3+hAPfXgPz/7wMKcOnGXb0r3kpLZOLb1GU9fsXj2WjCUfruQ/709j7sbZlasihoexecku3pk6z4ZC1o6p3ERabAYvTnwbAL9Qb27670RuffJaLGYL+1Yf4s95/1Ba3Lous+9veLVaUoaWwK+TD2Uv156MwL6wZk+t65pLPpXKL2/+AVhjHoZO6s+Ee0ez7LM1VWKB6sPDz61Gt+sOfq6Vit2/jO3Xm9fc/4Kml0OqShOsAmqtmlufuo6iPCM/vfJb1Q8TW1vrz7V385PX0al3MGqdmlcmvd/05DPNkK8lrXUX7VD53/T4bCxmmfhjyZXLpQuvCdmCEhVde3SlOKfsvNv5BW1U2b4GWX6YvYQXfnwYjU5DeVkFphri6ErLy5HNEiqlxLcH53B83xl+fuP31rME1dSPnbonu/u68vS3DxI2uAufP/lj3Yqdra11F7VXUljMP99u4OoZo3l71SxemTSn7TMpNnJsry97jiHXXcHBjVHMf/33FhKq5Xljxf8YfO0VAMy+5X12/Lm3Se2knElHrVGRkZjF9qV7KS0uY8/f+5sumA2TqCz/fDWDr+vPbynfMMX//ma11aJcOM6meHQJ7BI7VO4aT2GukU0Ld1RZplBITLhvDMNvGcSZw/EcWH/0fH2uczh7ONH/6t4EdQ9AlmVM5SZORJ5BqVLSuW8oixJmsOefA60SB3chRkUeat8CtFolBRlgKPJrXpKVGrjYp33mu3cy5dlJrabcXUxqbAafP/EjACqNihsevpq3Vr6AUqXk0KZoFs9dUeuMuy2RJIlnxsxu8X7aM4U5Rf/P3lkHRJH+f/w1u0t3h6JY2K1nd3f32XV2n3G2nn3m2S12d2GLcSZ2gCICItJduzu/PxYXkE71+/P1jzLxzDOzs7PzqfeHC9uvoaWjyeh1g1jaf136O8UT+DlYVdD9DSFf0xgT4RMYiCL6+7zUyjRk1O9Wk6KVCnFs1dkEAzYb31NtXS3aDGuqbiL+NXtaHqfAx92XT26fEZUijy49IyYyhqrNKvDHP32wsDPD/alHMtEpiVTC1f23/me8w4bxjruvfTdTYs2teTy4+JQJDbPWP/Oh03MEQUDXUJtYvzg+vg/F9aMnxQrYqbeZs2EnsiBz5BIleka6vPjOrXt+NCQSCfNO/ompjTHm+cy4e/ohvexH/BB1RSuGbGDFkA2cDNnFAe9NdLJIv27wR2J+t5WciXCkYsMy/Nlk3veeTpYpV7cUa0Zu4WQKyq2ZYXDZ8UgkEgYv+Z1T4bvZ9Kcjh5adzKFZZo81I7diYGpAg+61kgim/OIH4Jegynckm00mlUpRXbxtX8aO3jM7I9OQcPvkQ2wKWWJma0KIXxj3L7hwZa9zimM8vvwMz9efGLm6f5ZOIbNECeHE2XrTomYppnUdiqaGBo/fvWfU8t1oexXK1YjSjpkH6TapHQc/bcTI3JDd84/g+J08g/JYOUdXnuHoyjNIJBKa9a/P3GMTkWnKuHPqAUeWn04qk/w/xp4P61HIFcRGx3Ju6xVC/HNWAjlNcsiJoKmtQb953TiSyb6Doijy4aUXZWsX59nNV+rl/h5K9l+5QfeGdQFQKBSMX7MdjQAzEL65FzJTS5nJuhNDMwOa9quPoZkB1w/c5tLuG6nvl4lrWbxqERr2qMXhf07h5500W0CmIcW6kCW2Ra0RBIHoiBie33rDvXMueLz0QlNbI8UXB0EQ6D+/O9cO3Ob9U48MzyU3SRzxy1AUL/56SmVSuk1szcaJu4iLS/7ZLL88A6Uo4v7ck51zj6Reu5L480kpM09bk5iIGCrWL821o/fRDbFn4JQ1VKloT5ECNlz77ymfXyrRxQKUIp1thrL61lyG/dObtaO3p38+WeUn8aYXLleADY+WcPf0Q4ZVnpK5nfOojhRgw8RdjNs4FJmmLGejdxmJ/GQ1OiRIiI2OxfPNJ/LHK57mClmtA84E26btpfXQJrQf1YIZbRfj7Zp6+kWJasXoPL41NoWtkWlIUSqV+H7wIywwDD0jPUrXKoG+sR4RIRE8vPgk85PJwn2nb6zHby0rMXX3aHzcfYmNikUhV2JkboCGlgbebj5oaGpQtGIhwoLC2ee5kfYmfRMGSOV34rvU2f1/1R74Zdz9GEikEnT0tTMlbauhpUG1lhWp360mn975qhTTgiI5v+1K6v2jEnHzSN7Vf0WLEdhXj8PashCze/VQL69YpDBjujdm+cp76CuSRzRyCnmsnEFlJ2BiZUSXiW3pPaPzdzPuEqNUKjm39Qrntl5BJpPQYWxrll2dTVyMPEn6XlREFNun7yf4Sx4aQrmAk1JVtzO43AQAll6eiesjd3Ud6et7bvENxX9ctPW06D2zMyfWXki1EX1aXHK8wZAlvfj4yltt2OqGWbJmgzP7z9/GyFAXr09BxHgYoiNo5fT0k6GprYlDlcJUbV6RyNBInHZdz1ERoLqdq2NiacT6cTtUC7552ZDHKZLUN35LVHjK9UOiKLJ9+n76z+9OZGhklpURfwS6TGjDyfUXVb07U3gZk2lKGV1ndraPExcTx+zuqxiysAfXjt5HIkjR9S/CkwvR3Bdd0RUs0P3GsaRrqEvpmsWzfeyfmTHrB1O9VSVcH71n3bgdHF9z/ntPKU3Obr5Et0nt2PtxA4o4OT3s/vjeU8owA0qNw0lxgLX/LWBEtWnfezpZ4sTa85xYe57iVYuy481qru53ZkHPVer1MpmULpPa0mJgIz689OTUugvcP++iXm9X3BYzW1MAlvZfy9B/+tJqcGP+3DkKpULBvXOPeej0BO+3Prki2LbPcwOPrzzjyj5nFvZalWSdpZ05dTpX50i8dsOofwdSu2N1tjxfzs0j/7Fz1s9SjvGLn5kf0LgTKFDajia96xITGYsoigiCgCiKxEbFEhUezcn18S+3qXg/mg9oQPEqRfD3DsTl2gvmd1+JRCJw6/g92o1szui1g4iNimXZwPU53hgyCRn0hERLQ9F08Kdg/qJ8Cghg/dmz/NGihTpS16xyBVYYXoecKP9Lw5v4VbBm6aWZvH34Pvm26UU5crnuQy5XcmjZSQ4tO4m+sS6mNibqdVYFLZh1eAKBPsGsGLoxc20efrB6lXbGfdUpqKNr/EXB0gkpYQ2612LV7b9Z1Ht19hrx5tI5V2tViZLVinFqg1PmDbtE99vuuYcZsKAnG8bvRB6n8qzrhlkS8QwiAAm66GRgnMwcE0GCgYkeRSsVpkj5gugZ6QIQGxXDuyce7Jp9EMVXIYYcvH6f3b9gam2c6pxSXJ6YNOYiiiLHVp+lbucanFj7Y71wp+al/jai13FMS17fc0tIL01SIyLQanAjIkK/SdtNXGcnSdg+pcihibUJFRuUplAZO0RUBt7No/cRE9XbyeQCMrSBb6I8opICxW2Z0X5JOmebBhmJ9uZmQ+UsoKmtycxD49HQkmGR34y4mDh6FBiWfMPcnGs2v4N9HUaho6/NyVBHhq3ox9ape4mNzmaNd0bON5tqlFtfrEAhV+D0bdZAbpNVtew09ntz343JzeYxeu1gararytTdo/HzDiQqNAo3lw/0cxidouql55tPeL75BECz/g2o3qoSI6pO5v0TD2yLWVOubima92+IqY0xFnYqp7i/VwCOcw6p1dczfF6J6DunG0371uftw/cs6v1viqUiXzz91YYdqNIz14zciq6hLmvu/E27kc0JD4rA7bE7c7v8A6ISmUxK3a41CQuK4O2Dd8RExuS53gCCJN3a+v8ZfqVl5j2+H/1xqFyY3fOOpJhq1GFUC7Wqpc/7z9w7+0jlzU2EbRFr9i48hp9ngHqZUiny9sF73rt44Ov+hVvH7zN4cS82THQkLDBcvZ0gCGjpahIbHZcnPT/ixFjyVZaz96/ZSOJvttsvX7LpwgWGNm8OwL03rigjNXN9Ll95dc+VElWLcuDTZlaP2JzlYufcJDw4MokB9/GVN/fPu1ChQWlmHBiPVCNlJTFRKRLiH4q/dyA+77/g+fYT7598+CEifma2KmM18Q9GdGRMkma7b+67YVXQgmn7xvLgvAuOc3NOoS87GFsY0mJgQ8KDI9kxM/ueyciwKA4tO0nfud3YOnVPDswwfQqUzEfrIY1xPn6fc1uvJGQJ5PLL9NuH7yletSjFKhXG9dH7HB3byNyASo3LUrNtlR/OuEuPMrVKUKt9Ve6efsiT6yl31e07qwvN+tWjZ6GRWX4p6TOjE067b3L71CPqdqlGqd+Ksn7S7gzvf+fUAyZtHUYny0FZOv7PyJkIRzxeebF73hH8PAOSvzD/JMRFx+Fy9Tkdx7Si45hW+Hr48Xuh4d97WqnSYkBDCpTMj/PR//jyMSD9HX4CHjk9ZUi5CRwN2I6WjiZDyozPlJrp02sv8XH/wqrbC5jSbB5Prr3g4ytvTm90Um+jratF+fqlWek8H49XXjjOPsR/Zx4SHZnQuqdEtWJUb12ZV3feUqhsASo2LsdfLf5GLlfgULkwVZpVoPXQJvh+9GNC/cz3GIwMjWRg6XHqv+ee+JMtL1agq6+DhZ0ZXm8+EfQlBKuCFlgWMEcURYL9QnF9+B7rQpac2nAx1yLiZjYm1O1Sg0Jl7Xhy7X+9g3k8v4y7vCVF+fBEL1fHVp9BEAR09LUpUDIf/eb3IDYqli9eAWjraGFqY4y+sR4amjIkEgE9I10iQiJRxjdzTdzmYMOEXbQb0VxlCIgioqgy6CPDojEw0UOhUCKN74HUoEctrh+8kzmDLwMvG9FGfiwbNlRt2AHULFWKE/+pUkK/BAfz966T6EcXTF3Y5duXz9QiaRnsjTe6+jRkMimH/bYx+8gkRFGkpVYP5Ip0+jhls04yLb6mJaZXd+hy9QUuV1+kul4mk2BTxAq7EvnJV9SK2u2r0mFUC3T0tQFVSmGKc8wDb3mzfvHNu9M5vq+HH2Nq/sWAv3uw0nk+87r+Q8CnHFJ1zcJ5GpjqM/PQeOZ0/ocQ/7AcO56vhx8vb7+hQoMyuFx9nul5ZYhE17r10CZsmLAr+Xc8DzyZpzc6MXxlP949/YhSnkoNUAbnYW1vQamaxbEvbUdkaBThweHsyoQTILVauLyoB5FIJTTtW4/8xWx4fd+NjRMdU2xtU7RSIbpMaINEKkFDU4aAgJhaBCyVKB5SKfal8yNXKLAva0eLAQ04uPIMFx1vqtYn+hxEpUikzmfM7BQYG+rg9TmEWC9TtJX6zGy/lPUPFrHw3DSmtliQ+ZPOyOea3vcyj6N5sdFxXNh2hWv7U6hXzyvPfw6cs1yuYFKjOYAq1W/1nSx8fnmFIOHqgdsUKm9P/mI2TNo2DEEyHD1DVYbB4RWn2TjRMZ1Bsnf8ZOTQfRcbHcvZzU5UaV4x020qfNx9GVdnBk7KQyy9PIvmGt2TRfuiI2P47+wjWmj3oGqzClRtXoGxG4cQGx2LUqHE0NwQTS0NfNx9KVyuALqGuhQomZ9TEbvxdvUhLDActycefPnoz8jfpubIOc9sp4r2b3mxAoAr+5xxnHdEvV6mKUPfWJeCpewI8Q9l/OY/aD20CfrGetw/78KOmQdy7He//ajmPL7ynGOrz+bIeL/4/vxwxl1GEEWRyLAoXt9zU7c6sChgQVx0HB3HtuL6oTtUblIeU2sjQgMjsLQzIzoyBrfH7rg/+6juDRUWFKGWb/+KvrEexpaG+HkGMHhxL0IDwtEx0KHXtI54vv6E22P3HD0XiZYCGxOTZMtDIiPpumgpH92i0PHLn3qj5lxCLleoC4BX3ZrPudj9OM49nKO9nDJCrBgD+T9TqIAJSqUS9w8haHzOh1TI2q0rlyvxfOOD55uU65eWXZ6ZnelmmfNxB5BKJchj5ZwI2kGvQsPTTS3d9tc+SlQrxui1g5nVIRtpYdkkLDCcawduY2xplHnjLh3+O/OIQYt7ERcTl6vRgRptKnP/vEueROtTQhRFzmy6RI8p7bl1/B5hAWEEZKANi0QiUKlJOUysjLC0M0dbTwsvVx9e3XFNVSjqR8WhcmEa967L2S2XOb/taqrb9ZjSnt9aVGTtuB0IEgkn1l5AqVRmqs2CTFNGi4ENaTOkEfednvHZ3Y+z266lun2kzmcG9SpHvyb1AJWgT++l6/G+L8NQz5AiFexZ0m9tho//sxMZFsXtkw++9zRyjLX3F+NQuTDTWy/83lNJk+jIWNaN3YGTQpUdEfwlhHu3HvNbi4p0Hteaz+5+P12EHlQ1dh1Gt2Jxn6yrdU9t8TcLz/1FiWpFeZmKgq08Vs6dUw+Ye2Iyjy4/4+9uy9HW06ZcvZJUbV6RxX3+zX7z80xSMD4S6zj3UBIDWh4rJ/hLKMFfVM7qr/3ytHU1WXxxBvs9NyRTPc8qO2YepM+szjy69CxHxvsp+BW5+znw+6gSC/B550O5uiUJD45AQ0uDJ9de8P6pB6ZWhphYGzNwQU8W91mTLLe+9dAmWBeyJNgvlECfIOp3q4UgCOyac4hydUsyIoe8Nd+iDDLg4I3b9GhQR70sTi7n9fvPKN/mx0CwSb8Vw7eGXw6rH42pNZ3TEXvoPbMze+cfzl4D2EzMSRRFpIV8ODZvDEZ6egD4BgfTdcY6pN72WZ9DVsjNmhdBwpRm84mNjuPlnbc4KQ5wLGA7Nw7fYV7X5WnuGvgpKMXIRuKx0yUHzufKvlu0GtwIj5demdsxnfkpRdj05x7a/NEUs3xm3Dh8N2FlTnwOopJ6XWtins80SZ3E96g7+PDCE6mGlAIl82NoboCZjQmIIkqlyOcPX5DKpBiY6KujzIKgUpF8eOkZz53f4O99J+s9774TEolA+fqlqdS4LD7uX1g/bmfa9zPQYXQLuuWPF8DI5OdUvkEZStd0oH7n6lw78h/T2v+Dv3dgwgbfROu+YpJfrjbsAKRSKSv++J0O7zYTHaTNwNLjGLN+CMOW92VGuyWpvlymyw9QT5cew1f2QylXpK5w+BOcw7ec2eSEw8ah1O9eS9X+6Eck/rqWrVsSgAW9VnF1nzMyDRm2xWxoPbQJI1f359T6i3lnoGTi+yeKIhGSEJSa0WhGG6It6KrXyeUKxtedyV8HxtFycBNsi1pzbsvlDIuP7P24Ac83n1jcZ3W6372db9cAMLmJql1KaGA4l3bf5NLumxk+lxTJQmSzy8S2ADg5Xs/wYY74byMmIiZrWQKpoJAreH3/HYMW9mTL1L05Nu4Py6+au7xn2PK+7Ft4LMtCJ+e2XkEqk6Kjr41crqRpn3rU71pDZSCJItcO3Epm2FnYmdN6aFOGVf4z4cUij17u9ONM+XfvVeLkcrrVr437Z1+mbNyLws0GbSFVyYg8p7VeL5yUh+gzpyvb/tqXJ8cMJ5jBzaurDTsAK2Nj6lctxlWvULR+oOuTXRKnkvayH86fO0akGyUuXbM4w1b25+ymS0mWG5kbMnbjEAxM9VnU51/8vQKTrJdpyjC2NEy2PDtIpBIMzAxybLxvObXhIg2616THlPbsW3Q83e2VopJQIRBBFDDENNV03o5jW+Ht6pPUsPuOvHP5wDuXD0mWSSQCVvaWyGPlhAWGJ6kTyQ209bSwL22Hgak+nq+9c0VlUyIRqNetJg6VCvPg4hO2zziQ4ahpVETmzz9/MWtaD23Mo8vPOb72IkZmBhxYdhplBlNNjeIN6sRYGhmhoa2as+cbHyY2nMOgRT2ZtG04/UuOzfQcfxZuHv2PdiNbfO9p5CjjNg4F4MCSE995JulTsJRd/L/5AVU2ysdX3qwbu4MOo1ow+9hEdcrfj4JClKMo4MWgltWp6lCYIzfvc9n5Pdp++dXP5mfOr+iebwgA216txLaoNVWaVeCR09M0jVWJRIJFfjPM85ly+J+T6BvrER4cker2z2+9xjy/ac6eYBap3bEaAHdPZbzx+ubJu6nZtipzjk0iPCg8wdEFSVp7lK1dgojQSN4//Zihce+cfICptTE2ha3ALf3tf/Fj88MZdweWnEie2pVJFSaFQiQ8RKWelqKyZiKqNq9AzXZVGVd3RlKPcWp1bOnNKTVSOb4gSND5XJD1656wYZ8zymgZOqEWaAsy4AfwgCY6z8HlJ7L5yTKKVirCjul7VYqa2SWl6xJ/TLk0lmL5rJKtLpLPkgv4oZW6XmLuksvKdV88A7iy/xaGJnoprrcqaMH4LcOIiYxhWvP5hCYSBPraSiHAJwgzGxP2eaxP93hNJF0yPrkUzrd41aLU6VyDvQuOZnycLHB1/20cqhSh118d2fP30VS/g1EawZiXjGJMy3rExMnZfuYGYa5GaCuSGp/GlkZoaGny31kX1VhJVBiz+LnmglNIqRTxef+NMmoW++mlx+/TOwHg+tid0MAwekztwM5ZB3NUTlzXQIcBf3fHyfEGV/fdyvT+H196UaZ2CZ47v05yHURlyrV1tkWsada3Hpun7EMR/5J4cfdNGnarjtNuZ1V4WD1Gyr8B3p9DiZPL0ZAl/GQ6v3xNTJAWmqheqhadm4apjQlDK/6Z/kl8j+hWDt0nz26+QVSKSKSy7EeIfoAo36wjE/HzCqBngZ+jHcLZTZcYs24wUWHxCrGJruGbB++o0bry95lYGp9ljMUnjswahnV8CUo5e3uK2zmzesMjDOKSt3gaUnYC7Ua1oE6n6gxc2JO/Wi5I9RmkVCppa9ibI37bmHN8MlFhUXzx9OfA4uNcO3CbFoMa4fX6E6/uvuW3VpVo2rc+l3bfwNTaOO3nWmZ/51PSOkhnDLN4xW/19ygDz/Xja86rhVXORu3hVOguZJrSeM0IgZjIGJRKJUG+IRQokY+7Zx4xo+1iQCWwVaxSYcrWKYFCruSrz/PDSy/ksXLyO9jgtCvjUcSfml+Ru7wlN3qSpIZEIqFy0/KsGrY5z46ZEoIgoK8wBf94b9K3TZl/ED4892RWx2XU71qTtfcX8+TaC6Y0nZe9NM00MFCYsNvpNnVLl06y/MztJ+jxY3je8hIdfW0mbB2GibUxq4Zt5mMaKZAmVsbqfPyiFez58NILG3tzRqwewJRm8wHY5fYvNoWt1AZhpoy8RDTtV581I7dlad/M8vbBO2Qa0gQD7xuUohLzklEcmzNR7RHuWLMaractIe61fpIIXushjTmx7kKezPtnQUtXk/vnXXjm/BqAOh2qpftMVohyYolGC10kGTAges/qzK45hwkNyFp9pq6RLgapOD5SolHP2jjOP6pqYxFfl/fuiQd1O1RNdR+lqCBE9EOKDANMkHub0X3hv6wa9jv5zMy49uwFM7eeQC+qEPW6Vmfw4t/ZNn3/T1fnmB4yTRlKuTKJEdf6jyYIgpDntUm5hqhyho1YNYCr+52znlKbBzT+vQ6Td43m3ZMPKUYZX9x6g32i1jk/Cvny6akNu690r1uTjUduwafk28vlCnU2xdmovXSZ2JaNE3elOv5K5/mE+IfRw04Vge03rztDl/Vl1JpBiKKIkYUhoEoN/fTuM7U7VqNB91oE+QbnWo9DHX1t9E30sMhvhrerDyF+Icm2MbMxSe64ywQtdXqlud62qDU736yi1ZDGWNtbEOQbwtuH79k153BCWx+gcLmCaOtp5a4gz4/Er7TMH5zMeCJT8aBo6WqRr5hN6vUD2TlmVvfLqchQSsfMpvf29on73D5xnwW9VjF55wjOxe7nyfWXTGw4J+e8sPHjyJDx8nEAf+3Yx8TObYiVy5m/5xjer5WqmF1ax8urmqmcjuJ9HeObuqM/lvelbO2S7Ji5P0kz12/n0UTaDSfFASSJxCXc4tP8PN98Uht2AH2KjqRp3/pM2j4CgAtxB9g97zDvn3kQ7BuSsoBJSg2kNfL2MfLyzluUCiVDl/bGcd5hIhP1OAuVBDCudYMkRpxUKqVdncrsfO2KPkYAWBeyJDoiJkkblDxTLcyJezOH7++vYiTbpu+nxcCGlKhWjECfIN4/9UhVJVMURaLNvShZ0pjS9vm5//o9Hm/i0AmzTvU4EolAREhUlg07gKX91zH/1GSs7S05tuZcogkliuJ9bUcoCMg0pcREqNrqCOI36bkKRbJoXZSWP7YOcUxoUoOA0HB2XboH7y0JcDGhy+TtaOgoiQnSonzx+lSfWAqXB3foXKMdys8m6Ar6WT6vnMLM1oTI0ChVK6F07hNtXU2s7C0TamVFJXbFbanTqTq/taxEvmI2fHr3mbhoVS2nIBEQBIE+RUdm/nmXXi14FvqkZYevKWxzOi+jbO2STD84nka/16GjWf9cOV52qNCgDEsvz+LVPVemtfw75d8AYOOEnXQc05Lp+8fwz6CNKbaT+h7EpZByHadQJKRFx3/2RSvYs+7+IgSJQFxMHCIq8R7HuYeT7V+4XEEWXZyBhqaMZ86vVBHz+HF2zDzIjpkH052Xk+IAw1cNYN3YHWlvmInnrU1hK7pObIO322fCgyPw9w7kz50jmNF2SULqefw9/fqeK4t6r8nw2BlF11CHlgMbYWhuwLKB63ly7UWa6fXvn3rk+Bx+8X35uY27ePSMdOkxpT2iKHJ5jzMfXnhmaD9RFDm9/iJj1g3G9bE7m//8f+K1yCEW912L6+MPDPunDyeCdyAqRXzcfSlaoRBuLu4Mq5SB9KR00Am14trxEK7cWAMiyILM0RGSp3H8r9JmWDNaDm7Eua1X2DB+Z7rbf1VRyygXd17j4s5rOCkPIZFK6DO7q3rdmFp/ZciLHZLF+tjs8PqeG95unxm4oCdbpuxRv8QIokBcCpHkOIUcIZEyUZs/mmS1LP00AAAgAElEQVTox///I+e2XsHCzgxTa2Ne/5d68UWEgQ8LRrakQdmEyPqqU+c5sNsdPUVyBWBQ1WZq6Whka351O1fH1NqY89tTV9T8iiiKSCUSukxojcvVF8g0ZeR3sKH98KYEfQlFU1uTmEQ1jHIxjkKlRXaNTfDmd6nxG61mr0XwsEcjxA47axumnBjF+g3LMA30YtXvXQjv1JpZW/dy70ogOlHfL6tgwN896DKhLR9eeiKVSljw+xo+PE/+e9hhdAtaDWmMRCJBHqfAyMIAP88AoiOiMbc15drB26wbsw23xx9++AidRCLhWOAO3FzcEUURi/xm2Bax5uWdN9w9/ZBSNYsTGxXL5T038fcKoPuUDpjbmSGPkaOpo0n+YjY8vvKMuZ2X/bD9+hY7zeD42vOsHbU1ze2USiVja09npfN8/h29/Ycx7nw9Y7n/1o2qDkXVy/45egaFr5H6b5siVnSd1I6t0/ZkqPZxo8syJjebxyOnp6oFmTDAjC0N6TdPldmSk5HaDqNbomugzebJe4gMS3A62hXPR/EqRXj1nyugciyMWT8YL1cffD1yrqbZws6MOh2rYWCqz9nNl/Hz+t/ohZgr/Irc/fjINKTYFLZiSb+1dBrbimb966cZXq7WugolfiuKIAh4vPTk/PYr3D6RQVnnlPKqc6n+5et4+sZ62Baxwv25Z7pqeJYFzJl1aDz+nwIpXK4gAZ+CWdBrVeaOmQnP6tGVZzi/7SqxkdHI5UokEgn5ilmx5dlyukxqz6FlJ7PtedUV9CEo3iOeUbXzzHqHv+6WjVZeIgJRhANC+h78dK6Jlq4W7Ua24MbhOwyvPDldBcGvfI3cZeQYSfZLlJJpbGnEoc9bEv3oiaoL8814ZeuWZPm1uXxy+0xkWBQHFh/PvnJnJggLDMdxziE6jG6JhqYUBIF3Tz6w49wJ2lWvqu4dGSeXc+rmE3RRCRAUqWCfoe9SimTmHFK7/jkRicjlyLSfZwB+nmm/GNg5aCcx7ABGtGzC0YvLwCdl404ep8DPM4B8Ra35lEo6Unr99A4uPUnLQY2SRGwh9f58m6fsRs9Il7HrBwOwfeYBLu26zriNg+kwsin7FiYI9ERo+jG6TbMk+2tpaFCjdEGC7YtQtkpJBszvQZ/fhlGjug7ta9UAwEBXl+WjBtHW428in4kJkeO8qikTJKxynkepGg6sGrGF0xucGPB3dzY/WZZiurVVQQsKlszP4eWneXPfldDA8ISX5DyYa05u7/h+LbqGOkxoMAddA5XwTf5i1tTuWI2woAj+HbmVwuUKUqFhGSzym/Hq7lsOdTml3n/b61WUqlGcF7eXZfpU8gqJREL7Ec0pV6cEQytMSnU7bV0tVjrPZ8XQjQR/Sd3pJpFI6DevK+1HNCc6Kpbo8GgiQiKZ2nJBmvtlFd1gW8YtP0L50paUtLfl1jNXPr6JQ09U1dRXalyGiVuG89+5R5zbclm1Uwbuk9f33mfpWXjIZzOBvsFc3X+bawduZ3r/xMg0pDTqVYcCJfPhcvVFilHVY6vPqlq4tKyIXXFbila0Z/v0/Vw/dDeFEZOjpaNJ+fqlcKhcBIlUgiARuH7ojtpxU61VJUrXLI6/VwDOx+7x5aN/ts7pf55faZk/ByH+YexffJzuU9oTERLJua1X0txeEadAQ1PGi9tveH7zFbHRsVRqUo7m/Rvi5Hgd56OqBuIGJvqZ6p2UXbR0NCnfoDTyWDmPLz/Htqg1vf7qiCgqeXbzNVWalqdG2ypMbbGARr3qYGRuoC6IjQqP4dnNVwT5hnB4xRks7MzYOGEX+7w2cjrCEb+P/iy7Mlu1sSBgaKqPZQFz/D8Fqh7miV/Kv56yGP//b9+1BAFEkVI1HFL1er28+5beMzpSrUWFVM+3SAX7hDnlAFb2FoQHRRARkrw/XD4HG7zfppN6KwgUrWCfpV53erYarJk/n0IyM0RR5Nztp0S9M0VbzHhtUGLiomM5teEie/8+kv7GqXAueh8ttHtker/gLyHpKjLWaFOFybtGAdDXYRQOVYrQZ3bXDEtX5xTBfqFqIRdBEChcviAVKjan75rNNClXgsioGI5efUjUO1N0JBKKVSpE8wEN+Hf09jyd58+IQlSgEOVoCJoprtfR1Uq2TCqRIJOl/aN5ac9N2vzRlIPLTiZbF04wmIahjJahG2GBVJAmWe9QuTDLrszi8p7MyZZHhESyZ8FRpuwahX2p/Hxy+8z9C0/oNa2j2rgzMNFDomekFl35iiiKREjjaD+pMYYSU/4dsx33j69YNmVksuOUKpqf/55FICPla5abBPmqanrMbIwB1Tl/fO2Nma0JRuaGDFvRD10DHWSaMmKiYpncdD4VG5WhZHUHanesRq+Cw/J8zjlB8JcQprZUycJHhqqe/W8fvk8i+OXr4cedUyk7cF/fdaVE9WIsuzpbvUxTR5Nzmy5xblva7xJ5RTvjvpwI3knhcvZpbjfzyEQeXX7G2c2XUjV6qrWqxKRtw3j3xIPeRUepBezajWjOIZ/NtNDqgU0Rq1R7wWYFQRDQ8bPj5dU4XPBAG2N04+dXoUEZZh2eyMjfpuD5JoUCvG8wtjRk4IKeQMLnnVmGVpxEtZYqcZWToTv53X5EEmGyzDBwYU+cj93jwo5raW63b9FxSvxWFANTPbb9tT9pW58UUKWUyyhTuwS/taiA89F7HFhygrhYOYIg0HtmZ0rVcEAhV2BgrJ9nKua/+DkQMhoRyAsMBVOxmtBI9Ucue6alMikOVQpTumZxtHW1eHL9Jc+dX1OnczUKlSmATENK4OcQGveqjVRDyh+VJufaXEysjGg3ojkmVkZc3uuMtp4WJaoWxcfdF5vC1oQFhFG6VnHmd19J7Q6/kd/Blku7byT0ZxKV6BroUL5+aRr9XhfPN59QKpTcOXmfdy4faDOsKS0GNuL8tiuc2nCJJU7Tyedgw6ZJjkzcOozFfdak/qBJ43PY/mol+R1saKHTSy2/my6JPNnLrsxmYsPZGbxK6TN67SBuHL6Ly9XnydaleKwUzm3ppZlMajw3U8cVRZGSPTRY0a0bMqnqZTROLqf15MUo3uZPVYY/5cFU16fl4MYYmuqzf3H6sv+A+lyqtazI/FNTgKwLpICq/u7khousHbWVv89MZWGv1Wp5aZmmjGMB29kydQ8jVw/k7YN3jPhtCg161MbfK4BnN19laK65iSiKKPRjqFC3NOUqlkOmIUMQVEIa98+7EBMVm/4gOTqhnO+NmLXdEu7FxBGyxMuVooIYa2/KV8iHpbkxz9w88X0opW6Tuvh5BlC2TklMrY24dOsYs9vXpYB5Qpr02fuPmLvyJgYxyVVuv5KvmA2VG5fl1EanhLmIIlFWnvTsVIMeTRvg5uXNrPV7CHtuiDYJDpIZB8YRFRHNsgFpq8Cmdp4mloYsvTwL52P3qNu5OqEBYTy+/AzbItboGGgTFR3Fwe1LWf97dwRB4IWXF+uvXqWUfSEEmSbXn7+lR5/hBIR8Jn+EP02rVkpy3N5zlvPpVsaEZbJNCseo1qoSU3aNpIPZAHT0tRm+og/N+jdEEAQOLDnOlil7ku0zdc8YYqNi+GfQhjyfb3Zo3LsuXSe2JTQgTFXz/S3Z+M4ZmuozfFV/rApa4PHKm9PrL6hrl/OCaq0r02FUC64fusO5LZfJV8yGHW9Wp/oZfsVJeYgu1oOQyiR0Ht8GEysjdI30qNG6MssHb6D5wIZYF7RgRrslKapd73JdjbGlEXExcRiaGdDGoDfRkbn7rOw+uR3FKhVmXrcV2BSypOuktgT7hRAbFYebywdaDmpEvmI2iEolsTFxmNua8vDSUwRBYGn/demOL5EIaOlqYWZrQlR4NBKphKDPwcjjVOn7TooD2TpP60KWNOhWEzNbE9aP35VEqOQr2rpa9J3TlffPPLh+8A6x0YmyRlK4T9uNbI6FnQWRIZG4v/DkzsmUHRPWhSzR1NLg42vvLM09CYnmcUk8/FAUxSrZH/THpIqpqfigSZMcGUs4ePCHvFb/E5G7rKCQK3h115VXd12TLL9x6C43EoXKdfS06DS+dY4fv0rT8lRoWAalXEGwXyjH15wjNCBMXWB87+xjAPSN9ShbtyQ3jqjm5HzsXorjRYZFcefUA+6cVjVg1dLRpFrLCtTrWhN5rJwHF5/Qf34PGvasw9tH75nYaC6ISp5ef8GfO0bSY2pHZndcmqn87/4lx7Lx8RKOB26ntX7v7FyOn5owgqlX6De1YQegIZPRpXE1trx9iQHGeTKPMesG0Xqo6oGVHcMOYOXQjYzfMowi5QqirZ8QoanavAJ/n57GiztvcNp1g5GrB+JQpQi73dcxqeEcWg5pTPCXkAx5YHMTQRCQRWjz/Nw7np97913n8rMRY+2N4+KR2FurDDSlUsmQ1etpPbIxJibGXNhxje0zDmBgocc0+RkaFilI9RJFOHXnMSevvkQ/umCa6dPhQeFYF7KkRLWi6pq+CFkgf/RuSJ8Wqvu3askSHFs2gxbD51DSogJthzelUNkCFClvn6JKanro6GvTtG99jC0MmNL8b/y9A9kx8wAGJnr88U8fzm+/ipvLB8ICw4nSiKb9x7V0qFme86+es3fKZHWK78DGDRi7by8xL6z4EHOH6qWKYxjfh/Oay1Pc34ai8x1FVd7cd0PfWI8lTjP4s8k8/hm0gfXjd9F9crtUPfvXD95mzrE/+fzBjz3zs54pkJeUqFaUwYt70ct+RMYdi5kgNDCcRb3XULZuSeYen0zxqkVypIY8o8w/OQV/70DGbyrP+E1/8HePFSgUSrXqY2ocXHqCjS7LiA6P5sFFF7xdP+Pl+plKDcswZv1grh+6w4QGc1K9Zn2KjVb/30lxgE7jWmfp+5YZ9i8+wbyTk3FSHEAURTZP3o08TkHRioWo2rwCXz76M6T8RCBpH7f0KFO7BNVbVSImKpaYyFj8PwWipasJItgWsUIeK0cqk3Jq/UW6/dkOUQSpTIJSoSQuVkF4UDiR4dH4vPflxa036OhrY1nAHHmcgmotK6KhpYGGpgypTIIoQqBPcKqG3aBFPdm/+ESCQz4NdA10KFS2ICv/SF/F/bP7lwxdi1+kwK+0zP/fSGRSwgLC6DGlPdGRsZhYGqKQK7AuZMnO2YcwNDMgv4NNpiSwBUGg7YhmnN5wkXvnUla9+kp4cESqXpu0iImK5cbhu9w4fBcjCyMWnJ2K49xDnNt6NUmhb8CnICY3nUfZ2iVZdnU2h/85xYm15zN8nFE1pnMsYCsX5fv57P6F908/snfB0ZzpgZcGUZoh6NqFY21uwJfAMMI9v19DcxElKJJHwHW1NFXr8oivaVkAJ8McaWuQdYP73LYrnNt2BauCFqx7uJg1dxcS4BOIpZ05fYuNwsddVS/VRNKFi4qDWBW0AGD79H30nNYRXw+//z/9cv6HaNK3Lt7BzmrDDlT1OWPatmT+jGVYCIVwf/YRqUzKsKX9+HfUNjZffsUm7cdII/XRFezTrYsN8Q9j05+7GbaiL7HRcbx/4oFgFkHPJg2SbKelqUm5ivboRxsQEx3H2Dozic5kA/PStYpTpUl55LFy7ErmI+hzMGa2JuqXrA6jW7Jj5sEkwgM6cSZEvDRi2asHzJvaXG3YAWhqaFDO3ALH56+RYUWrkYuxszMiKjoOX49YdINsMl4XnMOcj92HRCJwZOUZNkxIkI2PDI1MZtg5KQ/h/swDIwsjTK1Vzqe46PRrUBWinEgjP3TNBKLDlGj4maEpJE/PzQ5r7y3EoXJhdSuXxGjrarLg7DTK1inJqOrTcsWwS8zABb1YO3orl3ZnLg04K2hqa3ImUhWV+5oNAbDj7Rr+2jeObX/tZd/CY2mOsXnybjZP3p10oSDh6v6M9ZM0z2/KEqcZBH0O5vU9N9qNaJbrxp2hqT7nt12heqtK/F54ZJr1Yhn5vG0KW1GvSw2iwqLYMnVvluYklUkxMNXH2MIQmyJW9J7ZmeiIGPy8ApDKVNczMjSKuFh5ggJmIiRSCSaWRlRpXp4CJfJlyLAztjSi1/RO+Hn6Z6sc4xe/gB/ZuMuiIEZOs3veYfYvOqby0mhpEB4UjlIpomuoQ72uNQgPiqBoBXtun7if4RcPmYaU2KhYyEy6XlqkI8cf4hfCiKpTsC1izfhNQ/Bx98WygAX6xrpc2H6Vl3ddeeb8iiHlJrD8+lw8XnolpDam8znERsfSSk9lRJStXYLGfery738LeHojUWpeKqm/FnZmWTrHKCEMh2oCZqZW6Ghqkj+fAa6mn3n+1kV1TVO7R1JrVJ3Ne8oQExzP3aRTrRrqFExRFLnl9REDUhaWyA12zTnErjmHOBe9Fx09bZyUh7IewYu/Jr4fA+hiOYgmfevx+NIzvngm/eHNV8wGQRAYWW2q2uDbPe8wpWuVYPCS3mybtjfBm5nH390fipyQec9sqwZBgpmNCZFhUenWULYb0QwTK2PKNyjJ9mXJXwTNjQz56OZJYLASiURgl9sarh+6Q9O+9XB97M6LW28ybdRsnOhIk951adSjNgeP7MQ/JARrs6TPBB+vAIqZQ2RIJH1nd2XjpIwpGotKEQ0tDep0qMax1WfpNK41Ad6B+HsHUqhsAd7cfweikvDgcCo0KEXhcgWRaWmwdepe+s7pStVmFXB58RBd1xfJxjYw0EUpKtAUNMCnAH7xpUl6AGl9RDmdmvvNeFKphC+e/lRpVp6zw3bTUqdnmrs7Od7gyPLTSCQCTfs3YOyGIQl1kInuta8prgpRjlDMl+3ThlPSviC+gYGMWPQvvveVWa4t/pZ5Jyfz/skHLPKbsvLmXDQ0ZYiAvpGu+uX4+qG7FC5XkNqdqvH6fhpR+Qx+5yIJRWkSjhgtRS/SHEmiOs/TG51oO7IFHca0xtTaKNs90bR1tdj+ZhWXdt/k1d23TNw2HB09bWZ3XMq8k1PwfPuJASXGJNmnn8OobB0zM4Jvm1yWqaLasw7Sbngzila0z96xM8DC83/hULkw2/7aly0hEEEiYF86P/W71uTIqrOEBYZnTDMhhWuiFCHEP5QQ/1A8Xnlx9/TDNI8LqmdO4XIFqd+tJqJSyRfPAF7ddeXC9msZOn79brXYv/AYAT5Bqc4rQ+RVO56fmV+CKj83lgXMqdu5OnHRcVzYcS3dF5zUkMcpkMcpksgKR4ZGcX7rVdoOb0aIf1gSw04QBCo1Lku+otacXH8xxfFCA8LUqZd5xad3n9k4yZElTjM4tf4i988/pu3IFrQZ1oxJjeYQFR7NsgHrWPtgEYv7rOHqvox5+77yzPk1z5xfExYYTqexrVg2aD1X9t5KVf0uK8IlAIJlIJo6xszr2RNtTZVwwUc/PwZv2Ew541ZZGjM7CIKEwNe6dJyxlKEdGiMqRTacuMyo+dNZcXoXsdGx9J7ZGY+XXukWUecELbS6AyrvfLYMvHiUSiUXUpGd93b1YXanpfz730JaaHZXN7R/ces1wb7BtB3ejGOrz2br+L/IGjINKYsvTuf1PTf+GZx2PVVMVCyIIgHewTx55oFcoUiSZrzu6BkiApVoCFFoKXUYVHYC2nraKOQKOo5pSah/WKZTcZUKJRd2XEMiEbAtY8us7XvZMGGk2kFy98UrzO3sCfeJYPuMAyy7MouGPWtnOEtCpiFFz0iXdiOas3nybmKj45h1eIK6Tsc8nyn5HWyJi4njzf13mNkYs/zabN4/+8iWqXu5feoeRhUDaFyhgnpOSqWS/zw90cMgU+eaFzSRdOGi/CCxMXHqzAmZpoz63WpxyTEhir7ihqqm2NDMAKVSiVIJZzdfYtzGoRia6qcqLBFp5MuuGaMoZqdSnbUyNWXP/Ck0HTgTPHPGuJPHxNGkTz2mt1nMg4tPUt3uxNrz7HZfi9sjd24c/i/L7RqiLLxp37oCvVs24sPnz8zbfIDQp/rqOs9Ljte55HidA96bMLXJvqNu/pmpREfEUKqGAzaFLVnQcxXVWlZi/umpiKKYzLDLawxM9Di49ATPbrzi2Y1XOCkOMHXPaBb2Wp0rx9PU1uSh0xOKVSyE0+4bWR5HS0eTDqNboKmjye75R9T1dHmJRCIwYmU/1ozaluF2XF+xLWKFeT7TBMPuF7nPL+Muj8lBj0KbP5rw/qkH9brUINgvhOsH05G8zeCxJVIJlRqVpXrrSpzedClZH6HWQ5sgkUoQlUqa9q3HxZ2qH1bLAuY07VMPbX1tTq5LbvSpyY7nJZ0onp+nP4PKjGfU2kEcW32WtaO2ssvtX9Y/WkJUWDRSmYR/R25jiuMYXtx6y5ePme/BsmXKXlwfvmfq7tFc3u2cqrhBkvlmojm0UiOaTjVqqA07gAIWFpTJb0tMcPK+PoJEAEFAkAiIyhSuTw541LXjjAh9YsDM51cRRRFDpSWNWzWhcUQTQvzDMDJP/jL4bfpU/GTj/81kGCQF7+yl3Tdo/HvdzI2TEuncU6F+KrU1+Tf1Bt5un8nvYEPBUvkTmiT/Is+QxykYXnVKklSm1Bwt57epjPcRq/oT425Ch8l/M6VvJ2zNTdl06hyvPnxkwZwW3Hn6lut33BB98hMdEYOOvjbGlkZ4u2ZeWU/t8Qa8n38h6nUEbbzmUbZMQb58CeKLTxzRL/SAQGq0rYKvhx8D5ncnPCiCe+fSd4x9bXXx8bU3Mk0Z/ed158CSE0SGRSGVSekzuxtbpu4l1D8hndlp13WqtaqEy5VnSAQpX15q0GnGUga3a0RsnJzNJy6xyHEtjr6neHT5WfonmcL3RRRFIgx9MbFVItFWEBEVixCjRewnI3Tismc0ChIBz9felK5ZHCflIURR1ZZh8s6RbJ26h/2Lj1OmdknWjNqq+g0SVLLqNkVUabhT945lWuvFCBrJXw0MLGRqw+4rWpqa2NgYE/gpwREgKhKeA0kiJyn9Zn1zfZx236Bys/JM3T2KHbMPcnqDU/J94hlcdgK73dfS+o+mXwWcATC1MSboczCf3D6zathm5PJvPoP4eYQLwfTs/BujOrcDwNrUlGNLptN8+GxwT/gcll+fjbaedradZK2HNqFc3VI0lXZNsvzBBRf2/H1ELViV46TxXiOTSZi0cyTvn3zA5epLfN77MnCBqj4MoJVeb85EOLJmxBbCg7OmTJkWJasXo8eUDuycfRB/r6RpixlVKm/zRxPMbEw4v+N6QqPuFCLPmSfRPZ1ae5ZE92/Hsa04ueEiHq+8kkTzMkKPqR04svJMFudJ5t9hUto+ye981qfyix+DH8+4y0H2zD9C/W41kWlIadC9Fv7egby49TpbY7b+owlWBcx56PSUdWN3qAVQJBIBIwtD+s/rzr3zj3E+eo+WgxthbGGEsaURZWoVx6FKEY6sOK2WHv5eKOQKLjleZ/iq/hxZfpq5XZfz8aUXsdHxalGChEDfEFbcmMub+26c3uSU6R5I1w/dZcz6Ify5YzhL01G2yywRkXJsTZM3CrY2McHNP2vR2ZxAIkgwUqpqzxBIsWZEIpGw691qrApY0GlsKxr2rM2uOYfSfInJKov7rs0Z4y4dll6ZhfOx/1Jc9++obQxb2Y9NEx2Tv2T9Itf5qsqW0Rec8KAISpQuwbun7oz/6zCxWmEM6FqThQP6ANC62m88rOfGyFn70A/Jx6CFPXGcc0j9HMwOOnIjOrZsx/F154gM0UBI1IJBESenQbeaAPSe2RnXR++T1JimRKexrdi36BjVW1Wm3fBm7Ft0nNAA1bPXoXJh7l9wQRDgzx0jCfAJQqlQoFSIBPgEoWugzYhV/Vk/wZGQx+HMdLmMIEgwVNoQ/D6GxRenp/j9zggxVl4sHtWOumVKAfDcw4N9zs5Ehiu55xSCrsIonRFSZ0ytvxi6rA8DSo5JEkkd9e9ABi7sRZ85qjlb5Felv36tb/tac7h3UerqvFERSoLCwjAxSDB8RFEkMCQCyPqcE3Pn1EMCfYKxLWJFlcbl0nwujljdH0NTfaa3Xpii0mHdztVZe38R7596YFnAnHNbr+DnFciQxb1wqFKE9lXbMKhN8yT7aGpoUKN+aea7bkIQBL54+mNua0pMdPYVIy853mDM+iGUrV2SZ85JFYWDv6R9L+cWHca0pEHXmoT6hzJseR+eOb9m6cCE3+vY6FiCvoSwy3UNw6pMydGG2wAW+VW/431nd2X3vMzVmQmCwMg1A7iw4ypvH6Te767DyOZo62mxb3H6jdGzStVmFfB668N/Zx9laf+Vf2xm0MKebJy4M4dn9osU+ZWW+XMTHRHN+W1X+PD8I5ramukbdilEP2wKWzF4cS88XnoiCAL/nXnE6fUXkuxWrl5pOo9vTZEK9izotZoG3WvhUKkwivhC23qdq+Pz4UvG+5BkpFYgm9Gm586veXXXlUnbR7Bp0i5iY+RJjnv7xH1un7iPQyV7Oo1vw+i1g1nUZ41a3S4jefxjav3F1ucrWNIvfbnizKDrb8c2pyv8M7Cfepkoijz/5IeFVsGMD5TCvCXS3FNDqNq8AtP3jWXjn46c3axq1GppZ8aeD+voO7srjy49ZeHvaxLNT8h4ZDMx8ds6KQ/lzMTTOXZ4cAS1O1SjbufqydJORVHk2KqzDP2nD3v+Ppa1l5gfpI1AtsjqOWShzi7hv8mjJoIkYduUvMq75hyiet/iRJvHYaFri3eQHzINiToCBFDZoSgW+TSIiv8oC5a2I/BzcCZOKnW0dLWICo1J1j7k5R2VqvG/o7dTvEphFpydxrDKSdvTmNmYEBcrR1Nbg1rtfyM6IgZv189JPeLx1youNo5BC3tiW8SaJ9df4nLtBb+1qIggCFRoWIZOY1sT5BvMpidLMbEyYkrT+by4/RbzAiaUqVUcUKkJBvkGs378TsxtTTi26mxC9Dqlz02QECvGULViPrVhB1CmYEFsX7ygRePKDHq1HTyybii9vPOWMbWmJzkmwJpR21k3bhfnolWCHd0nt6f75Pbqzb4ae7C2eqMAACAASURBVAVL5uPZ9ZcpRt8EHyNGLVnLjlkTkclkiKLIgu17ifTQRDfRPSgkSudN36mQtI+hibUx+Ypac2LdRdaO3ZH8OsYfp0gFe8rWLgmAREMDQZJcDObm0f94cu0F1oUseffYnUk7RiCPU+Dt5kuxyoVp3KMhX4KCKGhtnWS/9y89aSrrrv5bW1eTtfcWcj52P801u397mAxhZmvCSuf5+HsH4v78Y5bGyA0O/XOaAfN7sHny3gTn7jd0tRmCk+IA216uwOutNyN+m4o8LvvPZEEiMG7TUA4vP83+xcfTvle+uQ9KVC1C49/rcHK9Ex9feye5577dPioiRhWISicDJb25pozquOUblFGJt3wzz/Set6bWxpjZmhDiH4a2vvb3+W36/1p/l4fGnSAIzYFVqG6YLaIoLvpmfX3gBOAev+ioKIqZ68n1Df/Txt1XXt9zy/Q+HUa3xMTKiIjgCEytjZjbeVmS9RqaMv5Y3pfoyFhcH7kTERbFO5cPlKrugJ+nP9p62sRExma8T9l3QCFX4Dj3EB3HtkIUYeu05Mbn24fvWdhrFatuzVeJwGQCzzc+KOQKLsTtp4PZgCw3HP0WPUGfO86fmKV9gNFtWhAWFcWsnYcpW7MZvo+zl9qia5jzqpsymYRpe8cglUl57vyKu6cSirO/eAbQRNqNwYt70XFMS+p1qYFUJuX1PTduHU+57UV66BvrcSxwB5D9lgjfoq2rlax2tbPlQJyUh4hJRW3P570vW6fsYfCS3jgfu8er/1wzrXr4i9ynxG9FUejEIo/8wK4/Exp0X3VxwfHyZfo0bqxe9tX2WjNqG7U7/kbXiW1TbEqeWWSaMnQNdJIo+gJ0GteKKc3/5mF8BsHsIxPoPbMzjnMPAzB4cS/syxTA76O/qrb1yF0CPqVev/L5vS9X9zmzY+YB9cvN1/T5bzGzNWHlzblY21vy6t5brh5xZt2IHRQomY/mAxoybc8Y4mLiGLK0D+e3XU6zX1w0EVQvXSnZ8vL29rz7/BlTY11CPNK+RllFqVTSTLMHdsVtGL12MG4u7jjOPcLswxPQ0tWkZLVinN10OdX9tQVdPt4Jp8ngGdhaGeMfFEbERy10Y5NnUWSG6q0qoWOgzcNLz2g1sCGAyrBLg/X3FzGr0zLunEo7UhIaGE5oYDiiUkziODv+7zlmnRzHjEHD+HfkULUzwcX1HR/fhaGbKBJpYWeGdSFLtbM2K6y9v5jz266wY8b+LI+RW/h5B9JycEOOr0ldJXv/4uNEBEdgX8aOkyG7OLftCmtGbsv2sQM+BVG742806VOX2Og4wgLD+aNy6j2FS1YrRtM+dfH3DmT9BMcM1Vqe35H7qs1xMVmL7PaY2oEXt99QsloxNk5MKNEQBIF6XWuQr6g1j6885+Wdtzk11V/kMYIgSIG1QBPAC7gvCMJJURRffrPpTVEUc6zv2o9r3OW0tz41EnktHKoUoVa7KogixEbHsG2aystZtXkFAIpVKkzAp0ACvzbAFASOrjhNgE8Q1/anU+SfE96RzHrxM4C3qw9bpuyh518dKVbRHtdHCekNZyJ24+bizphaMwABYytj4GPG5wK00OnFP1dncSJoO28evGNktWmZml9q6AXbculQEBcvr0WUS9AKNadz77L4Pk4uWCIqRRDFdPPfm/Wrn7mG4xmgbN2SjFzVn11zDnHr+H3mHp+U4nabJ+9h82TV/Va+fmmWXppBkfL2tBzUCCNzQ/y8AihYSlXvkprB1mNqBwb8rVLIk8fJaaHVI+sT/+Y+qtmuKh3HtqJ8vdJc3e/M1ql7KVAyH+9cPqijNjMPjqeVbq8Uh4uOjGHjJEdKVneg09hWaOlq8fqeK5/cfPn4yit5Wl9ufv8zO/b/gmcznXNu/HsddAx0uHD9IItHD0+yrkGFCkzdulX992O3d3zxiuVrNzfno/cYvqIvgiAgpqKMm1Eu7rzG5mfLuHH4Lmc2XybYN5jw4Eh2zjqY5FxkmjJsClsyaFFPRKVIlablmd3pH1oOa8DuPRuxtNJGqqfAwtiBiuWroohTcPf0IzS0ZLQd1hQdfR0KlSvAhsdLUSqUREfEcP+8C/tSSEssXrUoFnZmLJ0zh+cX72BpYoDG/7F3lgFxJN3X//UAw+CuAULcPcSzcXd32/jG3d3d3WXj7glxdxeSEAgSLLjLMP1+GBhmgkf22f2/OV8Yuqurqruru+vWvfecov68/xSB93h/No9XPrcbniykQc/aLOu/Oe3gb7wF+qIB156+o2PNahptPPz4kTbVqhH0NQZdvsNYysIrkUqSkuqN837rqyH6XbRyQWT6uszqsDzjybJa3XqCCfiaEOwLCGZK2pGsZhEZeTwEAUNTfao2K8/YTf1RKBREBEcRGxmHREvZVv3uNbiyLyNCL6WnRKEQkah7a7LJ5/s27/vtvQ90shpMnHYE7fwW4lKhEG6vvfj0IRy9r/YazK+r787j5pH7LOq1Lvt3QSbPmbmN6Q+nhOQI3/GuWjl4C33ndqbrpDb0KjxCgzguFakLv9raEm4efcjUAyNzbNxl5vUStLToXWwUAMNW96b5gAbERsZlWFZmoMuW54uxyWvF0VXnCPsaQfdpbZHp6bI5VdQ9F565nOTQZYpvvXOCkHlYukYkRXovniDA44sv0uVbmtuZUqlJOVYN3kK3KW1xLGqPkZkh1/bfyR3pyv+Fb9evwj/nuasEuIui6AEgCMIBoBXwrXH3U/HvNe7+IQiCQP7STtTqUJXE+CR2TDuIlYMF+ka6yAxkxMfEY2CiT79F3QkPDMfAxAAtbQkxEbEokhVo6Whl38h/ANcP3KXdqGZ0GNeSpHg5CErDz9DUgINfNvH3vKO5zrtLxZg6yonErGNjcU0+yJu775Ui6j8IfcEQwlKmmD9ok+kZymg7oimDK0z84X6lYtjaP3EoZM/ImtMz/GBmhhfX32iEBanDNflg+m1q4ZePLj5ncpN5ue9sNmjQoxZlapUA4MvHALa9XUlcdDymVsaqD1sb8z5Z1pEYn8SL6294cV1JL1+sciHK1C5B5WblObj41+VD/EbGMLUypmLjsphZm1C4Qn4W/7meCgOKYSCTpSsbEBrOqfv32X/tBsHBkWjpahNHNHoYIhcTObr1NH3mdebDo0/cPv59HmdQeu4OLD5J6T+Ks+DcJKUXLzKO6IhY5AlJuO69hYCIsYURQd5fiQqJQqItYd/8Y9TrXoN9+9dwdPEA7MzNEUWR9edcOX3hDLZ6BWg+oD5VW1bg9EZXXt18R6BXMAqFApm+FNv8Nmx5sTRD427WsbH0atiNEY0qMm78YEAZbtxn6UY878YjFZTXy9vNj4Jl82V9foIOr176se/abbrUrg7A1ZcviYqPZ+LmfYj+uZCH+QYZvRuCv4RimUd5LV7desumMbtZ92gR8THxPL70kgOLTrJ14j6Gru7D7WMZ583+CGT6UowsjFh0diImlkYEegUTH6N8F3q+8WVSy8U8vZoiN5GyMPBHu0qM2zwwE+NOibH1Z7P86gz6lhxDbHQ8Uj0p/p8Cc90/PbkJUY9gxNUldHcehl6IQbpvyYOzTylZo2iu6wblwnC7Uc2Jjojh0YWsNW3/V9CRavP2/gdaVWhM4YoFVO/njCCXKxixoV+mRlhuUaJ6EeaeGIefewAd7PpnykeQGJfI5OaLCPAM0iDuWnh+Mktcp+Lx0ptTGy/zxT1Ata9ig9IUqVSQJ64vvyt6K6cQRVFDCzM38HHzo1iVQunGRohfGAcXn6Tfwm48u/qKDqNbcGrjJZr0q8fbe+95ejkHZE6/kTl+bs6dpSAI6mLUm0VRVFvhIw+gzrroC1TOoJ6qgiC8APyAsaIoZv4g5gD/3xl3FRqUpohLQbze+lKwfD6cSzpx/eBd7p99yufXPvSa2YGYyDh09aRY2JsRGhBBaEA4++Yf13ihyfSlqo9UjvBPaY/kZmVRrayfRxBPr77B1MoYf49AFMkKdk4/SIhfWIYindmyLWWAGW2XUrCsM/PPTeJ83N9Ehf0EdrCftDIVFx1PiH84c89MZHLT+T9Ul6m1MbNPjOfWsYcZrm427FWL6PBYLu+5kSERQHb9/Pacm+h143yccvVyctMFP5RbkNH11DOQUqNtZWa1X6qaAO6akX4imVu8e/CRdw8+MnR1HwqVz8fHJ1loVv0v8R1jPdPjfqTtnDDkZludcgJtZmNC18ltObH2AhKJwINzT5HKpLjUrMn1V6+oU7q06pjImBj0be3ZcMqVdQN6kN/Wlui4OEZs2MU7d3cqlnDC2S6ZIwdWYmZWlFZDGnNyXeYhXpnBsYg9M4+O4fKeW8zvukqjv05F7YmJjKPtiKYYmhoyvv4cZXiwqOBY8A4eXXjGxQtnGd26PnYpZEuCIDCkWUMuP1jBl9f+7FtwnPtnn9CkXz2qtqiIkZkBEi0JCoWIIECofxiX5AfYMuFvPF95M//cJJUnPz40gPIF86ddckFgQd9OtH27BWmEIwALuq2mbufquCYfZFHvdVzeczPDMaAXas+ajY/YcuIW6CaSmKCAWBlaXy2RCd8XFn5JnhbuN7jiBEJTIkxSUb5BaRZdnMaSqzPZO+cIgkSg25R21GhTKcP6NDxdyeosuGr5dDrKMuXrlaTHlLbo6Gpj7WiBiYUm4Yq32xde3nyHsbkBZWoXp53NgLQ6pFKE1ElWiicuODASqUyHY4FbMDDRB+DkRldsnCyZ0W556pHERcez/PpMTCyNOL/jGisHb9V4FsRkGLmhH7XaV0FmoMvHp54s6L4av0+BtBrSmHrda5KvhAOiCP6fglhzdy4fHnuyvP8GjRBzhyL22Dpbs9djLd3zD01/fdS9Nt88i/PPTeGLewBDXCZ9X/70L0SdztXpu6ArHx654/HKm9kdV2Rp2KXC3MaUuycfAVnkouXgGzR0dR9qtHJh+aCt3Dx8T3WcimlSbdwpFKKKIEg9v27j2N20GdaEawfvUqdjFdyff+be6SfY5rWgRhsXVg/ZRs8ZHfD/FPDzSGsyOB95YhK6elKlpExmECQUqViAul2qExsZiyDAk8uveHkjYweO11tfNozaSRGXgkxpvpDi1QqjI9XKnWGdC43D3/huBIuiWDGL/Rk9JN+6ep8CeUVRjBYEoSlwAij0I50SfjSM5mfCWDAXK0saKP/5BS9AK0dL5p2ZxINzT3Eu4ci0lgvR1ZfRfGAD7AvYEBYUybUDdzRovbV1tDLWTPkVL+h/4uHL4mGv27UGXm99+fT8c+7qyaS+rODSuCzzz05iaouFPDj37KcQTgxb+ydWDhZMb70kXTHX5IP8Pe+YMscmC6y6M4cJDebk2uhKRcu/GtKkb10W9libIf1/3uIOVGpaHl09Hf5oV4WxdWdlqiuVEVyTD3L72ANmdViebjtkwND5E4w7bS2B84kHeHj+KbPaLcs08f576gbQ0taiTudq2DpbsXfOke+r+5/GP2Xc5bZNjeLpvympE1ErRwsa9a6jut6qCZUoEm/vTa821WldswpvvH1Yf8YVI31bRjQsS6m8aYRFC44coX21ahSyt1dtG7puO1WbdmfflJMqps6MYJnHnGqtKmJmY0re4g7kK+XExZ3XiAyO5vz2q+n6mxmihVAqdiqAoZ4pV09c4caG8RoSKQCDVm3F/bqeRsh1tBAOZlGICdroR1uhJSjXOTc9X0L+Uk74fvAnMT6RgeXGA5CnSgQ7U7x2qUhMSqL2kCUYBDtrbD8esh1DU6VO2s/Oec0Mm18uI19JJ6a3WsS904/RlmpzPl4ZShcXHY+uvhSvNz4MKDNWdcyl5EMcX32OFzfeEeQTjPtTT9W+zIwXDbKUFLmEizHKXKFPL71ISpCzfMg2vN5+URZSWxicuHMwWjpazOuappkmqN8rtbq1JaAjk1K9RXlcGpSiVjvlQndoQDhSmQ7+nkFsGLObN3c/MGzNnxQs68z+hSfoPrWtajHSsYhSw3Bg+YnERkRz2H8Luvq6JMuTCfEL4/7ZJ+yeeVjDW9R6WGPajmjGnE7LcX/2WbX9ZNhO3t3/yIRGc7O8Pt9CIpFwPGQ7uvpSPF97s6jXOj6/+kVJlcqOZVtk1Z05WNqb4/XuC2uGbMXfMyhXTfSc0YHmgxowvfVinEs4Uqh8PvYvPKGZ45oD427+uck4l3DA1MqEpgY9VNsrNS6DobkhpWsUxe9ToDKXVz28UT0cV9QMdWzWry4WeSzQ1oI8hexZ0GM19bv/wcdnnjmb13wnilYqiKmNKffPZJ0D+tfynqwfvTtdv38YP9l5cFlx6Ek2Bst/GhWtrMTHbdv+lLqEzZuzvFaCIFQFZoqi2Cjl/0kAoiguyOKYz0BFURSDv7df/z7P3U+eEJWvX5ryDUoj05OSEJ/E0MqTKFa1CG4P3ekztwsSLQmXdt3IVIA3UzHM3BpiOTmv1DK/0sjLou6bh+8xeHkvds44RFQuDI7vwaMLzzm05CTjdw7B0NSA+V1XceNwNjqEqcjkHLZN3k+/hV3Z7b6arZP2ceuo0sskKkQGlhvHzKNjszXuQv3DyVvCifePchfGkaeQHeN3/MWHpx5ZhnZ6vfXF660vNnmtqNWhKjoynVy1A0ptoG/h+cqbfKWccl1XTiBPFhnsMpENjxby16o+7J9/LOeU2NmMZadieRi9eSDJ8mQcCtlxYOEJ5EnyLI/5VyCzRZKf8f7KrL4crMLmVPIgPCgSu3xWmNmYaEgKCIKAnn9etm94yfZ999HXMaF1x/a88rmmYdgBRMXFaRh2ALN6tKf5jPlMXDkfn1cB3D35SEWzD5AkJGJYIhJLxyQePjqAh2cURlH5+Gtpb3pM70Bzwx4a9WWmFyWKInG2PvRuV5XO9f7AMyCA5491OHDjNr0b1NUo6/c1AkHQT+u3lQfd21amR/26eAUFMXXTPsJeGSETDRlcfgIXk/bTp9hIjTq8voQRn5ioYTjucL0OoWrMlqKCTuNbIYoiT1xfUKFBGfQMZbkKyf5eDCg9hjbDmzL75ASGVp5Et6nKiUsLw+7pCJBSIQgCWybs1dBBzFXkR4pn5fTmy7QYUB95UjJT2y0nJgkkRspweTE+re3jm6+x8tIkBL0076QgVXv/qYVJyZOSkCfIuXzkIZcPPeDJ9XdY2JliamHIxb238XjpDaKIIJWydsxeZLraTN03jELl8rF3wXH2zPsmvDZZpKVFP6wcLeg5tQ3LBmxOOR1Rw1g4ud6VopUKsf7hQprqdVXJt+gZyjix9rzaZcjZhFyhUNDKrDcAlZuWY9axsRhbGuPx4jNuj9zZMm5P+mPEZGKMgtAzF0mIFtEJsVCF/maL7N4/ggT7/DZEhkbnKkJFW1vCvHOTMbM2ITYqjrCAcNoOb0aQz1f0jPSYdWwsMgMZ8kQ5r++4sXH0rmylb1LbP+i7iVNhO4gKi8bI3JDgL6F8+RjAV99g+i3oyqGlpzI16L7F2a1X0TPQZcPjhap8vsv77tBzejs8XmUsLK6xeJHJ+1PjfmfQ/sennnSZ2Jr7px9r7kh5niRaErR1tIiJjFM6DBJ/zcKfqZUxTfrWRUdXh7DACO6dfkyIX5gyF/pf4jH+V+CflUJ4BBQSBCEf8AXoDHTV7I5gCwSKoigKglAJkAAh6WrKBf59xl0uYGCiT8VGZfF86YW32xdsna2xL2iL11tfWv7VCEEiEB0Wzb1Tj5mwaygn1p6n56yOeL/7wuc3vtw9+fiHCQD+L0GelMy2KfvpO68Lx9ecx/dD7oWJc4MtE/ayZcJeilctzKo784iJjOXxxe/PSyhQ1hmnog7YOlvTY1p7lXHXpG9d/lrRm1PfSFhkhCDvYJxLOOTauFtwbhLj6s/J1OiJlYVg5JhAvkJ2SIz06TGgB4t6r8uS0S8jRARHYmFvjkQi0SA/GFB2HK7JBznkt4mO9gNzVWdOoCtTTmqb9a9Ps/71NTwSO9xW4VDYnvUjd3B89blc1TtwSQ+W9duAz3t/+i3o8t8w7P4PICkhiRNrLzDjyBhG1pyebr+haA7h5hQs60xcZDwmZlZ8DgzE2cZGVSajPBMDmQwLcy2WLZ2AbkhB/hzTgyv7b+P1xleZm+Lsy76Rf2GsrzS23AMC6bPob/SM9YiLyj7cKE6IQmIThlwrjsGdG9OjQT0AyhYowMn5U2kwdjLVihelcB575MnJLDx4ktDPOqSaE6E6PvzVpQZ9Gyv1zUrny8fRuRNoNHwefDbEoYgdoMz9Uzd65F6WtJmxnKk9WlHAzpa9V25z/OIbDBSaCypOxRwwMjPk4s7rTEzx8vwqmFgaExMZizxRjoW9GRUblQFg3M4h5C2mJF9afGU6s9ot1XjPmFqbcDhgKxd2XNU07L4D5eqUoMWA+lw9dA8TC6OUyXHG39QOwxsRn0vG5VRc+jsl/y4548XWuOh4prRcQtfJrXEoZJdpPV99QlSGXWZY1Hs9dbtUZ+Le4eyde5TPr30I+PyVQct6cU+N6Ti3eHDuGQ/OPcPSwZx5pyfScUxLdkzap5FDphCTkef3ZcPo3pQrWIDAsDCGr9yK36NkZKLBd7edCm2pNlf23ab10MbZF1bDyYjdPL/+hgFlNQnB1I0hqUyKiZURI9f352zs3zTT75YjbdNODgMxtjJRLSgvPD+JB+efoWeg9LLmFnExCQT7pQmiJ8uTv6ue3CBZnoyuvm6G+8rUKk6lJmWJDo8hMiQqc4fBT0CxKoXQM9Lj1a13BHkHU7NtZewL2uLx0otr+25RonoR3j1w/2nM5b+RPURRlAuCMBS4iDKufbsoim8EQRiUsn8j0B4YLAiCHIgDOos/aJz864w7UytjosNjESQCZjYmOJd0QiIR0DOUYZHHHBNLY7R1tEmIS0Qq0+HS7htUaeFC3W5/IJEISLQklK5dAj1DmVK7DQkGJvos6rWWL+6BPx53nZvVD/VV0P9FGNd3IDYyjvWjdtFuZDOC/UK5ui8TFtBsvBU5DVsBpdxCsjyZcnVL5sy4y8SDUa9rDS7vvcmEhnM1DJ8e09vz8tY7FRtlVvji7o9tPqvs+/ANgryDMzfs9IJp37koozq0BJThXN3GTyboiRFSIeMPQmZob9Mf1+SDXEzaT79SY9KFfprZmOa67zkZm2/uvqeBVidW35tHsUoFGbCkJ7tnHCQ+NgGHwkrvzc0j95B9+4FLCYUTMjAEdPWkGJrq4+OmDOHy9wikXN2SPLv6Ovfn8L/Ev+C5zRBZPKMtBjXExtlKxZqY2TPabEB9zm25QuvRLZnz90pWdeuMoZ4eoijyzucLUXFxGKl5YrZdvsycrl2xMjFh9Lad7Dq8goLOthhKQ/DwCGZCk9oqw06hUHD/w3ts7eHStV3ESAMy7EMq4nRDqVTHhHl9+7Ho6FG61q2tsV+qo4OzZR56TdmLubUWsXFJJAQYYShaKddBAWmeSLrX1fTs6Whr45TXmC+fYeurZWwevyed0SOTGJLopseYOeeR6CYhRBihjwOQdl0NTQ0o4lIAgCEr+zBpz3AQlB4y3w9+9Ck6IsvzywlcFYeRJyXz1ScYu/w2GvvunX7EkRVnqNq8Amc2XWLvnKNM+ns4+302IQgCZzZfZtXgLQxe2YevviEs67dJOUYyYZXMDloSgbK1S5AYn8TigWlsqhreODVYOZjzZ/nJCLoZh2KSGa29+hwns/lOCsvm/iVnGLq8B31mdWDHDDWdT/XzUmTNYAjQJe8QRm8ewJR9I8hbzIHo8BhG1pye7fXJiUcv2DeUfCWduHvqEfJkUeMexBgFsXFMb8oWUI4jGzMz9k4bRcMhc8E7B8ZdBs/87JPjKeJSCEFQEnW8uu1Gc6Oe2delhqV9NzBu+2DVoofqOqi1lxifyFefEKa0WMjIjf05HLCV1uZ9NK6ZugdO/VpFhUZjV8CG3jM6UK5OSYJ8QinqUoBDy84gaH8znjTGjLrXTW0cq4VgS7Qk6KSMSRVLpXqf1MarxndKfXtiWoi5qLYAqZ4X+P7xJ4pVKcK7++/T6hAVVG9VgfWjduUo2uJHQzTvnX7Cm7sfGLGhHx+feOJQ2I6gFHmYPzpWo0KD0pSpXZIbR+5Tu2NVBEEgMT6Rt3ffY2JljGUec46vPq80L/6v4x/UuRNF8Rxw7pttG9V+rwXW/sw2/3XGXY8ZHTi/7SothzTG86UXHx5/AkHA71Mgjy++QGakR8VGZbB2sCAhNoG6XWqQnCRHVCiQ6Ojw9Mornl15/dsj9wNIlidzaOkpXBqXpd+CrhxZcfbnJSNnAHminOfXXtNqSGO2TNj73fUoFAoCvb6mo/Oe3mox6x8vzOQoTfh7BFG4YoHv7kNGMHZMUhl2oJyErh7Zl3Yj1iENdch1fQ20OjFiQ3+2vlrGg3PPmNpiIXs91wH88nE/uuY0Ok9qQ49p7ekwpgWRIcp8FX/PQJZdm4WoEAnwUs/hyHwyZJnHnFc305LJz2+9ysBlPf97xt1/EIUr5mfN0G3pVpGLVy1MtVYuyneqCL6f/LFxMWZyz1kky6Jp8Ho+9qbmREQmkCxNYMKePTQtVw5na2uOP3iAhaEh5VImpgfGjWbs3r2YGRtgbaZHjQoFOfP6BY6WZtQsVow5R47QsGhRDg8ZhCiKnLhxmzgTb/QiMg4vNsobz/K/lOGSZoaGBIWHY2ehyS4ZG5eIYaQDiZHKj5s2qAw7AANdKf6hoTh/I1wdmxAPmPD23gcGLO5B84ENiI9JQEemg6W9ORItCbp6Uj69+Ezg56+EBoZz4+BdWg1pzOjuo9HJE4WhnRmLlkynVb0elKhUlGFVJuPvqWRwdFUcxlVx+Idy8LpNbYfHSy8Glh2bZblNY3apfo+tMxNQevk2PV9C8wHKsPSzm1054LuRNUO38eDsk+/y4B0J3IK+kR6zOq7ItuzaG9OIDIkm/GukpnH3C7B29B5mHRpBrfaVuXHk+xhA59gYPAAAIABJREFUQwPCmdpyMRcT/ubu6cesGLApUybH3KJWhyqE+IdSuEIBLiTsIywwgujwGM5svMSx09tVhl0qdLS1sbc1ISiH2uelahRFZiijab+6OBXNQ/CXUDrZ91fu/M6UjzqdqxEWGJHjcbJiwGaqtXShcIX8fHzmmWXZv5b3pFiVQhQok5c9c46ye/5xvrgHauRr5gb5SjkSrUbWJipEpHq/dswBvLzpRp1OVTWMu1odqvL2/sdf3rY6IkOimNNxBYamBiiSFSrN0Lf3PvDi+lu++oYweHkvnl97ja2zNXdOPKDN8KaUqlmML+4BGFsYKvkc/6/jHzTu/hf4KcZdSvJfFJAMyEVRrCgIgjlwEHAGPgMdRVHMNgZtzdBt/NG+CreO3KOIS0GqtqyoSpBWKESCfUN4dvW1kvQkNy+q/8XqeiYeppwybaWrQx2/Mi8vpc1H55/y5u572g5vSkxELGc2uZKU0cs9g77kaAVK7biJjecz7+wkTkbs5vKeG6wZui3TwxJIQEREppZHk1XdEaExCIKgGbfPtyxwSoT4hWJo8uPhL+qwME1fn52FBboGQGj68jnBqsFbOLT0FLs/rNagQE8nofCT88HkcgV75xxl75yjSCQSGvT8A89X3nx47E6b4U0BOJ6FGK46hq/rq5HrWqZ2caXO4m/kCpl6E7K43/vmH6f37E7IE+VEBEdhbGGIlrYWbg/d2T3zEEmJcuJ0wrAuGk99l2KUravLpI49sDEzY/v5K+w5fYdlg7tTo2QJbr9+zaLjx5nfvTuOlpaqNiQSCZ8DAvirRQsK5smj7JIoMnXbNgxkMsz19KhasKDyHASBeiWKssX+DjLHr8THJxHxRQuDuDTvVB6btPy2rrVqsfTYMRb266dapX/44QN+nvGkvhXkYhJye3/y5TdHIhHw8AxFjIHVJ06wdMAAVVjpw/fv+eQbgIlowcPzzylRrQjzuqziw5P0Y3HQsp60HdGUx5desPDiVOZOnULtpnZM6TwKY319zj9+zMxZwzBwL6bhPRBFEUEQWPdoEcMqT8qR+LI6Wg9rQr5STjgUtse5pBO1O1XDLr8NXm98uLD9qgYz5rcwNjekZrvK6BvpERUeQ3xUPM0GNGDVX1uZeTTNUFwzbBunN7pqHGuT1wpze3O+uAdQp1M13F/58i6FVr5t3uE06l6DkRv6MV5Xh3ePPFg8cCvhakaQoK2cYuQv6ciwhguVhp12xtMOMUmNgEfdU6J2rTQIWNTHfUpuX6pXZWa7Fay9N5sX194wYu2fGJkZMLnVUiUZlMbzokYQI6T/ZgV/CaNs7RIc9t9Cv9Lj8E6JMsgMgvonJgPSDOeSjozYMICO9gNUhpJUJsWhsC2jNg8kwtiLr+HhWJlqRmCERsQARmQIQUnaIggC2lJtXlx/g56hDK+3vsxqv1xV5nsx69hYTG1M6ZZvSPqdmRGDiAq2T9nHuocLaKij9k3SIEaBjqOb03poY7oVGUnwF+X0UNDRQdDRQRTVQngzm098cy+1tLVoN7IZsVFxGFsYEhUWg4iA78cA7AvY4vc5g+gaNSNSlKfNbzQ8enpqOY9aauM0ZdxZOVqgSJJjbKm8R7p6UjqObYnXO19uHLyXvs2fnaedAb7VzgNU0UWbx+2hcMUCuD10p1aHKiTGJ3H1wG2KVixI8JfvnJT8xr8KP9NzV+cbZpeJwBVRFBcKgjAx5f8JOano+bU3NB/UgNvHH/L5dQ6Xq37jlyA2Mo69c4+Sp6Atf87vwrv7H3l04fkvIQmY0mwBdvms2fVRSVd9dMUZjf3xYhzSfF+pX74gOlraXH36nngPS2SkGU8ZTXQb9KgJKJPCs8sB8HMPwMQyk4/od8IvMAJ5cjLaasbl04+fiA2VYPwD9fp/CqSBVic2PVtM+NdILu68/sN9zQ0UCoVGm6IooqWdve6jsbkhi1yn8uzKa40cPSNzw1znIP7G98HfI5DN4/eiraOFnpGeBoGSIBFQiMlYFYnnyNQRjNuzh03DhqmMoWFtmpEgT8Q7KAhBKEnNUqVw8/EhISk9O6apoaHKsAOlETe0TRvm7txJz4ppBGMKhYJJJ0+yc9JYrFMmtgev3WbNtjvoRyq9bF/V+mhlYkLH6tUZvGoVRqamaMlk2Bcpwu2vNwGY220ll+8e4ujSMVibmQEQHh1Nq3FzSYxOZPT69SQLAloSCc42NuyZNIrRa3axc95+es/uyJ/zujCxcXq9yI1jdrNx9E5AOd4NK/lxYuY01f6mLi4EhYezcv1trBLTCGga63SmbL2SzDsziYvygzn24DkWsWfTi2X4uPly49A9ru2/w+R9IzAyM+TF9dcUqpCfPnO7cG7rFe6eeoSVgwVnNl5SHd98YAOGrevHsyuv6GjXP41cJWVyeWaT0pir3+MPxmweyLA1fRFFkbldVtJtSjvyl3IiyCcECztTPN/4MKSMM/fOPePkpst0GtUMt8cenNl+nVptKhHsF8p+t2U0sRyQ7jyOrb/MWtdJ+LgHsnvJWZ7edCMuOmOil58BhULBoj4b2PdpNe8ffULPWI+jXzbg9ykQXQMp+kZ6JMUn8cU9gPFNM4/q6F5oBIgKWg9tzIbHCxhccZKGgddtSlsa965FYnwSpza68v7RJ4K8gzM1ttuNaEpMRIyGBywxPhGPl94MqzKFBDGekC4eLO7WVfW9WHn4FJHeOmS2lNllYmtkBrq0MO71wzmU36J8vVJUalqOJrJuuT72/NYrNB/YkGotK3L31ON0+wtXyEffeZ1oYtALxQ/q1KaibO3i3DvzjDwFrNE31lPJLd04cp9m/eqxf8mpn9OQGsztTOk9sz2fnn2mUuNyOBd3IODzV46vPvevNZSSEuW8uav0MB5beRapTEq+Uk5c2Ho1myP/j+CfJVT5n+BXhmW2Amqn/N4FXCcnxp0gITI0hn3zjmXfwv/CG5cb71omyIydKUNv1/9CmySDNr+4B7Bp7B6KVS5E2xFNCQ0M53w2LwJRFIkmHFEiYowZgiDJ1qPn7xlEn6Ij2PZmRTrjTrfAV87MGalirBvbVk6LGStIdjdQrpKLoJArUtpI88pZ2Jni88GPpITMqdlTER+rzOXMLWycrajRtnKGQsCJPqb0mreKVSP6YmliwksPT0at2IlhtFNWUYs5xpDKk5l3ZmLm+ZGp+MVjSVSISLLJSbF2tGDOqQks6L463cKNdV4rwoMi0dLW+uUJ8P9q/IL3WmbPnTwpmajQ6HSLIpGSECa0rEOiXI6poWE68pQ+jerRfdlKutVT5q91qlWLqTt2sLxPH9Wk9NqrV8QnJrLiyBFKODtTv3x5JBIJJgYGeAUHc/PDByqnhKC5vn1L21q1VIYdQKc6NTh6/QFhT5V9D/ES2HzmEv2bNUAQBArY2iIxNub1iWSS4qIQhCdcWtyN4u2MkMvDaNKgpMqwA6Wh2aVxTbYeOY+hqTY7x48hr7W1ysO2f8ZIei/cSah/GPfPPM72PiSRRGHHPOm2N69UibUHXUGNj0qhUPDU9SUtjHpwPn4/B/22oK2jRUJcInpGMjpY9dUg1kiFz3s/kuXJDFQjsrhzQlMkfp/XRvIWd6Bet5oIAoxY35/IkCgGlx/P8PX96VVkhFLgO4vzubznJkHeX1l4fgr9So9l1KYBHFp6mqv776Blo5aDrK1Fu/61mXN4FEe23yRO0CYyPJahdecAUKlRGQbO78SmycpogtToiK3TD7Nt1jGqNy9HvU5VGDy7HRa2piwatJVrR1LORz0MT83rqZEHJZdnWUY9LN3r7Rfa2gxQytqICuwK2KCrJyU5WcTHTRkxsOHhPNbcmsn8nus1RdC/uVYn1l5g4OLubH62iDldVzFgYTe0tCTcOfWYRX9uwtzWhD/aVqJ+txoYmRmyoOda3j9K0+1MzQdbPnAzFxL2U6xqIdweuKd7LnUFGW/OxzFe7wQyUSQoKAJdhQ27bm9DR6ZDUnwSUpkO0eGxuD10Z8PoXdRoWxltHW2qtazIzSP309/cb2Bhb0bnCa2IiYjjyPLTRIdnTq6x6NJUlg/YlHIOau+IlG+JRvRLBuNrfteVrLk3n3tnB6YrkxrSv/LGdIbXmpV2UGqdavcXrYzz9jQgijgVy8PtE48pXCE/YSExSi+xKBIZFoOhmX5aDriO2jdezTMsqnuMY9QSz9S9eGreY31rfdqMaEqAbxjH1pxHW6pFQmwCJ9bmXucTfm7+XS4aJTFBzvvHHqr/M+FE+r+F38ZdjiAClwRlXMOmFHV2G1EU/QFEUfQXBME6owMFQRgADACQZbo29Rv/FqQKT7cY1JAe09tz99TjDPVj4oVo9AuGMKheJfR1pey9fJ+wj4bIFCbpK/0G9gVt03mAYsUoOlYvpUFFrqOtTe/G1Vmx9iUmKHNvMmLwK1DGmQGlxuT4HBXfkbfWq9AINr9YQnx0PI8vvdDYp6cwwudeHK3cV1KuXlHe3vRBy88JiZC9lyvH+Be8jOVJyZSoXjTT/TXaVqbblDbMbLNElYukjqt/36J8g9KU+qMYekZ6SHV1iAqLZs+swxnU9hu/EvqGeiQDOlpaxCak97B4BQVhbOjAoI3baF6uFJFx8Tx386PtgrXkd7AiIjqON55udKlZnbblyvHa25vu8+axdsQIjt66Rc1Chbj27h21ihShUv78vA8KoquLS7p2nPKYEPI0GQla6EVbs3P3azxJRAe4evIZWgG2SAVdBEFQyiOYfWK4S3/uu7kh6Kf/nhTJm4d5vVpzx91dg/kTwMzIiALFbehTdESOIhO00cYvOP3KvJuvL8kRGef4yBPlKq9d+Qaleer6kvrda3Im9m8EiUB4YARPr7xiWb8NKi/MneMP2PVhDePqzSLIJ73sUde8g1S/u01tR50uNVjebwObXiwlMT6JkC8584ZLdaWEBUXg7/mV8Q3Tey1TcXTLdY5uuQ5qoWpiii7omR3X6T6uhcq4+xZ3zjzjxW03Vl2cxObph9IMu18Edb1SlfGmtsg1uNIUOoxqys7XSxlSbRruzz5To7UL1k7mHFt1XqOurz7BWDtaUr9rDeZ0WcWnF5qLU7eOPQRRQfl6JZl+cCShAeHY57chNCAcc1tTAryCcC7uiMdLL9weZM7GrIjQ5tWOCPIUsqP71B4IAqwctFmDwVlbR5spB0ax6/0q7Avaptc3zQBSmZSFFyYjiiKn1l9E30iPhRenkpSQhEQiQWagy6q/tuL2wJ3FrlMpU7sEgEqw/Hvw5aM/cdHxGFsaEflNzqL/p0AaSrtyKXHfd9f/Lbzd/HAu4YCfRyBm1iYEeqc9L6/vfKBMrWK8uPHup7TlXMyeZr3/4OCK8xQo7cjgZT25sOMaHi8+/5T6f+M3fgQ/y7irLoqiX4oB5yoIgltOD0wxBDeDUsRcteMfiEn+LvzCvmSm6fRvxOmNl9DS1qJZ/3oUrpCf89vUhIdFEb0CIZydM1q1it+hehXazFlF9BtjJNnk6NXtUgOAbW9X0rfEaACSETHS10t3nImBPqKaly41lyX1Wu54txK/T4E5omTOqC9qG9N+Z9D/DU8Wkre4AwvOT87wQ5u/cH4qNWlL0371mHBjLqFC5jky/0kIEi7suI5zKSfWPZjP2/sfWDdip2r38HV9sXSwYIjLZBTJGYcOhQaEc3nPTdX/Uw+MynVu0n8aqePqF7xjcvtu0Y40YPul2zQsXQozPT1ee3pSMl8+AOTJySw5cYYe3cZStVkFelYdjJgIA/+aTLBvCGc2uRJs4sXMAW1oVakSAHmtrKhetChd5s8nNi6RKe3bMLZNG3Zdu8b+x4955uuLpZ0dXevU0ehHaHgYMWiDNAGjvAkUtjTj2bVXTFo3i8lLV9LCOI31L4IQzAwTWXjyJLZmZjz++JHgyEgGtmihKnPtyRPG1a/PXXd3EpKS0FVbwVcoFLy8+wEhWlO7LzNIBAkf34dy/eVLapcuDUB0XBwzd+/DLMY5a6+8IOHp5dcgSLi89xaX994ClF6VtiOacT5+P/vmH2XH1AMs7LGGg35baD+mOetH7szSA1+3Sw2GVZtGbGQs3fIN1aQ8z4Ktb9PTxSQlypnTeRWCREAwTAt1Fy3SFuREdRZCtUgIQaY09LqNbU5jywFpuVDq400iUKddJToOa8j8vptwf+Gt4YnT8AIJmVw89YW3bLTHMh/rae0IEoHDy8/w6cVnZh4aiZ6hDEEQMDDRx9vNH5eGpajZtjImlkbERsYzq/OqbOUQnl55TfeCwzW2NepViy4TWtG39FgCPL+q7kWqRy8jHbUvH76wqOeaDNuQyxXMar8MgIl7hnI4YAsfHn8i2D+MuyceIkgkKJIV1OtaA8eieTA2NyTiayQ7ph3UWHw8v/0aAP0WdkUqk9JycENmnxhHfGwiLYx6aBjHuYLaWFvabwNzj4/FwNQAY3NDYiJjWdhzLe8efMLC3gxRFNHW1lItZqh/z7PDt3nzJaoU5Pi6SzT7sw5x0XHK8ZIylh5cfEGfme2Vxl0mkhoaOXxqZdQ9eogiJpZGGBpICQuMIDE+kYKlnNg46YDy1DNhBNXs+P9H37V/I36HZeYMoij6pfwNEgThOFAJCBQEwS7Fa2cHBGVZyW/855AsT+bUhkuUqFaEAYu74/HSi6v7bhOdHEWHKiU1cswEQeCv1nXx7uOMS/nK6T1N6h9tk0TGDRqKrkybLivrU65EFRAENq6cQZ8UYoJUnHn7jvUXlqOjI8UyjzlFXAoq9bJSykh1dZDqarP0SnotL31jfXT1dDREnEFJ2Z1R+awglyfj+9GfWe2WamyXyqSsfTAfyzxmHFt1jpPrLqryMTY+XcT8bqvxfpd1kv63MLE0wq6ADQGeQYQHRebq2F8JfSMZDXvWwv2ZJ+XqlgKUntSlV6fz+NILVg/JnCQnI8ztvIJBy3uho6uTo5Da3/h5kAgS/J/p0HTaYjrWcWHF8eMkiyJ2FhbIFQpiYpOwLGhK6Ndw/vijJpf33mLntAOq481sBVqo5dQBmBoYYKyjj6OBDQ3LKDXZ/qxfH4C5x09w+s4diuTJQ4XChUlMSmLj6dOUtbXhgfkj2tV1YXaP7qq6Jq/bhpZEB9fkgyTGJzK44kSk5v4s6jaIks7OgHKRafCaNdx5/ZriefOy7NARiltYYKSnR6dKlVh6+DCTu3RRvU/Wu7rSqE177u96SaRafl9WMAoowLjFh8hT4BSGejI8vIPR+pwH2Xd65dVzTi/tuqH6ffPwPVoPa0qIXxgHl5xOd1ztTtWo1tIFBIHiVQrx+NKLTLWsnEs6KglmXnqz+t48tHW0ECQC5jamOBXNg+/HAOK+c+7p4x7I+hvTmNZpDSEB4egby5i9fzhhQRE4FLbDzyOIkQ3mf7/R8IugbpDZOFux220lMw+PQkeqTf9y4/B+5/dDYe2X99zEobAd47YNRhRFTCyNkcp06FV4ePYHZ4NFvdZhYmmEsYUheUs4UrNtZUytTPj6JYTTG115d+8Dax/MZ0jlyRkeb+1oQbk6JTPd/6N4fu0Nw6pNBZR5707FHZh5dCxx0QnkK+nIvoUnflquoJ6hMs/OxMqIyBDlM1ynY1UKlHYiWa7A46U39btU50pGJCc5hF0+K3pMacPLm248vfaG7hNbsnfhz8/l+41fjN/GXdYQBMEAkIiiGJXyuyEwGzgF9AIWpvw9+aNt/ca/E2/uvufN3fcUKp+PAUt6YOioi9fV9Lc7OSmZLeP3cEhMLyaeusIVZ/iVhi3zM7d7B6Q6Org+esLQHgPA34xYg6+08VrBiNYN0dHWZt3JK/i+1WbymYUadUDOYtdrdahC4YoF0unfiaKIiAJRhDgzf6zspSTJk4nwBf0YawRJxhO3ERv6M2H3UAxNDQj/GknhCvmRSCSsHLQFl8ZlKFOrONcO3uVo0Faiw2OwL2BLscqFcm3cLXadhkRLgq2zFW3M+1CscqFcHf8zIZFIaNjzD5r0q0eyPJnI4ChEEcK/RrL06nS0dbRJSpSzb/7x76r/zEZXGv9Zh9MbLmVf+Dd+KvSSTQgPiCR/PmeqVyhPYQelbMfFR4/wC3Ll9rZNeAUE8/xVIFLRQV1xAEGA2IQEDPU0ve0JSUmUKZZe6qBagfw8ev6Od69eceLaNSRAm5Il2ePhTc0KhZnetYtG+dm9O9Nx4gJqPPgDqUzK1lfL+LNxY5Vhp+yDwOyePWk8eQq6EVYgjWPWEmXuWnEHByLj4hixbh0KmQx9Cwuen/Kn64AqHA4aQXRYDH8WG0lEsHLxRM9QxsmI3YCSjObYyrOcXHcBQRAwDctPzGOIAQww/+E82tR315ePaUl77x99RFQ0wtIxjY3UJq8V9XvUpPesTrg9csf7nS96BrrExyZgaKqfLpeqbtcatB7WhNiIWKydLIkKi0aekMSI6sqJd4/pHegyoRUVG5Zm0ZCd39X3gXXm06BtBWbs+YvP7/1o2Lka8//chJfbF4L8I4iPTdTQCfs3oVm/ugxd1QeJloTrh+4yv/tazsXsYePjRYyuOwu3h9/P5CuXK9g6ab/qf2NzQ5Zdzd0CYlaICI4iIjgKn/f+3D6mGeoqKkQiQ6IpXCF/hgywk/4ezuqh239aX7KCPMXA6lloOFP2j+TLR392Tj+ULo+uQfeaVG9ZgS/uAWyZfCCT2jSRv5QTn1N0X7W0lMyZoihSpEI+Nk7YR/m6JSlSMT/6hrJsasqmnZJOBHopwz1dGpRiw4T9Kkb33/gP4bdxly1sgOMpq5/awD5RFC8IgvAIOCQIQl/AG/h+gZ9/uwB4Llf0shNC/Z8k1f4EfHzqSURwFDXaVOLMzef0+aMGOim016IosunsDYwUdhqTnwQxDgEJOkhRKBToOcViaiLjytOnNHJxoWGlirxr7YWXtz99GvfmytPXjN50EO0QC4ySrNDTTsttySjhW/kzY7rm0MAIZV6B2tiJM/qKdQEJDrbmvPL+xLYRf1HcSTkZPXP3IQvXX0QvxC7DdlYN3qJxPep3r0mtjlUZvq4vQT7BmFqbULRSIaR6OoT4heHx2guf+BeU6qBNUmwiL5/5InyxRUfIWpNn7bDtlKtXiq8+wVRqVoF+C7ry1Sck48I5eS6yGL+OReyYemAUuvq6xEXFUbBcPt7e+0BSopyokCisnCxVYTWzO6zB3zO9g37Z9Zk578s38P3gR+O+dbMv+H8Bqdcns/vxE95xORGp1njnxOugpaVFEUdHAD4HBPDEzY2/R49SFXnj5cOAOfvQD3VUbQsPEllz/jyT2rZVbbv/4QM+XvE81PVkSHPltoCwMJaePYuhgRElSxThlJsbsxs0oICVFVtu3+Ft6FdcihbRiAIA0NbSwtrJkFblGyDVkuDvF0nRkpr08aD0FkoletSp3JJuC5oxdcFCpjRsgKFMRl5LS95//EqlMu3oO64Tm6VLcfe+wOQeZ8HUhGX3Z+B28xOORfNgmcecxPgkpLo6DK86hV0f1zB0TV8UCgWNtLPPdwKld23K/lF89Q3BysGCoyvOsFFNkw5gxpGxmFqnhEGqjYOuU9ujSFbw9NILEBX80b4K0w6NISYyFn/PIEK+hLGkzwY6T2zNihuz+PT8MwkJicgT5Fg5WBAXHY/788+MqzdbKQeghlSR6L3zT3DmwAPmHxiGmC+NKEbin/ZuEdQJTdQkDZp2rUJ0eCw3Tz3F9ehjXI8+ZumJkZz/+za3zinDAFU085mFYmqEJmYcNpdtmFsu0zlENUHzoav/5OaReyzouS6lKoFmRj3pO68Lyy5PR54kJylBztQ2S3G7/yGtEvU2c9D+lH3DMbUyZvecoxl36gc8hBpSEaprm8zHZx7U714znXFXsHw+CpRxxqVxGZr2q8vdU4/oPKE1xaoU5srft1jWd2OWofGac5WM5Z6+OUD18+T6S4zZPABLBwtC/MMpV6cEQ1f1IiYiFp/3fnx46kmVJuWUhdXuu0aIZIp4feUmZSnqUoB9i5QetM/vvmBXwAbbvFbcO/sUgGfX3hAdHoPHK28NeQ2NqCH1UEyNsabcrqsnpXC5vIQFRhDoGcR5NbZoFUmL2nOh/rxkK3ulLJTx9t/4jVzih407URQ9gDIZbA8B6v1o/b/x30KQdzC2+axJ8rGm+fQVdK7rgp6ulH1XHhDmboReykstTohGL18odcsUJC4xkfvPPYmXyxnStD5NXFx44+XF8DVrmNu3L61rVOfKgweUcnamlLMzjV3K0mfyTrTCf4yQ5NXNd8j0dTnguwljCyNu3biG95Wr9K5bh9efP/PMPZ/KsANoXq0SJ24+xOO6HC0h+0dHPZcGIF6MxbB4DCXLOPLhpRf+YSHsHNOP4k5Kj0ho2yhGHzmFzC8vn154Zd7v2268up2W1ur52ptRG9PTj/8ITK2N2fZ6OVFh0by994Gd0w5SsHx+7p9+gkKhwLGIHVo62nx+7YNMX8rUg6Ppv6gbs9UEjWX6UqYcGIWhiT6r78zl8xtvHp57ipmtKWGBEby9+5742ESqt6mE667rmfYlITYBmb5uGo37b/xjMIi3Yu7WwxxfWBB9mYyD164xpHFjjTIl8jri4KRHqBq3iHFIPk7dfsGngEBKODni7h/Ak7c+mIYU49O7QDZduEL/hnVYeOoU8/v3Rz8lZysxKYnOS5YQFxvLlAED8NOSYKijQ2BYGDZqzJeBYWEkx8ZycpkylCwyJoYWEycRl5CAnq6uqtyuy5dp2qIzNSr9wbF5V7h9OJwm1zZiZKhNeFgy0rA8VBlfgRXzZzG7QQNMDJS5ZuHR0Qz7azj1K3dheb+0Ce6ZmL9ZfHkarc16AUpx8rpda3J1X9pznhFmHBlL2XolAejqNIg/2ldh6Np+tBvVnMlN55GclMyCC1O5fuguD849ZWHPtRrH/1l0BNraWhwN3o4oKhfLxtWfzfNrbwClFtmmZ4uxL2jL6ztu+LlYjrfnAAAgAElEQVQHUMSlAEtGbFQRceTEsI8MiSYxMXch0L3GN8fKzgRtqRaNulVnyfA9DJnfEW1tLU5tuZ6ruv6XSIxLQM8ofV73tqkH2TZVSRCz+OJkVl6bTr/S4/BV86zmBrZ5rTi0/Ay3jn6fwHpuoC3VZsqekVjYm7F9anoPmPtTT05vvERyUjLXD92lxaCGHF52hrsnH9F8UAOOh2zniesLjff6z8Kbex+5dfwRk/cMxdDMgPCgCBb12cCHx0qW0Sn7Ryjz5rJB+TolMLcxYdfsNGPZNq8V53feQEtLi/pdq/Hq9nsUCpEPT7MWVM8KFRqUomGv2tw+/hCHQnY8vfpaZVz+xn8Mv3Pu/uX4t0ghaOz+gZicDAgVMqvv3+zRO7DwBJ2HtGffguNs+vgBBQqMsUMmSEACoqjAsHAop+eNV63I77l6Fcc8eaidkotTq3RpSjk7s/b4ccoXKkRBuzRvWVFHB6ztZcRGZC6toO6tywr9So1RXW/HJgLbhv0JwFtvb8qlCCyro2xhZ95dd0cfw5xfECBZlGNcIoyjs8coGT+7wukHD3j46aPKuDM3MqJ+vrzE/1GMERv6c3DxSe6cyJipzNzWlDFbB7L6r604FbXHqVh6SnYg25VkURRJJA5tdFQGq4mlEYsvTSMmIpZkuQIzG1PajmzGxjG7Vcf5vFdObKq1cqHHtHbsnnVYRTSQOmaXXpvJ33OP8uLGW/ot6Mrn1974ewTx5u4HzG1Nqdm+CiWqFcXC3gz7/DbsmpExw56+sf7/H4ZdblbsNcSDM1kxz66+TASI1euQKCTEvbOk6bAFODmY4RP6hdHNmqWrSkfnG8+aRBcDn+K8/BLMA5MHWFsaUMDOHh/RB8kXG3bu/sCOs/fo1KKKyrADkOro0KZiFbyio6hRvjw3Hz+meZkyzNq/nyHNmlEib15eff7MuO07OTAzLazN2MCAvs2b02rGLCZ37Ux+W1sOXr/B0UuPMPIryi1eAiATjOCrEeGxAVg4apHHRcD13n6s45JVhh0oZRPK21izefYO9IU03cvmBt1wVRzm788b6OY8mDkdlzHt0Bjcn3lkGV59bOVZqrepRPd8fwFw88h9bh97yEX5QUIDwum7oBuvbr9jQfeMCTQQJMiTRVqZ9UFbqq2UfEFU3asZbZdSsKwz04+MYWbbpUQER2FiacSSK9MZUX0q8bGJqtA3u/zWBHqFoEi554JO2jRAtDJDZqyPVEeLxFSCC5O08xci0hgPy9cqyqBZ7QgLimRCh9UANOpSlXWuEzC3NmFKx9V4PkvzFKk8dz+LZCIX5b8d35eSDqg8mAlxiSTFJ6Grr8vx1eeU3sRvxLZTMb7RfIat6c3mZ4toZdqbxMRkNAhadNSnVGo55yllBi3pQVKinDvHHoCo+O7vuHoYo3qbEvO0BRAxMoqZR0bi/tSTtaN2E+IfnuF8YuukNLbK59feqPp0YesVXt92Y8uLpeQt7oBXSsijsv2s+5fZ+0SjfVHBjpQ8XU0yHQk121XG0s6MUbVnpq9c7d6U+6MYJasWYvecYxrjQVtLQBGfwOcXnjw0kdFraht2TDukksrI6TytcIX86OpLscpjRr5STpSuWZR3995zYOFxRFFEQE1SQZKcUnda/0SNd3Du5H3+qxFc/xn8Nu5+4zdyh9CAcPSN9RAEASPSh0pFaoUypXU9jVCrD/7+dG/SRKOcubExPoGB3HvzhprFi2Nvbk5BeyWTXdIv0EGL+hqhIlcomz8/N1+90sjhAXj09hMy0q/uZodYwyBWD+6qIeXQonJlJm7XzHUo5exA/6k7OMpldrxdwYwjY2hl2luDmt2xiB3b364kIS6RzpPaULxKIdYO30HnCa24sONajklW4nTDMSuUSK2SBfD7GsbrN95IA/Kw+u5cprdeovqY6xvrM/XAyAzrcGlclku7b6RjkKvTuRofn3pw7/QTrJ0siQ6L4ezmyyr9urDAcD69+MyLa29oOaQR1Vq5aHx4E+MS8XjpRb5STjy+8CxH5/Mbvwa6gh74OOLvAwkSa7ZeuMLg5o1U+yNjY/H2jUQXM43jBEFAxzSBRcO7UaeM0msVEx9Pmxkr0PngSEyABAvj9NIo1ubGBCQpx/uonj2ZtmYNZQoU4NDt2zzZsQuZsSXly5bG1FBzgaVb/Xp8TJZzNzaWw9euEeehj5FfemmOOOMAxvSrRrsaVVR9GrB+PfGJiRrPZ357KxJxTyfQ00DSAVfFYQpXyM+0Q0qJlW1vVtJA0oH8pfPi8TK91/3V7XeEB0Ww2HU6g8qPw8zGlMrNy+P28CPjdw7l9vGHHF91NrNboIFU8olvJ6gxEbEYGOtjZmtKRHAU1VpWJF9JJ5oPbMiRFWdoM6wx5jamhAVFYOVoSVx0PPb5rVk8YKvq2XNwtuT5fQ+O3ZiIt2cw8ycd5otbQLo+jF7RDQNDGQv/2oHHmzSj9uL+e1zcc4vC5Zxp1qsm3cY0QZ6UzPH1rj9Eqf8z0XZkU17ffsfoOrMwMjckKjSa1kMac2JdzrTJ1gzbyR9tKzNqy0AW9Vqfq7bL1i7OwPLjv6fb3wVLezOmtlySo7Ll6pWkaouKVGtZkajQaGT6uuib6BMfk0ClJmU1jLtfjV4z2rOoT9bXVirToVKTsmwan5YzryPVplGv2ujoKqe2jkXs8fsUyKcXXhQokxf3DGSbMoNlHnNaD22E11tfjC0MAAmLe6/j2dXX33NKv/Eb/yj+e8ZdNpT0vxo/5JlLqyTn+3Owuv5vXNUxsTJGoiXREJVNhSjIMVWj2gZlDk18YqJGSNXfV65gb23N8mHDEIHNJ0/i6OGBka4+YX5CLn1nWSDlen/x+H/snXV4VNfWxn9nfCbuQgjB3d2luLtDkRZatDgUSrmlRdriTpHiUChS3N2CE9ziBvFMbDIz5/tjQmaGJEgvt7f9bt7n4XnCmb332cf3Wu9a70om9NUrCrq5UcLHh82nTnHpwQPqlCmD0Whk/eETPH+Qglpi9mS/7/0oqDIpWSAnu2YpxQ6w7fRVVBmO6AU9QypN4LPZvfklYB5bZu7Klq2WymUYjSLnf7/CspHrqN6qMqOWfUZUYDQVGpZBY6dGppCx6btdXM3KN3hzfjoxnRK1FKydNC7boH0SFs6yM/54F/W0+pDrdXpK18zJYgIsHbGGkb/1pY5QgJMbLyOJc+Pzb/pTrWUlxjb8FkQjL4NfcuXAdfpM68LGGb9Z9Q+6H8riYWtyjKuxU1OwVAFObj7Pq7A88gn/F/CRoxPyzpF593sGwBZntuy7Q3hMPL0/qcujsAhWHT2JUQJa+wjUie7ZDLAoihQqos427ABsVCqm9evAxB+OYpvuxtHrd+nXrImVAu6xhw8Rs4wstUrFxMGDWbJlC8eu38IQ7IZjpg23Sj9D7GOtnHvs2k3qNGxIq7ZtObL+DC3WNkQQBDo4DcTJw4EZu8dRqLQPE/p/mm3YvZ7TmPbt2XPlCr0aNMjevuPUVexzcU69RveJHUhPyeDu+Qe8DHpFvU41aPdlC+5dfMSzW4EE3g2mctMKfDarD5FB0dw6fR8HVztmHZ7KuZ1XuHH8Lod+OUXLQY0w6I1oE1Le65uW2zdIkAjMPf4NuxbsJ+SBydg6vO4010/cY9iCT9k1qQMrJ2xhz1KTMJGQVaOu7+T2fLdnHEqNEnsnG8LCEwh+8YpNC4/T8dO6zPipB0OazDbvSBQZt7Af0aExbF6QJYwlkUC6mVkXdToeX37M48uPEfV6VBoFo5cOovOwZoiiiEqj5MnNQJaM+vWdxwpv+b7ldq7yeF5eM11SqZQ+X3dmQCmTs+p17bU9Sw4jsTDsRb1FMetc/IhGg5G753LWS7MUixEsCl+/nuuZnVdYfOl7JrWYRZo2PdcIk9zKIgBILZwZgqtL9t96T/M9qrMz79NWL6JxsaflyDYc22kSWTG+Mr9LLXPXRi8bhKu3M0EPQhlYakyOvEzTIXzA2iePyALR+G4xnXL1ilOgmCdPrz/PvYFgeu4HfNvVzLRm7dPTz5PKTcqisVej0sjpOLw5BoOR0McRBN/XZc8rj5TO7OvUcXgLFGoFl/64Trk6JVk7bUeuis1vlmKAN667RdimkMdaLrf9vz6efPyHkB+WmY98/DkkvsqbPbLTu/LLgVM0KFc2e1vX2rWZuXkLswabwiK1aWncCgzk52HDstuM69WLIT/9TMDVGGy0Bf9tVbo3oYrzou+3i5kysCN1ypSidtmyjFq4BmeFEwaDSFqkClWGK6luwXgXsEGvNxAVqsMmwdtqkZkbxAQb9l70p1vDOuZtosjD8AiS09JQyeWsP32e81fC0AgmYYo0bTpLRq7j4t5rjF7xOb5lfFg7eQtB90JpIe+JQdST5hDJtXuhDO10hpQXGgxxAggSarerynd7JpAYk8TepUcoVskPZy8nnD0d6V98FDrHGGZ+NgxBENBlZnLy1i0yDQbkQirHNp61mrujmx22jjZUalw2O8cHTIpvBVtLSTt7iulVKjOieHEmbdqNSzk1Y+t/g15vRBAEarapQpVmFaxq2L0LqclpVkV78/H3gSa+IGf3aTl8bDMaHx2bv5tEIU9PHgWHMGLOCvSPPJAJCowY8HDJycyV8fVBotZBusDzy+n0nP4T4/p2QC6Vsu74WR5d1OJWyI3Jq1ZR3qcg/tf8mdKhAzNat2br2Uus3HmR9EAHJq1az4wBvdGoVNx98YIjz5+wYNJ41k3dxrY5e5n32Qq+2TGG+We+pWhFPwAGlx2L1D1n4fFyvr5M37ydhuXKIYoic7b/QdA9HYJbBAUL2iCVSQgJTUIW6YVMMC3eGnSpzcwe8wh9HEnXMW2xc7ZlUvOZyBUymn3aiPYrW/LwyhOGVplATFgsHUa0pt2XzQh5EI69qx0DvutBXFQCcoWUhUNX/9vXJfh+KF3HtKXDsBZs/n43j/yfYediR3xUAsNrTeVlmKnMQvEqhanZrhpGgxG5XMqiSTvISNOhTUzD4GUyGqQJKez85Qw/bTUVSPcp4sYnnatTspIvrp4OzPtq03svjtJTdcwdtNK8wSjyW+jy9zbuPhbsnG2p16m6SQzqPUtd5AWZXMrhtac/uN/2n/YT8iicUUsHMXfAh7F+H4r0dD39uy1hyfJPs42715BIJNTqWJ00bTot+jfEp6QXw2tMAf47zmKJRJKdy73g7L/4us2sPNvKFTKG/NiX45vOZatWvkalxuUIfhhGyWpF+X7/ZJYMX4uNowYndweC7ufNPFZqVJYKDcugUCsw6I08uf6cAsW9cHRzYOWEzR/tOPPxN8L/c+NOyI1Z+W/BXnAWa0qa/bnOH8Lo5eUREfLIXcmjzV+C9/H2/A0x8Pue/Do99xwqgDT1K4pVUjCiUwtSMzJY8Nshwp9n4lZESpWyftx58ZwRXbrQ7I1aWQeuXGH+3ItkZKZgV1CPrY2KqIhkpJHuKAQz6/fBqoDZ2wykKGMQbDMYv3gc64fuJT0lI/u6Z/i84PuhnTgTEIDeYEAhk3Po5DNsYvzevi9RJM07kG+HdKBZlUrEJSczcdVmgp9L+OTTiqSnp1O6RDWqVqvBsOpTch2j81dtKFmtCLP7LsEoGhALh/BV18ZcefECqUzGveBQIm7IsNd7AKa8vDGrh1KqRjFTeQeDEWcvJwIuPGLRz7NZNLAzT8PCWH/kCF1q1kQpl7P6xEkeXzKiTnex2vc3O8ZQpVkFurgOxs7ZhiE/9UOn1BJ+dB9DP2lodZzjjx3j+QEpfmUL0qBrTa4cuMGtkwHvvB7/8/iz7608xsgt/y7PHN48VeIsh7Dum+YZysFVU7G3yFULf/WKbl8uQBbrSro8EXXBJM78PN3K+bHy4DHW/uaPXxFbShT04FFIJH51ylOrXl2W99mZbTzpxUxkJUM59sMU6/6HTnDoWCzdxnbkwoWjOLhoiA/NpEmjVhQpU4iabapkt02MSSI9JQN7Z9tssYxWpepyaM4EqzF/O3+JuYsuoLAFEJEnuiJ6RrF+6gCKeZvyfWOTk+ny7RLkwX4MWziAZv0b4X/oJiVrFCMjVcfWWbs5+9slABr3qkfRSn48ufaMc7uuWF2D1+JAK2/MZWrbOSS8SsoOV84LueZS5vHNK161MN9s+4pNM38nOTaJa0fvUOWT8pSpUwK5Qk5cdAL7999HKhXI1BnQFTSH0yoCX5n+yNQz6Ov2NO5UjVdRicRFJ3LlxANuHL/D16sGI5VL2bX0GJcOZylhppnDxi3VBy1VCV/nIqnUcjY9WsDwut/kWJznijyYrFzv0zeekU3Pl2Bjr0Ftq8JgMCKRCAgSgVbqfjnaS9S5h9u/PgZLpmtPzFoEiUBynJb4l6Y6qQmvkvlj1QlunMh61+WhlNhqUCMKFPdizWSLEjyWz61lDl0BL5IyXpFhTMEuQ41KanrWDAU9stvEVTAzelof830izdIiuTDhMx7fC2fVnINEPY/G1dOBKYv6EBP8kuT4FK4cuo2/RYSHNWtpnQuXPa+sOVoel1Xb93xXeRX1oMvUZgQ8uUJMeAw9PutH6RLl6V5gaK5dnDwc6DO1MzvnHSA6+FWO3wd814MN35py63xLF6D9l83ZPPN3EvJwNguCQOfRrdFlZHJ0/Wma9WtgCq0OCMH/yJ3cVajfwQ5bqWVaFjTX5XFe3wevGce/aA14wvjbDVEUq7275T8T1by9xeuff/5RxhK+++5vea7yjTtL5Bt3Hw0DZ/bg129/e2sbvZhJqjIGRAGbdFf8yvjyTH+B6hX8qFe6NNFaLUPbt7fqM3/nHqR+BXGJT6Rv08YAJGq1dJ00G+MjbyRZmd5/1rh7fb4Hft+TLl+1pa1tVuFkQUKKkEj9tjY4aJR81bYtGqWS24GBTN68lfQ7vsglypzjWQ4timhVr1A4p6PXgTTGFZVgzuhZEzDPJPDyFsw/M4OxjWZQf1QZvDOiSRJFRnfrhlQqJTYxkQFzfsQxriJRT2No0LUWBr2BGb+PJzU5jSPrTpmYAqWcqxf9KeaXSnB0GAs+/RSJhRer95ylRF1zzKEIuvLmXE5uOU+zfg1ZNWEjZ/0PcnD2AJzfyH2aceAwddt/ztltVwg49yDPYsr5eAP/JeMujWSkXjF4udsRm5BCSqgadYZzLkNb97WrEM/v83MWPm4wdAy1Spei2yd18H/4hP2XL7N2+Bd4OTuz7/I1vt+4n0Gt6zK4RWMO37yJNj2dVKMR2/I1+PXLA9njGEUjpRpnsGzEQKvxE1NSGLXxMNJwdzoMa8GeJUeya8I17deASb8O58vqk3mWpYxn72yLs7cTK2/MRSqTUk/VnAr1VMwZ3As7tZqLjx/z28MnNK7SheVjTKUJdGIGNVvJ+fGz3lb7XnPkJGt/eYxtVrhm+fqlqdupBqe3XeDxtZxhZLMOTWV6h7noM/WUqlmCKk0r4OLtRFJMEkZRxNnDkSsHb3L37AOrvNq3nvt3GHcbHy9k/+oT7Jx3ILtNo+61eRkWx8OrJjZcUtAcIp6Xcbfgj7GMaT8fLOX100wWg0qj4IuZXfErXYBtC49wZa8FM/QO4040GBi+oD/+h29z7djdPI85G3/SuFPbqtkXv54bJ+7yddu5uff7k8bd60W7g5sdFRqVRaFWULRcQWq3roxCreCnz1dx28KhZbnP1bfmMqrOVOtC7rkYd3oxkzDXZPxKdsTNozIvHu8hOfQ2PkKh9zbuetauSHWDPX9svsygcS1QKmQIEoFNC49y5w+zWqdlOOlfadz1mN+E2Ad3GNGmJUqFgk0nTrN200U0cT45mjt7OdF7SkfWTNlqcrjmgmqtKvIk6BHhD6KwxREbew0DvuvOpX3XuX3GFHVSpGIhnD0cqNCwLAqlnAt7/PEo5Erh8r4cWnOSiOfROY7XCvnG3T8e1by9xetDc3cgfCiEGTP+lufq/09Y5gfE3+f10H6I0txHyb3j3R+ov2ue3bvmkpfPwLKtTCLHXmfyipeoUZSvd4xgxohn/JzlURm3ejWvEhJwczQtpKJiY9l30J8S5cP5ZYw5XNPB1pZZI/szcuI27FI93zrv9zlvMoWM+l1rs2q8WR0S0YhenkpSOsztZ17wVSpcmFFtWjH14UlcM811vnK7fwRBwC7DHSJBCVZhpUN+7EvA+Zw5HLmh1+SOBKVd4+nLl8wZMSKbgXBxcODnL4dyNimJIcOHZ7ePi05gUJmxpCSkWI1zxeE5/TpWICw2loKurtnjDGnbiAm3TuJoMC0gfMsUoMf49kikEhp2q8OEZjNJfJWEqJLzJDKKWsWt8/GePo3gQu8l2eN1GdMWJw8Hgu6HflBo5l+Cv1O9oXfVufsYY1so+CGVkiGm4VMpjQ2TJiGXyRBFkZ927mPfzkA0GS5vDGH97MQnpGAwGJBaLGBOXL9O/1aN+ay1qVxC9ZIl6Vy/Dh0nz0Kp16B7pcLRy4Z65UowacsW+jZpgr2NDb+dO8fZvds5bjhIM2kPWg5qjEqj5LL/Nt7Es8goChQqSIHSJVkzeQutP28KVGbvksOc2HSO7uPb8+W8/oxr/C8AkuK0JMVpWf/NdgbP6o2Xoy93T0bQOmARcqWAVO/AutNr0WYmgk8iyZE6yJRS0jenGEuFor5UaScl7GwCEolA1eYVWT91Gxlpb+QoZV3DvUsP8+l3PTAajMRGxPP7ggNWbQVBoGzdkvT+uhOZGXoOrDpOXFRC1hBv/x69Wfj5NT4tPYZ5p6YTcP4hj/xNBufFfTcYvngQagdbbp15gKg2G2x6G/MyILW2yehzuBOHTm9EdLAjzc8cWqu5bzKi0w2wcOwWFCoZP2wfiU9xT35fcTJrQIu8qrRcDFZBIORRBEPn9ObZ3dBsVuW9FryWKpFCTiPNMr8rTZtGb79hrLr143vlOFnly6nNCq6va5UJKrPz7rXBlxiXwvmdJmb2JLDmpyOUq1GYMcs/w93bkbXf7GD3kqNgMF3zopX80OsMpKfpQZBkX2PBIgddyMo7jzK+oFGrNdjamr6RHh5VeXBvA/E3D+AUbH4/uWktjPNy5mc2poKEumUKceV5KKcq6Dl1/CBOT8z9HAp5m/cZacGCpVncd7qcuXem43/7d9TKqMnDmDYa9ZzetZct08dmbxvYoilPgyK4sDsRldTs+CxetSgtBzZm1YRN6NJyN+xSlTEERh1nUNtaRNexY8tJf+ICPVk+ZgN12lfj0xndcfVxJuRBKC0GNmF4jSlkpOloO7QZEc+iOLl1a67j5jy4t2siCGIeBm9e+Xd5GND5+A8iP+cuH/n4cEj+RO0Xt4IuLJ25ggGfmEsjzujbl3m//87LxETiElIJe5aCItILiuVMbK5cvBiCTQZ8BJJo3JovCTj3kP0rj1ltV2bYIxNyLvqbVajATPvjEGtiG0BE8oEv6WKVCzO9w1xSbV7iWFDE1kZJRGQSQribSa0wC0mxWs79fpVnMbeo+4lnjly/kr6+LPllM9reKSTFa7l+7A5Lhq/NsT+jaEClFohMSubo/fs8DgmhbZUqNCpXjgRtKkXK+lGlYnWKVy6MXClj14IDBN0Po077ahQo5kniqyTsMjxYcPw8Gwr5ZqsMXnj0lODANNQW83L2csKQqSc16d01i/Lx10LvEMXiEV8iz/I2C4LAhG4dePByPU1qdc0KY5MgGo0kx6eg0iiRq+RkZuh5GviQmRu28e3APgiCgNFoZMH2Xfwx81urfXi5uODn7kXcDUeUohGFMopfz55lwdCh2YzxN717M2rlamJjY/l8bh9unbxHYEAIhduW59LDJ9QpXQKAjMxMvt24h3pt23HsyFa8m7hy9PRuXAxF+HLBALb8sJshFcczbOEAFl2cyXfd5hMbYco1K1KhEIIgIJVJUAs28Mq0oBZFkXVrFuOSlsqKaV14FBnJ7pt3uR4ZweA3zteuC9dp1LwPlb4tj0IlZ27/pTkNOwv4H76N/+Hbef4uiiL3Ljzi3oVHqG1V9JrckTtnHxD+NJLCFQoRdD+UqMCXH3RNAfavPI6ds1n4KVOnZ/FXG+g9sR1Rwa+I/Ei+QV26ngkdFzBxxUDGL+7Hs4AwChVzx8ndnrjoJNKTUyldoygLR6wn6IFZVXP/6pMM+lc3HN3s8gyZ+xiICY/DzskWWwc12sS/5v1zzz+QwfW/p3oNX8YsH2wy7rKgtlXlGk6YG2R2LtmG3WuUKtOXowG/4fSexM+cE+c4MKQfW/3voH9LMfL/BtJJpUHVsjm292halxOHtqHKNBl3Th4ONOtbnyUj1+Vo+xqZoo5S1RSsGW5m+XvVr02bbxZDaGEu/XGdS39cRyKV0HlUa+KjEjBk6tHYqylRrQjH3lJnNR/5+Cfi/7dx94EM3Yewce+bl/Ihfd/FFvw32LoPYQtft23WvwGX/shd9vpt4ykVKmKTzHWU7DQaZvTrx7e/buLuHwbUEmcQIDwyZ57GMf8bkGSTPf7bFP9y4I3zLlfKOLklZ1FilaAmNDpnUvatwEAMqXIyCoRRopgrCrmMR89CyAx0RiV5P01PqUxCvCyULz+rQ9/mpnDTlLQ0unw9F90Dz+wQSWdvJ16FxjJvz3xmfz/GVGvHwpC6FxjIw3MhfF5xPEaDMZsFeBMZHhFs+n44Rb3Ni4dvfv2V4l5erDtzmtKebXh+Jwj/QzdJijWLD9w4fpdhCwdQvUVFVLYq9q4/RKvpKyjoZUdqeiYRoXo0CQWsWMlfJm56r3OQj3fgfZTU3qf+3WsYRVQaAVcHa9ETQRDQxWrZ/MMeEI2kSmMRXSNwslciGBTERyiwzfDGxkGDXXEZk7ZvJyI4gif3QxBkevRvsHkAeoMBJw8HmvZtwKX7R/Hz1FiFAgMMadmcib0nE34mBYPeQOOedWjSrh7j+o/Gu5ACG5Wch8ERGOTpXD21B6lSoGfdZhTs5M7AGSs4tc2GnZGrWTN5C8u/+sXXrkwAACAASURBVJUWAxqxPXQlPbw/p8ekjjTpVQ+AiRtGMrXNrOySAinyOEpJitOzlYltLObtTd2SJWk9YTZfs50p3TugkMlYduAYly6Ec+vAZmq1rUqtdtUIfRyR+/X5E0jTprNu2nZafdaEL+d/ys75+2n/ZXP8yhbEtYAzXkU8iI2IY0jlybmqGb6GaBQpVsmPXyZbMxGCowM7119k8NftmfH0dvZi3+uS2SOmfGWyGgwOakSZFIODGkW8eV+ijZlNsXzv/Dh6C027VMPWyYbjWy8SFfQKTz837J1t2bvqFFM3fMFvCw5xYe91EEUqNSyNxk5NYEBI7geRC9vT5+tOdB3ThuvH7jJ30Er0BtN2mUKGo5sdMeHxkEsB9kydnsGze7Poy19M85Zas9fZsMw7zYVxtGRYrMewYP8SErP/vnrgBoHDmjNl/RfM6msqSt92SDOObzZHLwhZoaCCpWry6/DVXCQdjcZMjE42kGoRQmoxV/tH8VlDSFj07WD0eiN9lm9HTDUiBXR2FsxmQfNzr7aM8I02G5+WbGauyKMmr2AZymsZ8pl1SIXK+PDiYRCBUdE5hrz9NBCJToEgQMPutSlWuTDrpm176/rIrgSMatvEaptKoaBqKR+uheiQC6b5GA1Gdi04gEwuZeGF77l5MoDNM39Hl57zvnmvVJ/ctluxdXkUBMyjjuL7rrHep20+3oF85i4f+fgwlK9fmuMbPyz8LjMjk6plK7F85Sxa1aiWXQMvKSWF81eeopb4ZrdNeqFk3OLVfD/0U9RKJXefv2DO2j3Y6gp9FAXNfcuOMOvQVNq9zrezwKtncn49eYYBnzQCTPk/U9ftQekismvuGNyzQkjTdTraTZqN+MTmnUqaAKnJ6XgWV2QbdgA2ajVzhvfji0lbsUvxomT1YqQlpaFL11GzeVWGZk5i0qrVfD94EAq5nMjYWL6auxoxzIsYIacioCUKFba3MuwAhrVrx5BFi+hStx4rl5/G3uCeo196SgbzP18FmD80cooQGWH60NgA5MJu5uPvidQEgYchoZT2NYcUZ+r1RL5MRo4LiWIMbsVf8Wmdeng5OHDswQOSfTLwvxyJIdad6DgtifoMujWpSavypTh64wbfb93KzAEDsse7FxREcNQrJo4fxb4lRwkLSsK5Q06KPSYxiXtnn+KhKkSvKd0JDAjG0d0Bd4qTcCeVcEk8TmVkLBw0glI+PqSkpzN12zaa16rFkO5N2L7tCTt+3EfFxmVJjE3m7tkH3Dhxl43Pl6FUK/iy2iQy0zNZc28+h9O30UzSDQC5Wyrd6ta2mouTrS1+nu6c3ZfEmUuLEAQRY4wDtqIplK1cvVIsHvYLRsPHv9cPrzlFxLNoen/dCbWtivlDV7Pi2mw6uw2mz9ed+eXOj0hlUnTpOhxc7Di64Sy7Fh0i3sKR8+DyE+p2qMbFfdetxtZnGrh98QlThzbhTnAER+8+fe959RlQj87da7BxyXEObPfP8fuJ3037EpNNzqDYyART+BMwtvlsVl3+Dp+iHlT9pByJMUkMKD8h1/04utuz6PS3JMYkIRpFntwKJPRRBDVbV6aX3wj6TuvMwjPforZVYtAbkSlkIIrYOtkgk0lJitNiY69mfNPvGLl4EHKFjBWj82Z9/pOY0no224KWsermHJLjUyhYwhsnTwfK1y9NbEQ8RomUIuUKYutsQ2RwLP7HA7h3PRij0YioTSLm5V1c3Stkj3f79lJsnXzyjFBRqGT0+rwRNeuXZNOZ2xy6+BCt90eWlf434VXYnTUB8wD4bvIUAgKDKF/YD4DYpCS2HLlEux5dKVu7JKe3X+T09ktvddBq7NWUrl0YQy7MpDErRFKhktN5dBvkShlyhYzk+BQmNp+ZH03yv4r8sMx/NvJk6KRWrpJc+/5pdu2D82T/md6XtzFj8VEJ2DrZfJDc9LNbgfSb3pUv7acwauMW3IxS0jJ0XLsThDHIA6nFLtQZTlw/lEyL6zNRqWU07NQUdagvooURlacqoCiiVcQiOGgR0+RotB5IsX7IA849JDUpLVvVzhIarQe/bAhgx4mb2NkoiYxKwRDlSKNOrtmGHZi8hkM7NeXnny5hZ3R5Pak8j9+oN+DilJPlK1/YD0GjY8iMvnQe3ZqBpceg0ij4Y8VR/MoWpmbz7oz9dRsxwa8IeRyLMsYDmZD7Yy2KRrTKGCT2KUiVOet32Wk01C1blgpFCqNxfogYnfPezPMj+0+tyfN3n/fHquv5uu8b7J9G68nUzbv5vm8nyvj68jIhgZGL16GPdEUOKAtGsGPoMGyy8oDqFS/O7MOHUbgFIoZ6YFMikZ3TJmc7Y3rVr8+AhQuZsmYNlYsV43lkJDeePmX19GFsvHaAqs2r8XJNDDduBROTmJjNGhoMBuZtO4CtzpMOY1uye9EhEl4mUrJGcbp81YabJ+9xOeh3/tWzN6V8TGILNioVCwYMoM3s2ZTx88OpnMk7717QlVFLB7N4xFpOb7tIUEAIHYa3JOJpJJteLCMpNhmNg4YRiwfRYUQrfpg6ldSMDJLT0rBVq7HXmNgpXaYejeAAMabnUqVR0ml0a3bM3YfGXmMy7N5yTRxc7ajSrAL2znakadNQ2ypx9nTiwu6rPL0V9NbLdefMfe6ef0TJ6kWYvGEEs/stJT01k7XTdrB22g6Gze/PqR0XSU3OYMSiAWwLXEpHl8EmURbRyMW9/vSd1oXIoBgC74WaLreTKUzzyo0Q9m9MYWCL6pS2d6H7xDoUKuSKVCrhWUgM85YeQ5ZuQJSAUSXF10ZN1/51adPNVEagaFE31uwfjVQqYVAdU16jFVth6czK2q43GIgOiaFp77pM7zqf8GdZjM0bDJ1XUQ9W35iLRCqhf4nRADTpXZcOXzbn4JpTpKeks2aKiZGUuFiI/mTt0xhrYq68inowef0XlKpejLO7rqDLFM2iQpbCKXmkEAiWuXiv884MFoIilmPYWPxtUaeVqJdo7FREBr1ibNPvszcXLumBRyE33H3dUDqpOb33OhFaA8XLeNOkXwNGLyjE5ZMPOL7vBufPTcfWtQROLqWIiriEJCOdiqWq0XZNMwraO6CUyhAiMhAEePT4PufOnGL3vg2s+REUBhecASe1eU4GB/NcJToLVk5ukcuoMbOzEkvWLcOiluHr6/Ye0QSCRECmkOHi5cjXm0dSqnoxtvywmya96zJ02Fimj5lCWvx+lEo52jRoUKUTgbdDOLP9Us79kfMb1H96N36duZ2fi/qzZfwX2dtT0tO58zQKueBL32ld+GPFMWLC3+70/FC8MyrI8v7+d3Qacsu5zaM2aT7yAf/Pjbt8/HcQcOERVZuW58xvl9+7T2xEPCNqmpT3Ri4dhI27CrlcTm870+JPEKBweV8CA0IQRZHg0GDUKjXubh6IosiZn++9c+2badQR7/GQupWLMKFzL7Rp6Qxb/AtSUY1aoeRVlA51vBdfLRuCXCnLPVQD0KS5oX8G8YAKF1JJxtvVKUe7Qp5uGKV6eA/7oWSNYsi32+YIszxx6w59Rvalef9GPLjyhKE/9sPJ04HdCw+yZISlN1qNDT55MpdG0YiuQCAz+rakReUKjNqwgdT0dDQqs3DAppMn6VC7NvO2/0HPgT3YOefQuyeej380pIIM24TSHE9MYc3OfXgXLciC3ZtwsHVEaaNgWOOm2YbdawyoXZujAQ9JFuMZ2MjMsoMpHKx7vXrYajS42tvTuFw50jMzqVKsGPdeBHPoiEl8QhLqTZeJi6hYtgB2GhXX7weS8twBtSAh/mUCto4aEl4mkpqYyo3jd1CoFDg4KalZooTVXARBoJCrK7N69GD10VMs/vFH/lhRke4TOnBk3WlSSER0imPZgnl8NmM4Br2B7t5DOW7YQYcRrTj/+xX2/nyWuzXO06hCeeK1WtIyMmhRpRoxUQYUgIu3E7/cnY9So+T53SAG/dCb2Mh4ts/Zw8vQnIvFio3KUrtdVaKDY7h1KoD4qATUdmqkEgh/FsWEdcNJjE3m1ql73D59P9fiyK/x+NoLooJecn63Pxp7DQ5udkQ+j2b52I3ZC8tJLX7gm22j2Re3jqTYZEbXn07400heBIQwc+945g5YQcCFR1bjGkWRMgXdaV6lBJuWnWbhz4cBaNurBnP/1YWpk8xKx3NXD2DNgqNsX3eOmPuh2dsPhyyk2/Cm7Fx24m23WDZunLxH0151sbHLqUrp5OHAd7vGUqJaEQB2/LwfmUKGXqfn9PZLnLZY6L8Ltg5qfjoyBRdvJ5LjtawYu+G9+34sFCjsSuNBdZDJpRzfetHqtxd3Q3hx1xSOKs0yUEVPV6IjE7hw8gFOdioqVC9M/5HNGenUkcSkRPTGVIIfF6RWw7LExSSz6NEdLoWbrkWJTSloM5/Qol0NJn5tqg+3Yd0qjmw7i53O76876DfQZ0oHOo9sRY8CQ9kXs5bE2GRCH0fQzXsobj4uaOw1nNl5mcArqYgG8z1xlqtvGTUnUpJSSU/UERcgoef8VXSpW5mXCcnsvxRAu44DcHN1JyNN99ENu3z8w5HP3P0z8E7Z73coHL25/V34X/GU/JncwutH7zB6+We5Gnfvc96sjRYzfj71LcObj8OhSAItqpYmMSWN07dfYAwrgEKifCvbmqqMx6eynp+7D0UCzNq5k/DYWEZ1bkmX2rURBIGnEREMnr2OGq2r0KfQlxjfJwFdkKDGnuP+AQzv2NrKMFuz/xQanVMOg0sURVKdwvHyVaJSyAgOS2JojXGMWj+QiavXML1Pd+w0GvyfPGHWxl3YpBbiyOwruPu6olQreXY76N3zegMpmigWfdGFmiVMypZTO3Zk3MqVtK5ZkyJeXmw5fRqVQsGS3w/zPEBH81Y5jdU38cFyzvl4P7xHPl2e+BPlFF4FxeFiaMih7be4zyOOzRyPo7s9Ped/gj6XUASdwYCjsz3agi+RCGVy/C4RBORSKeX8/Nh14QJNq5jqzjWuVJ4Vq89ji7epll24LwFhmRjRohB8UGMy1hzdHJj463BEUUQqk7Bn0UGSE1JJ8ErhUVhYNnP3GslpaSjkcka0bUHYyzguHQzjybVnFO9gRzE7O75o0Z/E1FRm7tzI8Z1Hs8/BtLazuXDwCuUaO7Bu/PDsZzfk1SvGbd3KsRfHSHyVjIOrHUqNkqc3XzCq1temZ95WRfP+DXH0MDmfpDIpotGIawFnzv1+lVXjNyGKYva+El8lmq6NIOGnwSsA6Dy6NeXqlkSulPMyNIbnt4MpVtkPOydbnt58gYefOx6F3EAU2fB4IRKJgFtBVyKeR7F/1QnqdapO+bqlSE/JyK61Zudsy/oHC9AmpPAqLJbPKk+iz5SOPL8fimUAmn1QJs/vRnIi6C7XTz7Kjl04tP4yFQp7cPLUFJISUpn/Uy9cPRy4uO9WjvfhjTMPqdaoNM261+RVeDw7Fh7m7qWn1gyFaL4fdy08RNjTSAbP7M6xTRewc7Hh0NozDPi2C11GtWLe0F/4zM8NpUZBlU/K0WN8OxYOW8vh9WfM95abWQ0yoa45lFitNeDt6cSyOb2wd7bhwv5b/FDJXC9UYpkrqLEwLi0WeKLWQk1YaSFnn6VkKVhJ/Vt8azLMOYkStYpew5qQqs0g4E4IAf4vMBqMVvPOTUHUqDLvL9hbSXB4BPtXR6CONrFllaoUomwxT0Z/sQ69zohBLadoVvuwGgoqpbryxZejssf4/IuRvHj6jJBzqcgzzNdDFmlxF8jlpOoSSba9QUE/T1JTUnkVIuJlLGc+dsvcOUu8zm20ep+YnTw+xTzpPakjT2++YPertcgUMq4cucuSsab8689GtmLx8LXoMw0IUukHfUssv/NSuRTPwu406FKTVp81JS01lYM7j1CyQiWMGxI4sOgUKo3yraJH/1HktU6y3G6VG/3n2Lj8XLw/gXzjLh/5+DDIlaaY9rygFzPJcIvEu4At6RmZxIWKaLQe78xN0zgqcSuZzO5JZvn/r9qm0WHmaogokmc/o2jEvVQmO6aMz962/Msv+XzJErrWqZO9rbi3N0M6NeD0sZMoVIq31p2yhCAIxD5R0n/WIr75tCsalYqFO/dz/3oiasEtR/t09zAWT+hGtSxDK0GrZcKuPdRuUI89C0/S6ou5yGwzcfNQ80PXVsRpU1l+8BxPH8ej4v0EWkRRJFmIxyhkYmd0QeOszzbsADwdHVk2YACDV62iRZUqJGpTidLbUdqvBjVrunP01zPvtZ98/PORW95Y2R4uSB49xsPTg5jkZFztzMqLc48eZdbo0QSGRbJo2xYGt/wkW0BFFEV+u3CBKT16sPzAAa4/fYqHvT3HLl8mJCYOvdZaYOB1wfLXKN+gNC/uBnNw1XG0ianYOqjpMqYtG2bsRB7py4SNm9k6ZjR2ajWiKLLsyBHaVK2a3b9n41qcOrGXP349RIP2toxt3x0AjVLJss/7033uckTRmWnt5/D9gSmsXbGChgqF1bvH180NT7U9u+bvx9HdgZfBMQz8vhfDqk3KbpOmTWffcrMK4msIgsD71o7dvcjEjAsSAa8iHhQo5smZ3y6TkpBC8WpFeHEvjJchOcWjFpz5lr5TO7Pj5/3ERibQqGstzu/1R22rJuDiY24euU2dDtU4sv4MgkLO3uXH+XJuH55HJHHmSACJcaZ38+YdVxj+eWOuH7xvNf6c6XtZNOcQaVl12BxFY66Ormm9luJTzIOi5QuCwcBXC/oxqOb0tx5z8MNwStUoxoOrz+g0ogVf/mjKa/Y/cpuKDUojk0tRaZQkx6cwq/8yvt44nK+WD+bBlaeMafyd1Vgt65XmyIWHXNo6FqNRRATWz9zD7pUn3+v8f2w4udvT/otmnNx3k7AXryAzb0b2Q3H7ZjABl3LWUQTIiImkZauc9YFbtmvLvBOrcVbY5NIL9IZM9F53+W3VWooVLYooiqzfvJkVi7fgmlT5T83T3tWOH/ZOpFgVPz6vPJmwJ5FWZSUA5AoZ6ak69Jn/vnPQaDCye9Ehek7swOoJmwi8Z2JEq1Q2vxPeTK3IRz7+F/CPM+7eS+kylyKvH1pg84O8H39V3s6/kXPzrtjwj+LtyZpfs34NObsz9zAaURQRC4ez54cxeDiZ2KE/Ll7lx+XHUcd759rnNU6dPMFXHZpYLcTs1Gpqli3I5fAM5OTuZUwmgSFNa1lti9dqKevrm6Ntw/Kl2RL4jMSYvOW50zQxOBTU4+Jow7OICDRyNS5KFU8eptFn3HpkUgmyRCfUggdvxmQaRD1lSrtkG3YAjra2tClVkvq2LbEzuCIV3SlbUseyz/tkt2lZqTytZyyDkLyNuwwxnUyHGHSkk6mJxd3FHp3BQEpaJIZUOYmpqThY5lNIJHg4ONCzfn32nr1F7K0kQsWcKqH5XsFc8D7KlVlIkyWiKpiMp6s90bHJpIXZoNY55hznPcd761ysNufyzFu2zfrd2dOB1OR0ECSIokiCKprkSOjYejBNKlVg5rp1uCsUuNnYcPb5c7q0bUuZwoXx9fDg8JmT9Jgzhx4NGqCUyTh+4wZuCgWfL15OMU83elSvRovy5QFISEmhf8JGqhSrzuU91zHmci+lJKRQoLgn2kSTYoQ2IcVUWkU0YoM9L28aaPHNHHw8HEnOSOXz5s3oXMv8bN8LCkOSqSDTJoH+n7SwPnRBoHKRghy5Hsv9C4/I1OnZ9tPvtPvhzYIH4OJijwhEPovm05k9AbB1skObkLfTCtFoJnReR4/kUe/rzWsT+SKayBfR2f0eXH6GIBHM7Syu24t7ofiU8GbnvP0AFCjqQbWmFRjT6F+mwtgSgaObLyDIZQhSKXEvk1gydhN2fl506F+XqJBYjp54hJunA9EPo5HGmRWKX+fI6RSKbB4m2cKwE1PNSh6CICHsUQRhjyIQdToUUgkztw7jm97Ls9sY32Cp0rXpqDRKNnz3O5u+38PYlYPxLupJjZaVCLjwCG1CCnKlnFLVi1KpoZkV/mHACgSFHK+axVizw1TDUyIRmD6sFQkp6dT7YSUA7jd1ULEQAIqX5rlKUszzEDUW9eosviUSS+eiFbOSdd0sDVyLQtUIAm3610OpknPjRABhWQaGVb0/y7Et+75h+AAo4y3q7VkWCbdgGnQO5jGc4+24d/8+LZq3tRon4M5dVLZuVscrWBhUEYnH+WH0eIoVLZo1RYFB/fpx7MRJ4k+nI5FI3lg35ZI/ZvHzqOWfUbddVQ6uO82oT0w5hoJCbmXcFS3pSetP63N822WruoGWrOiHsnhNetZlVu9FVu+Tszsv0Xl062wHyr+NPHKfc/seWj3bedWwk+R1r30Ai5fHNyhPvYF3RGH9T33b/wcEVf5/H10+/is4t9ufVoOa4OBql+O3FHksX/VulW3YAbSvW5NCJWyyasSZkCpqSfcMQywcSbJNJEbRSGqSNkf+D4BaIUfMMqL0oo4M0VoBS4aMV4mJVtucbW15FhmZY6yLz55TrETpPI8tVR1Ljx6lOPjjRJrWLMWYbs05PmcC+74bxc4fBqFx1mGT6I1K0Fj3s41GXSYKn1pa4jPieRhiLQFe1tcHVCZPb4ZNDKPfkHVWyGRUKVkAvZh7eEmaKpYyTUR2zP+MUuVtWDJkIMenTWPPuHFUL1UUiUMmXX9aZMUq7L56lVqlSnHg6g3CAnXvpeqZj7ejarMK9JzcMfv/qUIyVRrbcHzBFDZPG8Gx+ZOp3cyJVGniW0b566BQyek1sQN71hwkxfkZNhWD8Kioo34Z08La3saGn0aOpHeXLoiurvTu0oW29esDcO72bWqXKEFdPz8KKxR4AAu7duWnbt34pGxpkuNSsw07AEcbG8a2bcyT4ACaD2iU63xe3A3Bu4hH9v+lMqlVTpq94IRNcGni/b3QRskpXcAcohmv1bL1zHVsDY6gk/M8Mmd9uAwFLDo3iz1xv7J89DrSAuUs3WO9AMzIzCRWZyAuPJ6Wg03OpAt7/d9u2P2F2P7jfjJSM9gevJwV/rNYO207Rcr7UjTLqAEoUMwDO2ezI6j90E/4fFoHpFIpugyT8ZCqzcCzwLvDr98Xx7ZcwNbBhg6fNc6zTXx0IrfPPuBoykZK1inE0iXz+G3XKqaOH09odCASqQSFSo7GTo1EKqFP0ZG0dBhEbGQC849NYe1vw1m7/AQt68yky+yNVB27kEZfr/xox/Bn4erlyO7Vp7l3NXd27T8JpcaRs5evExT8IntbaGgIp46cRaPIKZyV3c8F6tSsmWN78WJF0YkfpiI5Yd0X2Nhr6Fl0NJt+2Jvj96Jlfeg9phXl6xRn2aTtPLoR+EHjvw3BD8PwLGyt7Pzk+gtcvJzyv2n5yBsSycf59zfFP4K5s/R+vlce3Wtvycdi6P4uinofSznvA/Ahtf9ezy85Nok9Sw5Ts00Vjm04a93GJoPqpYrn6Fq4gDvhJCNBQZoigcqfODBn+AhsNRr8Hz5k3NxfqVZtHEu/n8avIwtn99MbDFy4F4gUH3QFQqhSrgBOtvZcCnjGq2cCchsQUhXsPHWNwc2bos4yDjMNBqITElhx+DBDW7RAIpFwJzCQs8FhTOj9BbAj10O0L6hjZMfWGI1G7gcHM7evuVyCr5sbX3X9hB+XXMc206zklmobzfBBNend2FRny2AwMHHDBiZ364ZbllrgrvP+SLV2IIBoENCm5wwlScvIRCCncWsUjXiWNrJyzFAOX7tGn7p1aZi1OHeysaFD1ao8joriZXwiDSbOplrFojwPDSU9w4hK0BAbBhqtt1VuYJ5s3d/lWfhvI4/zULNNVav8DplXAj8NHZK9yBAEgdmDe9Ps7lwIdch1jP8oLD28MiUdhrVgy/y9CJ5BHJw8HAcbG14mJLD4xAk61DaXByjg6kpUfDxNsrY9Dwtn1totDGlSh8jYWPYnJeHr6kp5Hx80SiWlnZxI8sip116xoA8a1yBungjIdXqCAEq1mYF383UjOpfQRABFlA+ffbeVAr5qpHIIT4yhbLGiJNlpSXuQyYJdx2hUvgyarGf+SXgE4Yl6kqJS2DjjNw6sPolGYs+pk8GkpK1ncNtPCI+NY92J80yaPZs7hx9z40QAyfGXafPZJ9RsXZmrh27lfW7fUTM1r3eplXpzXt59CwYgJiyG7T/9waglg3DycGD2gckY9Abmn5rO1SO3cXS1RyqXUqxiIVram4o6+5UuwLbZewh5bHJoSRzsSItPIiE0Br/CrgQ9MtXrE+1NBqGgt2Ac5DnzzwBIMRsAr/PYxrSbx5S1Q2jUrSYLp+wk8Lz5OgtZC6FJbeayM2IJqa6BrJ8xCWdHRzIzM/lp4yZUtgqqqWug1ChRqOSMWfk57r5uGDHiV7IA82fs5ei+WwjAo4RYXgdrpPiZ5huvNd87KY3Nf7veMd/36hizs0DING9XJFoshyyf79yedQtWrlBxT5KiEyA9w6ysCYj6PGrEWao6p5sYRanFIlH60hw1YvAwvyMynMzvfr3G3N7xXDBG0Z0x/Ubh7msy5qKfv8ROWwiEeAQ7s5FvyeKlx4tcuHKFzu3bW03vydNnyA32iOI7atwBXkU86fN1RxzdHNk2Zw8KKWSk6azuE6VGSf9JbZk1fC2JyUko5TYICgGSc1fTfl8mySgaSSKOJDGOqKCcheFfhcfh4GZPwsv3d6T9O/WLP6ieriU5+R419F6zeHmek/dULc3H/w7+EcZdPv55iAp8SYsBJg+uKBrRymMQ5Xoyk6QcuHydoe1aWrV/8DwMGa4A2BVJZ8n4KdkL4hqlSzNlSGcC7t/kWYCRfovWMqRlPRK0qaw4dIG0EDcE73B2/TAcd0dHjEYjUzdvxreJO22qVcP/0TMW7ThC02nf0LhiBWyVSh6HhaGQyfjl2Al+O3ILlUJBfLQRmyRPZNPzKDwKONmZGDltejpu9vY5fq9bpiRoLoDF98TFl2zDDkAqlTK+Y0d+PXmSsR06sOf69QWbrgAAIABJREFUdVJcnPh+6yBm9VmMrc6TTXfuUNmvUPY5iE9J4e7TaBRCkaxzKpJMPAYhE4koo28dU47E1ceP+bZTJ8BUa2/i9u20qV+fXTNncu7uXZbuPUD4LWfSI02Pfjqva9O9z1XNx7tw+/Q9JBYfUXs7JUq5dV6ZTCrF1dWWhNA3e//1MOgNRKc9Zv2YfjjYmBZk7o6OqAWBC/fuUa+cSVzh2qMn/HH8NifPP0OtkhMbpcMQ78Yu6TmWdumMr5MTD6OiGL9tG3N79OBWeDShsck59vfHtdvcPxaBrdE5x29gEgR5na9boUFp6nasweG1p3Jt22lEax5efcqtR1cpV1vF2W9mZT8vh/1vMmPRQUZs3YMYn0RGpoHgIC2aOB8iakdibxFVoE7w4trBdC6c3kLlehXYfHw/KQmpXEm+w+aZv9NtXDuuHbnDmNVD6enzRa5z+avxKjSWxJgkBpcbz+rbc7FxsEEqk1KzZSW+77uUiBfRLL80E4CS1YoQG5mQbdhZQiqXUqxcAeq0rMDWhUc+ytxmj9qMq6cDkxb0hsz2LBixjrAn1vue/fVsvh7YH+es8jFyuZwpgwbSosdIhKeepnlXL0Kzfg2Z/9Vm9Do9vz2dz7nj93Ps778BmVxKsfIFqVivJA3aV2VMq7n/1flIBCkOKUVI9zcZTA74IKjevrzzSq/Ez4sWU650aUoUL47RaGTNrxt4HhCBqyRvxg+g0/AWKNUKtAkpRDyPZt6QXxj/y+d0HdOGGV3nk6pPJFN2FxtFJsGxcuZMe0IRbx3uUhnXw2J5meSOhpz56O9ChpiGjgwkciPu5Q2M6tgUvWjEv1QYaQ/tURvNhqyrt9MHGXb5+B/C/0BYZr5xl4//CPSZBkIehlG7Z2UuX9vDt91aUtjLnV+PnmP9gROU9C1Ao4rl0WVmMnv7DkKCEnHABQGBArmEU7SsVZ0xG39Dne5GxE0D4+6eQzBKsTf6osBI6VKe2XXmdl68SLsaNaiTxV75urtTt1wpOo1bwsGzd9E4CmhUSlKSM1GF+CEzOqEH7IAiFQsRExGf53FFRCeRkZmJnVrNq6SceXnHb92DFOuQTMdcpL89nZzYc/YGfxx7yA+bZ/NV+fIs/n42NQbaIyrkFCpVll5L11O7iC+vkpK5cDcYIcJU6iBDTMNQLITyxQvgZOfCg8AQrj55xMAWTSjg4sLTqChKeHmx5vRpRvfsSdEsdcF2depQ1q8wfUYuwY6COeaUj38fl/Zds/p/XEwG0fHxVmHI8VotaZl/D2v60bUXeBWxpbCHh9X2rzt3pu2cOXh5exMVGUt8oAyZ1gPRMx6NjYyXqgTUZVSUKVqcgJcvKejoSGlPTyY3bsy03Xt4ESWlTtOmzNi7n4mtmqNWKDh9/yGr91/FxlAkT2dCYkwyj/yf0W96V57efMG6aVuJT4lDLsqRCyYmRiaXUb1VJT7pUx+3Yi4EbzvH7AFDrN4ZrWpUYU+9O0jC/Xh+JwgADaZrsG/ZUVq8ERaqFFQo07x4fiyaMfWn88W8/sRExNF2aDPaDGmKxk7F7H5LP85J/wjwP3IbO2dbdOk6evoOx97NATcfZ4Lvh2NARG2rIuJFNJseziPiRTRTO87LdZxrpx7QqF1lVJrc85VrNilNXIyWwMeR6PXvz9rHRCUyodcKPNQiXy0dRJo2g1un7/PHapPYydUT/swe3t+qjyAIeHg6Mf/QYoLuhzKt4088vvYCSZaYT+CDcHaemcTLyEQGdVj83nP5mHDxdGDNpRlcPHCLK8fucmTrRXYsOPxfmcu/C4VMhe5ZEbr3GUxBX09SUlKIeZqBd3rZPPuMWTYIpUZBiUp+nP39KnfPPeTx9ReIoojRKPJdj4UkG2Mp6hbIslo1sFcoCEtJYdDFi7Rr2pTGRUzOya+PneP8nQRspG83Il/DIOox+ITTsHoxivt4cOTWDXo2b0r7+nUB6Fi3Dm1GzUD/wAZBEPAu5knE8+h//yTl4/8v8o27vxa5JZBbha3klujMu0PH/mxR8v8VfChl/zZxgNc4vukc3s307PtubHY45OxBvej+83wO3rjOgj2/4+PszKAmTWhYqhQz1u1FDPIlNj5nuMbd5y/w9PLhAXFIRCkOmR7Z+zaKeuw05qTte8HB9GjY0Kq/l7MzBbztSLpbiIzodNIwoEGTw4iUSKW5FycWjWjFJNL08fT66WcWD/mc6iVKsOLoUYY0bYpUKuVOYBAr95zDJtPPavEaGpmA3mCwqgd26k4AxihnbDI8mNlyJQWbpfNTx3a42NlhMBr58cARQu8IhPpHI0WOUjAviDP9QpjYvyudso5RFEWGzZvP9adP6dmwIZ/Om8e2UaOI1mqzDbvXKOLthau3igwLh2Zuz8Z/NLn6A8RI/rb7f88x5DEeDPhpFUtG9KeYtzdB0dEMW7iOlPuudB3eAkd3BxQqOTt+3EdynPbtY+e1Pa82uYX7vLHt4bXn+DZ0535IKGV9rQ3+sgUK0LZaNSau3I0itQCV6wnMHzyaZadO0atKNzq0agXAoWPHmH/yJONq1cLXyYlHQbEQXYGz656QYkzn1MXVyOSQHq/GNr0kgtI0h7xErq4duc2N43fJsInBrVAKLUsX5ml0LDcfR1CpeHNaftqYU1svMLXtHNKcoihV1h1nu5z5vbqkNMKzDDvL4/cs7EFGWh5KhoKEJzcCAYE+U7vw9MZzdi86SO22VRnwXXd8SnpxYOVxi2m//TkRjSJaaSwy12REg4AY64KNYJd3KNZ7vIdf71MQBJQaJempOpJjk0iONTmbjKKRl5lPGNm2MzK5DHvfQuh0IJFYfO6zwr6D7gSx3v8J/b/ugKhNQaOQ8MvFb3kVlYh7AWeiQmOxsVOh1ij5Y+N5fl9hwaJaztXqG/1/7J13eBXF98Y/e1tueu+UUKX33pEqXUAElK4IIigWiggqfqXYQKWDIkWUIk06IiK9t9BCSEJ67+X2/f1xk3v3koQkCKL+8j4Pj3Hv7OzM7O7snHnPeY+1b/Exqcx4fhEN2z/Dx5sm8/oXL3Pk51PMeXsOofcjqV7ZKmoliiLe1XzYungf4xcMp9+Ebuxadsji6jix9WwEb082nPyA1yZ34ZOLFy3n6pPM1zdKSHKjnfXeZFSVJCA3WAs5Rktch/OsbqZijvW4mC+kIsjlpEZoOLbtLDtXHSE8OJr8G0GRkB4vSUE1Nd36t7vEFdPD6kapzLI+sw4hEjdEiSqnjTJlwfWlLv5O1o1G0dsdR9xxpAqGaxnYAYEyEAWJCI70HVUocHCxZ+u3B4mPSCYzNdvcL4UCB3cnmnVvSI+xXfhly8esbdcJRf5zUMHRkdVt2jDit99oN24cSrmcWZ1a0fPuYdRKPzQ5WgSJ66/FtVUyJ+h9Y9jyyUT8PcyM/8iunfh061YaP/MMFX18kMvl9Hu2ORtv3qSiXyUGTunFmhk/IprEx+KOaLveeczhMWUItymVuFlp2lTEN8tmnMqXxf96/OOMu3L8t+AkmCyGHUBSRgaNn6mBUi5nas/xBHhY3bPWzfRh6Iy1aO/Z8cOeQ4zu0x2ArNxcpn+9lrlffsPvC68VuoZSUHHxVqTFgBIEAb3BgFJh+3hr83Pz2AmFFcoAfCp6seDgLFZN21jotywxjYAWOax4/X0UcjkrDx3itytXSYgwsOXANdR2ctISRByzKhcyGPVRHgz99GsWTRxBBS8vfrtyjY+/242TpgoIkGUXzXvPDsYzf4Eql8mY0fc5jp37BmOYNT+SXtSi9Y6hYiU3i2EH5kXewokT6DrlHer4VkIQFXT73/9wdnbGZDIhkyy2TCYTeXm6ciWlvwlKQYUm2IcR09ehdgVNJqjTfVAJSpzcHDn0wx/kZOTywrt9+X7WT0+ljercAGZu2MGy14ZTwcuLPK2Wz3ftYkibNjSrXp1erW5z5HwIX46dSlZeHll2dgzo1ctyfu8ePQi+fp2k7GwclEryclQ45L8DjjJXxHTzc60EEAqvGuzsVQiIqB3tyMnIRafRM3BWN5KCD/FB18GWcrfjExi/7ABtkpvhX82X8OAoEhMTqVDNi/0XL9KrWTNLWY1OR3hEMioKK+LeOHWHZ5pX46Nf3iH45B22fbUHAM9Ad7LzstgZtw5F/twhmkyo1Cqa9WgEgIuns41xVxLyPCMZP6gZIzu3J0+n45Ofd3LyzyQcNL4ln/wAfCp50XN0RyJuRnPz9F3Cg6N47/uJfNDvM5tyWo9QVr/yHHUD/AEIS05mTs4FBg6axPYlB4v0TAio6kP/8V2YuHAYv205w5eztlt/NBioXNOXsTP60mtYa0wmkeCz9/j6rfWlbntidCqJ0SnI5AIB1Xyp5t2Az3/8kf9NeA1vDw+0Oh3zv1/LwGHD+KTnKtQOKuq2qcmuZYdQO6h489vRuHu7EB6WjEFnoP+odjbG3d8Fk0lk0+e/MvGzl1j/6U7Cgv8BvtV/I5a8t4lOg1rQuENtHF0duHHmLmcPXEWTreXP7edwe0ZBkLudxbArQGUnJzxUKo5FRNC1WjXsFQoqVPNi9OtDufT7TeztFfy++RTa3KIFw4KCXC2GXQEm9OjBD0eO8M6wYQDoDUYEBPpP6sHq6RvR5JSnQCjHQ1DO3P19EAQBQZG/q1YMQyfdSSqOrStyR+O/xtA9BXGVMrE5+W1KSbVdSNyJiaFprVqcuHzZxrADqOzjg6ePEm2ID6tXnmbzvpO4ONsTG5OBLM6bgCA/Pj9izqNUYEA1zJfLbm/XlwFzvuCdF3tRv3Jlvtqxk+kvWBeGv54+T2p0fnzZA1Ao5Cw4PBuVvYrLv1/n0A9HC3cnKIEf3nofl/xUAjMHDWJCjx48N2s+isiKFrfOotzN7EUnki+pGPLe9yjsjejT1DhpqyDkj5HK2UDtQNs0EIIg0KxNTU6kxqF0zUIhF8jUp7Ft1lTm7tpV6Boujo54uzsjRyAuNZVT//uQP2/dYu3evYzr29dSbum23eRGq3F8Gqk+SmKSHvc7WlamqyzXL0MdCkGBU2YgZGLOUpj/jGz9YjevfTmKle+sQ+2oLrqOMrav2F3qgnOl7E3+x80n0Iec9GY8P2s1Deu7UsHLg2Ht2/NMYCAAvVo04GJIOAq5nFsxMTTJT0ouRfNWrQg+cYLNF28haKtYRBzs7FX4BnnTuF1NXNwczekCTCKCAAatHplChtpeRV5mLpmp2bh4OIHJRFjaWaa2tU1fUsvPlxo1ndHk6rh/I5qqXTyoZqzB5D49+XL7djJycujfsiUhsbHMXLseU5SPeayLeO62f72Pms2r0bpvM35ZvJc89yS82jvQrnoN3hzxCi26tmfUuHFUbVCZqg3MKpSxYQn4V/GhVosa3D5/L3+8i78neWIO3dpXZUzXTgA42duzcMwwBsd8S+YtAXdfN3QaPbnZGto/3xx7JzWHNp4AoGbjIMb970W8Az3QafUoFHICa/hx9dgtqjaozBuLx+Dq5UxQ3Qo291wr5tG+np/FsAOo6uVFfScVKz/dwMQ5o7h3LZIbF8IxGU3UaFSZoDoV8K3sTVJ8BvNfX8efey6DXDJmGi33r0Xy4fCllnQA6y5+yqL903iv/1cYdA8Ib0hTBsgE1l+ZT2JUCl+/+QNtejdBrzfy4ca3UDrLWLZyJdrcbASFnOs7kzm90qx86erlROy9BN5cOpauL3cg4kYUzzSrRr3WBhQq8zM8yOTKyf1mwZacBub5U5VuNQ7yIiWbio2tzKIy27qGkEdaFVWNKcW44xe8O/nsZHxIDFu/+JWp34zijTazbVgn2/Mkc6xUREWyqLR4JUk2Ik0uVnZNrrGOpVIiLCRmSrxbpOxaEQtWwc7qcitI7pWmopUhVKqtZeSxkutorCyeIMjITMtl95o/EI1GKj3jT41GQSCXIQLL39+Msn4S8vxcj9JNzsS8PHSAIf/Z2HbrDmnRdoRdvU9aQhqdJ3bjzrlQwq7dL9R+c7cKz2sqhQJ9ft/1BgN7zp7AYG+PwWiwMeyK8jIqjedRqWCz7pQ91vpsUMQcJhQnDVDcWriEum085ErW0vl3ozzmrhzl+Gu4H59EeHw8VfzMQfLV/f1ZeuQIRqOxEKtkMBrJ0GXgUtWASoDUKAF9tjNqwQVBJpAan860bmaRAK2Yh3PtXJp2qAl2dvQY2YmcuDxmfnoY5EbAxLnrX9KgWkVCo+MJu5ONQ5Z/kcaXh787Rr2Rt3vMKbxQyYe/l4vFsCuAu5MTfl7O5EQWeYoNlIIKZUYgZIAabCZrXZaSa5GRNJDk3RNFkVtRkUwc0ZghbVoiCALnQ0NZceAAiamp5OTl4WhvXQScvnGDQS2aM7ZrVwZ8Oo/wxEQ61K5N8vnzTPv2W1xdXLh0N5SE6wocdWUPZC/H44deZ2Dbl78y/INB7Flx6Km149qJO/Qf35lWeU24e303Hw0dYvP771dukZSsIVujoaqPDxtv3uS5Ll1sypw4e45Du0+hyKtO+26tadixDnqtHk2ensTYdP7cc4W0xEwbFzKFIGLQG825LyWuY99dXsDpj3agrlqZB2GnVlLxGX9+33wcIyFsmjMVgMWvvcapmzcZPH8BKam5KMIr4fBAOpIHsXDkEgZP7UO7CQ2o6CtjzKD+lt82HzjA9PHjEaKisNNqybB3pPOwsfQNGsBXf3zE/ZvRhAdHsvF/vxAbGg9A42fr0aZ/c26fC+XY1tOkCuFk6arw4aZNdGvUiHb5McCt6lRl9610vru6CrWjHfdvxRBxM4a87Dy6j+wAokh6UhZL3llP1O04ZqydSJU6gZak0AWLNLWDik3hS3l1/nBWz9wEgEbMpUlQUKG+tqpWgV+O3mHFzJ+pWNOf+q1rgAAhl+9z6uB1Nn6x11zwAfGf4jCq6Sxa9mjAksMzUdopWDprK5f+vFOo3HMj23N89yVWz9kKOj03Tt0FhYIf/rcTF08nDHkacjPNBoSosy5Ety3aw08RywE4+MMfpCVkEHM3Dr0Rtn69n+dGdWDGt6MIPhfGTElevccFpUpBxVoBOLo6EFS3AjdO3ib8Rozl9/c3vMHbz859SA3/bTTuWJvm3euzfsFuyzGDqKNli/q4qusz98ABZjdogEwQyDMYmHbpEnKlkoouLnx+8jzn0kFM9WDXit8AuPqb2SOnUq1A2vZrys4lB8hJtxqwsUm5pGdn4+ZkFUxZ/OuvOLu68t3evew9dYpGlSvRq18jlhz8DpPoguxv2vQuRzn+ifhnGXcCVsZOGk9XTGxGiUzSf42tKw7F7PT/XdK3lhiQIq5nSndi+a5duLi44OniQmhMDFq9nvr16rHmt98Y3727pezCX7YzomMzxncxq2yeCQnlvZX78NLX4bsbiyw7oVpjHh4NUvnh7VdxdXQkT6vlk337GPXZ+1xpegtDrgFEExmXRH6/lIYCJxwEl2JFHDwCPUiOTcOgf+B5kYxlUkY2Wr3eRvkwT6slOSOHwnIppRo0y58O2b68vXIL698ZS4CHBzqDgfd+2ExVb29elLAXzatX50pEBG1r1eLlTz7hk1deoU5QEAfPnePngwdZM3EiAEsnvMZrS5eycdIkBjZvTr8mTZi/ezcRt7LwyKr50GZZdjbLkEi2zHjSrHMpYs2KbMuj4i/UER+RyLo5RafdKHXdpRnDgjJFzJmZqdms/8TshpfrEsOB2lfo2dTshnj9fiSxKjkrdq7l9WnT+OKlQeTEx3Pp2jWaNGgAwKXgYC6cC2f0q7Nx83LmyvE7fP/ZXusFVPmsgNoOVNb3x5CTCwqF+bU0Wvvp6u1C7L1INikVjJakY0jPzeXa9VhCxyzDq6EzQ7u3sXZPEGhbty4T+/Tm44//xFVRTJqJB8Zq2+J9yOsmM/vHxTbH81JS6BASQm9/MwMmiiLvLf0Ck9aOVs82Iy48iZUzf2LCgpdo0LE2vyzeR6VaAXw7+Xsq1g1AXzOEgRWqMKN3b+wUCrZdvMhXd+/ydv/+pJl0TF/1Bk5ujrzSbAZRt61KktLEzphElE4OeFfwZELrfI8FpcLybmryDLxc8y12JKzi9vlQzh+4giHHhQNXQxjUpKFNf3ZcvIWjyRVRryfyRiTR91Msv0kl+wW3fAVgaZyWdIdbwsqdPXKTs0duMn/zG9RsVNli3CmMBtr0qM+wt3ri7ObAGx0+Br3B0u6CaTgzId02dYBkzvkpYjkXDl1l5nPzbPpRMD+tvHKPU3su8sX+mQwZ25bNi8xKn9JNAhcPq1iHywlr3aZ0a8Cx8QFPoOqNq/DSrIGkxqURF5ZIUkwKt06H0PnFtgyp6ElANT/CgyNJikrmmaZBxIXGlplpF22eQbNhW5BKAkAmcU20S7GKdtmwddL6ikm5UMDE2NzfbGssoV205IOotLI2ors1flXIkrA5eRqc3R0ZPKkbIedDWTXD1o1cLsiJiYpj7vff8E5MDL2PHsVPrSZeoyHbKEOTFcCrm4IJ9K9F3+E9+PHKNgpmI9FoRKlSMHhqL26eDqFWi+rcOHUHtZ8Cx2pacnPdeXbWB0zp24caAQEcuXaNyr6+NK5eHYPRyG69Hpkg0PaZmqjkCt65fBBnjV/hsSphzViamLbi0yUUpCsoxRqsrN+jsuhIFINSpVz5/4Ry5q4c5Xh02GcHEnI/hjXjuqIzGvFr2pRFh4+y7egfeLu7ceTaNeyUSkJiY6ns64uPqytvb97M5M6daVWzOh2bVeTPfSnkZuYyqeX7ABi8Elnz1msW6XZ7Ozs+6tOHuStWEFSnAqFXIgDzgk9VRF64ByGTycwiKg+BJtyNd75fy+JXxqGQyzEYjbz9/Vq04e6PZtxJIBcUGMIqMviDdXh4KcnO0dN9+EACtbGFyraoXp17SUn8OGkSKw8dYuyn8/nwxRdYO2mShQUN9PQkO12k97yvqOjjTmp2DgqZD22f6c2tM3f/YmvL8V+GfUYQc5ce5xvfozg4qwmoXoM2zfvh5emDg6EhYxb/gpOzjOvLVuPm7EZiZCo+ARVoXKkzh346bUljgLrk9644XL94g45VqpCl1/PF4cN0q12bsKQkfg0Oxsk1kMFj+lGhqQ/3TxZWKQyPSUIllO3ackFWKE72zpkzjPa3ujYKgsB0Px9WXT9C/O1Urhy7SWZKNp+NW0HFZ/zJy9agzdFgMolcDP2NOnXs+WTgQMv5Q1u0YNHhwxy8fJVknYKASr5Mbv+hjWFXFAw6Ayr74tm03Mw8Jrebw7cnzCxSD/VL3A7V8+2R40zo1AaZILD25Fku3szCQSh7nF9pEHotiiGTuhIXkcyx3ZdYcnAarp7OzH5pKXdPF2bzSoPU+HQq1Qp8aJngE3cID45i1AcDeWHKc2SkZJORlMHUzp9YygiCgHegOzlp2eRla6harwKDXx1JRnImP3++G5VKTtPuDalQw5/A6n5Eh8Tx5SsrCiWsv3sp3GahrlDKMeif4AbYPxDVG1Si08Dm/LxoP1kJhV1YZYKckEtRXLt+gy+XLMFoNPL70aPMmjUPl9g6OMqVoDfgIHchPamw0nSb/s2p2qAy/kE+XDt+C6NHBip9DNMH9kGtVLLy4CG+P3KE94cM4c3nn7d8/+/GxKBWqbifmIhKqaRNrZqo3HZD/BMfknL8W1Hulvk3Q8TiPy4WF1tXqnoew278k9zp/5tQkq/5X6zc+nf+WBWlKKVESWqIN4O++IkALwf0CjlVazaHvCjadajP0YsXSc3MZMXbb9OoWjUAdHo9b37zDYuHDKFz/Zrs33cagLxs8y6ni5vCIj5SAJVSiTY1k+UXzRLZ3WQvFN3uou6raLL+K6aPbkYfzh2Jo2vYXAK83IhNTicn3Bk3U+HdwUeBQlCiSK+MNh2atKtF/Ra12bX0MKPatbUpd/ruXTrXr48AJCRlY0xyxFGltnFvvRsTiy5DjWNaDVLCwdXLmRfe7cf+NUeK7Jt0TB75+ShLbFhZ1SCfJJ62cudjgM3u8CMyoQVxQALgrKuAMQr6zRzAL4v2cGfHz6ybvYUeozvRo24ngk/coUH72jTqXJeAKj5M6zmfM2cu2LBODxpLBRBdrW5VOfWtxoZ9nJVRyM40kK7X8XHfnsSlp3MyJIQK3t4MataSawo/Im5E4+Biz47fLvBim1YWV62MnBx2/n4RJ3lQseNQ1Filxum4cTeUujWqW34ySpQTC+BlZ8fNczdJvWte+MscHBCNRqIjzQtdUacDhQKVl4YudZoWOr9LrVqMXvoLH3z0Ge/3/8KyoWQTgyVJOI0oUqVWAPHR6Qie1lQaBeyLqDWzVHevRXHjdAhV6lU0KxfmBbF5bwrbTq0HQcCQ7oET1RAkrKmNuqWU+UnPX3TbFZ0aoShlyPb9mnD6wHWaP1ubKZ8NJS9bwyvNZ5Gdnouok6iS5r9fNjOMjSq29fB3s35m/MLhyJSKYuOGTHkaXmv0Hp2HtmXmxsmc3HGO7qM7IRrM1zSlpjPh85eo2qAysWEJGHRGDHoDn49bTqXagXR+sQ36PB0hF8PYvfRgiTFJ0nbotVK2zFj038XOLZK1TcE6R8I4yrQSNlP6HNowL8VoEtgwT/nPl+QeiCar0SpIWFObzRhpAvv8Z6NW0yo0blOD1e//bNNu4YEFcg3XJqxc/iNpaSmoVEru343DIaIyChmAHi8/V0bM6MfKmT+SZUihctUg2vVqibuvG9EhsUxqMRPRJCKKJtwaJbPtw7csdU/u05vYtFQMJpPFsDMajcxevx5REBjZzpxLNiY1FYNGhrqUa57HFX9n9WAqHIf30LptjkvucRHPfana+qhKvE/SW6ccfzv+WcZdOf6TUOMISdVIyVdvbjGiPSd+P4GXiwvP1a3LvfR0i2EHZkNtTO/evL1pE/HJGahFT7R5OirVCiTydgyZGQZSsrJsDDy9wcD5P28z87l5zNkylaB6lYgILkUwXBmgtJPhqnRDYbJHptWiNjoyLH2VAAAgAElEQVSVfNIjwK+lIzf27aHBM9XYf/kyPRs1QhAELoaF8cP+02w7HIxOa8KY6I67qRofLN/NgC7h9G7ZiOPBd9iw7yz2qZWoVCcQlVpFpxfbsH/NEWJCy7cyy1E6ePi5YjKaLMIEJqPJJpn4iV1mpcIPf36TiV+8zOR2Hz6W69o7qOj+fBu+mD2buPbp+Lu5MbhFC7R6PQO+WEPvXhPJ0Mfww4db0Yve9H93EdWreiAIAqH3khFiio6rfRgcMwOY8O7njB3Ri2dbN+PPC5e5nJiKNjAQO8mCem9cAhEhDriUUH+glycxGYWTJ1+JisKkUXL40Hq6vFWboWPHMK7u+w+tq2XXukSHJT60zISFw6nbuiaDKky09glPSPN84i5XLh5OuHo4suit9ZY8eKK2aMXD0qL/pO5M+GwEb7T5wOb4s8PaMmXJWOLCEvAK9ODasZs079kIlVrF3CGLOLH9LG2fb8HmmJW833seDq7OVKwZwLQe82zHwWjk3tX73Lt6H5P+v64c8Xggl8vILCJF0YMYMKEbC6f/D2eHTIxGEX2uD04ysyEmCALDZ/Tn7IUjeFWPpH/PqtyOT2L7zu8QogNsYuTyyKVvw8IhBMPat2fi8uX8ePQo7k5ORCcn07tVK4Lv3eO5xo3NG8PLN2KX4VvmeaAc/89Qztz9fRBFseTJ9knurv8H2LqSGJlS5Ukprr4yNaM4v3SBxKgU2jfvybKtW9k3fSqLjx0rVK6Knx9+rq682aULn8gPsHbxWj74eSrjG0/DkKtmwqo1fP/6BJzt7dHodLyxYh2GOA8uxFzl5K7zKJQKa/9FEzlkYFBqsNO5oBZsHSmLYxmkyHVKYNIrrRnepQNgFn8Z/r/FJJxXltkNrAAm0YTGJ5ZKQc7YqRRERKVhivTm8pk/+GnWmwDsPHGCWVu3kp6dzZlzcbin1UIQZFZnUwHsE4LY8WM027bfQdDY42FXg3GLhhN1OwZHV0e+f38TJpNYqtjMJ5rfznqREo/bxAdIixhLszP+/wBlZOgs91g6TlLvCMm86+SqZti7ffnug59t6rDZNc5nHz4dtpg11780qytK741UmENStyBxZTOorc9dQivzRs2ZhW/w07Zz5FbozOB1+6jlJMdepeB2ZCo9n3+ViLtpBJ8JQ+bogB0OiCmORCWb+6TSORa5oCuO2RQczfOAQqHAKbMe3397kTXfn0Smc0YutmbQmbPMq1uZZ5yd2BEdz5LQTJzV9W1YDkHSZyHXzLKkZekxKpXcjImhTr7iaHJWFmv++JPVr/ajeeVKRKSksPDNSWiMcuyVVlauYNyadXiGl97oSm62hlljv8Pk6WK9Tn6uMiHbfL3e4zpz5cQdco0yZM5ONsaVjZJjMYsZQZKqBqd8PWEpq1PMu4jBwIylI8nJzMOgkbBD0nOLei+LeYdrta6Jh787tVs9gyAIhN2KQ1CpLHF0r332MgqVgglNpqNQKeg7oRsLRi5Fp7Fe73nPsexI/Z4lZ+az4u11LBjxDSad7skoGBb8XBzhIRSjVGjz7uYzYJIxlrJ1Nu+ctL7iXn/p2BZ33wrqlsbi5RTdR1O2mem7fvgK3t7taNG1Lmf3X7EydhI22N3PjU3ff8nC7v60q9AUURRZH3yHNWdDaNKxExlEsXbzN7TzdWH5y1Yl62tNo5mweC9OWUGWYypU3IspvKlx43403h6+eDg4E5eWjruXFxv3nKFW3YqM/GwVMQlZGGK8sJc92jf5cXz/bOuQ6kVIFFOlt0bC7tlsQhTB6BWrkFkcisu1KNW0yGe6/5Zv/z8J5cZdOcrxeHFsyylenNaflEPB2NvZkZKWVkg6efeJEwxq3Jiafn6snzSKt/buY1KrWRhEPTWaObNw6iss2rkTo16P3mQiODYWlbFKocWdUTRiqBjJsO7NaV23BjuPn+ePE2GoEwMt15MpZBiND5/YvCrLLIYdgEIuZ9GkUQyeugJVeoWHnFk8dP4xfP/ROJ6paD4/V6Oh34yFOCp9LGUGtGvHgHbt0BsMdBz7SbEKYA444+Fakefe6YJPJS+2L97L/ZvRT0awBLNaqcEzBRQmSHHG0eRZ8knl+EejaoNKtOvfDIPewE+f7SpVniiDwYRCWdYVR/FIy8ljxdo/kMtV4N+FqMh0EE0oc3MRtHZoclMKnVOQUuSvLk2cZN5gVFk8o7SOvZlw4waCMRbB4IuTqlGpNoJ0Sf4oFGoORUay8eJFBODYjZss7t+PZvlJu4M8PVnYvj09z22GPKtxV7txJV6Z3pvE2HTeH72avGLyfkmx47tjDJnYxRw7bPp7NznsHOz4+p0fH0tdb34zhir1KnJ672UGVXq90O/L3lnHrI1TAHMs4o5vCsdcAsTdS2DT/J2c2H72sbSrHGb8vvkUL77Th+SYVMIKErhLEJ8cy4BAaFfBnJZCEARG1a/FydQTBFbW8NnwSXyyZAkTHgg1aFCxAv7+CrKyrMcUgorLwZHcjIymTiXz9zE9O5sdF66Td8Gfa4Yc7OT+TFgzie6jOnFy5zkWjlqKIltTvqgtRzn4Jxp3f/cO/H+BrZOihMX8Y9udeUSjQTSJpMals3zqOlqNrcUv584zvEULpi1fzpTBg/Fxc2P9oUOkxsdTp5FZsU8QBJr4eHNRG4nBIYe3R48m0NeXOa+9Zql34fffc8vtNndv2C40NZ4xbPpwAkG+5vieZjVqsLnqCRZ/ewYnvdmIUjvao9fqH9onV+fCsikBnp4o7EVIL/s4GEUD9er4WQw7AAe1mreG9uKzn3YWKn/65i1MWXbm51LCSFogyBg+axDfvf8TcrlQSBCgoExRKOszkWeXTu02auaNfws3JyfWHfidtVtO45Bc8eEnluGdKo6hs9nhLkuMQGkUOv+lTGBQvYr4VfamafeGbP96H3FhCeYfSlANlTIsfV/rRkJyPKvm/YBK62AxYoplPCT3wWgSUTrZY5TSCZJdYtFDwjrlWa/p9keY5W/7euZnJyU2kwGDmrH1wGUA1PnMgK6GK3/cTWDJyrEc/ek0f/56Ga8AN/Q5GsJuRKPT6BFVkjgx6fUl6RekcYEylYpKNf2o2rAy2em5XDsdikbyu8JgwtXJVnUSwOhqnQsEjZX9kOVf04kgDhwKx9E7Ew8XNbGJWRgMKnZE3mdnVCRBajVjmzTBQaXCz92OLNEOv8qevL1oBP7VfZk4dDmZ6bnme5Wf/1BUSFLG5LNroo+Z7Vyx7TyNu9Vl690v6NN/Eaoka/wiOdY8ZUjdJfOsx20YnILdbHnR99IGJpHokHj6junIuV8vlFzeepFCh/yreJMck0qVehX5+MXFRcb2Xf4t2Hx6gQpkUe+/aOLg2qNMWTqOE7+clhwug6Jsfj1/GWVRPpR2RSz6nZOWEcSi2eii4vJEqcGvLXrDptg79sD7v+Wrvbw6bygp3x4iIzkLQcLQa7SJPOsf8GANNPFwpeuzHbBTqZAJAooiGEW5TGbzHbKzV9FnwBgWHT6JQjyKQiYjJj6b1PNqlIKIWnAAE3w+djmRt2MJrOFHy16N+GPzqYIai+vR40UZlJ+LY/RKxeI9KmzmQet7XpwC/f8blAuqlKMcTw5XNocTUi+LCu6hdKpTi+nLlhGRnMzzjRoxvWdPm7KpWi0quR0GijBaMDNpX730PBO+24rJZJ24/CraWQy7ArzQoQ3LN/8B+WmLFEo5RsPDJ7jo+AwMRqPNh+nI5WvoU+1QP+S84mBAT0Xfwup1NSr4k50isurgYV7pZt6Nj0tJ4cOVW3HMq1Ck25mjqwMvz36BM3sumUVnnuBkLYoibtW1rHrvHYsB8Eqf7qRkZrFrQzSOgnMJNZTjccKvig99xnclNS6dgKq+tgu5UqLbuLb8duwHmgd6MKKfK3suhZAS6Yq9oXRsbFJMKhPnD2PJzIekdCgl3py6kW+WjuTM1Qii4mwV+XJzdWjy9Bz46TQtutQlJT4DBy9nBk7ogpOrA2kJkl0WqUec1HUy303Rzl6F2lFNZEg8t65G4ebpyOCJXcg1imxff/Iv98PR5A8J/qREGzC6h/P53Bm0b94MgMvBwXyyaRNzOnYkW29k7sbX8fB1YcHEtby9bIzZsCsj5s3/lQXzhvDl58OY+/qGUjF+jwM6jY4D+YnX/wo+PzSL9OQsBgZMKPJ3DdnonGN4b/xEmrzmyYkfb2Of4VFk2cp1K3J+/2UWHprN9O6fFFmmHI8GURT5+Ytf6TG6M9u+PWjzm0J05+j9eOp72+ZRDdNoCMg/1rV9e365fJkhTa2CQ7FpaUTHaXASBJzcHBj9yVCSolK49udNIk5rsBpqziiL+P4d+P53pq56rcRveDnKYYNy4+5fjH/pTnyxKI3K4N+FEq5fXByX9Lg2T4f2vB2xJj13Yk+y9PUxVPL25o1ly8jT6bDP34lPzsoiLE/PN0fn8+ee8yxa/zMb5s2y1JOn0RAXE4NH48b0blKL+zH3Lb+ZFIUXOUaTCZ3JQMGeo6kUzJU+yo3h/1vMokmjCPD05Mjla3y0egdO2qDSj79kzFSoORMcWqjIhoPHcEwNwLV+c15dvo7k8ESSEvKwS/K3umRK6mk/qBU1mlZl0/wdZKXmB7z/leehhPfEgJ4GNSsWck97qVsHdu1eDhmOj35tKcrKMpbU57K+O/8URr8E1ceE+0mEXYskoJov6UkZxEckPbS6B9kOQRC4cnM/60b2w9XezEiNa9eKl1ZtIuGWKwoksXOSvnvXCKTLCy2pUN2Ps3/eZdy0Xny97oQlDVpmVSu7pU6zXtM+1mq4yCSMkSrRummzdsUfzJvSm1Urj3Iu3VzGxQjTP+yPJldHRo6ew7uvmAsbjZw9fheZXIZSsqlTUuyRyWRCXxAnprYjJiKJGxcjaDe4JaMmdyM3R0uODM6dCyM2Lh1VotVnTBo3KNpJ6pYyh/mKgzp9Ni3bVrcYdgCN69XjeJUqLPzzOCOnT2bvlvOc/f0mAB4+Lnz07ct8vfggyfGFRVkA5Nr86xut4xeaks7g11bRsG4Ftp+cxY4DV/hqzREcYq33T55grU+UPsfSDYGCeyKUnMxcFE1s/PxXpi0fx8k9lyzHBb1k7peUFxSFlxsFrKGdvYopHT42u5UKgg3joBd1eDTXsnnpYuzyx/iXGkc48nso9w7mbwDk98fZ1Z6+E7pjMol8/fpqK8tXVo+Vv3vtYJO/t5h3vjgyyiamVCocU0S7i8sTXFw8tvTdyT+cmZyFs7sjiKJNfKej0YGdd+/xbOVk6nl7AbDrbhjxyHHIn1s6NGvG4uBgPti5i34N6nMuNJKtx4MZ9vJEvHw9SUvMZPvivaUX/xJNGHR6PPzcbDY+y5LP7q/c61IxwiXVYSyGxZNO1WX5pksVZUsjGFSUJ1A5/vX4bxt35fhXwF7mRPr9DA5fus7Y7p2ZOWQIr2/8EXtBgUEnIPPzoXf/scx84Rv6v9KJZwcM4pXPvqRvi6Zk5+Rw9tIlZnTsCEBEUhq1WrhiMpmYsHgEm7d8xtWwMBpWrWq53rrffqNl72aMHvYGAEq1knUfbX1oG9UmZxIuqBj89ioUaiP6NDVOmiBLvE9ZIQgCqSFKxn+xjE/GDcPFwYHluw/wx7Fw7IUAPJw9SYlOx6jTI2DCiNF2oQ30erUredkavn9/099m4MtREJ9ceNEZEh2DMa98Onka2LfmCDKZgEpdjHz9Q9B3clfCL22zGHZgfjan9+rIhHvHcdEXdrV9btyzjPxoCDfO3ePknstUbVyVn5cd4XGFe125fJ8pb6znq8Uvc27yeurXq8A3X73EjDc3culceJEOVyajCa1EWMNGzl1Rehet44dvcPzwDRyd1cgDXZk37wVOnwpFlqMlISadm1fuExVX+PmvXa8C1fxd8fR1JT4qlaTwBK6eukuuMZO2LVoXKt+gQQOmrD5BpT//oOPIzny0cjROrvbERKcSFpbIm2/1wMPDkatXIlm17Eih84vD1RvRDJm0ho2LRvPVmtKf91cgluSGWQx6jenElMUjSUvMwM7Rrth4QY1bMms/et9i2AEM6tKFDT/uB7xsymamZnP/VjS7lh20UXctx+PFnQth1GpWldsXw22Ou8ubM3HPddwcrmIwimRmuiD3NZCQkoKvp9kT4Pnu3Xlx4scc2p+G3GCPr1sD1Hb2fP+AgFNpkZORS+y9eFr1bsrxbWf+ct/K8f8A5W6Z/1KUEGvyr0JZmYV/CIrbOSvuuH1GAN+tvcmmfRfwDfJCzAsk4nweMkFAJuixf15B5+fqsXnuFgS5HBE5i25t5rMBPRnerx+CIJCUlUWsnZLJgzrz3ItdSEvKZPePKvacOsXec+eo5u/P9YgIHBVK/KvU490uc0vqhPVvQYZKsLOIp6gBhDIqTT5wz+x1ntw+mseAK0tAbkJIdcNRrIBGyGb3xuVseGs4zvb2pGZnM3bRd6QFeyJDhhEjLg6ueFf0Yt2HW/7WZ0EmyAi5k8qlu/doUsOcviJPq2XBul04afNl6B/HrncxypnFxdw9cixpWRnw4vIkPmWYTCKa3AfiaYpiA6TjKpOjdrKjKKtMrVLaqv1hzutWq3lVpq4Yz5genxMfZWZNfr9pjvFTZFnjuJKbWJ2V3W5ZPzP2sZIKJfE6JkmibnmOHn2Onqy0XNxc1Eye2IXBAxaTnpYLKjmCq4Ok49Y/BZ3EQE2UCK9I406k8WUu5lhAo481JlDraW53HvDF28/x6/aL7P31MvIcHX6B7tRvGkSf2gGYTCLhoYlEhibQtnNtQu/EcfFUKMnxGQRU9qJyBTde//QFwkJCOHHxJAO6dbEZy4PHT+Imq8LyUx8TF5fB0nl7SIhNR+9p7ZtMZ+L5F5qzYs04zpy/xw/rj9vcKpkkCMsh2sp8pmQZCQ1NRJVuwOAkGdc4icFbXPxNPhNjk7+smPxqglxOVroGR1cHFh2YzrEdF9i50taglCpxCo7mvu2P+hrRJCLIBN57bj49R3Vk2+K9tudJWD6Fk4nKAYVjudw9XSwxkgXxo4JcTmxYIlO+HUdcaAIXDl3N70/J8UslegWUZq543LF6xcylNvnTSpVbr+BQMWxdMdcvLs7v3K8XGL/wJTLiUom9l2A5LztBhwOV0Waar+MIGCN0DBrzEZWqeiCTy0DlgGN0JWSCHCd3R4bNHMDRn0/xVxBxPYqWvZuUunxROXkf5dyi8Dhy5RWuswTF1tLcV9sTytKs/ybKjbtylOPvgZPeE6I9SY6GKUtHsubuJnIzzbLQC0ctZem5+Vz67TqJ0akICHA3iGnrD1Mt0AWjyURYdDbyhIrMjlnI0nPz2fjpdmp0asaLAQF4OTkRm5ZGn7p1mblnD28MGcLhBVeeco/BTrDHLt2WHRH8kvn8hSmo83eqPZyc+O6tsfT+5Eta166Np6sr16PjObD9wNNoMuqEACZ//CMVghxQq5Xcj0rDdN8Tu3/4hkM5bGHnoELUwpXwJLR6PXYSY+urA8dRawvnisrOjwcrMOyeJM6fukuv5xqSkpqDh4ej2bj7G5GbqyNC4uYaH5NGfEwah/ZdByCgogfVq3uzcfUfaDV6ZPlxbtHhSURfv8/J/VdJyQqlQ09n9h09ynOdOgFw5ORJkuJiMYhKJnaYy6LfZuHj70ZCbGFlph1bz7Nj63n6DWvByuVjmb/wVyJuPdxlzWDQcu7YPmoF5XEzQolM9uQ+8yaTianPfUadpkG8MndwIePuQXy95x2WTv+J3Kw8zhy8RnZ8Ktf+vGX+sRglUmOaHUfPnufZVi1srhsdnYKcwjG+Hw1ZTIWa/nx1+ANyMnKY1GImOZmFk9KX49Gh1xlY8d5Ghk3vx9n9V7h7KbzYsnJBgUN8BZLjoV7bWrg6O2OqdI/Gz9an96tdOL7jLKGXiz+/NNjy+S4uHbmGSq2ySY1RjnL8f8W/27j7/7aYfBpxdk8p9ujy78E07daA479Y5aznvvAVc7a+zRutzIl/VaggoRqR8eZdKzvRBwSIumOmB15fNJovXl3O5B3b8PWQ4eJojyLQAz+vllSpVals/Souf2BJio2lvE9qBzt8g7xJS8gADweLYVcAT2dnOjWpxYKxY8zXFUWGzfsao+iCXPiLr3EZ76UgCDikBJCaT44oKTq/2OPCY2HoSnWhR3y/ntI7ohM16NDhKLggCDICa/jRtl8zti3ag+nBeJuCtjzQ1qC6FTDEV6DPonWM7dQEPzcn1v55iYgINQ5qFwQHMxvm7uPCtOXj8PR15rOpPyIWkT9J72FlnQKOWe9TrsRzLrO6k+Vvxxjrc6uXMEwGRzNTtO9WOOsXjiQ+Pp270amQH98mSvJKKlOsjJUgVYOU7p7bW1lE0cNqDOhdzMcNTtZ2GCWKDf5+rsTcSUCmMSKTGAcmF/OYxEUkESeNcZSOeb7apJ3KnUru7phSUpi9YAEATatUwdvJGTulEqMosHfnZSZ90JfxQ1fY5J+SYtu+yxw+cZt5cwZy8lIYG345iyJPEsObYmZNc9NvUjXuAvWWqKghCHyZrCHZuwv2ThWKNZ6kEOyKcO2V5q1TFo7F8/J3442vRjDjxSUI9vYgFRaWqI9iNOHp58qvq63uklKGTtRJcuVJqnAy+fLhwu9w+EhNq4YNSElP5+3/LUIX42a5VEE9BXVE345lSNAbvDSjPxvClnLj1B2aPFuPnMw8RtR402IA2DA4Uu/dIlmvv5AbtKT5pBRzT2nyp9kGapVUR2lQzHUEEYPRyN2LYdjZK0E0FcseSY/fOn2HbqM6Mm/vDO5dvc+Guduo1rBycY2VXO/hHkwiAlOWvsLBH47y6/JDJXeruBjHx8DO2rCdJeQaLC2KvG/FxU8WU6YcEpS7ZZajHE8Hx385y5vLXrEx7hLuJ7F39W9MXzeJhaOWWo4XiHsUxH7kZWvITM3m7Y5zuH8zBkQPokUREyZ+F3+gt+MIpizQ06x7Q4vbTuU6FZj2wyTSkzIIvRTG2kf0/y8Lhr8/EJVaiWgyocnVER+RSPOejTh/JxudwYBKsvDJyM3F+YG4qGlD+zLu3iock4KwFx6TkEk5/vEwigaMgbG0aVaNIH8fDp66wp2YaEz27uw7dwfsnSFfW6d2qxpUaxjEnpWHC9WjdrQjIykLteCIKbomizfexyQYcFNXxkGyYdCkU23Gvt+fZXN3cfNihPmg/aMlCS4LvDycSM/I4Y031oPi7/8Q2zuoyMzUlFzwIXBSurPjzBl+mvEufVqYmafwuDj2XrmKb21nZn/xNoJaZTbsSkBGZh6T3v2RKZO68uXsQXww5xf0EoVAo1FHTc1lvg+w5s1rW9GeYZlnMbjWICfhEXK2lAKrTnzIkllbyUwtWskYQO2goseg5ri4l32eEgQZg3uO4VxUMp8umkZaXA7yZE/s7d0RRZEcUzoGkw4XmWehPabNX+2lx8gONO5cl90rfsPOQcWOpDV8N+sntj+QJ6/g+1GaXIblMKNhxzqsmlH6PIdGg5HWfZuxb80R6rWrTVDdiuxZ9dtfbocoilw+GkxK7JP3KCjHfwTlxl05yvHk0X5QS3Iz87h4+Jrl2OWjwYz9dBi7lx0kOSYVgH2rj9Cyd2OzEXSgeLdKJzcHYkITLP8vCAJy5JiMJr459QlzX/iK/+2ezpYvdrHuw60sPvEJG+ZuQy4XGDbzeao1CuKDPgvK1IdqjYLo+EJrFCoFRoOJ7PQcYkLjCT5+m/SkTJuyzh5OVKodyIIR3xaqp0Gf6szdu485vXuhUijI0+mYsvYH3h821MZ9zsPJibH92xAWlcy5P5Nx0HgVqqsc/z3o/ePYvOBNAjw90ep0BEeF8d7ISbSuU4eI+Him5Cwn5YIrjoIr7Z5vSVp8Ot4VPUmKssahyeQyXnr/edbO2QKY3w9XvPCv4s3UVa9h76RGaafEJIq4ebnwWqdPyNX/vYvekLBE0tNy8fR0Iinj73XJBEhJysLNw4H01Ee/tiiKVA2qyerjx9Fkmy1uFw8PvvpwDgs2/Mzsl5aS7e5WpjoXrfmdVk2qsOLrkSxeepgbt805XbKywhjraMsUCIJAe6MGRd+qHFqVRm7WXzNWi0LYzWje+3oEvUe0w2QyYTKJKBRyqtevgMpOSfCFcExGExf+uMXwFh890jV6jenClA4fYTJ44IgHCKAx5aCsFMvLPdrg6erMT/uOkXxDgb3emiJh1OyB5OVoGOk+1XJs59KDDPywPbVjZCjydNwJiSYxI5l6fj7o9SLhsVpUqZUf2SPCwcWBbiM6sHf1b7zx7TjkcoEvX131SHX9k1GndU1unbtbpnNqtaxBelImQXUr8vnoZYXjhP8C4u4lWNYJ5SjH/3f8O4y7p5HS4J+SRqEMiVCBf62raq0W1TEaTDbG3Z9bz3Dp8HWGzhjA8e1nCLlgTnq8aPwqpq97g3P7LhcbvGw0mDDoCssA91ANY86WqczZMpW32s9m8Nt9OKj/iZtn7vLc2M4EH7/FQM8xfLjtXSZ8OYoV76wrVfufn9ILBBk/zNliSa2gdrSjWqMgeozpjIOzmqiQOHwqeOLq7cLx7WcJu3q/yLqu7QklT5bFiWNf4+6qJik9Gztvke3nz6PT6zHq9cwYNIifjx1jVKdO+Pf2YGDcIjKviU9m17ms78LjeF+Eol2gyuQSW4q6y/x+FVVHac4rzXtZjOukTRFRJKiKKwH5ynM//fEH43v1on6VKgAE+fmxZfYsurw5iwZBbbh9LpQKNf2RKxQgyBBkAvXaPkPb/s3ZsewweXl6kMuROakZNWsAjdvXYvlHvxByOdJ8QalbkUoigOJsZe4MjubjemdrWVWm9d2zS7W628klSb8xWl2JVFJvKImL0enjIXTtVJstm89KfreWNUkZRMnfUtEVUSVx/ZMk547pZHbLVEnEL/XtrZsw5xMSqDW0HnvO3sLjprUOea4kKbokLYKN2km+i6agNxb2ECQAACAASURBVJCanMV3X9iKNx07e57Te+6jNviD3Pp8W9IcACalta0mleR9EOHsxXBC7sbz1vgujHVtj2CEqxdOkf2Vdf4sQIYRTmw4z4T3+rJq/h6MBhOCq1VEhmwJ45a/my1KjgneknyHEkGagqTI7/RcyIHU1cx5dzNZGXnI0s1pIxQqBQZpEvUCF02pS2MVSbzxbWvclSzfSyGwmg8fbpjIkS1nEJVKG6EXRYUofv32Y9T5oi2Du3Ri6MxPSbuhQi4oQS6jx6iOLJ+1BZmnlc28HnYD15U3WTzqJcuxNUeOUN3enk7VqxOVmsZLK/cgT62Z38jSz2eNnq3PggOzuHP+HuM/H0FKXBp+QT6AwJevriz+xGLmu+LSCNm6qpcwFz2mFEoPipC0f745q6ZttDCeJbkGqh3VjPnfMAQBrvx+w9awewzfjH2rJQxgWfr5qN+AUsDmG1WK8ZYKp5RZJKUcZUM5c/f08PyUXlRpUJkjP56gQk1/9j4G+r4c/0zcOnMXbZ5tILS7rytD3uuHQW+k05A25GTkEXM3jvTETDz83Pjg57f4btZPxIcn2pzXpEt9EEXaDmjOyZ3nC11r7pBFDHyrN8vOL0AURXQaHevmbGbS12Nw9Xbh070zcfNxpWmPhiUad51ebMMzLWpwdNNxQi5F2PymydFy4+Qdbpy8g5ObI65ezpxKzKBqg8p4BrizbdGeYuu1NzlDrDOZMUZcG5jY9fkcSwL1uJQURixaxPPNm+PvYd6lbtugBjuuxWFPuXvmfx0KibR/WFwco7t3t/ldrVJRKdCD9gNbsnfNEVw8nIiPSMLd15UOg1qidlKzctpGG8Otbotq1Gpahbd6LrBROHza0BuMqNUl51x7EjCKj2ezJCVcYNHqdbw5bgQymYzouDg++nwNdtp6fylWNS0jlw8//xUAZZYRUTShTtXR1V6NIr/d2UYjZ+SOmLQqdq47yfDXu7Dhm8Iuun8VE19cxtJNr3H7egxrPv6F5LiMIjfXyooZq15h9pBviL+fbHNcZ9LQtklNi2EHZpZy6svPM3XWDtxEs9G4ZNpPzFrzKjOWw7BG7xMTdR0vp+vMG/4/m/rGPfss72/YQKfq1ano4U6z6h5cPqtDKdjGICpUiiL75erlzJKz89Dl6ZjQdDoRwVGWBXnrvs2Yu2s6mlwt/Sf1ZFafBQ/1Ovk3oNuIDpzafaHUaTDyxBwMRi06jY7EqBQUKjkNOtbh2rGbT7il5ShHESiPuXsKkOxu3Dobil5vpFbzavhX8y1c9jEzBP8JPEnRlSdY94kd5wodkyvMH4CAqr6kJ2XStGsDMvMTdbv7urJ/7VHGzH2Rr15dad4FzG/f2HlDyU7PIfj4LfOxItq6ffFeti/ey2HjZuLDE7ny+3UCa/gzpfX7OLjYM3rui1SpX5llFxbyerPphepw8XTmpVkDObHjHCvf3VBi/7LTc8jJNLt33Th1x3JckAkPDXLPVqcwe+RAi2EH4O/pScNq1RjQsqXlWHhsEkrKkOesLO/O0941LI7FE8soalBC3Y9lTMrC0JURgkxAQCAsIpWs3FycHRywV6lIz87GzcnJpmymVkvo5XD6T+zO+YNXmfjlCGLDEjm9/wpJ0amgUCBzc7WUVzrZE3s/xWzYST56hopWV1+j2vq50Lpb/zbli5DoHa33Q2awPq9yifCHPMd6n2RSVTsJwSPTWo253IRMKvm6IM+xlpUycVLmTJDskkvFXaRsmMbbagw4Rpv/m1HT2iafn6zjqO6lxzHJgFuIFnmmlWUQJAydIF3oSw3BAoZLJuCoD2DHuhuEJS8i8nYsKYk67A11ERTmfsrTJW6f2da/bR0sfSx/aT3M77lMQgpoPM11ZTUcQPe7e+mIgTwRzuUqGTZrPi4u7vQZ2JRv3t9Ct/6NOLxDknTcQ+IWmpUfrCllbB+SCqEAEVciGd15Ic+92IIle95laNt840kiqGJyNP8tOFoFbqTjJ7i5Mm3JSBKj00iMTaPbkFYc3XmRhPhMq9BLQf5CU9GsvSAI5vbKlOQpdez7fQf7qvxK9wGDWP3nbN5p0gRfUWmjDFtwnlIS41wjyJcmg/vSrnsbAqv5cv9mDHk5Gqo3CkKn0eHi4UxejoYz+y6RHJ3KoDd7Ma3HPK79EVxofM7su8wAz7Fsi19FyIV7zNs7k+7KoUW2/2EoXia/6B0CQW4es2ITWZd1Hsqf26o3roKrlwuH1h0r8RStmIeySiKDOjfC2V7Nki/n4OfRiGefe5bs1Kwn9m2xd1LT6cU2HFp3DKOhDB4ef8Vjq6Rk4KWoT3xEZ5THApu+P71m/BchCEJP4GvM0/oaURQXPPC7kP97LyAXGC2K4qVCFZUB/zzjToLb50K5fS70aTejHE8JyTGpfPXqSrqN6MDGT3eQUxB3I5rwCvTg1QUvkZ6UyctzBnPvagRHNx0HYE6/hawLXUJGcuZDajfDZDTh4W922Tm79yLvrZ3E3Be+5Ic5m+n4Ylsib0WzIWwpI6pNtjlv6PT+rPtoiyVVw5OCoDDiL3EpKoCrkxO5Wi1O9vbcio7myo14KlZoTNsBLTh/4LI191A5/nMQon0Y8N4Cxr/YjbqVKzPju+9YNnkysnyjbOXevaSHK4hSxfL7z6dYdu5TMpKyqNYoiPaDzBsCoiiaFQbz14WuHk5E3Ix5Wl0qFvWaBJVG5PHJQBAe29qzSkAtRr70EpF3E9j07SFyTX+d1SoKKhdf0ru9wracDASZHP+bJnb9fJUGTSrTb3BzLh27Q/Nn6/DCa53Z9cNxdNrH2479m88x+ePnH+ncUdP7kJ6cjaefGwaDidVzd3DzQtES+XYye05fvo1Wp7NJbv71pl04EUCOPJw2LTyYMnAk2Xl5zN2wja6N9zMoIQ5HuUhobCzVJbnzUrOzLVtjoihy8FIIb/WrSG5mHid2nKfTkNZE3o6hv8dYACrVq0T9drV485vRxNyL543WH3CvGDd7Nx8X3l0zkbSEDKZ2+oilZ+dxSP/zIxl4TxtuPq50eakdq97bWKryyiqJ7Jn/Nvb5DOvYZzvy8uIVfPHKHZ7Etnr99rXJTMnCwcWBZ1qY4/tO777wBK5Ujn89/ibmThAEObAU6AZEA+cFQdgtiqKUtn4OqJH/ryWwPP+/j4x/tHFnwT+IObDgabepNHjcTNtTYDnvXYkgJSaV1z5/iY2f/EJipNk9JzkmlQUjl/DR9ndZMOJbXl34Mlkp2Vw4dJXUhExEk8hh01Z6KF7ElL/TrlKrQJBZZLB9K3ujzdMhk8nYr/0JhVLBnlVml6WU2DQuHrxKh8GtMehtt9McnO1JT8wsZNgVtYNaGkbpYUlP7XI9WLXjIF+++arlmMlkYt+pC5y9GowoiMSlp+Po50mHzi04tuUMrfs2pd/r3dm36jcib0sW7I9D3l+KR30HShEDUprE5f9I/A3zgkpQI4ZVYsmX59EoMjG6JdN39hwCvb2IS0klNjQPz4xa3Dxzly7D27Jw1DLC7+eryEktJWcrS9WkZRCzVozllTkDWPndSctxk8o69kY76/2R6wo/14LJWrdBUlaqoK4sZr/FhoGTGBwKAS6eCEGQqEKKEoJaVBYtNS6NrZNJ4sfkGiuTpNCay/hcsPbFId5KIcq1RuQaPcpMLTKtRKZfek0JKyBKYhLRm8sXSPS36tmQRW9uwGg0MvjVzqxfLJFr10hij6T3R5KCQBlvHTi9kzkGzqiWjLHkS65zAVxcEQFNqgENcDIinp9WHMXR152jh27iHeDOqGm9uXgqlEu/S9YY+akvhHx3Oy8/V5JTJMyitH3S/kreS0EQ2H9rPj3rzrKJzSy4x0KmJMYvP01Fy061qNa0Kh+8aVZelCVYVQ9Fb+vmlpCWZfnbEFuBPpM/ZGivdni6urBx7x8kRrmiVEDDho4seHWEpeyGGZPpPWM+9+6LLDJm8u433/DyyJG0ql2bqxH/x955h0dVtG38d7anh4QAAUICJEjvVXoTEFBAUCkiIAgvAmJDuiCK8Co2RKRIk947offeewmhhIQU0stms+18f+wme5bsQoBIeb/c18V1hTNzZubMmZk9M/fz3M8dxixfwS8dO3AnIYFvduwmMa4YUwbPZ/ruMZw/fJ3m77/O6T2XUXm4Urt1Vc7tPMeZdC2H11fEw8edm2du8TAEuZxRi4YQVDmAlAepaNw0GA1mBtYYwYpIq//dY9ZZu+DVdv7GtsvO/PKy88uUEpb9GVm8d4a/ycJvVjo2x3yoDJ2YyRu1y+Vs7ABkMhl9WzRkzNE9eBn98lTnk7SvYee6xEcmcHTTaWQyAZ9iTyZWlKd6Hvfb+IJC4zw1/tes1/KC52uWWRe4KYriLUvVwnLgbUC6uXsbWCRaJtYxQRC8BUHwF0Ux+mkrfTU2dwX4f43kB6nMGbGYTkPbodKoOL7lDJcOX0MURX4bNIcPJ7zL74Pn0mlIWwLKlyDyxn1c3DVo0zL55Pe+TB/yNwCbMxYjCAImo4n74bGUCC5GljaLvuU/pUL9EI5ssPfPm9j1J77d+DUNOtRm6vaxfN3GYmqky9Dh6umSq53/BtSChhMH7zNCPpdPunYgMT2d7+euICE5mekDPqVciRIA7Lt4kZGzf8Hj/mtsnrUTuULOB990Y8+Sg/YbvAL8T0AQBFwMXrgYvBAzS5EQnUScmIUXAfgKMlQaJV0/74jRYGL30sPIC3k9sjxjlpG9606RGJPKn/8MYPZvOzl36s7zeZhHQK6QU65KSQ4duPFC6s+rT9HjcPFoGK3fr8+Nc3eJvZdI+ZqBXDvjmOn5t6DXGxk2sRO/f7OemPvJzP7vNuo3r8CAUR04vucqF46H5+RVqhUM+qYzdZtXZOakDRzZefmx5VepW4ah370DPLrfskxazKIRjdwjx+XQZDLj4+dB/2GtmZtHn0AXuTvi/deYP+s6RsGElzIANxdXksVb9G/fzi6vIAi0qFWJtdciuZWZxq8REWz66SfGenhw0WBghJ8fA1auxiswiEkzZ+LvV5Kj284R+Fpxhk+3xBat2aISb3zQmOunb/HBqLdIT9ESe+cBq6Y59p1eFzeXg+tOMLDm17kO+ZJik/Eu4knyg/Q8PeuLRs3WVSlaxo9tO7YSmRmOp1gYlaB55D1vDXkDF8P1XNc1SiXw72x0/vrc4iNfuVF5gHzx+yxAAR6DwoIgSOnh2aIoSuVxSwD3JP+PJDcr5yhPCeDFbe4EQQgAFgHFsMzY2aIo/iYIwgRgAJAd5XW0KIpbH1vgE55udB/VGc/CnnZ+TzVaVObaiZtkpkscOZ4wOLUUDtWrXhY1zf8nSEvK4J9vV6PSKGnarQE1W1VhyfdrSYxNJvp2LE3fbcC66dsY/Gsfmnarz6T3fiHs7G3mXfqZkNplWTBuOfFRifQIHEy99jWp0aIyB9ccz/F/O7LxtOWdPvQux781le3GFdRsVYWdphWWjeGtWBKskstPEszW6QmrkzzZ0CQX5ei6VA7u+ZNazatjzCpM/7YhORs7gGZVqlC78jHO38tCI6gxGU0s/nY1/af24q8vFj2mdyWwjusnad8TwxlbJ6lTUDpZmqTEnd29joIOP2FbX/ScfkpmVRDNeOJtMbG0Xu/yaXv2bbtE7L0E5L6Fchim/t+/i4u72uKbJPkAL1rOn/uRiazceYWNJ24xavRbdO7VgEk/bkZrFToyS9g4hVbi32YNAm5S2d6f2cv2t+sDW1/KtDY2SpAKKElVGN0tByfFShSiQo1SfNzuZ3Cx0XWCRHFTyqKZPCUskTSguDS2uM7WbhfrL5NMwgrKM21ly7IMyLR6ZClaO4ZOkPgTSlk3wSj1v8v2v7H08e3LkQz6/l28C3sw+5s1DJjYlfthsaQmZdg9OxK/L7uA4ZJ3pdBa8pslzJlM0pVuMba8GUVtbW3WoRpXz0fw7sdNMZnM/P1zKGeOhXFi/zVqvh7Cx9+8zYyJK/HxKcSQ77tRq0Ew/8zeR0iVkhzZYfElM0tUSPGQ+M6ZRP679D8c23+dAV3+sLZfbtf3ejELUXuYhhV88NKo2HvrGmmKari4+nPsUiRnJq1m0oi30Aa4o/CxHZ5pIiVyphIxITRqBMCD4jlB4zGZEYxK7sUlUK1sWaSITUhG7daQfsYs6glxeIkiol7PnKAgiiqVVIuI5+rFUvzYbzlvdG9A8dJ+doI6QRVLAjC2x592AdcRReSeHtaOsORXKGToswz8MmyhxY9VanEgipSpGkhKsu6RVhvwkA+WZE163H0AZivza7euOlPilFpHPLRu9pvcnXNRe9hyYA2d6tVjUNtOrD54hGNHInBNLOm0fh8PXw6cicRkMiGXlP/nhl24G3xBsKhkN+nWgHmjl2F0xio+Ba4eC6NYUBGO/Bsmma/S996T/Ka8Ss+VH8g/5i5eFMXaj0h3NFkf/jjJS54nQn4wd0bgC1EUzwiC4AGcFgQh++jtF1EUf8qHOpzi3N7LaFxtP/xKlYL+U3qydPJaCpfwJTUxjb3LDj+ihAK8StDrDOz85wAhtcrQ7N0G7F56iK1z9zB68VD2rzxKZlom4zv9SGqCxXynnUtPdppWMHX7OELn7Qbg+JYzHN+Sd1/VH/v+SVpiOl/NH8yGGdvpNKSdXcyw5wGN4AqprtzY+ADfVmYavPZarjw1y5bmMBfQWMUXjAYjp3deoF77mk/0vAV4tSGTy/D09SDWwRit1bQ8f45bA5BjrgxgLupF2PUYALRaPePGrqZ6jUB++6kHW7adZ/3ms8+n8RLERCURE5lEvWblOXY8t9nbq4ZFUzYREGIRBju89RwV65Th2I6Lz63+y2fu0qnX6/Rq/V/ad63DR1+0QwD++HYjm0J3sPnIMTQ+wWQpopm/cBXlKnxKizZVuHD4Bo3aVeXMwes445lCz1lCPUz41HlAa1F7mA292+FlDXPwZRMznf9ZS7rZD5lMgVanR6NW8t3XbzFh/Lqnfk5PhT/T1+6gda3qOf54MYmJnLx4Dxd5JbJcyvCfAE8quNhbXxRSyDAZjCTEpLDsl1AA7t9+QO8RHQldfIjI8Dj6jeuUpzaM/Hsgx7efd5g2+9QPuZShX2b89cevaPxSqV22LKUKF6ZOSAgNypdnbqmd/D37Mu5GH4f3LZ+yHoWPkvdS/uSDZvVw12iYs2UfkRfBxRpDsEy1IFw9XR3e/7R4b8TbnAw9x67FB/K13AL8j+H5mWVGApKYL5QE7j9FnifCM2/urDah0da/0wRBuIqFTnwuuHrMaq5jPaEw6I0MbzgGg95I0/cakpqQbpdepmogzd6tz7wxy3IX5oRRcMS42J1uvWo21q8IKr3+GiXL+bN9wb5caQqlnIwULaJZRJ+pZ/UvW2jRoyEbZmyn89C2CDIZy6euR5eRRWa6jk/qjeaeM/NE0UyVJhX4cu5gti/Yy9LJa+2Sd/2zHwQZC75ZSfeRnYi5E8e+FUestz7d4UpeWDyH94kiEYdT2eV/gWrW2GbZOHz1Op7Y/9Ce3HaGj3/8gJunw0mIScEhHPi6SU9ypeNYNDtgJ54Bdr51EiZCUDr257GDNNaTEx+Up2/YU8ZG+jf9F/LILJapUorLR2/YqxxamZ/UZC0XrTHsTIU9cpLlFyxzQwEY/C0mnMWKeLJ331Xeal+dPr0a8smXS4i8n2xpitHWxy4PLCyGKLMdsomSpkrj0tnFhTNI2A/Jqb2QYrvh2z6zmLJiCMc3n7alS5/LxcYeyXS2nzNjIdsHY2aQxCRVMtf07pZGqpNtyWa15CdRECzEkyBAlsQvziB5OCmTJGUesuuRMD+XT92mcoMQfIsX4vrJm3z8bTfO7rmITho3T+rTJp0DEiY7W/1TbXNLs2P2jK4SHysJ2zp7+i4q1y5NYqqOlPh0atQvA6JAo44lWL+9FIFlBubkvXJjCes2bqdr1zeZM3s/weX9adevKS5uahBFS3UKGWlpOq5cimTvniusXXsKirijSdMjkwsoFHJcvVwICi6CSdShv+CVs7EDi//V5w1rMjL0Kl6uwch0Bj7r/hd/LhuEt96MNt3S56kVbWua52GJwIrMwXvwcEMAsrSVaT/yZ8oHFUanNxB2Lw2VrDYUUqHxrMbMxM38XsLWFrMocjxDRK1Ug9GE2eqnuWzianqP6EjbXo14t8ww6jSvwM+bvkChlOPirsHdyxVdpp5hrSeTmpCeMzarNirHu2U/s81B61o1ZfMIChXx4i2/AZax8VA65J1RexyyrRny4qfsTKXRr2xhHrjdYPz73alfrhzHrl/n0zlz+LFvX/q2asGi9SfgvuX9+BTzpk7b6mjcNWz4w7I5NiYKJB/z5vsTRxEFEx4mX1wE2zNtnbPLPj5dPiCwYgB3r0Ry68LzNXt+aVDwPfqy4SQQIghCaSAKeB/o8VCejcAQqz9ePSDlWfztIJ997gRBCAJqAMeBhlga2xs4hYXdS3Jwz8fAxwAa8ucEx2C1s96/8miuNJPRhMlYMMhfBXj5eeBV2NNhWvm6wXaxgq4cuU6vce8QcTWKhRNW4V3Eiw8nvsusL//hh16/M+PEZN4rPtDeVNeKwIoBVG5YniydntJVS7Embh6nd53n5/5/2QVa3fzXTjb/tZORi4awZ+mh/H/gR6BR57q07t2EmNsPSEtMZ87S76lZtgzNq1TBbDazaN8+Ll15gKesUK5FfOH4FfQa3434qCR2/rP/X1f4LMCLReN36rHzn2c/te7e63VWrjnJ+o1n8fR0YexXHTl2MpwFS4/kQyvzBqPRjFKtYNzc/mxbcoRTe1/tuFib5h/gg6/aM3vsSrYvOUz9NtXYt+rYc6vfYDAx8PM2NGlZEVc3DZOG/sOO44cJLG8fzqVEiR7s2zeGqtVqEVjGj5vXorl5zf5bw6xW4OHpQuUqJREEgbp1y1K9einEtCzMJhGj0URGpp57d+JJSUmgkgOGxttFjcX4x4aDO68w+It2/DRx/VM/p0bhBbpahF01AjJclUqy5RnVCjfOycoxPOoGg31dSTSamBqvQyfU5GFP6jFLhpCWlEG3wCEgCHzd8UdLgmRjGZo0l+JBfjkHySHVSpGWpMURajSrRHvvvk/9XM8LVZpUJDU+jaxi91n0wXBbPNWKFSnh68vcHTsY1LYtQZUCCHm9PtWaV0apVtKuXwt2LtqPh487adawRYIg4CX6WozMHjrHrNasEj7FvNm7PP+sq3yKFyKkVmmOSQ6E/g0UDfTjQWQCZlPB9+Qrh+coqCKKolEQhCHAdiyhEOaJonhZEIRB1vS/gK1YwiDcxBIK4ZkXCSG/HMYFQXAH9gPfi6K4VhCEokA8lik9CfAXRbHfo8rwFHzEekLLfGlPXlGudlnSkzK4Hx5jn/BYFasnjLH1ok9NXkZFpCdgPx5mt5QqBR9OfJeU+DQOrjlGzJ0HeBX2pMeoTsz8fBGiKFK+bjDN32/Ish/W0e6jFvT+phvf9/ydQ2uP55QzdvlwmnZrgF5nYPXPG5k/djkAw2cNpGqTCvSrMDxXmz6c0I2FE1bl6RGfeJw4QZEAH+q2q4FMJuBXyg9RNDNv8R9oXHUYzWZirpoprCud+0ZJHxcpVZiO/2nDia1nuHjIPtZezt/ZzJ00kLVkjRAlCn6ik9NmZ8iuJzu+F4AgiYPlLIaWXZ1S/yRJnaJRomaYH8zdk+BZ5vYTrDP2VeY+3Vco5fT9oScH1p8i7Nxde79FK8M1ZfUwRgxcYClDEi9OJjn0yFarnLp4IF8+ZGbXb1BzqtYK4tupm4iNtSg4ZjNJOl8bc2d0sbVbk2B7Z5p7lnsydHEIiSeQiwb0LiF4qMvksFZGfxtTY/BU4u6uoWX9EBo1eQ1fXw+GvTsDo/WAzuRmGz+Z/jYWz6S21W9wtf0tl1rDWbtQJmUh42zj6KPOtYm5l8iu9WdAorgpZdGQSZUzJWMzm5V0wJ6WrVyS6q+HsObPnfxn8nsc3XqOcweuWTtIMh8kzJ0oeU5RY+ln0UmMCLPGVqdM4mMoyzLgWciVAZ+1oXRIUYZ1+h1RFLlpiKRqraW5ytHrR5KpL8Qn/Ztz9MANBJnApXtxZFj9JmUmyTyTTgHJkFY9sG1yRMMetvbrjFzyUTVwzTYmrFpMaoIBuVKGh6cLRf292bzmFNOnbAEgNcSm6up9MMJWnrftenYMPfn9RFvl7pLNpDSmopWFzTJpyfCJxyx3wdWvEq5htkDp5nhLOd5FPPl6wWBcPTR82uzbnHRBqaTbp235aEJX7t2M4eMGE5i8ZjirZ+3h4/Gd+enTRYRJwzhZ18rQlHmcP3iV12qWYeqAWRy1mjvbrXGPUb+0ZnKY3+l1B+mPwk7zKqJuxvBZ7w9YOWpYrvTxS5bg4eKKR/DreMl9ObH1DB0HtWHLnJ1oUzO5edZxCIuH0aJHI3z8C7F62qY85c8LBv38Ib7+hfi++6/5VqYjzD4/jWVT1rF32fM96H0s8sF3fJe4+vRj/MheadQuV048NWNGvpQlvPHGS9lX+cLcCYKgBNYAS0RRXAsgimKsJH0O4FhS6gWj87A30ev0/PLxLJp0a4Crhwuh8/a86GYV4DEw6I3MHbUUH/9CdBzYmtM7L3Dx4FVObDtHm77NCJ23l2snbhJxNYo+k95j+ZT1HN5wkr8v/Uxr+XuAxSyoabcGduESsvHHJ3PZrHXsP2J+3psHIC4ins2zbCpyMrmMdv3e4/aFCK6dvknhR9wrLePvUUvoP6Un92/FkXA/F5FegFcYb/3nDUL/OUTUI2IcCs8YNG7eX3vxL1GIb77rwvET4Sxc/OQsnjblEs3SjjHaRUADbMg6wjTtLdx9WjvMn56uY8umc2zZdI527asxfEInfhq71mHeVwHhlyIpGVSYLoNaMnP0Ct4Z1Irk+BQuXD2J0kWLySggywzAVZmXWf1kSE3SMm38OkZ+34X2PeqzeclRVDIzKSkXlSRfUQAAIABJREFU8fKqkpMvIyOMlNQEXNy8mfX7LqrVDMRkMvN+t3pcvxHDoaNhT1y3Vl6Dt+avYVijahRy0TDv9BXaDO7L+sWnWb/0mJ04jpBPh86PglruCiUaPDJPclwqN87eoXWPhoDFLD5NGQ+eZiq2KcneNcdpbo0dWaNJeYqW8iUlIZ2bFyOdlhl25jbVGlegWOAThALIB4iimUyfKEoGueDqouLuvWQMET65LKaUKgXbF+5j2vDpiIHxmM3mnBiaAEaTiVM3bxJ3U8A3PYMuw9+kVIWS/NDrtydu079hAbP8h3X57sfnCKPafkdSrBNXhwIU4AUjP9QyBeBv4Kooij9LrktjNHQGLuWtwGyVsefDdG2buxu/AEvcIKPeaJPOldT/3tedOb71DHcu2ZRKn1j58Hkp8b2MDF1e8JTtToxOYuGElXQa0haNm5pTO87T7ztbcFhtWiYLxq3gox96MGPYPIx6I616NWbX4oP0/e49Dq8/kWtjB2A0mpAr5PSf0pO5I5fYK5U5+UDOL5buYQRVCqBW66q4F3LLKdtsMpOVqefDie+idlVx/WQ4iyasIjP9YQW23D4biyauZuBPHzBv7HKnJpqCJDCw9FRZykTY9YLg+Hnt/KOsinYyN8kPr53fjJPlSDpfpKqKErbuheJJ57YThdDHrX3OxlR2HxcP9uf+shMI2X5oEva1z5iOVKtThsN7r+awLHax5VIkzJS1TZmpWvw0ShLirLHFrB/fsTfjGDJ4If36NWHGr72YPH4tsdEpyLMkrIHkvdpdlwsUSTvLJFdbeie1glu6ONa66XF1LcKDWm45aWmBlmdWpqmoWao4bdrVRpugY8qKgQhAbGIa384MxWg2U76CP5+80whXjQqj0cSCTSc4fOG2HcNlltv6UJb9+JJXIFXOFAUBUSFHVMgRPG0skVQ5E2cmWdnPL/UrlDBx+1YepdfXbyHqDaybvRvfWkmMGVKfdrVrkpaZyZj5y7lwxYirvLh9eYBgjblnP/8krLd0PZNsknTFbT6WX63dy7wx3Tkp13IvoQhX9n5DiYzOFPZuRmr6MRSqHeiDQzDKFbjGGjkWZvHJPHkrml7v1ScyJpnIyzYzzYcZQltf2driJvPEQFMm7IpArUml3yefYda7sWFRaC6pOGkZntclwj+FJXHLJNNOnm0GKWHrRLnUEkCSWaJ0qZYqcUr6SmpRsGXBAd7/vD1KDyWpxe7x2dfDqVGrNkePHuDcuWM0frsWMqUCQ5aBSR/P4861aJDL0fIATYk0ihXxIvZBMpkRrrRx640gl+Pi4cLAH7qTkpDGnuVHH/J7ljTJyvhpXFWsjpmDUqVgSMOxhJ2WMGN5YPEAdIUjmT7qfWoGlwEgy2Cg07if0F/RILPW713Ei55j32HRNytIkt+itJsrf27bxpD27XPK+XHtWhpWqsT60xGYBTOrf365zu2TH6SS/MBJQM18REL0S3o4mi/xZ/OnKS8tnm+cuxeC/GDuGgIfABcFQch2ghoNdBcEoTqWYXIHGOj49heLCwdsPhwPxznLhkchN1w9nk9cswI8HTbM2M6nf/anWrNKnNl1wS5Nm5bJut+3MujnD/m00TimbB/LF3MGoVAp+KrlRKdlft5kPD8f+JYW3RvTI3AQYNnYPYkAysMoWy2Qmq2qsioPZiglgovRYVBrbl24y46F+0hLysiVZ9W0Tbi4a2j9YVMmbx2NNi2TWV8u5N515764ep2BhRNW0XfS+6z5dQsxt+Oe+nkK8HKg0uvlOLfPcSyyFp1rUsTfm+E9/0J0toF2gJh7iQRXLE5CXO5YVQDz5h2gxPaLjJnYhVPHw5m75vG+Y3pjBtUFPQ8733RWmFmddhNci+S6x12l4pfu7VDIZfRZsBrlRZsJaeM6ZZn/XS8yMrMoE1CY979ZRFxSOp4qFV/0bE7/Tg3Yevwqy/ecy1Xui4Y2LZPiZYoQdvcGncsG8madWgB4urry++C+dBz3C6bU4v9a/cOmreHHoW+BSgZD3+fi2XOcOL6EN1vW5cf1Ve03aRIsW3WcwQOac9HHk8MHHY8NZ5AJMrzcLJuLpX8dpmX7qpQuV4zbN2Iec+eLw4NIi4lmots9Fqz4Bz8/yxjt2PEdihcP4O85C+kyuCUKpcKysQMyjInUaOHJryPHIggCoigy+te/OLQhFjcKM334Iqo2Ks+I2R9bNncSFCrqxcwT33Pv+n3K1SxDenIGhUv4cP7AFU6GnmP6oUnI5DIeRCbQs8zQPD2DWTRTNsQrZ2MHoFYqGde7E59N3EyDmk2o37E26ckZzB+zDG1aJkJJPZN7fcw3K1YQ8eABLioVmXo9LapVY9G2g3hS6BE1FqAALzkKNnePhiiKh3Aco+HxMe2eJ56BOZs7yuaPoHZR0e2Ljmjc1CTGJLP2t6d4zMe15VVl3/KCp4w36JS1yFYvFUX++mIRgkxwKJoSeSOaNb9soXXvpvSv/DkzTvxA4RI+nNvnXKDh4qGrDKz+Jf0md2dd4gLcvS2Mwp5lh2ztdaKqKr3Wpk8zMlK07NywmwfqS1yLiEHxWgzpYa5ozJ4ON4vefp68NbgNc0ctwWgwOSw7m7HRZRrYNHMHm2buoESIP5/PHsjdq1FMH7bAnnFU2NqanprF7JHLGPLbh+xdcYyws7fRpmbaFCulbZKyBlJfPAmL5pRFkPoQZW8spMyn1E9JEjNMlI4HZyyotO8l/n8OFW3zgCdSLXXK3MtzZ35kfrtMufI6Sgd7RrRIgA+tezVi+tD54C5hmNSWd5mcoiMjPcvyHiVsBlKfKTszOEueiLBYgoJ8OaG1sLtmbxvzI9Nb2hJ9O4EhQxfRt19jZk7rxcSpG4mJTUWut5Wn97YxVuoMD8IFBfYBC+GMWcDoWwKjl4oMidbyf1rXoG1ABb47uYdjsRFQElwzbGNqc/xtzq5IwCyKTO7QikytHkGEdK2eiXO2I5PBgO6NWDy+B1tPX2fValvcK1NOsyT+ee4SpUm5AEoZokZh11d2MfQeWs9KlPYjuEpJ9q85QS5IWDxBpWTr0iP0G9uJqd+e5J2G9jFtBUEguIQvV1JMyGUKOyY0x2RROuYlLKw0r66kbTykBtieTakVydDqGTx1tV29ZdW+RD9Q8H3vNoz71hKOQO9lu0+0DrtpK/YzrHU9ju60rJ/SjaBdWyTjTbSOR2n7Du69TrcPG3LrVjyC1IdXskbIpIdaZsm4ka5FmdY13y6mm2QuSn3apO8sysmmUrpGyeXcuniPMtUCcjZ22ahVqy6L5sxl7qIZmEzmnHcs87rPf7+YlGPlIQgCk4YOoM2Z0RBn6Qe5Qs6lI9cJLF+Cy5fPoiycxOvtqlHytbJcO3WTcjXLsuCblQRVCuCvrxajTbEIlKz8cSP/3JzOjoX786SECSBiprBk/majrH8xaneoiEan4tfRf6A2u6ISLP2qT1WQqdfTo3FjToaH0+eNNxCAaWvXERMuQy44XusK8JzwLNZg/8vfmHlFweauANlQu6gYsfATrh4NI+buA4IqlqRU+RK069+C8HN32P2cFRQLYA+psqUUCqWcz+cMwqA3khCVSGJMMt1L/Yft+mVM2vg14zr+4LTMWxfuMrbDlJz/u3q68s5n7SlfN5hrUof5R0CulJOsS6JcAzOzPh2AUqHA+K6JrxYsxT+oAUW8/VGoFURciWSfVeG1/YBWbF+4D/+yRclM0+U5rl5UWDRftv6ejgNb8duBCZzYdpbQBfsc+tcZDSbK1SrD4Q2nee/LjsTcjiN0SUFMyFcNTbvW5+8xyzEZTTj6uYqNTMTbx81ByqNx62okb/ZqlKe88+cdZPPuy3zz9VucPHOHv52o38lkCm66lmZ/1nWaWs08E8xmpss8cfMJzslXu2Rxvm3bip2x13h7xzxMeuc/xFHJFhOsuzHJVAkpztGLd3LSzGb4ffNhft98mE/av86CST3ZevAKK3c8Pm6fSqOgR/+mtO5Q3f7wwuTY7BERvP3cUatVvNnD4suldlGhTdMRVKE498Ji7MydBJmAT1EvGOfCpTsRNK5Sya7+mKRUZEKpx7YzPxEVncziVceZ/VOvR+bz83FH74TZexJk6QwkJaRTt1EIJ/dfe+by/i1cP3Mb0YGtml6vJ/5+Ciq1krnf2dQ9PTzUOTH2sqFQKPDydEFrNZRYO2sXLd+pyxufVqb05gi+7NgThVzO1ago/lz+G301I1j7+zaH7Vk0cRVfzRtMqQol2L5gn51ytCPIBQVXb91HFEU7t4Kl+49wbNdxGtYOZMSXdTl04TonzkSiiSuBd0ppJi5bxaqRX1InOJj1x4+TmplJTIaZ4m5liY1/kNfuK0ABCvCckW9qmfkBT8FHrCezOtW/aHVJKaynHBo3Nf1/6MHloze4ezmS++ExNO/ekNsXIqjdpjpuni7otFmEzt9LXES80+Keu5rf84Iz9u1fPFXKk78jMOT3vmRp9fiXLcquxQc4suEU1ZtV5Mfd39Ba1u2JmudRyJ3eE99lxrB5j2yrRyE3NK4qHkQmoCt2j+2/fo6r5LRZbzDQ5vNpfPf7byyZvI6aLSqze+kh4qMS8fT1oGHnOhj1RrwKe+Dp68n98Bg0bmq8/LzsfqQz0zJZM2uvrU+sDJlHITe6DG5FSLVSGA0m9q89yeEt59DrDIiiGf+gwnz224fMHL2SO1ei6PlVewxmuHAkjGsnw219KWUcpIF/7ZQrnTBtkth12b4u5kyJn5/Uz0XyMSSNeSc9RRczbaysWfJ3Xk+wH8azmNjm1P0C5rNMotg46KfelK9TxhICRmp2qVCAKCJTyNBmZDF+yGI70QqZVuJLKY3RZi1bpVEwYWYfRg5aCIDZxfZOzGqJ76V1s2Nwt6R/9EFjatUKZMIvW4iOTUFusPWPOjYTURTRxh6mcOYtNJiJ1BQmuWMn5GoXNAoFk/7TBpVCwbBdmzH5JFqbZHu/KVG2uHWKFFs7OvuXo0WVsoxYvA2zZPiU3Gnzq8oo5c5/PmhCraqlWH7gPJuOXEGVbhu7mkRbP/TpUo/4uFS2bzqHTC9hqaWbGgnbbKeMmWH1ATObKRboS2JsCvosU668leqUocRrxdi2bSZrJwzH3Tq/tp8+x3dLjuGmqgrYK5tmzzVBL405aRvHUt+6lLK2OSWTNNslQeIjJ3k/mrhMPD01jJ30DiP7zwPA5CmJTaczoNYoGTD8Deb+uJWsTGuh0qVfshaIjk7HJRYE2T55bbvWJiMhjUOh1qDu0vmslawXUkZNEuNQTEnLVY1ULVa6bkjXJLvrTiwOZECHfk3J8k6kSKlSvNn+7Zy0X3+aSqeubQgqWYYeFUfklJ2uvs6qmV9QrLBNFCcpNZW3+n+HPNoHg+YuapWJTK0LDd8uzU8dOti1fdPps7i2bM7UjgttbX1ojXNx1zBl+xiKBBTmytHrJEQn8een83P1A0BIzTIUr1OI6MiTjHmnI4U9PZm/Yy9/bzzIyF5v0kXCHO89f4nRP27DPcOfTEUKbmVTaFClLEnaTCIe6AhQ1aBUSAmH8WcL8BLD2TeZg2+YXeaVL6UCZH6hdvny4qk5c/KlLKFJk5eyrwqYuyeALiOLP4bZL57b5++j/YBWhNQMYuc/Bzh78Dz6UpG4FNGjEORE3c3AJbYUSkHlpNQCPA/M+mox7fo1p2RIMSau/Spnc/Tb4EdPcJNoIl0VDwK4Z/kiFxToMnTodY8/tX7nsw4EVw9ibMcf8PJS223sAFRKJd5eGtwLuZMQlYjGTZ0TMyc1IY3QebYNm0Ipx8e/ELqMLFITLWZK2RuTak0r0nv026z8LRRdhu1jMy0pg4XfbwCgXPVA3hrQnNqtKhMbkcClozeo27oK3/WdRfOu9WjfpwmB5Yvz24gVlCpXlEGTurFpwX6iwgv88V52NO5Sl5XTNrPhzx3IfGyCE6K3JEak4slNUPQ6I/KnuO/vfw4SeuQKv4zvSo+hf+dKFwQBt2KNSPNqQRqgBuRqNU0qlmZkl+aMO76LvREWwYgn0bwLPX+Doe1eR6NSoBWNTvPN/OcACoWMTwa3pFuzasxffYTDZ/Im3f40iLlrZd0dCDFdPnmL+m/WwJhSmY5j/qRy5WLci0zkQbIr7soqufI/D3Tr9Tq7Njlngnp93IwV8w/aNnb5gNDVp2jfpSbN36rB3o2PZ1WfB/xK+NCia11c3TUcCz3P1aM3SPfaxtolqygZFED49VuU9Q8muHQIH9UdZ3evOjOI3qOm8vvoTyhfOoiwiHsM+346pgRPipUIY0a7JhRxc+NEdAz/3M/tH92sYnn+OHiYP49PpmhQYe5ejeKzxuPt8mSm6xjRahIfjH+HPUsP0WHQG1SoF8LV4/ZKph6F3GnRoxGzvlyEXjTQ5fAsBIUJIdELr0A3Or9e1y5/82qVKVQ8FEMYuBi9MF3zZM+1NOTIUQjuNJxSF73OgEwue24x3gRBQKGU58Qwflr4FvfBu4gn4efu5E/DCvDqosAsswCP8oszm8xs+msHm/7aQefhbdGWCKPfGy15r0ULBEEgPiWFbqO+xxxWOkeR6mnZgpeK8XscG/ecmNe89okhy8DGmTvoMLA12+bt4ecBs9ictojNf+1wek+mKplilY1827U9cpmM6Wu3E3FehCwfzu25SLuPWrDtbydhMwQZK3/cSM8xnQmpWYbz9+6RkJqKr6ftgzs9M5NSNYKJDo8mLTENQ5bBXolTcqJmNIrE3bOwGDmx6KynyxdO3CYqQUfP0Z25eyOG3Vsv2spIs/hp3LgUyU9fLqduy4pUrF2GLCNcPRdBkaCi7N94hveHt2Vk198wyxVE3Y3n+L5r9BvZga1LjnBfusFzlTB3UhVNiQKdmCVhM6z1A4iG3D/M0hNywUmML+kpvjTOnd08EnMrleZpnjlhXh2pfFqyy6x1SPzfnPhgPVH8KbsmOR4Ddtcl7Tux4wK715xA0KgxF7PFiBPlDnxipD530nfmaut7s6vtIMqskGHwsbxz6XNK/bCyhVLTi9uuXdOnsv/yLdbMHcSevVf5c94+AJIq2XzAUoJtbenToCYtg8vQav4CZMFJuBazXG9U0rLpOhBR1tZuma3/TC62NplUMr5Zv4s5A9+h79QVOdczAiV+iNZbjUYzP8/ahUalYHTf1vTpUI/pc/cQHmWLoyaYTIh6I7IsA4L0MEc67qWmd1L2OltRU8pASz8mpHllAmq1B5hqEVCpAQZVLNqwWES54FC0TrD6OxolprbSGIM6H1s9ynRbCa5xkniDUTY1QSHJnvWqUrEEC75YnGOKqki0PINMLsMsk2FM0ZIVmYAgZSIdKA7DQ8rCjjYCEquArfP3U6VBMP2/akf4mVvsW3sSs8lsNxfNmba4eYJ0/Ga/EycxOe1jEDrZIEjmRo3G5alQtywb5+whXeLz555SlPRDIpcO3EWOmtuuccjkMuRqhcXfz7pWKVFgjC7HR5/PRe1uJDPBiGtaMRRe51nauQNK6zPVKVaUf+7kPlg4ei2Mjb8fwTWpFJcOXWPO+Z9o1785odZ5JJpFvl74CSajmavHbnDrwl3+HrWE7qO7IFPIuXzYZuLafXRnlny3BgCVoEaVXNLWh2gdqj/b/QwJAmosLKmHjzupCWnERyVSvGwxIm/cd9yX+YzeE96lZLnifN/9l2cqp3XvJrxWJ5ipvf9Al5HbN/9/Gi+TNVwB/nX8b29dnzMWzZ5H+ZJFeL9ly5wFs7CXFxMH9iRdU2Cf/jLg1I7z/Dl8AQAd3J37lYiiiO9rWawa/xmNKlWkQYXyLB3zKcUqmjGLZk6GnkMml1GudlmnZWjTMsnM0OHioaEwwXw6fxmxyckAxKemMnbjRjRpJZnW/y++3zKau1cin1heuULdsvQZ15liAb7M+2ETmRlZNHmzKgqlHP8AHyrVK0uVBsE0aFOViQsG4O3rgVKtoFmnWui0eirXL8ukpZ+wZeFBu/h9ZpOZeVM20/mjpsge2iRpDSmkGG6QZojmZTLr/v+KTXP3UveNqi+6GbkwbdUBfli6h2aNXmPL8qEMH9jSYb7xbZpTpVhRei5fjdHJJiGvMJkhxoGJnjPo9Ea+m7aFsZPX0/f9hkyd1p33ezXgj1l9ada2CtGRefN1fRYYJQIk+3Zc4s1ONVE8BWP6rKjdvAKTFg0iPjrZYfqn/32fj8d1Iur2A9KdhFB5Vlw8epO5k9YTcSOafuO70PaDRk/FHj8LqjUpz2u1SrP0x82kJ2tzpQuCgEJQIggCWZl6Zo5aQdUG5XLlU8iUeJqCUaeUxzM9ADkKAnyVORu77LIa+foye/funLX0fmIik1dsx13vx5bZu9CmZbLk+zV88ktfAFQaJf1/6MH2Bfu4dvImxUpbRF6ad2+ELkPH/Zs2kZiWPRtzbPNp0pNzKy0DpMXI2X7anqU9fi2MxGjH87BJ1/ocWH2Mu1ciCaxY0mGefwMb/tjGogkrHp/xMVg+ZT1unq4M/eOjfGhVAV5ZZIdCyI9/LykKmLt8hNwtCx8JM5ONOuXLI3NfCY71PgrwlFBplOh1Bmq0qEyNllWYN2bZY+9Z8+sWeo3raold9wikkUyfFnVznWr2ebMJE87spxB+7Fiwjy/nfcKPfWdgdHIavPhbixrdeyPeZtfq/XT54i88vOW06NWKe4dUpEZawj9eO36Dw+tP5MnfsFyt0rTu1Zh74XEolIockxWAI6EXaNylLl37NyU+JoX425Yfep+iXuxdf5prZ+6ye+1JPpnUFZ+iXmz4ez/GLCOTVwyld62xdvWYTWZO7L5ChdqlOXRoHwbvaESFgbrlSjOs69ucC7vNnPV7UaRVRlFwTvTCcPtyJM271WPvquMvuim5sO98OKe3WeTyl8zqD2v2AVC7QgANW4RQrXgxjt6JYMKevY8oJe9oXC6QXVfDH5/xISQla/n62zWU8/Vi6s89mP7zdo4+wjQxP1GpdhAjf+3Bpn+OcCH8AWuXH6ddp5psWnf6X6+7Sq0guvasj2chd+7eiOb3USt4EOX4gCk1KYO/p27519sEcPN8BDfPR1CjWQUatKvOoU1n/vU61a4qug1/k5g7D1j+c95VsHevOk77D5sQXLUUNy/ec5rPjIkUoz7X9eYlStDz8BFuCwKRlyMJu/YAfbIrSpLwFH1YHP4HyXEpjGpvEf7qMuxNdi89xO2LEZzbexlEM29/0paE+4l2lihuXq4ElC/B7iUHnbbJTVuESbO2sbfeFdrVq8qeM1fYfeQmbqkBDjXQ/csUJeZ2HJ6+HlRuVD7PffSsyM/YdXuWHcK/TNF8Kevfhkchd/r90IMVU9cXhCzKb7zEG7P8wMu3uXtVqGMHDqmmdA2xiYm5su44eQpzssaxnP1jTMdeuCnmi3gfeaxz5ump/DF0Hq16NcYlj3EIE+4ncWTDSQZN682p0LOc2nHeYT45CpJSc592JqdnILdOG4PeyIofN/LZnEGc33eZvUsP5vgEKFVyWvRohKevB34BvpwKPUfCrWRcKYkpEd5s9R47vx1vzaugUsNH/1BKx0nSgzT0WQYSH6RxeLPlo0dqunRy21kO6wwWJs66OT13NBzUKirXKU25mqXxKeqF2lUNMhl1WlaksL83osGAkG0OaTU3O3viNkKJK9R7pyRfjfodd3d3ZkyfzsZTpxjZtStt6tZk2J/LyLjkg7u3K/FRSYg6J6cYjsa6k4DLdqaY2jywBHbmi7nDVDgLMC/tN6mIiyARa8ARO2lyEsja5MRUTZLHbs4/rRCMtN2uLlw5G0GT915n941Yh/mzJekV6TZTNlNhm/CGWWUrz+Ri+1nQikZUIR4kp+nQe0r6U9Jso7Wr9DadE7IK2/ossZqAQiYDdxkzJncjpFBhQm/fYFfMdSbu247RbMajgu0de7va/r6QYInz5u9tE0WJNNnaYUiymZMq02WU9fFh7b6LCJKuN0gCpsuzJOIuSZa5qtDaDmYiYqLZv/0SpowsuwDcdpCau0oFOaTmyNljSTqOpAdFWbaP/DnTtnPjYiQfj2hHcFwa65ccpUSnmsjNYg6bblZLxEGyQwpIi/O2PaNJbUvQJFlelEwG5RUqypQvRqVapQkI8MY/yI8Lh28wZ+xKm29ttmm0QjK+DEYKFfFEqZBBhmQuSoO529vx2f62UxMVc1+TmEvarRvWD69Lh2/wztA2NnNsaX6pCab1cE0qouIUDn67qzerSL2OtVn9x3YSY1Ls5ldO6ASpKbadia3ItoUH6PV1R8LOSkwsJWajglyOzCyg9vVh3rVr9CtvWe+NZjPfnT1LzF0dvaZ/zrK/l2AyH6dfx+YkpKTx98a9LP1tJa26tKRogA8XTSaCqwexbe4uu+fw8vNkw4xQu8fsPOxN1v/+6E2qIAi4PAjgyCYtuzbvwEV0x0Uo5XBj9/aQtjmqnKkJaXj65g6t8CogdN4e+n7XneAapbl5NrdJ7MsEo8FIlhMV8HzDq/LNXYAnwsu3uXuF4aYtwrkrN5i6dCnDu3ZFrVJxPjycqfPX4q4PdhwNsACPxetv16FKk4rM+mKh3fXv3/+FiGv3qdGiMskP7ua5vCtHb3Dl6A3qtKlKv8k9mDd6aa48boIH6w+coX+7lrhYhVD0BgOLth3CA5s5yq0Ld5n20UxKVynF0Bn9ibkdh8bNkr9mq6pkpGoZ3W4yJqP9R7zaVc37X3fi9c51Wf7DOuIjcx8KOMODyATmjF5uvwHB4hMzcFJX4mPTUKoUKFVy0lN1bPnnED5FvOg37m1i7yWyZ+NZDm05y67VJwGoULsMO5YdcVhXkjaSckXc+W3GHznXxowbx4gvvyQuKYkihQrh7S6jw/B21Gxeic3z9rFjnhM/xAL8azi+8xIfjuzodHP3tIiKS6FCaX+OXni2jyCj2UyjxXNAKbK8/XuMOrQDweXZxBEcwdNFw/3EVCfRBl9O3LgYCcCeKEYHAAAgAElEQVTs/26j/4h2AISuO03vgc1ZMPPp51KxQh6M69cCdzc1JqOZ1HtJREckcGjHRS5uPcW0rV/zy7AFjzxf8CnqRa8v3uTm+btsmX/gqdvytDDojajU/+5nSru+zdBps5g9duVTl2E2izyISqJms4qccRI7teun7bhyawMlatTg6/370YgiWUol3Xv25OT4JXj6uxN95yxLv/k8557OjRrQ44c/2DP3OLPP/4SHjwf7Vx+lRc/GrP3VxqIqlHKUaiUGq5Jrg461SYpNzjPbpRY0Fr86J98o1ZpVIjUhnfP7Ludcc+Sr9ypAJpNRIrgYgRVLvvSbu8x0HX99vvDxGQvw5Chg7grwWFhPPmQIaO4Gs3zVKUKPnEYpVxB/T6RQShmnC+FzZ+ZewVOa9KQMkuNScl2/dcGyoZs3xro5e8LAnCe3XwBBRv8pPblz6R4nQ8+REm/7MdTf9KPD1z/RqEYwcpmMg2fD0IX7oHnoXZrNIuHn7zJnxGIMemOOo/a8MctYl7SAUP1yzCYzE7v+iMkaDDmkZhlKvlaclf/dwIiFQ+gTMjR3A+3eleQk3Sp3bxdQXCZD464mLjqFNX/ZPggLFy9Et8GtWDN7Lzcv3MPNQ025SsUxmaH7Z+1QKORERyTg4uGCoFZTPNCX5p1rI4hmsjINbNyzgIaN2+ZqWs8PPuDA5s10bdwYnU5P6Py9dP+yA8WCCnNm22mqN6/E7qWHHYt62IkeSEQRpGIpzsIiSIuRnPTbxdMVHcw1KdOlkARIdpfEf5OGEZAwK6KjkA/OGAKpnLrkx8PpLJe0yyQa0XnfpURxV4wmMzH39billEJwMK7t3r31b7MokBxs2/BLWSplhqXdcglLpYi3MdOCuyREh7et728lJhEY7Mf+2/aHJzqbbgtGaxdm+djGq3+Izcc4OcPGqmfGugECmAQahdjMJ90UtndpFm3Pey3F4k8Un24TRTGbJe9XbaszrbSIyUUkrbSIV5gtjyrNMcMkz7Ku23pJMHCdHowmBIPRTtLfTkRFY6966xDZIjzSsZuuzZ0OliDpwIhJXZAr5IhKOXfvJZJuNqEp4UVamg55pu29ZflZ6lem2q4ZXCXMtALqhpTky05N+Wn8Bm6FW96FUrJBN6elEfr3bnp/1Z551mDlIGGVrf3UpGMN1szZR9RtSxl2AcB1NtbSToBI+tHkiLmT/ObZzS07Js62LsgEm1CJHeuO5L1Z56MgmcN2ZUvntqSeDh+3JC0pg/2rj9usFiyV2v7WWVlByZpkJ9ZixbZFB+k3vgtXToRblESVkndiMrFj8SEC23nTuFYt2jRsmJM2avoclCm+HDy4l08626+1KqWS+tWCuX1JyedNxvHb4e/pVXowyUF+dBrajvXTLXHwNs3cwYffvkeWNgs3L1fO7r7Iltm7crXxaVGtWaUcUZZsvKqbO7PZjAhUqF/ukSarBfgfRrbP3f8wCjZ3+QyFoKRwWjm4Zvmg84UCxu4ZceHAFS4ccHwa+qw4GXqOk9vOEFixJC16NiK4Wmn+Hr2ExJhkNIIL3A5g760UQEQtlETziLLSktJzXetcqA+v1Qlm8K99qN2mBoYsA6JZJOZOHF82n0Ds3QdEXIvi9U512Ton949xrVZVqdyoPIsmrcmV9jC06Vm4uNm3MD4mhcyMLORyGcv/2AlA+RqBGE0ie9efxmwW0Wt1nNl/jQ+/7kBCbCorZ+zEkKFDpVGSpNIRHZ1bqvv2rVsU8fLibNhNtOkq1IJAxI37uHu5UqyhjIthmylc08ytGxm4aQNzlGILYA+5Qk7F+sFcOXYTnc8NVo4fSHEfy87p/O27DPlpBa4JZfJUVn7E7HsYYVEJtK+Xv741BcuhParUDGTEt124eT2GiDvxqNUKsrKMbN16ns6darHoH/uA8GvmDWLrrot4KlXI5TLuRyex5l44D1IyUMhkqDUKvurcjA9+Xo57uHNxmS2zdzHj+GSQbO6yIZPL0Lio8PX3ztnYvSjcuRpFYPni3L2Wf8qMMrmMPt905XjoOS4fDXv8DXnEzmVHaNq5DjuW5raESE/W0rBOVz7++Q861KlJ2ZL+zF+/k4snEnAViuLqqsGQnNuCIyEumb6TPmLFtxuZ+uEfTN42hi+bT6Djf9pwMvQcUWHRPIhMYO7Xi5HJZJglhxIaNw1mkxm9Lvdm9EmgUClyWZ+8ysjSZqH8lxnhAhTgReL/xej2KeZNxQblOLTuxL9f2WOCQ+b53gI8OZ6h/+5eieTulUhcPV15/+u3SU1M59Da48TcjkMtPGJLl4c6y1QpRcUGrzGu4xRSEy0bwOCapYm9a/loOrfnEm37Nc9VnmiWkZ6cYSfFbUmwnjxL7amyGTDrabV/UGGq1AumWFBhZHIZBqMph426dizMzl8mdOlRGrWvzsoZu8hMtH0M6jOyUKX5s2/Xfj786B4BAQEAaLVaZv85k1JKL65ejUcdF4AgxLHq5y3cTTtO3yrB1HizgeXZ7kQwdMZ6XJNCbKyJ3YmvE05L6nOnc+KX52yuOWAL7ZhAKVsnDbiclntz/vC9OUyMMx8jKVsg/RgyOf6797guXDsZTrvhr6OKc8/Z2AFUKx1I7ar+nD9oQiWonQZwRq2kkJ8HaelZdsGpzRKGMtu/TJYl8VmS+FWZNZLAzpKuvH4/jo986yDKIUviU6eWEOkGK6mm8LP5Y9X1szF9p2UBOX9HaVW4qpUIHgYiM2wFJmTY3kmgt03QQ251njOZHW8JSwfYRAZ0/kpUrkaKVoojRumXc12VZntOz1s29kzxwDLWpWEgRI0KFHJEpeKhd+xkLZcyz17ScZVdniRkhMLGPgrW9zB5QX8Cg4sy4dMl3Lhynwo1S1GzbmmO7r9OarKWrCwDfr5uRBktzGa39jXx8/VA4a3iTHgcyelaXgsswtjGrXBTqyji5UZGchaL/jmC6qYO1R3bxswsYZsEheV9370WzVtD2rJp3n5UGiUyhZxipXx5Z/Ab3LkRw+kj4fb+d5J+kDJjTi1QHPmrShlRaaBxg+NwKpcPXKZGi8rcPnfbaZiQnPsk78MuhIl0TRBFeo7uxM6lh4kMi7Ex7HIn7zi7jdJ+UEh8vCXzPDNdh9ya7+Gg4wCzP1/N+ujlrF28jiGf/42b0QdXoTii2cjcARtxKXeflRO/zGHFMnQ6jp8NR/37Lhp2qsuMT+dhMppYHjWLjGQtt87fJfpWDPtWWDaT5ofUZodO/4h7N6LwKuzJ8S1nOLf3kuNnfAReqxPsMC6cXqdHpVE988bxRSDqRrTF57wA/39RwNy9+mjbrwXl64Y8n81dAV5paFO1zBuzDE9fD5p0rU+JcsW5cuQ6V45cf+IwBdnYNm8P7j7urIieQ7/yw4m+be8X9e5Xb7Hmly207NmYwIolUWpU7F12iBunb3PtxE2unbiJLC9CAYDRaKLf6LeIvBXHhSNh7Fhz8rH3pCSms+WfQw7TlDI16Ze86NG5OyEVg5ErFFy/GI7+iitZKNFInO83LdpBk7ZyagQG5txfPagUresEsTs0A43c3WEd/59x5XgYleqXY+WS5YzsUS1XetUyAZzYfxuV/NEfIk3aVqFsBX+mtalISOkieHq4kKnTM3DcMu5E5d2f82Gk6/T4eDxJKPHHI8uUf8GvpfiwdDNidY5l/F9W+Pp50KP1jzn/v3Yxkn5DW1OhagBpBhMbN56hZ6/XWb3nApXKF6dx3WB0egO/rjiAzLq3OnEpgszjFmVNd42K0ilqIpwoXj6M6UPnM+vsFPyKF8KgN2IwmEhP1vLL1yvsQqO8SMTejccvwDffyus4oAUXD10nMizm8ZmfEK6eLmSk5g6fIEXPkE9ZE/UXh+ZfIvyCLa6iPsNIyiUlb438gU5Na5OYmk7owQukRyi5XvIgcbvPkOF7j51L99Lv++7ERyWy7Ie1tO7dNCdEgRTFgopw4eAVts+3KNEO/q0vlw5dy1F2zmsQ8kZd6rFg3PJc1928XF/JjR1A1aYVibqZ/++/AK8ICswy/zewfMp6FKoX8KgFTNwri9SENDbP2olSpaBUhZK06dscpVrJ9gV7HUoS1+9QC7lCzsnQc3Y/eC7uGiauH4FMJkOhVPD2kLb89cVC/MsUZfTST5Er5Zj0JjoNbcehtcfZveQggiDQ9qMWNOpSj3vXorh2PIx+k3uy7e89FCvtx+ZsYQPrKXWNZhW4cPIOpSuU4MKxcK6cl8hxS9kHKUvlxHcm5zbJSbebsijcLUrkbTNk6XERAnF5aF30KerFnZgEagZWyFVWk8rBbNp+Ehe5V3bltkSpX5oTZQe769J7nbEp1vxShk7QOFa/tGPrpGqZrpINjVrC3GX3p9TnRvoRLPXtkfr5SZ9BctJ/YvcVTu+/zvujOrN630rqhITYPcres1dxU/qDILfzJ0KtIkOfCMaryNON6IxFKVuxDr8tO8jl69FodXqCg4uweFofrt+K5bPOFkEcQaq8Jmm3QtIncg9bP3hEGklPzKS2xpcrYTYWKCXI1vcK6/BRudnK3ry7jq0eyRBsGVKa2GQtQryaW7qiDvNcTLS9N9FkSZBLxFfMabb2Rcm9AegWVJ3XVOXou24d4IdcbytQW9T2t/s9yXjLPjCRxJkTXZSIgoAol9mPE3cnqpdK24GFydPG5mSzplIfR8FgP75d3TWYHtpAGVUKZs+yfIx/8nU7gsoWYc/+qyz6vS/r91/k3J37fDZyOa5gR3q75cQlyyI+Po7s0St62vpSJvUjNRjwL+1H54GtmPbpYi6fvGVRn8wes24SZkraRrlkrdBL1hMnc1dw9AElDUqeapt/duyaYM+6yRUK+3Sw94vLbqMztk4up0azCtRuWZmTOy9y/kiY/Vx9OL8U2YqlyZLA706C03u4q0iNTbZjHh9GeoKFMW7cqQ43T98CbP2nMXmSed6NuecvWlSZXV14650ARr3fGblcTkyXJHp8+wdftPiGn/d+y+Lbf9Kr9GDa9GlG43fqc3CNbYMXXCPITjBEl5GF0WCkVIWStOrVmKBKpfjzs/mPldg3GowYDAYyXOIR3LIgxRU3g88j73mZIZPLSIlPI7h6aQoV9SYp9tU6ECpAAfKC/xebO7P52W3OC/D/Ewa9kfDzdwg/fwe1i4q3PmmLm5crmWmZXNh/hZBaZShSqjCnd5wnIzWTXuPeIfJGNDsW7gNgxMIhLP9hHWd2X7Qr98yuC6Q8SOOND5vyrv8AuzRRFNk2dzcAQZVL8dEPPSlbLYgB/+3JvWv32SoJOO7p487gKd0Z/+EsKtUri4ub2n5zl4+QyWSITpzoxy0Zyro5oew8t51OdWvZpW0+eQkPWaF/pU3/C1CqFRjT4MipWOYH7KN3i8YYzWZ+Xb+V2+Fm3ITcH53arDvU9rrDt7Uq4qZQsOfkHj6YtQSx/ns5eW7ceUCj939m7YwBdO3TiNULDlG1flk+Ht2RbwbMo3KdMkTdimPszD7s3XwOk1X63+hl28iYFeBfxIvrN2NB9fQnnSqZjApF/BjduBkDNub28XoW/B97Zx0d1dV28d9IJsnEhQgJwYK7S3EoUNyLO1SoUQpFi5ZiFawUd7fg7m5Bg4UkRCDuNhn9/phkcofM4G3p985ei7XCnXPPPddmznP28+xdt1AxOherQret29+pnx5DG9GwTWUcHGw5dezN09feFF9Mas+GBUfNfr5g4TFGfNGc4yeDCI6I48iVhwSFxfC+eNQi/l5cOnSLe1dD31OPfx+yM7Jx8XQiObagsNar4OhqzyeDmpDwPJnlk97tGXkVstKzqdqoHLfPPngp8/ksJIY1k02rc4pEIuzRL4TJiqYzsU83w2deLi7M+rwn30zZzczef/D72emIxWKOrDnNJ0ObU7ddDS7v17O4Dq72lK5ZkgZd6uDj783+pUdx9nCi/+TuBCw6hJ2zHXERCQWOb+9sZ2R6npmVgc4/kjlDulGjVEmOXr/FrxsOkZGeiY3cGsXfLdX/nqHVaPWM54DGZL2CZbXg/zEszJ0FFlgAkJOtZPu8vQA4ezhRtrY/14/c5nlIfnrH4+sh1GxZhe4/dGD7vL34Vy1eILADvbdd+Xql6V/yq5ce8+m9CJaNWc+vp6YS+/AZ2RkKtHl1JRIJafGpDKs1AZGNNXuX5aZ7CmvEjJguQWAmrE0x4Uul1QqUKM2p3wn2WzhqA89DY0kmg/VnL9KnQV1EIhG7rwdyNSgFO9sy+TU6wroQQd9Gq+FCVTwzqndGK+1Cdi2PRTAzVl1GQf9CeIGtsxf8LVwYyluRN1Pbw+soyAnZBbGICvVLc/9GGPbKMqzcEsb2s/dwcnci8oYWe0nRfFYrt85Op9PhJA7mt7o1Df009/Ik3D+Tv6LCsHPy0x8mR38dhvdfxs6A74iNTOKLce3YtvAoq0+N4/rZRzg4ybly6gHhMfmTZk1Gfu2c0kHMrNXHUdqLEQtO2fVR/j1JLCfFViZlQtFeZClVhCYloS6mple9KmQolCQlJpCemoK7tw8/B5wkOiQVG0RoUk3X+Smjokm+GIFOKcG6pAynehXAOj/gtE4X3EulPTNbdqD1hjWIBWydxjb/GdNKhUqOBe+PKJfRatmpOtO+3kBkaK4ypODZVLnnM2Aa6/z+hCqWRs9YXr2jcP4gfDbEIkpV8OXXcTtAmt9IWBPp5CynSsUi7Fx/CSvEzP68HQO+XEW0v/66ZfjlP98ldpmepIoyBEybnRyxREypij7Ua1qOzHQFAStPG9eo5l0fc+l6QjVKc16PwrphITuW9x6be4eF437hPh1YcYpu37Vh7RRBQKQx2sH4GIDIVs8+fj1/AKsmbyc6NJehMnNMo/MRBmbqgpYdxgq6+raFirhRsW4pngSG0X98JxRZOZzfdYXIx9FGNYl7klYRF5FApiYJiXcKLo42JCRmI471wAprMm1iEdnloEu3pYJ7QS+5WqX9EdvncP/SY5QKFW2GNWf/0mMcWnGCAdM+NQR3h1aeRCKVGAmhiCVibhy/g4efO0tHrSuQlmkjt2b63rEELDrEmW36Or5LN4+wfdpI3BwdAejcoC5FPNz5aclmGndpxfEN/7xFxrvA1t6GtsM/RqvVkpP931v0F4lEWNvK/nNB9QcHS3BngQUWvIiUuFTDj+iLuH70NmqVhh6jO6IzIRgit7ehUbd6tLHtbbb/EfMH0/Gr1uh0OkQiEWe2XyIzNRtprtl0zZZVsJLbYC2XER+VxIO/ia17XYTd0x9fTgn+2hrG2mO38K/sRyHPshSyqkJMRjA6TSpSfJCL7F7R2/8WXD2dcPV0omIdf6xkUtQ5KrbNP4S92HSqmxYNJR1kBbZ38ivMshuhkBvc5SErU8mn9Wew/OD3uHo4smfFKfasOAUOgpRCt/y/VQ4ytFoNz6Mek5iZg5UNFK7gj63MueDYXeQMaVeP2iV8WXbjOrZWUn5o3ICAq/fpv2gTj3ZdJCemAlpVcST2G3GpY4VzlVIF+slD+r1QUo4UQpSzFpHImoyIG2Q/mYHnkAYmpdd/bt6SbUF3yVKreRcdzr7ftiQnS2kI7P4JpKZkIpVJKV3Fl9mrhrJ38yWKlfbGv6w3Z44FsWLZKVYvO037LjVYv/UyTT4qTdOGZdkU8uCNjmMlk1K/ZUWKlfNGJBLx8FYEmxcczZ/Y/gck7TNTs0iMTqZoOR/CHzx7o30jHkXnB3Z/I5p9Wp+i5X3Y8+dRbp99gK2djIZd69Cyf2OiQ2M5vf0ymalZPL4Ryr6N+6jSxIrfPxuFzMqK1MxMes1cSLZCzdwh3ahV2p/jN+/w54FDBY5z5eFjmnVqSfxlBTZ21pzbJdASeOHn5kWFS61Ga8gKMQVFVg67FxzgweV8BVEbqcYQ2OWhZml/VJptlK9fhmdPYnh09UkBMZcPFYWKuOPj78WW2QH/9lDeCh2+bEWFj8qycMQKkwrdFlgAluDOgg8deSuy/7H6xVun7uHs4cjToEgjc9kiZQqz6sF8NBoty27PY2afBTy9l19U3+7zltg7yen4VWs6OPRDrdagVqpBJKZig7LY2tug02ioWL80iKBEpaLcOh1E0MVHhj5EAv+31/FjMwlhvZHgR9u4LkbQh2C7o8QXUn3p1KUvbn72XN3Vk7ElHChvL2fT8yCOJdhiJ81N3RSu0JtjCAWMntGKuZAxkxqr4RUYn5FSoOCamPtbeHxB/Yyhlka44q15jcmxqfqgXBzbcB57Zzk5CjVqlUZ/LhIpIonUeEy57KRYZ0VMdkFRkrvJqYgV9ljH5v7gC84/OzaRg2vO0n5w4/xzEShkagSqjlnuEh6evExa7HigFpBC6vOJ+NdV4OjmBYC8qANzvu1AicJuLNx7gd6bz6C1AqlYTJ8KlVmy7ixPL1wlO2wuIlFRRIA2tT9Jpyfibp2AtYMbannB6xZ9JgWxcqEhThOLaqB51h/OnsLBrzgAsoz861fdyYvZs47gjgiRUEhVULNlm5B/rdQ2guekkH6RQZJlTfNutZgzOQCdlRlWR5I/VrWg4FTpkC90IxP4zkkUuZNqM19bOqkYn6LurD0+BmdXO/p1mM/oqZ2Ij0qmWEkPipcoxO9LBnLvbiRHDt6mUauKnDr9gEoVfHE6oT+OY0T+mGLr5C+YeFzPZ/HspSKG/diW/Zsvc/7wHUPqrc7WGqz0z5ORn5/aBOstqNVDoGhp5OMoeB+MvOiE74mpGlnBe6FTmTO114/pwLLjDJ3Zi+XjNuUeU+BzZ9JOM/dc0Rl/b5it4TXzHptS/BR+rFYhd7TFr6w3gSfuGsaVlZrJkVV6z1GvYh60G9acjNRMxjSfSsX+jiz4YiCS3O9NJzs7qpcvwrcdOlDISZ+S2a5OTeLSU/h55y5+7NQRqURCVEIC627fZNrcBbh56eveUuPzWXeVUoXUSmoQTXkbvCjMkqMquMikUCrJUWhYNno9VZqUp9/k7sRHJZq08/mQYGVtxaejO3LrTBB3z73ZIsmHglun7mHnJCcz1ZJS+tawCKpYYIEFb4vTWy9y/9JjBkztgSpHjUajofuoDszuv4DjG87x84Fx/Hl9NopMBV3cBrE+ZDFuPq5IJGKGVRpVIO2iTptqrJuyHWtbGYV8XTmx5SJxEYloPtAVU4lUzJ8Tx7GjchEccoOUSf5yvK1jWPssBnup1788wg8HGSlZ5gUdXoBIJCIqy4WDkc9pU6QwAGlKJX9GJ/LZqCm071GXrKwcAjZeJjoqUS9nDwSsPEWHoU1e2X9ieDBp8d+iD+wAnMnJWkjk3V406KD33Ns5bwgLtp5l16nb5NgKAh+tlkfP4lk7qTe1az1CJCpq1Lc2ZzSyxB9ZNWsEUpkEtUZLSqaCuTtOERGbhEbhVYB/E2lbkB65Hcfc4C4Pi8d2o7C7Ew5ya9LfIUWpUjU/FFlK7guUC/8JxEWnMLLvUr39AvDjF+sQZeUHUmo3OSNHt6FSlSLs2H2d2dO7E3g7HBcnOckvmdiJJSJ69q6Pvb01EoWaVb8eJj0lC9Qf5vfE60Kr0aLT6RCLRW+k5PlPmG33Gd+FouV92b3gME161OPygUAUgvTm6NBYtswOoP/k7mi1Wp7deYqko/H7LhaJDIFdHga3aEHDbyZz/NRD7OUyvEqX4Ksfx/FtvYnERSaw8ekSo7q3hKgk3H1dXymS8iZwsvdj7dFTDGjZ1LBt6rptqGOcUOgUXDkQyJUDgfQY3QF3H1cS3kGd9+9Gz7GdcPd1Zftv+/7tobw18mybLHhHWII7Cyz4F/EfY+xeRFxEAivGbgT06VE5WUp8y/jQ7ft2zBmwmNSENA4rt/Bx/8Y4ujnQxqaXyX68irmT8CwRVY6SKk2rIbOVkRiVgKuHIz7+Xkb+TjoEq+sagQKk2ExtmInVa7OBhnC7MKgU+tLpdLh5O6PMUuKUmYyDnbGYygAfD9Y+C0On80CkFbJvgr+FrEB2/iQJ4Yq0lZXJ7XnsmpEnl5C5E9YkChk/IROYLahVEjKX1rlMjfD6Cfsw422nyxKcg/B6C2uYcp91kVhwXjamLRDsbKox8949lj4KxMXeGq+a5Ri/dAUVqpQiNTkLkVbHiHFtiY9Kon+VsWAlJSslC41ay5zdIxnTbYGRumNasfzjJJ9OBm3jF44oQqL15sCKL8jKyiHkeSI7zt9GKwFptv58cmz012TK9uNs+7oXRTzcCC3goS2jjK8HX0zajMpK376krxt/DGvP9p1XWev9jPBw4z0kkpuM6t2C1q1aIJWK0Wm1VKzsh0qpZua0AJZ825k/5x8j6G7+hEdtl39dhXVxOsE9Vjnpr7PE0w6xTEK2mw2yVKHCqeC+awTKolmC+ypg9KTZ+dslWfp3UGRGyGvgsMZcOnATUXI6CJ8NP2/Dn1prCb8uOMLkSZ1w93fn6zk72f77YE4sP4vycTyZ5fO9/HqUK4+HmwM2MitEtVQcORFE1LNkrGNy60ttZIiNVD7zz1MnLL9V5T73YsEzLfSfE37PGDFg5ijKVzB3gjEJ6+yMfPMEfVw+EEjNllW4cvDmC956mgLH0OUqcepyjO+BkReecCjmPPTyGEAziqBFShcm/H4UqhwVkQ8i+Gr+AMLvhRN6N7JAH3l5k3EJ6Wg0GgNzB5CenW1Iw8+DUqVCrJYhfeaLAnj6KIMf9k01fB508SFFKxTh0bUnACQ+T8Kt8PsN7mpUrs/S3xax99wNPN0ciYxOJjnMCjudsWLmviVH6Tm2M6snbn5vx36fcHRzQGYjI/x+lFG2jAUW/H+EJbizwIJ/CCql2iDI4lbYlWZ9GlDIx43woCg+Gdqc8W1+Nruvk7sjaQn6mXKZGiW5e+4BT4OieBqUO6E1ZwvwGnByc6BMzeLY2Fnz6HoY7Yc348i6c+RkK4mLTHyrPht3rsWF/YFk5RScEGVoNGgtXz3vBJFIhINNJZSAuLAnnw7vxMrZh0mL21vnyZ0AACAASURBVE3NxmUpUa4wt07fx9pWxg9/DuLysXtc2H+TPtUncjBy/kv7tnWyBUIAf6PtPoXhdlAksxYeJlhdIGozwpJt5/EvpSHkSTIiUX5wL7dbTqVa5Qm7kF83FRKVyG/zD9OqZUWs5QoQbwFtTwB0ukTsnRZz+GAljh7WL5J071KTa5dD2bTuAgB9BjRg1m+9mPHTLq5cCnnNK5iPUV+15N6D52+839uiQjU/Og9vSs/K416r/dTpAUyf04OW9cuy8+gtpi4fxOBmsxGJwN7OhkE963P45mMOnr+PRqvDNuHVKXkikYgyFX0IC45l0KjWKJX6fZKfJxGw8sw7nd/7glthF9KTMlBmCwzNLz5m4LQeXDl487X7+buZO//qxbl34RE+pbzJzlDwTf0JuQc2/52seubMT7sDmNShPTYyGUnp6QQ+imDZwWN81ralod3UDTtQRTtSsMJWj9I1ShIfma94mRSTgk8pbzOt3x62aR6k3YJUnQ6RyBNbE22yMxTERyZQtrY/D68+ee9jeFekJabz8EowQRce/ttDseDfhiUt0wILLPg7kPg8id3zDwJQs+UdytYtxaNr5iemj649oXGPegQHhuHi5YR/tWIELDgoaPFqj7gX2TjvEh583K8Rqhw1d84/ZOj0Twm6/JiytUpStrY/0WHxeBZ1JzU2lWehMahy1CQnZRIVHENOtgqJVoNYIiL0bqRR/r+VjYxyNYvTZmAjBjTZxS2ndKo65qu+TQ6OQqqtCmjRiYT1OaaZRWGNnFEylpAlkwj6yZ1UGXn5CWsPhWyYsBZHwFAY9S1kKPPGYoZRM2ITBRAJ1TyFrKARm5K7r63AU03ACgptKEQCplKVlIqHlyNfjm+HVqniwbVQTm88g9jamlk7v+PUjquMWzqErIwc1GoNIpGIb+b1Rpvr3da2XVUys3Jo3XsBAD7FS5MUNonstNWAvo3Eaj3Dh9Vm8aLjxIYkIvHOP588Vksem38PboQ/xVbuRolSI4mPaU1aWjHsnfcxsI87x3eEIMeYRQu+H0Lw+RA88ETldZSYhD1otTbY2aTj710a66R8ditghV6dL+/KPLwZQbHihfj2h0/o13Ke/rOM/GulFXqcSvLvZY6r/nr/uHAff47vgVMJJxKj84NWYQ1fnm8dgFgtYPEyBWxdpoAdVmlo3LoSzT+pzLwxW/VpkdkKpFIx30zsyHdNppL1XC/eIhYI2wjrW4X3e+ofB9i5eBjpWTkEXgtj7LJBPH2eREaGgr17AnkWkmCYcOukQlZZoMSp0FG1nj/hwTGUKOVJ9YZlaNGqAtUalGLGl+so5O3MR83LIVKq0IlNP/86c/6Or4O891FnJtgSfD+JdSr6TeyCWqVm8y8BhlQ/rUZLemIGzh6OJD5PINMpBhdPKdkKFepoR2zV+cIfIrGIklWL8fxJtNl6vhdVOQuMlXwW0Ryb6OPvyeW917CyljJh80gu7rnKqS0XCrCZ1rYyg+iIjcaB5OeFmHziNHHBzxHL7fjss4ncvXeNAb8txcfHlchnSYRdzcZOa9pT7vdz0wsEcnIHWxQZBX1L3wUyQW3uqwLl/UuPMeSXPsSGJ3yQ/nEXAq6+utF/BFYyKZN3jmb/0qNmxd0seAkswZ0FFljwd+L60dvERSbSc2wnNkzfYbKNcyFHMpIzyU7PRqlQsuT7NW99vPJ1S/FRx1rEhMezbd4+ilX0Y9y6EWQkZ5CZmsXXDSZTtkFZhk/vgaOLHW6eTsz/ZjWZqVm4+BXCr5Q3jq52qDOz0Wp1tBncBPtcz6RzAdeQO9mh08HmufuQKsryzf0blLVPwM/GiutpShKzi2MnsX/lOC14PSQ8T+FMwA3WzztoZPNQ45NqACyfupM543fqN6o1TPprALWalEWh1vL8WTIzpgcwdnwHPqpVkgvXQpBY2VC2gT/ht/uTk+lCmZJyvv2mKUWLlWb33tdXmMtWinErU4H1a0ozes4WFk7/nMLuhfi0y4KX7lfYvTiF3V/can7CuujnfdRvWg53D0ezbV6Gh6GxXL7zlF5tarBo5em36uNFrN4/Ehu5jPTcVNg8jF86mN0rThN6783UbX+b0JXx8/YQGBTFyO4N2LcnkKCIfGXPl1VryqylVKzqR8MGpblw9B6te9QmOiyerAwFmekKMg/fpWGbKty88Jj5P2zSm5n/y3DzceXJraccWXOaAVO6c3bHZcPi15G1Z2j3WXNWrv2VFROGU7F4MVRqNTPWbOH4vgjk2fkPT+Pu9VgzaQvvoqT6MjgXckSVo0Kp0C8MHVt/hpBbT0227TCiNUfXnDb8P/hUBAcObGT/0mMsGbmG1Qe2AjDkl6GE3gln4NDqzDpT8F0Zu/5rmvdphE6no628j5GHb2F/L+6dN89M6XQ6Mq2SELtnockUY5vmgVRkekEKoG67Gtw4dudll6AA9iw6RIMutdm3xLyHowV6kZ2OX3/CoeXHiXhorADbf0oPQyAdH5VIdEgMYXcjSIlPM7RRKdXcu/CAp0H/rlK2BR8mLMGdBRZ8AIh4EEVwYCi9xnbm9tkgEiKTiMtNt7FzlDNwek9Wjt9EelIGy0avf/2OBavHrQY0xcffi8c3Qlnx43q0Wi2eRQsxeuXn2NhZ8/DqE+wcbNApldw9FcTIptMoU7MEUrGI9FxFtsSQGBJzff3yWMGbx+4gspLi6edOx88/Jjk2ldmDl1CtWQU+/7kXOl1P/pq0heCsHKxFcuykZtQ3zSnXmfLKAiMVTW1q/o+eobZQ6KclFyQSqcx4/GXms0NC1k9Yu2dgMYR1UkJ/PiOVT5npv63MtM+7FsLaP2Hdl5DxEzAoumwFIo1GH9jlMpRTNo5AZmvDqYAbJKfm5B/HXs70b/Vqg6pC+QG2Y3FnZk3oQr1ev6J0ECNydqWYTz0cnyrp3LE6lauU4cehq8kbudQp/9rmqUc6Rytp0rQcJ08E4V3ei3WLBqPV6jh84BZW0U4c3hnER/X9kaTnjl1QryYWCImY9BUUesgJrr3I053520egUqpJiE1DaiXRq8sKAiqxgHkS1hlKFPrtWqmEdbuvMGd0J1asOW9IUcz7HIxr7mQCFlEsYKx1tvn3eNWvhyhTyZeyVfzIymNS7OUULVuYad9uMlZbFbC6aheBv6IwHlFruXM1nDLFC1GhUhESkzO5F5WfjidN0x/Ds7AzHQc2ICdHjdRKgjZO/17cuviElaPXkpmajZdTU55cfMyJlfpUVKMa1LyxCK+3yrTSpM4cc2fEehl7Or7Ytzl/vLjwBDyKuKHMVrL8x430GtsJK2srgi4+Ii0hlcA7Vxg7pBMVixcDwEoqZerQvgTen47iXv741DkqNGqtWYZOZ8orr0CbPAXRgnWFpWv5c+vkPVoNasLTexFcPRhosg8AtUqNIjM/xbTDl63ISs8m+IaxifzhVSf5ce1XHBEEgkI079OI6T1+LaBqCeDh505suGk7D51OR3bhcH7o34rO9esQEhPDhNVbib4iw0ZX0J5GJBJRtVlF/vp+rdlzMoWEZ0m4+7i90T7/i6j+cWVuHLlFk54fsU7g3ygWi5HZWBlq9d0Ku+JTyosv5w9m7qDFqHJU2MitGTKrD1lp2e+1vvJ/ChbmzgILLPgncOVAILVaV2XoL31R5ahw8XLGzdsFRzcHNszYQXrS23naSKQShs7qw6mtlzi48jhZrs/xrGCDRgdNhjVn/lerUCpUJD5PIj5c/0MhspagVmkIuhRs0sTXFGIjElj/826KVfDl12MT2fhLAGt/2UP3r1oiEUmRiPRfN/8+L/D/D+f3XGf4zJ6cCrhGg7bVUSs1TB225LX3P3vtCd/0bUKdykU5F6ZfCW5YuTiZkmT69/mIr0ZuMGrf4ePK9O1Um7iEdOzs9SmqpYt6APDd958gk0m5cDWEcTN2YZ2kD9aOHrpDtx6138fpGjB92SBSEjMY1WcpS/d9pw/s3hC92tag48dVsLGWYiOTGIK7d0GdpuWIiUwiLSmT6g1LcedSCDNWD32lrL45uLs5sOavwfh4O9O79xI6dKhOmVJexMWn0bVDDeRqHRqNFpVKw8pVZ1HkMknWYfkBoC5VH5RaWUtR/AfMm5UKlcGEe/OsAAZM6U5cZALxkYkEXrrM7KETCuzj6+VK8N18UZK/m4RUZCiwc5Lj4uXMzt/2v7TtwytPKFunFFcPBtKoW12Gz+3Pxp93cOWAcUD4LDiaG8fucGDZMaPtS27MwcFVvyDj6m0sUpUHiVRSwJg8D5nWCYwb1JYO9fQquKUKF2bjj18zdOl6cu64kvjcWOWyYdc6nNt55aXnZA5KhdLIAsiCgji86iQNOtdmz6LDRtu1Wi3xkYkULunF85AYfPy9aNi1LjeO3jJcTzcfV+5fekyRMoURiUQfBNv+n4Kl5s4CCyz4J5EUk8LSH6YbvsSLVfTDv2pR/Mr54ubtQmJ08hv32e37duxfeoxnT2JR+kWyYeoIShbW12ocvnaDNbcOY5uol9Q3rE4LvN2E7Jqwhs+celxmugKdTsfVo3doNagpp3de0bc1V99iCmbsHYyYPsd85kmUY0IKX6jsJ2TaBOMwOjdhPZHwOMKct7xzFrIPRrV/gh8Mod+g2ERtHRjVAupyf2xEAgEJMvPTLLWZ+SyRsceYhieBoUQ+jKJVv0Z0GNaUszuvGrF7BhYzQ1AbmZa/WJAZm87O1eeY9V0HbGxlxDxPwauwMzqdDq1WR9d21Vh25amhfTVPNy4fvsfyeYfQOOnZJnGOmomzutOweXmehsQxZ+RmbBX598DGxgovHxdEucGTSHhP1KafpbzrrTOzwFDc34M+Zb8HIPDcYwZ81oR1vx8xYlM1gvpIneD2SJRafHxc6Nm2Bn2+WEHxIu78Me1TBn+/DoDCTvasXf8ZVlYSBrX5lehI/eRXlpODPLdeMV2lomQFHwoVduHiab3XZMf+HyECNi48jruHA1OXDeLhrXBSY1NYMmYjJKUgEtaACs5drDRdw9ev6yLkdjJmzutJxv0YNt0/SIuB9alexoddm6+guC6o17W2Ju+MdQn5gkgiL33w/eBJIp980Yq1vxpPKgFDHZ0uLa3gZy/ibZWMBffSyBNP+F6KRYQ/iKJoBV9Cb+vlU0PuhGPvbEd8ZCKadBnXHj2mUeVKRl3HJKQiluSriCIqWFdnlnE0U7f8svMMuviIvj91w8bOhuxX1LpFPXpOq0FNuHowkO6jOzKr3wJKVC6KXzkfI59S0E/wRSIRHn7uLL/7GynxqYTdieCLGmOwtbdBpXjzoEnimk37ujWNtllJpdiLIekFaw2RSET5+mXemLXLQ/CNUPyrFefB5cdvtf+bQGYjo1rzilw/cruAafuHDK1Ga5J9BX3g12diV1ZN2AwiuBhwlZsn9ZS0WCym4kdlyM7M4dLe6/Sb3B2pTMqq8Zv+yeFb8IHj/3foaoEF/zHcPH6Xhl3rGP7/9F4ExzecY9PM3fSb0oMSlY19w6xtZdT+pBqfDGlGo2518fAzLlZy93FFo9HyLDiaDFEyAzo2NAR2AK1r1aBMBWc0undnK/JQvo4/9k5y5hwcS5nqxYh8HPPe+rbAPHKylOxdepxOHsM5tvEcw6d2xb1w/gr/qgs/sfLcJOYfGMWo3/sU2P/ApsvY2MpYvfQUly8E06HFHAJ2XWdQv7/o3LUW/b9pwe+bP2fD6bGUr1aUov4eBfqYMXY7GekKlsw5yB9rhxl91qZbTW5fe0xa9m0yFRdIU4WgfQerk2HTuxMVHM3QGT347dh4Slf25dPPm+Hs9nr1nGXLeDNvTi/GTd+NQqHmQXAM+47d4ZshTflyQGM2bfmS8PAERn2/ie+mdDbs1/ubVjRqV40OAxoyfslAJi0fQoM2VZm98TM6DWxIk/ZVmTdGn2aVEJPKhEEraNWjDsc2XyL8YfRbn29WppK01Gy6D2kIwKGDd9i+/Sppadmv2NMYfb5qwbEd1996HP8UHl8PpXzdUob/F/JxIyl3cctOUYhpy7YRl6z/v06n46+AA8SH6J8ne2c7Fl/9Bf9qxWk7rPnfNkZNrkBRzmv4LKYnZ+Do5kD15pWwkVtzftcV1k3Zxkeda2MjNxZoykzNwtHNgb6TupKakMbn1cYwufMcQK9KqTYRxDi42L9U8MTN04OY5IKLg1qpGEWmcWDaa1xnjq8/+8pzModH10IoW9v/1Q3fEnZOcpr2agBAywGNcXJ35Lu/htP52zZ/2zHfB0QiEd+v+IJPhjR7abucbCWObg74Vy2GRCqhuOB3v8JHZbBztuPK/hsEB4aybso2kmNTcHJ/u5rj/1mIxe/n3wcKC3NngQUfEB5cCaZYJT8+HdORHb/tN6xEKjIVtB3WAu9ihXgcGEZSTDIuHk4osnK4fSqIsLsR2DnJadClDjZ21myfuxdHd0f6TOrG8jH6lDqtjYKPKpcrcMwKJYvw6EwEtu/p6+DMjiuc2XEFBxc72g5rQcOONTi3x6Lm9U/i7vlHxMZn0X3Exyz9aQd/nhjHrfOP+XPidtRqLQcj/mDnkuM8FQQbslxVyW0bL6HOrVlbPF8vijCk/zIW/tmfs4fvMqrPMn5ZM4RS5XxMHtvewYYK1fzQarWIxWKDQmCdRkWY2H8oq3s1o3ShQpwJCWXygYvYaOu98Sqjo5s97QY3YfQns5i67TuWjNnIufOC2iWZeZEIgDr1SjJmQkeGf7aKaAEjevfhc4b1bsCWvfrg57NhqwBoXs+f39Z/hkatwdHRljP7AnFys0en0zFj+Coe346gevOKlKtelPEDlxsdy9beGrVKw/WTQW94lgXx09jtrFgzjMf3nnH9LWpt7J1suXMllOfhCa9u/C8jNjyewv75C1EuXs6k5trBiEViVMFejFu7l+Rn0WRkKsiMkmGn9ASgwxctUeWomNx5LrVaV6XjiFbsWXzkbxlnanwavqW9cXC1f2XqfIkqxWg9uBl9in1p2HZ6ywXqdazFqc3nDdtObjzH7+emsXvBQX4d+tdL+5RaSWkzrDnOHk7smn/AbLtvJ/7ATyt+Y+mXQxHnTkoPXL1B0gsuIH0ndSPw+B2e3Ax76XFfhtSENBzdHV7d8C3RcURrfPy9uX74Fpf23aDb9+0IPH7HbLqqEKVrlsSzaCHO7TTNmuXh70h31Ol0RNyPRJH18rRov7I+eBX3YP6lmQQev4NGrcGnlDdrf9rKg8vBVG1WkdaDm7FnsZ59f3g5mHJ1S1lUM18XlrRMCyyw4J/GoRUn8CnlzRe/DyRg4SGiHut/fdUqNT+2moGVtRX2znYFpKbjoxJ5GhSJZ9FCTNw6kuDAMJaOWmdYlZVm2XLw0g3K+fkZ7XfjQSjWGLMdwtQlnUYg8W7O3NxoZ/2+6UkZbJ23j/rtqvPpN63YtuDIS38shcINRuvPwi9h4d/p+SmLIhuBfUBu2peR4bJQjESYpmUm5VRk7os/b/zC8zBKGRR8pQrSL3W2+SvzQhsDoYiLKFfoRScINoTS+MJjagUpn+bMn+MeRfL0dhhrrk4jOS6N+V+tMqSNBt8OZ8mJ8bR2HKTvQ6ki/Kr+eWpZtyRHd1zTd5I7pudhcXStMcXQ98+fr+Gvwz8gysxBmpcillvrE/YomsxUBfu3XmX4dy1Z9uNmdj2ay2ederNtYBfkuQIeTfxL8kdXG0asv4WT0FNP+IzlMhEG83gArZY6n1TlzsVggh/G8HXLX/jtwGisfjvCyT36+iWNveB5yL2VO4+ORm5nTXxcGiqVhtFDV5ESGo9dSv5zNOXXfjwPicNXJGXl4hNIsvXntnCUIOVJpcLLz40chYrk5GzDOAMvhRCY57OX97ypVEQHZfIoMMzoeTASMRGK4wiYF4lAfEOcnv/8jmw6hRk7v+eT6FS2LDxK+ONYoxRkYWArEtgsoNbQvlddTu0JNE4rFj7LeWMR1G3pBM+a0ffCa1ghiK1MGIPrzAkqFTQ8v3fhEZUblSP8wbMCgZOVSEbtsi04desixKSQJwlS4+PKWFlb4e7jhlgs5urBm3Qc0RL/KkUJDjQWL3kfuH06CHdfN2q1rsrNE/fMWgCIRCLSEtKJeRpvVIv2+EYoTXs35My2i4Z6uZT4NGb2nk/zPg1xcnckNcF0mmy9DjWp3LgCh1acIOJBlNkxlq9XGgc7JxIe2tH950W4ymWkZSiICs7GNsXLwPj1GN2Ba4dvGUzR3wl/UxmY3MGW22eCuHf+IenJGZAMS39Y98r9JFIJ3iU8KVzSE6/iBbMOhHD2cOKbxUOJfPSc1RM341m0EFWaVMDOWW6wMHpb7HhFbSaAu68bB1ecYNno9Xy/4nMu7b3O7vkH6fxtGzbO2Mn6qdv5qFNt6rWvyaV913lyM4weYzpagjsLDPj/HbpaYMF/FM+Co1kycg31O9akRd9GeJf0NEiqq3JUL/UQig2Pp37H2myYvsMo3UaOI7sPBXL6ll7aWqPRsObkSSIfKRCLXiNoe0tcPXqHnj+0o8d3rRG/Sd2dBe+Mg6tOc2zDOb5pNsNo+64lxwE4nLaaHRGLAfikvz7N6ZPe9V7Zb1pSJvHRpp9BByc5DVqU58HtKDwLOwOQo1ChjEs0BHZ5qObrg50801Q3ZtGqVz2+/bUPa2btBSAxJpXPG8+g/8hWVKpdwux+Do62DOq1hH7dFjG41xLCQwuqCu5cfhrXQg4061SdgG3mxSRiIhJJjnt1Xdrn07ux9vp0Ktcv/Rpn9nrISlPw/ccz2bvmHP1Ht+W3gO/4fuEAvIoW8I8wgkshB7RaLTGRSS9t9yHhyoFA6rStTr32NbhkYuJ6dO0Zev7Y0fC9UqRMYYpX9GPtlG3cPHEHq1w2eu/iI3zcv/HfMsbQO+GIJWIGTuvJN4uHmm3nW9obOyc5sU8LMq4nNpylywsphU9uhrFh+g46f9uGFn0bAeDk7kiF+mVoNbAJX/w+kJwsJUtHrTUb2MlsZBQu6UVybCr3zj9ArnNCHuePq1Vtsh96IU/1NgR2ZWv7kxKX9n4CO0ClVBmu/9vAq7gHpWsUfJ+//GMQrQY25c7Z+0bbi5b3NQjOmELNVlWYsms0Z7dfZsssvaWLnZMcK2srXL2cEQvqpQfN6ImzpxOlqpegcY/6TN75AzIbK/yrFDdcLyuZlGIV/Uwe610RePwO6HTU71SLG0fv4FHEnbknJtNmaAvDOV4IuIqLlzOtBjZBpVRjY2fzil4tMIIlLdMCCyz4N6BRa9g2dy81Pq7M0Fl930h5LDtTwZBf+rBy3Eaj7TbPfRk7NQAHr92o1Vom/DWVpo/cuLjnmvnOzEiEG0Eofy5gpqRWEv44OZHIx9FcP3KboVO7sXLyDkO6qZFQhlB+XciiCY9jSrYdjNmzPGES4X4C5k70QoBhsg8BjGwRxCbS/YTsmlBERciuCcVNhD8IWtMMiQFmpOKNrrdGuNnYaFkHePi6IspRoNXqDNc1M1ovtNHNayhbo5aizcrCw8sJgFmDl0J6LksiZH4E91VuLaZ4GW8905PHOOb2fXF/IF4lPFHGJxsYpaTYVNKyCz6/aQoFOTk64x8i4fXJeyZy8q9fm4GN+Kbdr4Q9jDFcn+zULL7uu5SJ83oyZsgq+g78iC0rzvHL0v5IAAcnW66cCCLu2M18fZy8yZyAUTu2/hzH1p9j0qphrNz0OVvnH+bguvPmLS6EQj6CZyzvmek4tCnffDyTlPh8c3SjYwM4Oxn+lMalCvoWPDOCZ1OUKxDz4HoY04euBKDKR6X5Yk5vHJzlqFRqPLxdGNtrMTEp+vGJxSJ6fd+a1QuOoXOwRZRlZty556MT2oUIhU4wzbSZE0Yxdc46pZAhf3nNZYY0joBdi/F0sydWHU22TI6t0t1wzLTEDPYvPc6w2X0JDgyldI2SrBy/CZ1WR1J0Corc89Qh4vGNUMrVK8ODy8Emz+FdEHE/kuPrztC8b0OzbaIeR3Ns3WlK1ShZ4LMnN8Nwcnegz8SubPp5lyHDITM1izWTtlChfhkGTPuUlLhUnj2O5vaZ+xxde+aVaYNKhZLnudY1W+fsYcC0T1k1fhMxJgLMBl3rsnLsxgLbXwdanZYM6wQQa7HNdsVKJCPifhR+5X3N+v69DI5uDnT5ti1iiZjHL1hFRDyMIi2xYPrrwGk9efYk2mAh8CKuHrxJ6O1wtFotrl7OuHg603tCV54GRVC1aUUOLDvGyU361NgDy47z7HE0ToUciY9MwNHNHhcvZ/4cudpwzRv3qE/Hrz5hTIupOHs4ER0a+8bn+TLkia3IHWzpMKIVKfFpHFpxnC7ftiUlLpU9iw9zcPlxGnTWM3ip8al4+LkTF/Hhp1x/EPiAA7P3AUtwZ4EFHzhuHLvDg8vBfDavP76lCxvSNM3hr5tzsbWz4eCy42TJ43EqosHR3obnMenootxwyPKCUP3LX75UJbaH/X1ms9sj/yTi0XO+bTINgMy0bPqN78iaabv+tmNaYIy0hHT8yvkazG5lNjLGrB3BH18uJy0xA4lUwo6oP3FwsefuhUfERuYrLGarUtGJ7+NuJyY5W4NGXRq5lTuDxrVnwdgthnY5mixyJCGIJWqi4mtQvLIfKqXaUNtjI5eRkeHOsvNXGd5Ab4eg0+kYvfsIoqyiYKwnYRZSqRhPX1fC7hd8BzJSFZSt5Mu6w6Pw8Ham/5fNCbz0hGnDV5KV/nIlwxcxffBypFYSAsL+0Ad3b4mMlCwWHBvP9oVHWDXbfD3Uu+L2xWBuX9QHLa1616Npx+rEPksGO72Sabmqfty6EkpWxquFPz4UZEmSaNXClUld2xm2TdiymzPHU5BrnQ3bwu9HsXbyNryKe3BiY/690rxgCXBi43mGzuptHNy9J5zcdJ5e47vgX60EhYq4Ex9ZcIKt0+k4v/sqpWqUpMOXrdj7p3EN4I1jd0iMTmH43H4sG73eKHALuviogKLmm0Kj1rxUyP8nZQAAIABJREFUUbFMLX+WBM7hpw6zDR6rrwOFJB3nchlM+rQNrg72LAo4yv1r8UQ8eEbJqsXeOLiTSCX0n9KDVeM30W1U+wKfb5u71+R+cwf/idaMWmbjHvVx83Yh7G448VGJtBnWglLVS5CakIaTuyO/DV1CdG4Na+8JXaj4UTmmdf+V5yEx2NjZUKJyUZ6HxOJburCB2Ty99SKhd8LpNqo9VjIp1w7f4u65B4ZjCuuN3wVZ6dlsmRVAsQpF6DaqAyvGbqTdZy1wLuRISnwaj66HUuPjyuxfepze4zvrFTYt+J+HJbizwIL/ALLSs5n/5XK6/9ABK2spmSm5dVki/Y+ISKSvExOJ9GzZx+LuZNnHMXRgdQa10itzZefk0H3q72TekyHN9ZybN+RP5p2YjFqlJjtDgbXcmoPLjxuZqgrxOqa/wtXw3YsO02dcZ3RKFSKJhOgnMWSmZFK7RUWuHLxpzGAIuxawIzpNPotgxAkImTZ7gQlv3qROyDiYY16E281YOxhZKigLsk9mLRSEbIaQeRHURAlrAfP6Ma53NKLlTI/PDPLu1ZpJm2k5oAlNutfh8fUQJm4ZyYIRKzi06hQAh1efpPWgZmjUGjb+vMvApiq0mXh5hrG6S0tsraxQaTR8e+AU95+pqVTHnyVjt4JWS5YoiRJFnzO7XXPc5HL2PLzC4hsXcHGrgq2dNeQoUeeosVN5s+FcBPvvbsXd3ppnydkoUgpjK3Ewb1Sv1TJ68UAatKvG8AbTGLt0CJGPovWG7WDE9v62chAHNlxg94ozxMfkM2C6FEH6pJARzrNIMMOkqrIUbP71ADsez+P3b9Zyfv9N/QeZxrLx+QcS3Lfce9nN/zsADsUvpXjlokwatELfQPjcC9g/bUJ+yqRYyJoKn9O8ayUV1ibq+2vVozaNOlRj7JDVINcbtoslYhp/Upml0wIQ5T1baflMolFNXd71MeOVZgTB+2+WgTPxHSF8/s2x5SKxCGvPVMZ37mW0fUq39rS88xfEOBsxhNkZCsLuRhj3IRIZ2ui0OnQ6HcoX/f2E43sHFi8nW4kiU8G8wYtpOaAxG2fsNNs2OFBvE/DlH4P46/u1RgHA03sRRD1+jqu3SwHfub8TPUZ3YPf8A4Tdi2DG/rGMbTWDpBjzqf9CyEumETBtDJLc775lIz9j+O9LEWWKX2kPYQp9JnZl1x8HkEgl5llgE8hKK/heSq2kFC7pibOHI2Vq+1OxQVlunrzHxhk76TW+M+mJGRSr5EdKfJqh3lGtVPPoWrChpMHKWopUJiU8KJJe4zpz69Q9AhYeQq1SE3onHKVCRevBTQm/n58a6+PvxVcLh5AUk8LmX3YT9fg5RcoUJvLRyxdmX4anQZFsmLadwTN7sX3uXpr0/IiAhYdQZitxcLVHkakgPTkTRzcH0hLTX93h/zIsgioWWGDBhwKtRsvW2QHYyK2RCmoZtFod5PqRodPhVEgviexeFENgB2Brbc28L/oycOwGHDL0vnZ3zz6gt9/niKUS3H1c0Gl1DJvTl0/HdGTrnD3vNN6dcStRKpRM6jzXaPu2efup36EGPUe3Z/uCw/8pb6L/IrIzFAZVtfoda/HgSjAnN54zfP7b8GX89tkKjio3GU0+NDZh/NWhGba5aYtWEgkL2jal7abjiEQYJqV2DuGs+rSLoRalS/lyKIKCOJmVQIXcGjipTD/xs6MYqhSITtEHvLYvmbvZyGVsvjeb8/sC2bfqDKuvTUeRlUNn368KtJXKpBQv6833nf7QbzCzaPCm2DBnHwfWnmHD7dl0Lz2SrLQ3n6wCjGg2g4XHxjN+UT9mfrX+vYztRXzUuhItutZi9ABjtc6WnapzcNtV/ffDfwgOtlZIXpiAWUml2Nla8ToW7KmJ6bh4OpEcmx/oW1m/XEX1XXBoxUk+/20At07efWm787uucGX/DcZsHkHh1iJ8PB1Ra7VcPxeGNsQVG7k1zoUc/pHgTmYjY+D0Tzm1+YJBbGbHb/vp9PUnr8UA5eiyaVHZ3xDY5eGrTi1ZeyWc6ztuv9F4Wg1swr3zD3keEsOAaZ+y6493Y7ub9vqIriPb8d1HE43MwnU6HZt+Np09ohde8TL8Pz0pg4UjVtJhRCseXAnG1t64ti3q8fMCqaDRYXGkJqSjzFFhLZdRyNeNmYcmMLbVDKRWEqNA8E2QEp/Glf03KF65KO6+boBemdRaLsPDz52HV4Lxr1ZcX7NnwcthCe4ssMCCDwmKrBx4ia9S2N0IipQpDM4FhSrK+PogkatAULKgyFYBKiIeRoNOy08d57D42mzaDG1Bdu7qpUcRd8a0nM4TodqcUc2NcBVfP2N3cLGjT7HRxAnS/ABEVlIu7g9EJpex4PRPXD18y/CZVqNFJBaRnpLFsQ3nyEjJMlb2E6a5CIygRQImwvClLTQLF/ahND011AkYFGHdoKkVfXNKgSJz7F+2GfVBU6bw76kOyBQu7rlG2N0IvlwwmD2LDhOSaxANGu6df8jGkIUs+m4N+5aewMFBi7OtrdH+UokER7EWRbYSRCK0Oi3+bvIC/lptS5QgyDYJqVRC31GtSUtMp++o1mAlw0om5dz+QJ7cCs/f4QVTeKlUzPxDYziy8QJ/TdgGWh1ndl8jNjLJuE4x95rtiv6TeV+symf0hBAEeiKBoTl555adr1iqyymoDJsSl0Hw7XCWnZ/CwCpjUQtYNCMWVsgK2uZOAHPZgNBb4bQr8g17wv7QH8+IMRbWdAqZJEEboRG7o56lFglYka5DG1GzcTnGfboQpAK1zKxsCnvac+T6E3SKNwhMX4MxEdbf6V5jfSbvepozpBdCp9URn6AiLi0ND8d8/66opCSSEjXYi8TGqpsmxnv14E3qd6zF/r+O4VbYhfaft+TqoZsvHujVA39NpCakMbv/QgAqNy5Pi36N2bPokMm0RJVSzeI/fmb5lwNxttcztFFNEhg2dy0DpvXEe+1pQm4/5fDKk+9djl+IHqM7sOuPAyQ8yw8kH98IpetIfb3bllkBZKQUfKea9mpA0fK+ZGSm8yzsVIHPkzOy0KlFqFWv759aoX4ZrOXWBB6/Q5la/sSExr7SWuJVOLHxHLdPBxlqL18HO38/UCCAUyqU7Ph1HwANutShTtvqXDkQaHJ/Bxd7Pupcm/lfLDNiLse2nI6VjYzpe3/k6zrjSIl/tSCTKdw4docvfh/ImW0XadG3Ecc3nGXr7D0MndWHdVO28cnQ5pbgzgJLcGeBBR8CBkz7lJBbTzm/y7xC32tDBxq1luiYFHQ6ndHE+/jNO2hSbV+ysx4jav1o9P8lgXOIM1GE/zI8DYqkUBH3AsFdHsrVKknA4sMc33AeqUxqJBiz4u48veWDk5wrh28RdOn918n8ryI6NJaFI1bQ/otWFCnrw+mtFwEY2Xgy/tWLs+TaLPYtPUFahoi4jAw87PPTA3PUauIz1Lh5OdNvdFvWzdlPsgnPpieJSeRkSOnqN4KMpAzWzdgN5KcaDp7YifjIRFJNCCMA/LhsKOnJmfrALhfBtyJMtgV9bHg24O8x5pZKxXzXYiYj5vZm9a1Z9Ks45q37iolMpEG7apzfZ3pi+Dbo8UUzSlXwYVwvveqp8Fe9bPViRDyOeW/H+ichS/Hh87U7mNCuGTWKF+Na2FN+XLMHm7TiL+Rnm8az4GjK1S3F4Bk9SU1IJ2DRYVKEgjV/I6o1r8TCESto99nHNOxal5BbT4kJiyM+MoGU+DRq9KxAJV+FIbAD8HV3p0Xj8pzee45FX6+kZquqNO/TkOMb3t5M/GWwd7ZDp9MZBXagTw39qeMcqjaryK+npzCqyRRDgCeRSug6si3JsamsmaSvudWVeE5yhwxccs9Fq9Wy9vwl2rcdjiThOrdO3XvlWNx9XKnXsRYrftyAWCymed+GLPluzTufo1ajfWOBEaVCiVJhnht283ahUfd61GhZhb9Gri1QV9ewW13qtqvBw6tPeHov/zvr2RP9eziqyWSjwM6vnC+FfF25cezlAZm9sx0zD45n1YTNaDVapFZSlLm/maocFSc3naf3hK6E59ZWW/ASWNIyLbDAgn8CGcmZJEUnv5e+rGysUCqUZMfaM/z3pcwd3hdne3uuPgpm2soA7BXFjCdHr1i9rtSgHEqFirTXXEXNY7W+qT+RTU//xFouY/nYjQTkpsXoVGpKVilKpy9bMazKD2jVapSC1fzxm75l94KD7P/rGCKRiDbDWlCivC/7lx03XvUXMm3C+p88Rk/oYSdU/9MUZH5e/PttYY7R02lffwX7fdUBvQyqHBW7/thP014NaD2oCYdXnQTgyY0QdDodWqUSSY43Q7YfYXWP1rjb2ZGmUDDyyFm8HKoQHRpLx6FNWPvTViLVOs6EPKVxyWIAKFQqJh++QE5CJSQiqREblefht2PePtp/1oINs3PFEYS1jBoNjs5yNv4SYKhxNKprNHGNrWRWlKniy8NremZZJGRtcx92uaMNPyweTPyzZH3QmFfjmJUt6Ej/7ExYNZyH10IZ9FNXpFb650ir0SKWiKnVtCxXj+gnYkYKp0L2KG9B5QXW5fevVjHvwI9c6VidqT3n65sKJxnC9DZbAXsg9MhTqHB0lePm7YCLmwO1GpVhdMdf89vmPuvWtlY0aV+VJeNyhW9M1IvCC16Kr6q1M8PomfNaNEqPzVOxFb6Xwk6M7qsGK5EMf7dmTN9xk4S4vZSuWBlvcR3iSSzwXph7746v/3sCo1fh/K4rNOhSh90LDiIWiyle2Q+PIu581Lk2t0/d4/yB83T6qXWB/ZzFVkzrMxd7kRP3zj/k+2WfcXHPNbLSs00cRQ8PP3cSniUZasZeBzIbGV8vGoK13LRycGx4PEdWn8JGbs2olV8wtes8/Mr50uHLVuxZdMgofVsd4k7nsX9Qu3IxPDyceZSYRKVyLfjru3UMm92XxzdCTdbDCdHhy1asz63zbjOsOUdWnfpbGct3wd4/j1C8clGsbWUmx3hw+XFObT5vtt7wxWCz3WcfI3ewfWVwl5GSyaGVJyhdqySHV52k9ZDmLB211vD5o2tP3puNxf8ELMGdBUKMXjWCiEfP2Do74N8eigX/j7Dz91cbm74utGoNYokYG7UTD09n0fb+fKxtxWQlSLDLLFoghe6VEMPzJ2+++j98bj+srKUcXXeGLt+2MQR3AL+e+ImNP+8yWXvgW8qbmb31E1+dTseB5ScoVa0403f/wMx+i1460bHgzXBq83lqtqzC578NYP9fx4h6/JyU+DSsrK2o16omflW7Meb8PnSZqaQrwFFag8o1ShIXkUD4/We07NuQEpV7szZgI39d2osMHRGJCrSZpbGVmP95SU1MRywRYWNnjSLTOGXKp6QnHn5uBJ4MMg52XgKdTke7oc0NwZ0p/Lp/DBvmHsC3lCdLz//Ej90XkpKgX7CQ2chYHzSX7KwcRCIRyXFpuHu7sG5mANv+OIRHYSda9WvEzdNBzNg1imm9FuDs7ohWq6X7yDZ803AK6pfUjhYq4sbgaT0QicXcvxZCIV8X3H1cSEvMRKV8/cC/St2SDB79CWkpWWQkZyKViPn581Um23b5vDnb5h82+dl/BRmJ2VQtVZ+LD67ja1+GRPcU4sNNZwJ8SAi59ZSGXesCeiYr5NZTQm495dK+6/Sd1I24uHg2HDlP3bJljfY7euUucvTWGIpMBWe2X6L9l63Mzjdqta5K+XplyEzNfC1zbIDRq0dQoX4ZfEp5v7SdvbMd7b9oyeTOcxnySx9iw+NZMnJNgRppK5EMIv24GqGgfAN7/EtU5ODv+qB6y6wA2n3WwqzCJYBbYVfSEtPJyVbi7OGEi5czIbefvta5/BvQ6XQs/noliERmA9A3EZJZOW4jMhsz9jwv4NDKkwyb04/mfRqyddbu1z6GBf97sAR3b4j9y46RlpBmMNd80YPFAgv+btRpWx1ltpKbJ02nu9jY2RhWcW1EcojXy6E7wGulM72IuIgk/Mr6gEhcoLZlU/ifXD18i9UTNqPT6uj+Q3vaDmuBvbMdiqwcxrX9BTsnOW2HtaBMrZI8yp3Yhj+IYu2UbSbV9GzsrAtsj3z0DGcPJwZP/5SF36wGXqizEazka034xRnVQ5ljGQQw6lswlvfB7n1ouH70NnfOPqDVoCa0Hd4CgEHTe3Ah4Bpbp17NbeUAQCrPiLz+jNNbLwBQokpRTm48hypHjUhSDAAZubVtJpgEIQO3d9EhBk/sTODxu7h6u+BW2AWxWISbtwvh95+BWpN/vc0oiOaxQP3KjGT9o99ZPGo9iiylcW2mVsu38wdw9/wjLuzRp25mJmUwaflQnN3tyclS4uAiJy0pg0ldfyU6LNfcXMASxwRnsfanrQCMbjGN6Xt+JD05A5m1FTFP45m5dzRjWv2cf8y8oFQkwrOoOwvOTGbmwCWkJWag0WjpOLwZX/02gOIVfTmx7TLrZ+rFi0S53o3O7g58NrMnnkVcmfnVOj7pXotKdUsht7fm26bTUL8YEAonmRIJFer6kxKdTMLTWNNtzCmy5l1X4Tsi/FzIkJsJvM0qHObdE5EJhlN/oAK7pCVlULJKURCJyUxTYOcoN933q44tgLl3/n3DnFDUppm7aDO0OU8Stfy8eQffdW6HUq1m+rrtPLuvwVZwjb1LenJ0zWmT/ZSrU4pKjcqzavwmBkz7FFt7m1cGFfXa1+T87itUa17JsG1bzAps5NZYyaRkpGQSF5nI06AIVk/YjIOrA42712PDtO3kvKg0+gKsRDLqNK9tJFTiWdSd6NCXp/O3GtSEvYv1thDdvm/HppkfftAiXJDxr1ac8PtRb+RDK0ROtvKV11aI6NBYnt6LeOuaPQuwpGVaUBAPLj8G4Mv5g3Bwtmf2gIX/8ogs+F+DdwlPss2wV2KJGK8SngXqKN4FseHx2DnbUaSMN1HBxgze/UuPadi5Dm0G56tyLh29njPbLxmN4f/YO+voJpY2Dj+buiulBsXdLhR3d3fXi1zc3Z0PvcDFpbg7xd3dW7TUaEvdNcl+fxTShiYVKFCgzzkcmt3Z2dlkk513XvlFhUVTv3sNXt17R1RYDHqGumhqayJNSGkA3Dh6n9lHxjK1VVKVzaWXZ7B99gEsbMwxtzEl2Dd9ZbqzSR/xsfEcX5Ood7hl8m5FLkdauD3xSJ/IvQpCPoaxYcJO8hTPxZvH7gT5JOaIOhSzp9vkVqy4NoN5PVbj5x6QZl8BXkHcdn5E7Q6VObX1CsYWhlRr4Ui5uiV4/8Kb0jWKsmZ8ksbXic1XOOmUqIkmxsVRvHIBFp4Yz9Zni+nnOAGvl75qz/XyzlvaW/8NJBkTiy9O50T4VmKj4ogMiSI2Jp6osGj0DHURBAFTS2MqNirDhsmJBuLKEdsU/fWe1YGVl6bw/sUH7l50wfOND9O2DGTVtMPo6mmz9MBQctiY0jh3oqyCmIanz76gNY71SuI0J+tPktNC10BHUQwjNioOC1vTNI7I+shlck6sO4e+kR75B1Vi5rmrRIZE43IuHj2ppVJbC1tzwr6YxOcr5UDdbjV4++g9Wz5VtHTecIHG/eqmWl3SyMyQEtWLsmHcdlxuvqZ624o8OP+URWen0cG6n6KdubUprYc3peO4VkxsNAe3px5q+/wSQRCUwkNzFbFD10AHQY2XS99ID4mGhMjQKErVKMa7x+5phnBmJTS1NBm9cRB7Fh7hyr6bP+ScJ9amrksr0ZBQ2DFxITUzdPZ+W7KNu2xU4TRtb7pd6dlkk5kcWXlK7b763WtwITMS8JU0rASeX3el2cAGrB29TanZnE7L+e/eAtpY9sHQVB8jCyP83qdcqdU10GHVcCcQJESHR3Nm62UOB26mtUUfpFLlB9Ad54dUa+Wo8MY1+bsuIX6h3Dp2n3rda6Krn1g5UJ0enCqPgroVfXUeOnVkRHcpUzwEPyD/7kvSY9gpe3tkKbYJ0mR9pHINcVEyXt19o9SmTsdK7J5/hKqtytN2aGNWj3RSOkbpM0j294yOK1hxeRrWDpaUrFYEdxdvLuy5Sdm6JRAkAjnzWcHZpLwW+ScvoiCRoGeohzReRq9iowj4VABIrq5ianJP7qfPeHStGRwJ3sKz669Y1HsNMw+OYmz9OYqx6hvrs/rWHNaP3ZHYRbJKr1tnH+bU1quUb1iKZr2qkaeoPUPrziXwYzgSiYTwEfUZUmUq8uBPk/xPHkUlLchPY7W0M6Nxt6psnLQHUSZPl05i8vtUoQsnU73/82f9JRn5XnyZh6ggmdah8MlAiAiNxtjC6NNhIhI1XneV50/Pd+cnfL8+Ex0Rw8lFl2g3qhmR0miqzcjJlinK8gOh/mEUKJsXT9cPxEbF0nVKWwK8gtg6ZbeS9yjAKxATS2N0DXQV+myODUoT5BtC3pK5yVMiN7FRsYrwzrDAcE6sO0eDnrVSFA8J9gvl2OrTVG7hmCHDThWXdt9gxc25icVZ+q5VjO0zDXvX5syWy2hoalCtbUX+G77lm873o5EmSJnWahFBmbiY+q30ntOZwA9BmFqZcOv49ykylU3W5/c2Xb8jUWHRhHzM9h5kk3UwtTLBrqANbx+9z9R+cxexo0HPWuyYfUDlfomGhJrtKxEZGo3vu48p9tvkz5lCYPjg8pO8f+bJwKU9E/uQSNDUlGBX0Jp2I5vh9jSpfefxrZjZfikAL26+okoLx8y6tGyyIEf/O0uxSgXZv/QEUWEqpA1SYXyTBXQc3Qw9Q13+He7EzeMPWD1+T6K0xi71K+vPb7xCR0+bgmXzfvW4fdw+4rzxAtHh0YytP0dpX3R4NOHBkZRvUErlsR+9gjix8RITWi+jU5HRBHyaLE7Z/De7l51OVwiWuY0pHcc0Z+v0/RkqrpGVCfUPxyxnYg6aTf6c+KpYOMqq6OhpY2plkma7A0tPEBcTj2PD0krtE8R4Ll0/hcw2hPZTm1G8amE8XT9w1umyyjzNY/+docPYForXXae0o/OE1rg98WDzpF3smntISdxa31ifsVsGs3a0U4q+AryDFDpqGUFDM2lhTVNbk5W357Fn4WFWDdlM7zmdFOksn9HR16Zi07J0ntiaE2tS90hlVQK8ArOUhywhLoGT686Tr7TDzx5K1kYiyZx/WZRsz10mUaHxXwT5hqjUtMkmmx9BmxFN1QqzfgueLz+wddpe1j5YSLf8QxXbP6+cH19zlrGb/2Hwit50sB2Q4vgAr6DE5P1PK+OfjxtZczrb36zEecM51j5YxEePAHI65AAS8xDOJiRW+XtyxUWxuuz7zo+46FiKVynI82svFedQmyOkYn+6wggzsIqf6Tk8P9iDkGmo0T1U5+1JjiBJOjbYL5QTGy4AULWVIzZ5c/DmkTv5SuamepsKgIBMKkPPUJdLe2/i4eJNmTol2DRpN68fuOH73h9/zwCFN6v14Aa8vPuWuLAoJf1CxX0iEShYOT/HN5xn+t6RLP17Lac2p9TuUnWdyYkMiWLO0XE01ums8EYn94DN7riU/52bSq8iI5T7UPJYJb1v8sgo9PW1uLbzsvLpP407eaVO67xWtBvRhPXjdhAXpSbvKh33leJzU+OpVNefKp3LxB2i6r9T2/bl+UURRDnWDpbcO5UkIaEu5y+rsGP2QfrM68zhFc74uqVc9ErOpd3XcXviwcKzU9kwfgfXLl8kfzktpnRrgpaGBpuvniJQJqOQfX61fWjraFGrQxXsClizZcoeXG6/ZsO47WrbT949HH/PQO4l0xlNTqh/GNZ5rVRGYqjirzolcLn5CkhcrFt6dRabJu7k4YVEUfc1I7fSe05npToFexYcwSq3JRY2Zni+/JCu82STOqIoIk2QoqmdPb1Xyx+Qc/d7X90PpHLL8jToWQtdA920G2fzx9B9enuKViz43c9T4K+8+Lp9TBH28jWYW5uy98M6hq/5m+Fr/uZswh6aD2iAVe4cKcRdAYpWKoiOvg6v77vh9HpFiv3SeCl6hrpUa1MBAIlEYOKOoThH78DS3pzl12YTH5tA94LDFcec33GVIZUmMar2DMbWm6XU3/G15yhWqRClahT95mvNJuvQakgjGvdNyt3UM9Tl9omHeLj6EBMZi1VuS57feE1Li34MqToNgMKO+QgLjEBXX4elF6ex/c2/2OS1YlWynLbSNYqyb5n6XCSAuUfHU8QxP2ecLnPtK7Um53ZejtcrH7WTqgCvIAJ9Mkfu5DN6hrp0Gt+S6q0rsG7sDuJjv66oQ1bm8+KJqsqqWZnYqFjWjd5Gq6GNsc5jlWZ7DxcvBleYQOvhjSla1wSn8YMpaGtLnpw5mdW+LcYmkTy+orpcvoWtGTOPjsPARB8jcyMa9q7Njln7Ffs1NTVS/HaXb/QXXfMMUjueY6vP0GZ4UxyK2Su2pVZpuULTcgpDcdm1WexdeERh2H1GVZEZf89AXO9k65hmFqJcRKKRPbX/0/nupr0gCI2AFYAGsFEUxQXf+5w/g1vH7lOyelFkCRnQs8rmt0cikVC0ciEs7S24dvD2dzlHzQ5VqNzCEd2cmgi2cRyaeQ5dIW2hcpUIEmp1rk54UAS3jt9HlMu5tPcGz666cjJqO/3mdWHlsMTS644NSjPj0Bh09LRpYdKTmMhY1j1ahImlEaH+ymFkPQoMZcvL5QR+CCanQw6iI2IYU28Wz669Smokymli0I1WgxvRf1E3KjYpS+fcqicf+/53jN6zOxLgHYyv28c0vWfK+TnpyL/JAOmpvvlH8JXvHyQuEMRExmKb31rxHjb9uy4n1p5VFFQRAAMTPWQJCXi5Jkpo2Oa3ZvjqvmyespuhlSYBULN9JQI9/ZF/Cl0zMNQhzDsAmQqxdUj0rmnpaGJkbsji3qu/avzFqxRm9OZ/mNVuidoFlql7RqOhIUGUyZTuk+TvmvhlZVaRFO0/U6CMPfV71GTfoqMEZZJG5o/wGivp6iU3FpLrTya/Xonw6d4S1N5jilzB5MfuUTlYAAAgAElEQVSJavr7gcikMjaM20H/xd3ZPmM/ESHKWqFyUU64EISAgLFogTReyvDGE/hnZOkUfXWqUZFRU09hhrKhKIoiRTtYMb5PPzQ1JBSvU47A24Kicqa5tSm7PNcqJvyhAeEYmxsqKt6qQ5ogZe0oJ1oPb0LNDlUwNDUgIiQSiUQCQmL4X4BXEPpGeljny8nDc08QRZH2o5tz/8wTbhy+m6JPiYYELR0tRWVJs5ymFPgrj0rvob6RXpaXvbGwNadsvZKc23blZw9FgYeLN3lL5Fa5EJtNMn5zz913Ne4EQdAAVgP1AW/gniAIx0RRdPme5/0Z3HV+yF3nh2k3zOaP49Dyk9TsUIXOE1tzcv15pbyHb6Vhr1qEBoQxY+wYmtYqTs0yReEfB2488CL0jnbGNe1I1GjKXcSOCduG0NYqqYra7E7L6T6lLavvzKNg2cTcieNrz/Lv4E2KNpf23KDrlLasHqacGO/73p/Gel3Z8W4VnR0GEfTZg/HFZE0aL+XAshPsX3KcvR/WUbJGUZ5ddVU5zm0zDzBoaQ82T9lDdHjWngRkox6JhoTaHavw5uF7rh9O8ppVblaWloMagAB+7gGY5jBWyg8a13AuRmb6PDj3jPWPFrF7fmKxiCv7lRdRNHW0EuURUqGBZifOyfZ+1fhzF7Vj+fU5jKg2BffnnirbFK9SmOptK9Ej/5CMda7m61uufimKVizAmpFbM9bfL0hwaBDBcn8iozPvd/NHIk2Qcmj5Sep1r8Hhf50V22O1wrAoFsuIZnVIkEnZfPwyYS8N0ZRq4f4hZSikp38QGvKUUzZJvhAa2pszfWpiyPzHkBDmfrjIWdk+hRdnSosF3DnxABNLY/b6rGf7rP3oG+lhYWuW9FusArlczsFlJzCxNCYsUHnBTktHC0s7c6LDYwgPilBUw6zWpiLDq05R2d+FHVf5Z3kv/NwD8PcMpETVwni/9iUsIFwpXLPD2BZY2llw/+yTLD2vKl2rGPW61eDynhsZ0qv8nvi998cmnxU3j9yjUZ86nN588WcPKevxB4Rlfm/PXQXgrSiKbgCCIOwBWgK/nXGXTTaq+PzAu7LvJvo22hiUiiavlgS3N77Eehuil5B2wr06tHS0sCtky8HDO1k3szul8iYmUFcuUoTS9s+5X0dCl17deXrVlWX916a730k7hhIeFIHXKx+lFe9bR+9z5/hD6nSpipaOFt6vfHh2/aXSsaIoEv+FZo+eoS7NBjYAICIkikDv5JXF1ORjiXIW91lNl4mtmJjcuEu2Gi9LkOM0bS89Z7TP0CRXOR8sCUFNCk+mrPqnJ4cpvfu/7C+rkNExfbrOxn3rcHrLZep2qcZZp6QVcLcnHoysMV3t4Y/OPwVRzra3q9gx54Da85tZGacv30wUKVA2H28fZky7tP2YFtw4cpcXN1+pbbP8+hxmd1iC7/tPuVfJvUrJckRF+RcTRLmYeP8la1+1VQVMchirLXD0PclQVcwvUFynoCYnL/l5NDSQi3LizN8S5yUwb1hprrqeJNrYG/0wh8+DSf18arx4PwO/9/4YmOhjamVCqH8YclGORdFYDs8ep1iAa1W5Is0mLCDexYYLd14yuGUE5kaJlUJj4uLYevIqRmIuJYNfJkopXdKSSgWTQv9zmplR3tSSKlr1MZSbKY0jLDCc7bP203pYEyQaEmzzWzOz3eI0x/+lYQeJnjtVuYSpGTler3xYMWgDOnraCLaR3H5yCH09bfSsLUgQ4tDXNqT3nE5c2n2DNw/d6LegK663XxMRHKm2z5/JxV3Xubr/NtIsFLH17rE7DXrVYu0oJ4pVKYSlnXmmSiNlk3kIgmAO7AXyAO5AB1EUU6y2CILgDkSQOGGSiqKYZlW572262gFeyV57f9qWTTZ/BAHeQeSwtyBOjEXH1ofN/3Rj/eBenF06nhpNLIjR/vqKqw161uSc02Vy2GkrDLvP1C5eHJcHdzmz5RJ5S+TinHw//96ah4mlcZr9BvmG0N6mP6NqzUixTy6Xc37HNU5tupjCsAM4vfkSjg2UQ4qOhTlRuVlZYiJjgPQbSvdOPyZfqTyYWqkfc0RIJBd3XWfA4h6JQuvZ/HKY5jCmZvvKnN6SVMSkYtOy3Dn1KM1jNbU1MTQ14NTGCyr3l6ldApdbr9M1jr7FRjD76Pj0DToZDXrWYlEv9eGcuvo6+Lp95OqBbw/LLlm9KFa5LXHecP6b+8rqRBm4s6p/E8Y1qEOD4sWY07wR/7QsRYTmr1MxMzmH/3WmzfAmAIQTTJ9mtZQiKyQSCW1qVyCKcPCwptX45fRftobBKzfQdOxi4t/kSBGJkUA8+XIqa+MBlC+cH1FXdXjwzjkHaWfVl5CPiTILaZEgxhMqBhInph0d0bhfXYLTESIcqulFp6aFODZ/DHumDWNVj7bY1ZbRa15HDi47yZtPCyy75x+my6Q2iaGgWZQfYdhp6Wilu61cLufIylMMWtYLE0tjIkMyVnH4jyFrVMucAFwQRbEgcOHTa3XUFkWxTHoMO/j+xp2qZT6l2Z0gCP0FQbgvCML9BH6dZOlsskkP7596kL9MHmSWAfw3oh+6n7SpJBIJc3t3Rtfu6354NTQ1sMlvjdcrH7Vlz71e+bJn4RGGVZkMQLBvCDvd/0tTn1Emk391iJqugS55SuTC0FSfjc+WMGFbYhjaqFozOLDmKGHSYIwtDNLd3+75h5jvPCnVNq/uvcVp2l6KVi5En7md6TqlLaVqFMtOKv9FOLj8JM4bL+DpmlgtTxAEytQuzl3ntI07abyUJ1desPreQpX7S1QrgtsT93SNo06X6ljamTNsdb+0GydDEIRUhZcFiYDPO78M9akK6zxWODYsoxTa9ztjZSVQKpe90rZOlRzRs8yaXpy0iAqLxsPFmxLViiAgEK+iuEiCVAoISI1CyZ/HklbVK1A0tx2iIEdCytACbXS5/8o9xfZDV++gEWWkchyiKCKKIpMaz0UjjYqjMea+FK8L06ZUp0YrY2KsPVWKkQMUqViQpv3rMa9LyqJaX2KVV4OudWsoXhvo6jK2eQNWzFxKkE+SlykqLFETdeDSnhSpUCDNftNCW1ebRn3qULtT1W/u60fRamhjJu0crrRNEAQqNCmr1uj98MaXVUM3EREcSfcZHWg1tDEGJvo/Yri/DCJCpvz7RloCn3MNnIBW39rhZ753WKY3kCvZa3vAJ3kDURTXA+sBjAXzP7gaQTa/I28evqf//7pzx+UElsbKHihBELA0NSDoK/qt3bkql3ZdByDIR86VZy+oWbK4Yv+Oi1eJ9tPB8NNrf89Anl1zRUtXiwP+m+hTbASB3irOLMoZWnEi5+T7U813U0ee4vYIgsDojYN4eOEZtTpW4YbzHRIcPGlYpTiuBw6St4mcmAAbXp9Ku/T1kZWn6DuvC+OdBrOwx0q17WKj4zjzyfOjravN6rsLuH/2MQlxCdw/84SnV5NFgqsLkUxH6JbKwg3pIaOCyn8AAiKW9hYEeAXiF5nkFVDMHdMZSjez7WLWPFyUYnvnia2p3KI8I6tPTVc/TtP3Eh8bT6Vm5TA0NSAyNH0LL4IgMHzN31Rq5siHN74p92sKOBSxJ0+J3Cpz8lRJd3wmNCCMio3L8PjSc9qPac7aUSk1yb4Lau5FtcLh6mQRkoddfzYkkhsHqYVOqlNJEFWcX10fWYwLO6/xz/LevLz7ls0nLtOmaiXFBD1BKuXw5QdoYEqlGlYsHtAj8SBH6F6vJi3GLQfvXEr9SQQJfu9Elp5wZnDD+mhranLk5h2cL71EX1BuGytEo5MnmHwOFsTExvPezZs9iw+z8s58hlacmGKskZpB/N2pPH0aJlawbV6hPE9quTNw5m4MQ5WjJIpXKcyQVX0ZVWNaut4HC9OUC3wlHByQa6f0Dro/9+S/EVto3K8ulZo7Eh4UwZPLL3j/1DNd+nK2+a0p36gM5jZmyKQyLu2+jllOU7pMbqNSOkhTSxPHhqV5ds2VqDD1izY/iqv7b/HmobJ2rYWtGQMW98DTxRs/d/WebE9Xb2RSOe+euNNySCO0dLTwc/vIpT03U4jXZ/PVWAqCkFwtfv0nuyY95BRF0RdAFEVfQRDUldUVgbOCIIjAuvT0/72Nu3tAQUEQ8gIfgE5Al+98zmyyyTLIpDIu7LhK3pJF8PD3x8Eq6bubIJXiFxiBFqYZ7teugA3nt18FwCDcmskrjlLur7tULJafy49ccXkagmG8LZCY8xYWGM7BZSc4uOwEbUc2Y+urf2lm0FVt/1cP3EYuzfhE6Y7zI/qXGcv6x//jzJZLdLDpT5y9J0eXjcXsU/5IV2DCrl10ndWG3TOOpPmAHlh2HFtf/cv/eq1O18M8PjYeYwtD1o1JLIdfq2MVuk1rx75Fx7IfaFmIcg1KM2BxD/5xHK+onvcZtyfuFKlQgJd336arL/Ocyt+ham0qUqhcfgaXz1iY5e75h/FzD2DH+/9oZdYzzfathjYGoGn/+oQHRjCl2Xxio1NGoFjaWzB2y2CiQqNY1HOVyjafkYoJROsHIkhgdrclrL+3lAdnH7Nr3uEsU7ThR+DvL/LIw4u/HJKMlB037xEbZET6g9SyHgeXnaDtiKY4LdlB0/HzaV2rPAlSKUevPiTmjRkysxDGdeysdIyJgQH5Hczw9BJThGbKPxjgULkB9YYsRENTINZfB8M4e6W4Kbkox7hYKIdnjUVbK/Hde/PBh/knLlJXWl3lOHWsY+hVv5bSttL58pDTXpuoL7IJRm8axD/lxqd6XyfHyzcEUVS+Fue7DyBcfVTH59BrYwsjytQuTuUWjujo6eDh4sXFnddVPhs6jG1BdHgM1w/dUaoo6/XKB1EU6TiuJac2XSQ8KAIDE30a9q6NWU5T7jo/pNu09sRExODvGfhTi5IE+4US7Kf8hgd+CKZ/qdEqpSWSc/3wXXrM6MD7557smnuIgmXzsfTqLOJi4rm89+b3HHaWJxN15wNTC5UUBOE8YK1i1+QMnKOqKIo+n4y/c4IgvBRF8WpqB3xX404URakgCEOAMyRKIWwWRfHF9zxnNtlkNV4/cEN6P4H+Lzeycezf5MqRg9DISIas3Izsg9lXTVSSh8YIgoBegD1PzsRz58xj9DBET7BV7J/nPIn1Y5PEbA8uO0GlZuXY67OBjrZ/o2+sj3WeHPi89SM2JnGSbWplnGqBiNRwf+5FI+0u6BrqIhdlFCuUU2HYfaZvzZqc8vNl/8eNXNl3k6sHbvP40nOV/ZWuVZwT68+lfHir8YbZFbRR0hq7vPcmOXJZ8veiblzcdR3X26pzsNR5Jb5a6uBrvQhZ3PuQWTy++Jyl/dakMOwAzm27wsClPdUadyeidnJpz3X2LDhCqepFCf6YNPkxtTKh16yOCmmEtNAz1GXji2XIpDKCfELIV8ohXUZlpGYQb8NvU91mPTJ/E6asG0ul5uVUTpoCvYMYX38Wjg3LsOTKLO6cfMC2GftStIvRCSFvWQ0mdO2ChkTCkr0n2LBkLXlzFlMKV8sKfJMXLy0ECYbReRi24RQ1StlQuXAeTj96xWPXcIxkDiCoLpjyK0iRfPQIIDI0isq1qvDw/FO2uLoiIKCPHbqCQCQhyFTMPGXqBN+BvLkKMH/1ClxuvmL/kuMpEmKidAOZ3rW5wrADKGhnSy4rPRb1VZ0vqq7QsqAiFC0yNCrdhh1ApIc+g1duZEG/rhjr63PDxZVF209hEJdHbYXYz4QHRSjlrxZyzM+AJT14esWFu84PFQsgXSa14dk1V55dUx198uyaK3HRcdTpUg2znKbERsVyfvtVAj5FtHw+rljlQgxY0pMds/ZnCU/eZ9Iy7D6ze94hukxpi5/bRwqWy0/fYiP++AIropipxl0a5xLrqdsnCMJHQRBsPnntbACVblhRFH0+/e8vCMJhEotVpmrcCerip38GxoK5WFGo+839FK1YEA8X7yyvkZLNn4VUTCDW7CPG5hpERUgR/C3QFb4uDr779PZsn7k/1Ta5CtsydutgTm24wKkvVh41NTU4GbOLuNh4dHS1QRAUD/Nd8w5TqkZRlQVVMopMlFG0gcjq0QOVtrv7+dF56FpaNOvIxB3D6FV4mMqQNkicrE/dN4rRtb6onKjGuNvycgU3j95jw/gdys0FgRrtKyfmQEpleL38wMPzz7HKbYmuoS7vn3kqqrKpqwr4XY27z9fzhxh3aVGpWTk0tTW5nkxUfMf7/4gKiyIsMIICf+UlIU6KubUp01sv4ubRewD0nNmRIN8QTqw9m2r/rYc1odOE1gT7hZDTIQdtLHrjUCwXH976Ik3DQxZj+YFBParTqVplEmQyNly6jKWjI/e2vktXKHPXKW2p2qoCW6bsVmh8yUU5pn8Fc3D2GKW2A1Zt5M1pAQ115Vy/BxkMEVZbRTNZPxmqtJnsuFhpJDFiJAaCCVqCmnzhtIy7LPid6ji+FU8uPU+xkBAnxlCqviarhvZVbAsMD6f1uJXo+ub6shsgMYfb0s6cKftGMqxySodAhJEPe5f2UYocAVh47ASnNvqpfF8jtQIZNLAsPerVUmx77u7J39N3YBiqnAvZdUpbTCyN+G/E1rQuW0GMGAU5g9HWkRAdqIFBtBWSbwhNL1GtCFValidXYTt833/knNMVRWGWb8XIzJCOE1oRFhDOtQO3Uw2FzKrkL5OH3nM6c9bpMvlL52HbjH1qDcTz4oEH6S3c8StSrpyjePPm/bQbpgNdXeGr3ytBEP4HBImiuEAQhAmAuSiK475oYwBIRFGM+PT3OWCWKIqnU+v7u4uY/wy6TmlHVHgUF3Zez9IaKdn8WWgKWhiG2iMPBT1Ic4Xyaxm0rBdFKhYkLDCcpX+vU5nnI5XKaKjVMcX23EXtWHl7Pm8fvU+xLwXpmDBpCBq4vPYiOjYWfd0kUdUlu4+gFWZOaEAYF3ddU2vYAYT6h2Foqo9N/pz4vktWelvN+e0L2bJp4q6UwxVFruy7yZV9iZ6VQo75qdmhEkE+IUSFRtFiUAMS4qQcWnESaULSQ+9byr9niCw4Af2Z3D7xgC6T21CmdgmFV1ffWI+x9WYq3Qe6+jpKXgO5TJ7mqrahqQH1utdk08SdGJoZcGj5SQA8XLxSPQ4SJ981qjjQtWY1ADQ0NBjasAGTDh8mOsI8Xde2c85BDi49wditg2k/pgVTmi0gMOYjfWpXSNG2TeWyTD5zPoV49XclPfdicsF1NUaVIEku+ZD+/hCTvLnacm20MU/MOpGoHpfi/L/Qd2jvwiP0mt2JyNBovF8nlSLQEfR4dDOQTqEraF2zHO+8P3L2liuaPjZqnxlyuRx/r0Ck8arfZM1wEzafusTMnkm/+aIocuX6C7QEG5XHGCZYsn7nbR68ek/TKmW49ewN52++Rj/EPsU4ds45SN/5XZl/ejITG81N1/XrCQbgnxiGaQTf/Dx8fv0lEcFRdJ3Slq1T9mTq4n5ESCQbx+/AwESfDmNb8uyaK7mL2GGSw5gr+27i9tQj0871vXj32J1rB24R5BNMtVYV0NHTznaA/HwWAPsEQegLeALtAQRBsAU2iqLYBMgJHP4UwqwJ7ErLsPvc8Jemaf96PL/+Eg8Xb8W2s06XqNKyAiWrFeH5NdfsGzibXxI9Q126TmlLXEzKPLGKTcpSumZxjMwNGFljuqJa3yaX5YQHRTC8SkbCuZPoNrUdTtP3UrxKkW8ae3Jk7pY0HzOPni1qksvKEqcTl3j7KAo9IQfdprRjZtvUdZYa9q5NvlJ5mLBtqFpx3M/s9lpHTGRsunLzXt9/x+v77xSvH11ywTZ/Tgav6M1Zpyu43nmTvgsksVS1qvDCbL6NXXMP0XxQAzS1NHh61RVP1w8MXtGbKc0WKNp8GQ5mlduC92rExD9TqmYxnl114azT5QyPKVYnnPa1UkbaFLey4rQ87TLwin6i45jdYSnLrs2mcnNHTu07i19wSmkU36BQNESt77YY9CVFKxYkyC8Uf4+AH3PCP5htM/bxz/LebBi3Xel3Xj/akoB7cpbeu48WOugJudP8/POVciDIV3W4nZ5gwIXL79HRPsjAZg0Ijohg+raDhL3TJ7XYEY0gc26cCebamfMYYIRuKuPYNHEniy/OSH2QPwBRFL/bnC8qLJotU3ZTuYUj9XvUZHCFCfT/X/cfV+joGzmz9TIAL258XcrF78KPDMtMfRxiEJAiXPFTGGaTT3+7AaW/bJMWv3x5Nkt7C8y+SKaXSeW8vPuGQo75Kd/4r580MmjYqxYt/mn4086fza9N8apFuHX8Adtn7k/xb0jFiYypMwMrhxxUalYOSKwSaW5tysSGc776nNVaV6Retxo8u64mtEyUJ/1LD6IcHXQQXtuxesk9xk08judVbfQic2CTNyfxsfEqBXKT03VyWxb2XMXcTstTbWeVyxJLO3NaGHdP39hUjNXnrS+rh22mdK3iFCqXL12HmVkZs/P9KixtM14YJ5u0Ob7mLKVqFmP1vQXIpLJUJRLK1i9F4QoFlUI5VSHKxa/2yGok6HL/VUrPtkdICF6PMy55sGHcdrpObUsZx1KcvPqEiOikvJ6YuDi2n7qO0VcUXfpa/l7UnZ3v/6N620qpN8zob0EaiDJZ0r9P4u2/Qg7dtyCXyRO9Xgu6oqmlvNYuESQYC+aJHq50UKVleZ5cdlG7Xy/YlpN7/Gg29F/2vPXF56Y++lIT1eMS5cTaeFK5hQEjR5SnZBV9ROO0JSj0DHXTbPM7cOvYfaLCopHL5Hi88MImX86fPaRsMohcnjn/siq/tHHnUMye05suKhVi6DatHVFh0eQpkZuNE3cqQrB+BtIE2R9V3SybzMW+kA1eL1OXCzA0MaDzxNaUb1SGloMbcv/s4wwltkPipOBwyFbOyfejpaNFwbL5CPUP+5ahp0AQBIwxx1RuhYaQOIkZtKIXG7/Ii/uSam0q4v3Gl/Pbr+DvFZhq26VXZ/HwwrNvHqs0QcreRUep3Lwc9brXSLN9yMdQJjWZ98cnqX9Pts3Yj/tzT5b1X8ex/84wdstgukxpQ+3O1djpvobt71bTcVxL+s3vytTmC1Lta/CKPkzcMSzdlTi/xFA0Ycepm/iFJHnpXLy8cD72RH1OWCq43HrNqBrTGLSsFx07D6TlhGVM3rGHUf9tpemYRSSoEK/+nrx74s76cdtxbJjhxeJsvoKQj6EcWHKcHjPaf1M/N4/eo1Gf2gxY3ENtG32MMAjJRYsWHdAU1JfyirX0YeOU3izo15WudWrgNGEwTZrmJUpIfSHu8r6b9P/fVy6u/aLcPfWYDuNaoqGZmBObu6g9+sbK/tB/VvRWLMJmk82P4JcOy2w6oD4yqZx1o5Nc4rr6OggC+HsGKIoj/Cwu7Lz2U8+fza+NnqEuMWmElwyvOhldfR1mHZuAtq4WA/8am+7+JRIJxyO3o62rzfPrrqwesZW3D92wsDVjwvZhXN5z41svQS36xvoYmxvx9rF7qu0qNi3LxU96fuowNjdkjvMk7p15zIqB6ZWXSR1RLsNp+l7KNyrD4OW9CA+KRCaV8eyaK0+/LJghSHj72CMxZ+gXyvn5lZAmSJnf7V+GrOxLYcf8FHLMr9h3/dAdqrQsT5VWFfjHMW3pA2NLI5YNXM+l3anfV6kh8bCn7bj/cKyWj9jYeJ7d+oB+cMpcpPQSGRrFyBrT+HthN04+vcjFfddY+vcaNOS50PjOdl27Uc2Qy0VF3mF8bALBviHksLdIfydq7nulapmfPaVqimZItDJWlVZJH/AX/975ewby6OJz6nWvoZC4UUeMZijatpEYGeoQ6B+LVkBOtARt3J56MKTCRFoPa8LSK7MUFZUjQ6J4++g9L269wuXGKwo65iMiKJKa7atw4/AdpCryU21z6VPYXlnLblTb5jhf+h98NE7R/jP7Fx9j5Z35X/EOqMc6rxWFyxdAU0uDawfv/HQ5G4mGBLks6X4L8gnm7smH1O5UlfM7rtJ7die8Xn1g8+TdAOga6FKsciHCgyK5feLBzxp2Nl+Qlb1umcEvbdytG70txbaNE3YC8Oii6rLqPwKr3JboGuji6eqdduNsslFDWGAEplYmitLMqnC5lVjWf0m/NWhpa2Yosftg4GbCgyLonEu5kmWQTwiCRMDc2jSFvk5m8c/yXuxdeCTNdnlK5Gbr1D1q9xtbGHEwYDOh/mHcSyVc72u5d/ox9848RaIhQSIRaDagPgnxUlxvpz8fL5vMQS6Tc87pMrmLJE46G2t3wsDUgLDAcKxyWabbc/rsmitlapf4JuNOU9BE0z8XtgZVKFA1D27OTpmSE7dh/A52zz9Mq2GN0RS0kPP9ZyAWdhbY5LXi8ApnRFFk65TdFKlY8KcvTo7eMABZgpTl/2z6qeP4UTy68Ix2o5qRr5SD4nc8p0MOYqPiFKHrMbrB1Gtqy9SubQG4/Pw5UzftQ+KeXxERcfhfZw7/66zoN6dDDsrWLUm1VhXoOLYlJjmMiQqLpnCF/DT/pwEaGhIiw6JZNWQTHz/lWWqoCFnWkEjUyiMkJy4qY5EjqaGrr0OnCa058q8zgkSg29S2BPmE4Lzxwk/Jca7WugIlqhclJjIWkxzGVGlZnptH71GqVnEOLDkOwI7ZB5Q09UpULcz6sdup3rbST8/NtitoQ2RIVJqpEL87WSXn7nvySxt36dX5+NG0Ht4UU0tjFvZc+bOHks0vzPunHuQt5aDWuNPMYan4O6MTVW1dbQxNDWht3kvl/u0z9zNwaU/mdVmRoX7Tg0QiIU+JXCzu81+abXfMPsCUvaMYWX2qyv3hQRHsXXSEKi0rUK1NRQ4uP6koLpNpiHLk0sRp9sl1Z+k8qY16405dKe9f3LOQVXC98wbrPFa4PXVnwJKeGJoZUKN9ZQI8A3lyxQWJJPGhraOnxYLuqn9/7QpYExUWlSnjubz7Oq2GNsqUvj4TGRqFppamknfge1G+USEAms8AACAASURBVBniY+IxNDWgWJXCvLjxkqYD6mNgoq9WHyxDqNS8U12JNrmXD+D8jmvkKW6PlpZEkd6gStvud+LgspN0ndqW6m0rIYoi/p6BFCyXjw1jtxMbHYdx7jhm9OjATVdX9t+4QfVixejTrCY7z94g7rUtOoJeij4/egRwavPFFHI4yek2tR0Vm5bl2H9nAPD2isQ3OBgb86TqrxtPnUfmb5j5F50KeUs5cOvYPdxfJFaxff/ME7uCNnSf1k7hGfsRFHLMT71uNXhy+YWieIrTtL0MWNyD28cfcP3QHQqWzUuQTzDvnrgrHfvuiQfdp7fPEvPVLpPaEBkaxdpRTpSoViRzvuPZZEl+aeMuq7Jx/A40tX6gJlE2vyXBfqHkLmqXdsOvoF73GqkKsj65/IJ+C7shkUjSVXkyI3SZ3IYr+26lq+2dEw8YtKRnquMoWaMYVw/cIoe9BYXK5VMrhp4ZJMRL0dL5Gtn5bDKLD299ESQCbs88CA+K4MWNlxiY6ONy8zVyuRyJpoTxTkPpO78rmybuVDq2bP1S1O5cjW4OgzJlLIbmhkSFZX5lvh+VY1e9bSUMTAw4tekCL268BBJLpptaqS608aOYvGsoN47c5+h/Z39LI04doiiyY9YBpW13nR/Sc1ZH1o3ZhpWlEdFxcRy8eZOlffsq7pOuNWvSdPxieK9aBy8t9Ax1lSrMavvb0GnaatrWK4dj4bzsv3yHe3c/YiBXLZuQWVRsWpYnl12IjYoFICwgHLuC1kptPrzx5f1zL8rVL8WDc0+V9jkUs/vme7dC47+ICovm9f132OTLSe3O1YgMjeK/EVtStL128DYthzbi6MrTjHMagpmVCffOPFaKIogOj8a+kC3nt1/56RWVT2++SN95XclVxI6xWwYzotqU7xadk9XJ9txlk2FkUlmWWKXJ5tcm2DcEy1TyXmTB6S+7nhyrXJYMX/M3bx+5p9ru4s5rdJvWjm0z9n3VedRRp0s1+hQdke72x9acYeubf+mRf0iKfcNW90NDU8KO2QdYdG7aDwmFDvkYipmVMSEfPz0Uv0F493sQbeCPpYOImYk+3n5hxHkaoytTnyfzq7F91n4s7cy5vFd9saytU3dTr3vNFNsjP+VhF61SiFd3331z/k6ofxgaGhKaDajPiXXnvqmv5GjpaKKtq/3d84ueXH5BZEgUd5LpwX5ezTfNYYw0QUZkaOZ4OVUZacrad0kvAjwDeXj2MaI0W14k2C+US3tu0GZEU46e3MDZR4/oWL260gKAno4OlUvl47pbJNqCTobPYZLDmEDvJINEQ9BEw8uBPZvd2K7xAn2ZKXpq9PBS8JXrEiWrFyVXYVvK1S/N+e1XeP3ADdsC1vi9TykY/vjic+p0qZbCuOs0oTUSja/7PbbNb03LIY14dtUFh+KFKVOnBCEfw9j3v6PERMaqPMbl1muKVSlMvtIOGFsYYprThBrtK2NiacyZLZfweedHXEw8R1Y68+Tyi68aV2by7rE7pzadx9PVm34lRv30/MWfxZ8Qlpm1ZiXZZJONgoR4KboGmV9aevz2oUSFRrN/8dFU2x3+15kKTTJfSiQhTkrhZAUx0qL1sCbsWZAyP8/UyoT8f+VlSIWJABStVPCrViELlMlDt6nt6DS+FRJJ0k+ipqYGQ1f1VTKwi1cpjPtzT4pVLpTh8/wIYgwC6NejLEfnjmbruEGcXTyOvBVE4sTfR+vz+fWXFKlYMNU253dco1iVwujqK09055ycRGxkLIsvzmD9k8WM3zaUnjM7quklfYypM4NanaqSr5TDN/WTHLenHljYmmVaf6oQBIF63WpgZq1aZqHP/K50GNfyu45BHevGbCMsMJxKzcpRpEKBnzKGrMTr++/48MaXqnWasPvKDSQqPLsaEgmJSu8Zx9jSiAAV1YgNBGNM5Tm/ymDMCLoGulRrU5EDS0+wZuRWilQsSLdp7ajWpqLKEPhaHauoLPi1etjmVCNSUqN84zLsXXiE64fvsn/xMXbOOYjzhvNqDbvPHFp+klEbB3Hn5EN2zz/MoeUn2TX3II4NS9NvQVeqtanIzaP3vnpcmUl0RAxntl6my6Q29J3fBUgsWJNdyfP3I9tz95uRI5clTfvX48q+m7x/lrqQbzZZnzcP3ShWuZCicEpyvlYDqkiFghxZdYriVYrQckhjhZc5LjoOHzd/XG6+UuTwvXvsQdXWFbhx+O7XX8QXyOVyRm4YmK7Kns0GNuDpFRecN5xPsa/l4Ea8vpcoQi6NlxIdFsOCM1NY2m9tmrIJn4kTY8hRU+D6o6MYGluy3XMVJmYmRIZGYWRmCEKiMaqjp02+MnmwL2BDQoJUOURHXdhYco9e8r+Tt0/L65fBkDQTexm9G9RSvJZIJCwb2J0Wr1dD0NeFbGVFHl98TqkaxXh6Vb2u19FVp/n31lz6lx6Dtq42GpoSzKxM6GI/AE1tTeyL2IFczqxjE3CavvebxrN50i66T2/PzLaLv6mfz2hpayJN+L7RH6IosnnybjxcVHu7T6w5g7+X+mJOP4Lo8BjiY7O9dwB3Tj5EV1+HAvUrsurkabaOSFrgiEtI4MaTt2gL6fuOa2prsurOfCJDokAAE0vjnxqe1250M/b97xiQeF8e++8M+sb6JMTGq4yCsrA1Uypa8hlft4+p5tNqaWuqlafyeOGNfWHbDL8Pcpmcu84PlbzfcTHxivxFxwal6T2ns+I6osKiObjsRIbOkdncOn4fs5ymCIJAl0ltMDIz+OMqef7unrvfyrjTM9RFW1f7j64E1LB3LQxM9DE0TZ/waTZZm+sH79BrdkeVxt3XMrnpPCbsGEYnu/5K243NDSlcoQA12lWiTpdqTG2+gHVjtjH35MRUjTttXW02uSzjo0cAOXPnYGbb/6UqcaClo0VEcCQOxXLh4eKV6lg1tTQU+RdfcmTlKabuG6V43daqD2eke9npsYam+l3TDDmJESLJXTGB4RX+wqJeTV57f6Bn4zaUyd2QHDaWPL/mysYXy2g2sAEvbr7CaeoeHl54RrU2FVWGCmUFTIxSenotjI3R+s20he+eekS/BV1TNe42TdxJx3EtOSffD4CHixc+7/yQSmVIpTLePnQD+CbjQVNTg0Ll81OwbD5KVC2Cjp42cTHfHupkk9+aoA+Xv7mftHjz6T1QxesH6vf9KFL7fP9EYqPjeH7UjRgdOR0jltOjUTXCYmI5dv8ZzVr0JId5DjQ0JLjeecP904/VGjKDlvbkwNITnN9+JdPHmL+UA1P2jkJHTxsRkQvbr3Flv+oQaolEQrvRzXh9340gH+Vqt+oKY+Ur5cC7NCR0vkRHT5vha/oTExlDXEw88bEJXN5zQ1GoBRLv977zu/DusTvxsQm0H9McLe3E/OrbJx7w6p56XcztM/fTZ14XLu68lqKgyv2zT7h/9onideXmjpSqWYynVxLv7cotHIkMifqhxU3eP/Pk/TNP7AvZEuofRpBvCNVaV+B6Ji7iZmX+hLDM38q46zi+FdZ5rFjQ/d8fet6KTcvy9pF7ih+nn8GL66/ov7g7giDwV92S7F14JFMmG9n8HORyOZGhUVjamacs9a7OA5SGt+fxpeeEBYTRdUpbds45qNgeHhyZWPr/9GMa9KzF8htzGNh0IGv/nYtx2TB83kZhEGaTotjDxJ3D8H7tw8RGcylbtySr7y/k0u4bvLr/lujwGCSCgEwmR/7J02iVy5Jbx+7TYkhDFk6bQS4HQ7S0NfDwDEXwtlIKATrrdIX1TxZzauOFFAbjpF3D2TFbufhAfEwCugY66col0MkTjtOEcWhpJv4MFrK3Y8Xg7szdf53Xh94RH5tAW8s+5C5qh8ut15SsVpR63WtgX8iWm0fvpdl/urxuabXJwOcK8MEvnLiEBHS0koq+3HJ9RWywFr9TGRi5TJ6uBawGGh3SbCNRUfY9PVRrU5G/F3YjwDuIOycfsuffQ3Rb2xTvp/6cXnz9q4uiSDQkyBJkmV7IKJvfB704MwLvyZl+7woaaGKEKc5XkqphFqtciI7jW6GpnVh1VRRFEuISCPUP5+WdNzy96krT/vW+2biLEaOQGoUhxmlhFG/J2kf/Y+Ok3Xx098frlQ9GpvoMXzuAPCUSPYpyuRxBEBTfDS0dTS7svJahKCNtXS1KVCuCfWFbBEFAU1uThLgEDi47qfYYQzNDPnoG4DQt0UOvqaXJwKU92Tx5t8KIjA6PZuuUPfSe0xlBgD0LjhDgHYREIqF256rUaF8ZTxcvnl17ic87P6X+ZVIZG8fvoO2oZpRrUIrja86qDee8dfw+Q1b25eWdt8THxlO0UiFiI2N/SuVK79c+vH3sjlVuS8ysTSlcvkCqRmw2vw7CZ6HLrICxYC7WNWtBdHjMVz3YjMwNKVy+AG2GN+Xl3Tec3XoZP/fvu8Iu0ZAwYfswAryD2DBu+3c9V3owzWHMXOfJ5C+TB/fnnuyef5i7zg/TjBvPJuuiq69Dv4XdWDNyq/pCPRk0AiQSCUfDttHcqJvaNgZlYhjesQa1SxQH4P7rt4xcvAtd39xoamvScnAjOo1vRXxcAt3zDlZ8Z8vULsHYLYMJDQhTGEESiQRBIiDKRT56BHDt4G0KtDNkYKW/KGSXWBE0PDqaVpMWI3mbS2liXKBsPsZsGqQUxjl4ZV80NSSs+GeD0pjPyfezdrSTyrCXXIVtiY6IIcgnhKqtKxBv+op5bVqnaDfm0DEWrP0PTS0NgnxDuLznBg1710aUi8THxvP40osft4CUwc81ligsSkWw/J/uOFhZcenpc6ZvPIK2lwOSLFb45Vso5Jif3EXsOL8jdcHn9LDh2VL+Ljkq7YbJz18uH+O3DWV07RmE+ocRY+5D5Up2dKpbmVd+flz18KJ+9U5sHLlLpUh0WvSc1VExEU0P+sb61O9Rgwdnn+L92kdlG4lEQqeJrdDU0sz0IknZZH20dLQwtzalaKWCOBTPRaWm5bh3+jFyuZyI4EjePXZPrOxY2Ba7gjac3nxJZR7eZ6ItPtCwdgG61a+O20d/dt17wLx1/zG0zBQsbMyo07U6MZExHFhyQm30RWZhamVCmxFNiQ6PpkjFgsxo/T+6TWnLbedHhPiGMGbzPxxfe1ZpUc4qtyXNBjZg86RdSn1paGogkQgqvZ42+XJSulZxbAtY4/3Kh7NOl1O0sbQzp2Hv2ugZ6vL0igt3T6XUX7WwMaP92BasH7M9SyziFK1YkL/qlcQ0hwmbJu7kZPSuB6IoOv7scX0vSpd2FE+fvp8pfdnaClnyvcpynrvB//Zh08Sd6RakTU5EcCSut1/zrlYxilQogM87v0w17gxM9FMkxcplcnbPP0So/9eHghapUAC3p56ZUrkoNCCcrVN303ZEM3YvOEyDHrXIV8qBLVN+nCZMNplLbHQcB5YeZ8CSHmyfuZ+ITxX/vgVtXS1iomI5I93HujFOHFp+kpaDGzFkZV/qS9ojFROwt9RSGHYAjoUK0K5FeZqNHkn+AvlJiJcypvZ0pZDRGCGCu69PM3fCe0R0ebrbFy1BO8X5pWIC8R8iFYYdgLG+PsO7NCbEIi9/lXZEJpUTFhBG2XqlAKjTpToB3oEEeAZRplYx/i45OkW/fYoOZ/3TpSmMO0NTAxadn4aWthZaulo8ufKC/808h9haVDIkA8LCuHnsKe8GvefdI3fuOD+k77wuHFl5CqfpexmwpGe6xNd/FroYEPZEm07jtqKlLyMhVAeDmN/LsANwe+JBlZblM6UvfSO9DEt+GFsao2uoS6h/GFGSULq0Ks2Qlo0BKFegAA1LhTPCaTun4vd/CgOLZ2ilSXx445uu/rW0M/ZoLla5EOGBEdTvUROfd37YFbBOoQOmracNYuJz0sjcMFN+R7L5dUiIS+CjR4BCqDz54oGRmSH5y+TBws6c1/fduHXsPr3ndmbtSCeV34tISSjdW//FP80TNR7zWVtTpUhhpk2dTp+5nXl+/SXbZ+wjNjrzBM1TI9Q/jM2TdlGjfWVyF7VnzYNFmNuY0WF8SyKConjz0I0uk9sQ6h+meF75ewYSHhRB3a7VubTnhkJXUiaVoW45xtftI4XL50cmlWFgqq+yTeCHYEVETLn6pRi4tCdPLr3g1vEkYyLIN4TTmy7Sfkxz9i5SXdhMQ1MDmVSGoakBHca2YP+S49/tO+t65w1aOlp0n94ecxszePddTpOlyAI29Xcly3nuKgp1M6Wv1BJnv4ZC5fIxdf9ohlWenFQC/SsoWDYfI9cPYGilScikMiQSCSvvzGf3vIOZGu9sYKJPxaZlkWhoULVVeZYPWP9H5yL+DhiaGtB2ZDNe3XubacnPJpbG7HD/j/fPPDAw1kdDS5NehYYSIYYyZORfdKurXE7+zqtXeNvmpmmL5gR9CKZX4WGKfbFaYZSuZcDigT3Q1dbGLySEnnNXE//SGk1BebIaI0bRqoc9YzooV+N74eFB37G7MYqxRltXG5t8OSlaqSB6hrqY5DDG1MqE8g1L8/DCM5b0XaPymja/XIHrpwf4/3qvVmw/FLSFNha9k8agE0KDlvZM7dYOQRCIT0ig+/yV+N7Vp6RjMWYcGkcXh4GK9l0mteGO88MM53t8FzLo0fsd+XtRd7ZO3fPN2lFtRjSlVscqDKs8OUPHbX31L70KDyM6pyeXVo9HW0s58LXLnJW0bPsPRSoWxGnqXgYs7aGo7JoWBf7KS92u1dk4YWe6ZXXajWrGR49AcubJgbm1KYeWn0yxSNp3flfcnnrw7tF7PF9+SLPP6m0rcfv4/Ux9lmbz9VjntSImIvaHPMtzF7Gj2aAGHF11OsWiRJSlB5fXTkhxzw/fvAPXYyI/c15pV9CGCk3+IjYyjmfXXAn0DkImk1OoXD5s8ufk/HZlb79jwzLkL+3A/iXHFQaeOgRB4J8VvVk9bHOGxlS6VnEqNSuHp6s3Z52uKL7TnSe25sTac0SEpDTaJu0agZ+7P35uHylTuwQvbr3i6KrTGTpvRvlsUJ4XD2RJb1RmUbq0o+jsnDmeO3v7bM/dDyUjD6NilQvx/plnqqGLAd7BxETEUrxKoW8ywjxcvNk6ba/iyy2Xy/lfr1XpetBmhKiwaK7su8WAxT1IiJNSvlGZTAlhyubnERkahdP0vfSd35XHl16kO9QlTowh3igEMUETw1hLJS9OWGA4zQ2TQjMXX5wBgB4GXHv8MoVxd/b+M87uvsDtOi/oPFE5pFHHPpKVQ4cpPGHWZmasHNGbnuOdMAy3VWqriz43n76BL1Kidp6/jla0CQgQHxuPh4tXiqIrG54tZcWA9Wqv95+y49j/cRMaWho8v+aKoVmiUXxgyXGldnpxZpw94s31B/MxNzXA72M4sg/m6Aq61O9VG1OrJG24et1r8OGtX9Yw7LIB4MyWSzQbUJ/D/zp/Uz+Hlp/8KjkELd3Eia0oE4iXSlNMdKVSGRsn7OR0/B6eXXclh516zcovMTDRp3rbSgT5BHNgafoq631up6WjRb1u1VVW/bt3+hHVWlfk5pG0n2EGJvo07V8fAxN9Tm++mGb7bL4vgiAw4H898HH7+ENSQDxffmD9mO20HNIIkxzGvH30nqv7bwEgyiSERUeTw0RZMPyjdxCi+HUSHppamljltiTkY+g3pZF8eOOLvlEVTm28qHhG/r2oO7r62qwcsilF+/tnHpMQl0CfeYnSAHoGOni/9uXMlktERyhLyIiiSFhgOHYFbZQM3hy5LGnSry4Hlh5XKXnw5PILnlx+Qd6Suem3oCuut19z9cBtzm+/Sr3uNVT+hnm9+sD2mfvpObMjq4Zt/u6e9gpNyuL9yidFPuHvSHZBlV+YjuNaIkgElfpYyZFIJAxc2oujq05xYec1te1CPoYyvfUiProHfNO44mPjuev8UCksJnnFpsxEJpWxbcY+ilctzJ2TD9M+IJtfgqOrTtFsQL10TfpiTP2oUt2W/s1a4hsUwsJdxwh9YYSeaKiyfbEqhXGO2YUgkbBm2RKOPXhA87JlEQSBi0+eceaSK3pCLtyeemCd10rpWOscximKSBSyt6N6x9Lo+edRyncQBAH/1wLDV21iRq+O6OvosP7EWa5c9UBfUDYEv0SaIE01j6le9xo8v+HKkn5rmXtiAuFBkUxv/T+VieJ68WaI78wIArQwURQdaTW4EffOPAag1+xOPL/+kvufXmcJ/lBvXXI8Xb1p3O/bIz1MLI3RN9Jj7smJGFkYUbRCYon5xtqdyFMil8rKrw7FcuHvmZiPJASYMm/XIeb17arY/8r7A96eUYxYOIyXdxLvu9joOPQMdVVOXHX1dYiNjqPl4EbUaF8ZiYaE4VUn02xgA6V2EomEfKUTqwWq844kxCVwapNqY+zpFRdFlb60iAqL5r/hmxVhfNn8XERR5NSmC/i6/Z+9s4yO6urC8HNHkomHGHESXENxd3d3LRRvkX5QSilSihSXUhyKU9zd3d1CEkJICHH3ZOT7ETIkTGQCAQLMsxZrNeeee+6Z6czcu8/Z+32DPtk15SlydYp7+fplGL7oe5KTUgiPCmf6jn0sHtRP3fehz0v8fOIwJOfgrnilwrQY1JjglyGp6cKkvj5v9xcUKl6LLX/s1jinVPXi9Jvalfk/LCfkVfYWHQq5AqmehMQ3rgglqxQlIihSvTP1LmnBVxoORW3pMq4tqODUpvP4e70NeHbOPUDjPnVpNbgxt47f586pB7QZ2gRLOwsMjGWZBndiiZjGfepyZd9NVo7diFvd0gyZ15eIoEgK2Gb+fplZmSIzknF8/Vka9Kj10XftKjVxw6SA0TcR3IEuuPtieXTJHbRQK1MqlUzvvpDBs3vjfsMr25qIvPpRLVOzBOPW/8igcj9/cEpRTsRGxukCu6+MUP9wzG3McuyXqIqnbj0nZv6Q+tBZ1N6eGtNL0uJ/s+B55sFdS1kPJBIxVVtVpH63miz5YzF/bziOSCQQHSBgFOcIAshTFJhammQ4Nzg8BpUqYw3by+BgVHJ9hs7vp6EwaZBgyZ1j8bS6tRBEKggzxUiZfWBXslqxHFVpe/3eCc873oS+CmPIdzl76WXGtll76DGhI+s9lrD6l835K7DToSYvhBqiQqPxvOPNf7P3EeoXztLrszC1MmG9598ULGRNO/N+xEfHY2xuRJO+9di75AgNetRSfyYMBRMunA6km/8iGlYqzTPfAG7e88Mg1JHyDcpw7WBqCvXN43dZ77EEiUSCRE/M2t+2Uqi0IxI9KQ171iY2IparB26x8n8b1DYE73pZthnelMS4JJr2r8+eRYe1suQQiUU5pptlRV5nlHyrOL3xT/tQI+vMxDk+Fe8GQKbFpXSdsYQyznb4h4Tz5GkoslAH0EIktl63WiwbuQ7jAsapO3VGIdgWEyhX1In7D+9SZZALvifisC9SkMrNK5AQk4BCrmDrrD04lrDPMbgLD4jEvqgtz256IZaIefn0FV53X1CwkLVWwYu/VyAbJm9HT6ZH8wENaNS7LlJ9KcmJybhf9+TkxgukJKUwZH5fQvxCWT9pOyKxCHlK5hlj5jZm2Dhb0WVcWx5dcufGkTs8uPAEA2NZln61m//cxaDZvdg6Yw/WTlYIgpBhQadys+8o4vYmnTQPIpXlY9Z/8Bg68g9fbXD3+MozrfuG+oXhfsOTqJBPU5P29Jon07st+OiBnY6vl5jwWPVqf1bIzSIY3j5j3qNELKZsMQfue8kRC5l//eVyBVf236T9Ty2wFDsR+wKUgDGob9zx0fEoFUr+2PsLUzrMSW3zNWTCms1MH9ATiVhMZGwswxesYfTvM7BysMj0WjLBEMIyL0zPjMS4JCztMx8rjXPbr9BhZEutx8yMdRO3cWT1aTqPbcNlLVLYdHweJNK8uYUNrzxe/d9XD90iwDuILdN303JQY7b4LCM2Mg5TCxNkRvpEhcbw6pk//f/swc55B3Et58yzmxB6S8mGWx7oIUMmOIOQarYeH5OASCSi3fAWzO73N153fXAp40jHMa25f/YxiXGRdDDvp5WqplMJB1b+bwNntlyk45jWhL0O16ghSkNmqE+nn1tj5WDBxd3XuXPqQZ68VzpyT8fRrYgJj9UQufmSifaQo1KZceZ6BGL0MBCctArsIHWXLiVZTkRQJHHiKFq3duG3np3Uxw9eu0VSTQUu1s4cXHYcaydLHl58SsFC1lRoVC7H8YtWdFUrWSrkCpYMX027Ec2zDL6yIjnxrRk5pP7elKhalJ4TOwKpmwivPAJQqVTZBlhhr8ORSCWs+20rQ+b15ebRu6hUqmzTTyODo1g5dhN9pnTByNQg1SJFrsDSrgAyYxkJMQnU7lQduVzx2U3RvzR0aZnfCEqlUuu6hry6ntfdF5/sejq+Pl488sO1nDNPr3tm2UclFxMaFY2jlVWG9tj4RAT0szjrLfMHLufPg78y2E1TlRKgm8Ngll6fxZ6wfwl8EczpLRfZtGQLTe/NwszUgJCQeMSB1iwZuIaGnevm7gVmQeirMFzKOLPm0QJ+KJu5fL3vk1d5Yv3hVq80pzdnnaqt4/MhCALdf23PsxtZf/7fl2b9G5CUkMyW6bs5svoUR1afAlJTIk2tTFj7eCH6hvpsnraTXcFrMTCW0d1xMGGvIzDCNMNYhco4UadjdbUtz5X9t4iPjsfnkS/ntmdu7Jwd22fvY9DcPmyYvJ3ts/ex5MpMTAoYZ1qzU7VVRW4eu4fHrecMmdcX9xteWRpD6/i4RIZEExsR97mnkecIgoAeslyfl5KUgkQqQZ4iR1owmnFdh2U43qZ6ZVbumcuDZ6klK2mq5+EBEdgXKZjt2C5lnfG4qSn3WNDFWp1K/b7IU+Q8vuzO48vuuT43OTEZY3Mj4mMStBacSU5MZu2ELRna2v3YHFMrUxYNWcmMHguJDstdLZ4gCPy8ZiivPALytfLzx0YX3OUjRCIR9brV5NLua9kKpqhUKuKMQjCzY+bDugAAIABJREFUT1WjjAhQYhRtmytTWVNLEwq7FeLe2Ud5MXUdOvIUj5teNOhRO9vgzjDOiukb97Bjys+IRKkiKn4hobh7hCITnHK8Rrsfm2NfxDbL4/JkOUMrjKNoxcIATD/4K30md2HH3P1snbkHAyxBgD/2j2fbzD25fIVZX1MiFVOotBMtBzVmzMoheNx6zqiaEynoakOFhmUZsWQgEqn4g65TrGJhCtiaZ+pjpOPz03ZEM+6fe5zBhuN9KVe3FM2/b5i68v7moevwqpMa/ZRKJZHBUXSyHkCtDlWZtP1nWhv2YtLOzBc/JBIxpaoV44eyPxMbGUeof3iug6t371khr8LYOmMPHUa2xLmkA37P/BFEAha25hkEVKR6EsrVKcXtE/eB1KCw1eDG7Jx3IFfX15E35Maz8FvA0q6AumpGqidGKtF8FNXXl/BuXkqTfvU5v+NqtmPbFbbh5ZNXGu0i8ee1gzm4/AQ9futIUi7sISxszWk/siVxkXH4PPbj1vH7bJiyA5Eo9c3TJi37XVQqFR63nr+X3ZiOL4cvKrhzLedMzbZVSIhJ0JCCN7EwpvekzojEItas/JvR7RrQukaqD9I192eMnb8dg0Bnra/VanBjytYuhcet5xqKSTp0fG4iQ6KxdspefU8sSAh8oE/LX2ZTuXQhwqPiePg0AGmAdnURFRu78fRazg/PXndS64O6OwymcZ96jF4xmOYDG+LvGUBkUBQVG5VjfJNpWr2unDAyN8TzjjeT2v7Fnwd+ZXyzP5l9fBJHk/8DICE2kZXjNjJi0fc5jJQ1dTtXx9rJih1Z+A/p+LzYuthgZGrIrSt3SCIBE8yRCNKcT8yCjqNacWHXNYLe7A4cXn0an4e+2Z5zee8N9i45wsG4LUikYhYOWanRp0qLCnje8SYyOApIVZ+s2MSNOye1S4/Uk+mRkqyZuh8ZHMXmP3chEoto2LM21o6WGsqYA//qxY65B9Q1XpEh0RibG2l1XR06ssPC1pxqrSpmKdpTt0sNKjf7jptH7yJPlnP39MMM5QNlapXk7plH6gX62CAR5x88pp7bW0/VgPBwAl7HY0TGe5zPI18citny/L5PtnNME2lJT2RwFObWpkR+ovKbd4kOi+HCzqu41Sut9TmF3Qpx7eAtvO+/pGydUjTpW49j685k6cOnLQeXn/jAEb58vvadu3zvbJtekc/7wUvObLuoXo1MjyJFgVgi5tyeS5QsZqYO7ACqlyxBmyZuJKi0T4vYNf8gK35e/0UFdh1GtqR45SKfexo6PhHXDt2mQfda2fYxktqg8i3M5aMpuJ8WkAU4Ixa029VyKGbHnkWHczWnU5vO09qoFyc3nCfgeRD1u9UiNirv0pF++Ks3exYfIex1BMMrj+fOyQc0EXWhiagLrQx70da0D8fWnCYpIfm9r+FS1pk9iw5/Vq8mHVlTunZRtu9dQbs+Dsyc1pgSDVUkmL+/wptSoeT65evc8T7NPd/T3Lp7hQQthFpWjt1IG6NedLL6Xh3ApafDqJaUrlECI7PUoGpKuzkMnNGTktWKaTUvebIcmWHW6dNKhZJTmy6wbdbeDO0Oxex4fs+HEL+MKWhJCUlI9d8/CNahA6DLuHYUreCKzFAfPZke7UY0RyxJvaeUrV2S7xqUZdGQlUSFRpOcmMyQ+X2xcU4tDXAp60yLAQ05v+NtSrJxfEEmLtvD8oPH8QsJYdfFq3SfuhT9EDuNaz+56oGrW6Fs51e0gmumi5IHl5+g9+QuNOxZ+0Ne/gfx7KZXrnfPVapUpd1bx+/hWFzzPdGRe9Jq7vLiX34lXwd3tq42jPxnEG2GpcpBq1Qqrh7I3FA1PiaBDVO2kyQkULmYi8bxBuVLoTTUvg4nJVmO37PX7z33vMLQxIBhC/szfuNPdP5fG0avHIJLWc0dSLFETLGKhWk3ovlnmKWOz8GD80+wdrKkTM0SOfbVE2RZCqhkRUx4LAVdrN9rbpum7WTJiDUMqzweA6OsazLihRgSbf2QO/kTIwvKMaAqVqlwlsqVyYmpAV1ifBJSfQnFKxVGZqjPjoDVHJdvZ+mNWVrNXRAJusDuDUmqBGJVUSjzkfWCR9hNVo8bwC/dOtC0YgVW/DyY9m1KESto+rppw/VbF3CrInD877Gc++c3xo2qRWJB7exp5HIF0e/4T1VrXYnZJyYhM9InMiiSVffn8cfeXyhTuyRjG07l51VDsg3a0lAqlYilklyVEwBUbVGBe2c0ywk8b3vrFv90fDBGZoYIIhGJ8UmY25hSrVUljM2NqN2xGkXKu7Bk+GqUCiUPzj/h9skHLBv1L91+acdP//xAp9GtOLLmVAb1VkEQMAgoxMa1HnT9eS3zFl1D6lMISbr7lUgkon63mmrVyOyICIpSB5PpiYuKZ+lPa4mNiOPHvwd+MTvZ6V9uWECEVkrZOnTk67TMwBfBHFx+HI9bmsWxmRETHkvEmQhUqOheN+PqzKk7jxDFy7RWc/pc/Pj3QMytTXntHYS/ZwA+D33RN9In8EUwRdxckBnr07RfPVaNy2hiqpArmDdgmUZe+fBF35MYn8S637Z+ypeh4xOxY+4Bhi/6nmc3n2eqBKZMSLfznM68PCeftPL1yyAWi9i6ZAdyWRySJEMM3xGL0AafR76EvY7I9FiCQSj1m9rye68fkOnpcfreA6auPIBBQOYrs8UrFUZPJs10l+RdIoOjqd+9Fp1/bsPB5ce5ceQu0w9N4KRyJy8evmRUrd+zFF3J7cP014hCJUfh9Jp6lYpQ2N6Go9fv4/8UDBM0H5o+NQYSOU7WGefxU4eW7DvxFwSZ52oslUrFi2e3WTagj7qtY+3qeAcEs3ezr9af+ZLVitF/Wjf0DfV55fGaWb2XqD+nEzaPJNgvjD6TO3Nq03lWj9/M5N1j+a3FjBzHjY+OV6vkaUPdLjWQp8gzlYp/ctWDFj80ei8xCB060vh7xBp1cBbsG8pvLWdQpmYJytcvwz8j12n0T05KYdaE6dgVkeFa2pFbns/QV9lqLDYaq8wg+k3g8s5PcK0OVSlUxgn7IrZqI/WsSIxNRKqX9aPtjaN3eXbzOc0HNmTX/INavOLPx7vWQh43n1P0OxduZZK9piN35Oddt7wg3wV3Un0pihSFWlb26sFbGn1qtqvCw4tP1Sbg6ZEIUh49eM3eS9doX6sagiBw+fFTjpx5kirVm8958fAl1VpW4uVjPwRBoPnAhkQERGLtaEmIfxg3j92jaosKGuelKU+9K8d7cuN53S7EV86ZrRep2a4yF3Zdy7Mx9QykTJ4whvb97GlW8TtO33/IzlM3MPAtikjQfsO/ZLViWNhpmrSqVCqsiiqZMaCnuq1xhfJ4tw3m39VPMVZmfEh3LuXA9EMTmPv9P1pd18LWnC7/a0sP56GEvnnQbSLqAkDroU1Z+2QRyYkp/NVnCe7viNLovi+QYveaHX8Mxc4i1Xbi+6YNGLF0LQ9PJaAvGHy2eTmXdCA58aVGu0qlgvf435ZMEsVsNO8LHetUZfe+pxCrXXA3ZuUQVv5vA3dOP9Q4Nqv3EgC6/dIOSN2FyMmrMQ1DU0OtAjsLW3Oafd+AAO+gLOtp4qLiMTLT3nZEh47MSLNwkhnqM2hOb8xtUn+r/+w6P9P+idav+WtMO+q5lQXAv0koPSYvReRbSOuFtMt7b1DkOxekMmmOO25iiQhBlP09ytbVhojArHf69WR66kyQz0mIXxhFKriqrb38vQKp06naR7ueVF+KPFn+Xr+lXxI6K4TPQM+JHdGT6eHvGaCWoE6PnkyPLv9ri76hPme3XcLMypRhC/uzbuJWtcytQZg9c5ZcYPnOM4jFIiJeqzCOdfwsu3ZD5vXllcdrDq/SfC2ZcWzdWcrXL4vMSIZjCXsCXwSzfc5+Tip3cv/cY3r82oET68+p+4vEIobM64uZlSl/9VnCqOWDCPYNRalQsn3OfjzfiF3o+Hpxv+FF/e61uLTnRp6YmQL4JbvzY7nvqFa8OABurq40Kl+OAdPXUSBUu5ohAPfrnrhf16x/UCCnpItm/UC7GpVZ/98tiHob3Nm5FmT6wQlsn7Ofm8e0MxNPC+Qy49CKExxacQIDYxmbXyxD31AffQM9fqrxW2qgp3q7WPKt4upirg7s0pjcuxPtby9HP/LzLZI1G9CQ5Uvn4RMUhEvBt5LoC3cfRBmS+51lKVL8IzR3gu95+aBK1BRlSI+phTG/bhmFqaUxlvYWNOlfn8jgKLwzEWMZvqg/sZGptacmFkaYWec813J1SmVr9ZCWCmdf1JaIoEhObjyfrQKenkxP6x1AHTpyQiQWUaZWSX5pPI3kLGqcFSo5pcpYqAM7AAcrK/q3rcPq5Q8wVmou/GWGUqlk/aRU0awRSwbg89iP6LCYTPvaF7XjeLpnpKzmLk/J+rswZfdYPO88Z+OUnXl2T30f/J69ptWQJpzddglIFYWxsNXuPcsNhiYGNB/YkDodq7Ng0HLQbe5/8eS7mjt5spy1v24hIiiSCg3LahxPTkxmQosZ6g+7nkyKQq5A8c4X1TjBBpW3PXJPW0zi7D5LqpVUT4KhqQFed33UbSYWxvyy8SfmnppCmVolNdIoFXIFz+/7cPf0Q1b+bwMqlYpBc/owb+AyStcswX+z91GtdUWGzO/Hr5tGMnHbaJITk9UFxO43vIgJj8WhmN1nl/7V8ek4sf4c7X7Mu3rLoIBn6sAuDTdXV+ycMq+fi1fFEmMQSIwQobHzFR+byPxzfzDr2ESm7B7LnFOTESPBN1AzdeyO1wuUCW8fqi1szVn9cD7/Tvovz41aE2IT6WQ9gNZGvVCpVMw4NAEAid63HdgBZLbwrSeRfPa0dqVCieilFX2nrOSPjTvYf+UaA+Ys4/BhL4xUua9FEQli9KxtueL+dgEiLDqaJbtOYJxikc2ZMG7Dj1zceZUfq05gaMVxOBS1ZcGFPzkQvYnekzpjYPz2u3L7xAP0DfQpV7cU4zeOxO+pf/bzEomo06l6lrvxP/zVi5aDGvHw4lPW/baVvYuP5Cht3uv3Tpz7L/feeu8iCAKzjk6kTqfqHzyWji+XElWLcmT1KaLDYjKoYaYnmSRKuzpqtFcvXQyVTHtLgPRsmb6b7uPbU6dTdQxNDJi8838MmtMHa8dUZU1JNimZaTy/50P1VpXUFkEVGpZlwMye6jEOLj9G6Rol6Pl7x/eaY16hUqm4sPMqzQc0VLeJP9DmJw0DYxmtBjdmwMyetB3RjMt7b7Bv6dFvpizhaxdUyXc7d2ncOHKXH/7qxd1MCsMT0ymZhbwK0zpV61NjX9QWWxcbmvavT/n6pdkx9wDG5kaYWhjzyjOAKs2/o17XGiwb9S/FKxdBEASe3fTKYCy5Y+4BinznQsXGbiz4YTnjN/7E0bWnubr/JrauNhT5zhVrRytMLExwKmHP8X/Ppp64UtOnScfXi/eDl5SuWYJS1Ypl7X2Xvs4uXWqlSO9tMKVMTl2BVSjlGvn+mQ6pUpFY0I/WDcvQrlYlrj31Yv3BS0h8HdU1FRNbzgRSbyamliYsuToTQRDw9Urg8PXbtKpWCYDwmBjmbj2McZKzOohYcW8eL5+8Ui/mfAxMLYwRBIEfq6YGd6r8/Iv9iXjhG0l0fDymhm/T+ObvOoQovMBnDfAEITUg0/d35uy+CA4n+2GMGQZC9sbG2ZH0xJhHJfRYMGUxYhH4+8cgDXTI8rNvamHM/9YOx9TShJMbzwMQ+iqMkTUmAqkpxL0mdmLVg/ncOfWQhYNXcP3IHQQBFpybxtKf1rL/n2NZzkckFtF/WjcOZfEbbmhiQFRoTK5V90RiEa88PlwkzKWsE+d2XKFoBVcu7s67VHAdXwbm1qb0mdqVmPBY9W5aVuhjwI3Hz6FTxvb9l24ijjN+r9+SyOAo/Nz96TSmNRWbuPHv79sID4yk05jWPLzwBM/bOWs0JCcms2vhQbqMbcP2OfspVqkwhiYGmFqZUKZWCZITU7BxtqZ0tbcLnE4l7Al6GfrJ0zUfX3lGmVolcChmh79nAP4er3Esbv9B3+Va7atSrFJhTqw/x+vnb5WGtVXy/Rr42m/z+Ta4U8gVPLz4lEpN3LitpS9QfuPlk1f80WkeFZu4qSWoA7yD+L31LBr1qkO5uqV5eOEpRmaGDJjZk7jIOP7sukBjnOf3fLC0t6DNsKbsWXQYnyd+tPihEbYu1kSHxxITHktcdDwdRrVk9S+bNYQiekzogL9XYI6FyDq+bA6tOEG/ad1QqVS43/D6oLHcatdg95WrdK5VU9127PZtgoLjSL8/EisLYeIPzWlTrTIApZ2daVa5PF3GrcAoLGP6XkJsIgmxiVjYmiMz1EcVbsvOKz6ce+2HnljM/esvUD63QywIHIzdzIbJ/2FmaUJX2x8+6LXkxNh/h3Pn9EMCXgQBn9/sNl/gV5B2vy+kb/OalHC2Y+Pxizy8E4WhkLWp/aegfP0yrHm0AJmRjKSEZPQN9Fjz62ZiI+M10qdE6bYfwwMi8H6gWasHqaqz8uTirD+zn3ENpxIe5JPtQ+fcM1NYOnIdDy88zfS471N/ZvVeQtGKhRn5z0B1e0uDnpxU7sz282XrYkPn/7Vh75Ij+HsGZNqnUGnHTE2as8Pa0ZKo0Lzx9wr2DaVe15ocXH48T8bTkT+xdbFhwIwe3D3zMIOnnUoFEUGRbJ62K8cxRIIIP/dk5u7Yx+iOrZGIxRy6fovDp59g+AEaCGI9CZf33VAvcPSY0IE7px4wcdsYejgN0WqMFw99aT2kCZC6iK4n06NoBRcqNSlPdHgsnre9ObvtEn2ndqWwWyEu77tBk371MxWnMzQxoPfkzmyYvP2DbHiyYs+iIwya05vlY9bz5KoHxSsXfu/gztzGDFc35xwDcx1fNvk2uAO4sv8mQxf0yzK4E4lFuJZz5vk9nzy/trG5ETbOVlk+EGhLfEwCl/Zc12g/veUip7dcZNL2n6nQqCw3j94lMjiKhj1rEx4QibG5IZf23lD3lxnpk5KYwt6/j1CwkDVPr3rw6+aRmFmbEvwyhHM7rtCgay26/9oemZGM5WPWq88VS8QodbUW3wQbJm+n79SuJCUk8yI7I+Z0u3hpu3Vp7YVKOzFj4QxaVKnF1WfuVChcmKd+flx97In+64w3ZAObZFpXrZShzcHSEjsHQ6I1sy4BWDthCwdiNnH37CPc6pRi1bhNmFgYM2N1FxRyhdozqcPIVjz6BMp+T6540GFkS/XfgT4h2LraEPgi+KNfO7+iJ+ij8nZm5fJHKMR3MJZbfPbADuDpVQ/WTdyGzFhGZHAUxuZGjF45mLioBJKTNA2/UaamCFdoVJZnN5/j89iP188DCfENxeN2aj2ynkwPKwcLvO6+YPmduTy8+BSpTErR71yJCo3m7x/X8OiiO1Gh0cw9PYWd8w9lGdilx+uON67v2NZkVgtasbEblZqWJzEukcjgKFaN25Tt7kBBF2u87+fuvtR8YEN2L8ibtOa4qHjdg+E3QMcxrbBytCQxPuNnMSVZnmWNXWYYxNiwd4sPB0/Pony9Utzc441xYt5qIFzYdY2GPWszvsk0rc+xdrTE0s6C6q0rce3QbTr/3JqE2ESuH76tfvbqNKY1xuZGXD1wE9dyzlzZfzPTsSR6EpxLOmJhV4AA76Bczb1qiwpI9CRZjg0gT5Fz/fAdaneoir9X4ActQMaExyKR5utH/4+OTlAlHxDsG4qxuZG6GD09ZWuVpO/UrmyduYc7p/J2d6/NsKaUql6cye1m5+m477Jv6VECvIMoXbMExSsVxv2GFwVdrDVUzS7svKreeUt76Fw26l8a9KiFtaMllRq7cXz9WWwKWVG1eaqapkgsQmaoz+Y/U1fYZEYyBs3uxd3Tj3h02V0rSXkdXx6b/thJj9864FLGibP/Xc6xv0gsUvsCWRQ0pcXAhpzfdpmT965R27QB502fI0o2wDDKGZmQUWRCoVAhVyiQSjL+lKRks5jw3+x9mFga02pwE3q7DldbJWycukPd56RyJzbOVhgYyz66ctl/s/dRvU1lRi0fREJcIlI9KYHeQd90cAeptVUmFAAFn73WLo3EhGSSE1PUNT6xkXFM77aQeMNQLF0V2FqZEhgSTbiPGIP4jHYJbYc3o1zdUrjVK41JAWNsXWyQ6kuQ6ElQKVWEBUQgT5ZjamlModJOJMYnYWAsY9Q/g4iOiEVfpse+pUc5tem8VnO1crRElo3HI0DDnrVRKVWs/mVTtv3SI9WXkpyYSSCbDXoyPeJjEnLuqEPHG/YuPoJUX4rv04y7xI7FbMmtoLCR0hxem/PDoLG4b5ua578n/p4BbPpjZ67OCXoZwuT2sxmxZABed19wZf9NOo5uxeV9bxfVj/97lhJVUktmzmy7rFYKfZfosBh+b6Odj+q7lKlVkvjoeKwcLFJFXlQqIkM0d9nvnHrAwFm9eHDhKU371efK/lvER8fn+noKuYKo0GicSzlq/L/9ltAFd58ZmZF+ltvcbvVLIxKLqN+9VpbBnSAIlK5RnKfXPHOlerR3yVHO78g8jbFUtWIUdLHm3PYPL05/eDF1BTh98KYtDy484cGFJzTqVYdmAxri/eAlhcu7EPfmC99xVEuGzOvHX32WcHrLRRRyBSoVtP+pBTXaVGbugPxZq6jjw1CpVGydsQe3uqUZvuh7jv97luf3fTLtKxKL+GFWD/zc/UlJltN6SFPO7bjKviWHqdikPNYiF5JD33z/MrkhywNNWLT3MOO6tFO3nX/wmJBXcoyzmaMiRcH6Sf9l6YGXtsNxLPk/Nvss43/1JuP37MPrhbJidO3f+WnpQAK8gji4InMpeR2fnzsnH9CwV21ObbqgbkuQRtK2vSvju3VQt83atodDu/wwlL9Vljuw7DgHlr1NJZx3ZiruNzy5d/Yxt07cJa6AP241HDGxMObyYXcMwuwpXqEwY1YPY0SV8bmea7k6pbIVOXGrVxpTSxP2/X00V+OGvgqjeOXCBPpot/hQokpRreqQdOhIT2Y7UFYOFtTuVJ1N6RbivnQOrThBo1512D5nPwsGrchwLDYyTp05VqCgOfZFbXPlE2lsbkTRCq543vEmLirzQEwQCZzfcZWWgxpjYVcARYqcv39cm2nfbbP20mdyZ7bN2kv/P7uxauym9xL/2r/0GMMXfc+yUf9+VjVQHR+PfF9cItWTZrpaUqmJG/2mdiPELxT/bHKPq7euRMOetanc/LtcXTcxLjFDoWl6qrSoQJVmFegwsiXl65fJ1bgfg9NbLnL7xH3K1i6JtaOF2mD00aVneN7xxq1uaSDVn2bpT2uZ3m0BW2bs/pxT1vEJeHDhCSv+t4GS1YrSe1JnTCwyhlv6BnoMnd+Pw6tOcXTtGU5tusD9c4/VYg83j99j5LLs692MlObsP+BJjz8Xs2jPIQbNX8nExQcwinprcyAz1Kdio3JUaf4dZlapEvAtBzXm7LacdxXbmvZl87SdrHu6mLVPFlGmZoncvg1ac2rzRa0fmHV8Hu6dfYSppQnFKxVWt+nZxWZYXAD4pWs79O01sz3Sk5yUwpbpu7l1/B6J9n5smDmAFUP7M7drZ/6d0Z9kh1d43fMhyCeYZt83yPVcu45ry/H1ZzXaRWIRHUa2pHLT8rkO7ADunnlE8cpFMCmQ3fLJW9r/1IKrBzT9YnXoyC2NetXh2NozpCR/fjVhkShvtv/6TO6SqtSUDkNTTT/IDiNbUKmJG1YO2avopiHVlzJoTm8Auo1vn2W/2IhYYiJi8bzjzaNLT9m/NGuxpfjoeG4dv0ep6sXYveAQQxf0w9Ak976jSoWSE+vP0rhP3Vyf+zWQlpapU8v8jCgUCkQikcbqQpUWFfC6+4K46ASOrD6d5fn3zz3GxtmKe5mobr4vG6fuQCwR0/3X9hQq7Yh9kYLExyRyfseH7+Rlh4GxjFodqnJ680UNufnts/dRo21l7IsUpEbbKlRrVRFbVxsEQdD4oYoMic5021/H14dSoeTwqlOY25jRanBjDE0NOfDPMWRGMtr/1IKtM3YTns7MNSVZjsxQn4SYeBYPXcXu4HXYOFkR7Bea5TUMom0Ivqlk100/9NDHQHCg9+TOVGhUDgCVUoXv01co5Er6/tENP3d/vO6+0ErgITkxWb3jUqtDVf46MYk2xr0//I3JBKm+JNfpRjo+PXsXH2HI/L7qmjljQ70M4ikAYrEYI0M9EjMb4B0SVHE0r12KYg726rYSjo40rFGcM7simDdwOeufLebm0bsZvis58fSqB47FM3o5CoLA99N7cGL92Q/aib5z6iEuZZ3UmR9ZIZFKCPQJ1vphPFYcitQ5ClNjGaGvU5CF2iMV9KjS/DuSEpJ5cP5JjmMUreBK0371s80Y0JH/adC9Fm2HN+efUesoVNoRV7dCPLr4NMtF709KHv5QT+++kEpN3Pjf2mG88gjA7+krSlQtRkpSirqkBVIF8lzdChEVmrm/3ru41S3Fma2XCPAOoli6xah3uX74Dg261+LImtMMX/Q957dfwcrBgi5j23J68wX171wat08+YMj8flw/fIeNU3fQ/df2rJu4Ldev2+O2N4XKONF3ale1Mnbj3nWZ1PavXI/1JZKfA7O8IN8Fd0+uetCodx21xLRUT4pYKkaZlPH/hJ5Mjz86z6PloMbZbkvHxyRkKzv9vijkCrZM302LgQ1p3LseDsVssS9SkG2z9ub5tdIoUaUobYY24/aJB0SFRmPrYpPhhzZtdVaeouDVs9dcP3wn21UgHd8OkcFR/PfXPqT6Uibt+JlQ/3D+Gbku1dQ4nS3Ci4e+FCrjiPs1D0QiEaZWJtkGdmmIBBEGGAGpalyVmpZnTJ1JmfbdHrAamaF+rl/D5b038M3BH+x9MTCW0bBnHZYMX/1RxteRd6hUKh5ffkbZ2iV5dMmd0KBkXoWG4mj1tsbONySEsKDkN5/I7Ekknqqli2qid98BAAAgAElEQVS0VyldhCOcJz7agLnf/8PcM1PxuvuCWb0WazXP8zuvMnXvuAxtwxb258ia0x+cYizRk2hVh1rkOxee332h1ZhRRn60bVeM37qMRiQSERwZSfeZCylt3QIbZyucSjpoFdypVCrEUnGW9Uk68g9N+tYjIiiK116B2Lra4O8ZQFRINPW61aRMrZIsGLScUtWL8+ymF6e3XPzc0/1o3D75gMeXn9GoVx3+t3Y4d08/5Mk1jwx9Tm+5CLl4D14/D6Lt8GboyaSs+HlDlv1ePnlF5WbfUaxiYXbNP8gPs3tz7+wjRGIRvSd3Ye+SI9w9/TDjfE/cp3bHaiTFJ3H/3OPcvdh0pD1np3F57w2CfXO+3+vI/+S7tMy7px8i1ZdSoVE5ChayJio0WuMm0XJQY+xcbajZrgpiiVhD+v9TcnTtGTZM3c7iYas+ukDJvbOPGNfoDyKCIqndsRqDZvem37RuGv32Lz2Gv1cg5jZmNB/QgNZDmqBvoJfJiDq+NVKSUpjZYxFrft2SGti9g5G5ETHhsQAolUoUcgXG5to8Ir9l4rbRLB6WdZC0ceoOte1Ablh0aTpX9t/IueN7UKVFBQ6tOKFOadaRv7m89wZ1O9cAQD/clt7Tl3HuwSNS5HLO3n9I3+nLkYVrp+5pjBkHLt3WaD906S7Gb4w/bh67x8DSo7EvasuSazO1GnfS9p+Z0X1RhjYzK9MPFjEwNDGgeutKPL78LMe+1dtU4vqRuzn2U6lU2BRX8Xu3rupdUBtzc+YP7YfSPgrP295ai7g8v+fD3yPW4Ov+cRZidOQdxSsXQZEip3TNVD+3Fj80os/Urjy96sGCH5bj9+w1Jzacy7X1xpdIYnwSJzddQCFXYGFnzouHvhm8Lg1NDTWyoMxtzN4dRk2AdxD3zj7i0MqTOe6c7154iEa96hAZEs3eJUcoU6skXndf8OjSUyRSMYPn9qFG28rq/reO38PWxZrSNUrkqVXY8/s+n/V5+lOiS8v8DBxZfYqhC/oRH53AnkWHNY7Hx6TKXruWdWLJ8DWffH4VGpbF0t6CU5tTi/q1Wc3MK9JWay/tuY61oyXWTpZZ9nUp40TDHnUQS8W8eOjL4yvPMLc2pUytktw4cidf5M3r+PSkKQ2qSWeLIH5HYvnFQ18a9qydQYgiJ0RiET6PsrZhOLzyJKOWDdJ6PIC6nauTEJvAluna1YrGSsKQWMejVADB5hhikmVfsURM2doldT6QXxBKpRK/Z/44lnDglUcAyhcu/DrjGILxPlSxMowSnBEJ2q1dSgU97t56zYpDx/mhRWMAVh0+waN74Ri8Y//wU7UJ7ArOXOwgPXZFChIVGs2t4/cytB9edZJqLSty9eD718B1HNOKjVO2a6TmZ4ZIJNJqBy2BOErZ2Wi0VyhcmFsLN9C8ck+2ztjzXvPVkT9QqVTEW/rj5GKEvp6EyAQFty7c4W66kpW8Vh3/0khOTKan01CUKhXN+ten79SubJiynfY/tcCphD3BvqEkJ6Wwd/ERHIvb88v6ESz9aS1iiZiGveqwfPT6DCVEVVtWRE9fyr6/jxL0MiRT1fc0ts7YzQ9/9WLl2I2sGb+ZTmNa41LGGSMzIw78cxzXcs50/rk1u95YmuzKI2uTb5FvwQoh3+3cpXFw2XGe3fTK9Mvw8rEfr70CSYhL+iwBiks5Z4pXKaL+W99AL1PfEEEQcKtbOsPqT16hVCjZvfCQxnZ/iSpF6TMlVWnw3tlH/DvpP5b+uJbHV1JXeWu0rUz5BmUYMr8vv20ZRdN+9fN8bjq+XHyf+VOotKP672GVfqFRb+2LrrVNtwx5lYUJXhYMmd+PWT21S4dLsHrFkGEVOLf8N078M46GXQoQVdADpSrzX/OOo1txaLlOIfNL4+iaM7T/sTmQmhZskmiDcagzJok2Wgd2aRhG2LNhzWMajZhFw+Ez2bTOHYOIzHf+7p97zKxjE6n4pqY0M4Yt7E9EkGYmxyuP17QY2ChHi4SssLS3IDkhWauaaddyztkusqRHjITgCM353vL0JD5MxaU910mM+zZW9L9WEm1esez3HmyfMoqNE0aw7bfBXLt/QKtFgm8JuVyBUqHk6NozmFgYY2lXgOKVCnN8/Tl2zD2AjbM1pWsUp2b7Kuz/5xjeD3yxcrTEzNIEfcOMGVL7lhylWKXCDFvYn2EL+md73ciQaHbNP8jwRf0xMjNk98JDnNx0HqmehKbf10ffUI+wgEi+a1A2V6+nTK2SFCnvkst3QceXTr4N7vy9Arl++I5Ge8FC1rx46Mv+pcc4uUE7v6G8Zu/iIywb9a/675HLBtH/T830SKcS9oxZNYSCLtafbG42zlbYOFshkUooUNCc9j+2oPekzurjR9eewffJK5xLOuL37DXu1z0/2dx05H8cSznzyicstQ7vzT+ZoZ46NdPK0ZLD8VsYNLs3g+f2oUGP2hSvVJhVD+Yz/dCvzDs3NVszVkjdTbB2tKRc7VJAqlFsdnQZ25a7px8S/SZdNDvCVUGYOylxKWiJSCTCSCbjz769qV6lEIlWmaeJmVmZ6FLIvkDkKXKu7L9Jg+41UShTiFVFE6uKJtHWD+OyYaQ4vSJRrBkEtR7ShOmHfqVE5SKolG8fbI0VFuj5O6L/2gljRQGN89KY1XMxp7dcov2olqz3WEKV5t8hkYjVxwfO6kVEYBRjG07VODc8MJIXj3wxMH6/4K5+t5ocXXtGq7412lTWKiUTQF+Q4esXydw9e0iRpy6YvgoN5Zd1G7GMdXmvuerIPyhUCkqVtOS7Iq7qNiOZjJ+7tSBWT1djlRW7Fx5iwtZRyIwN6Dq2rbq905g22BUuyOktF3Et50xht0JsmLJdI6XR75k/CwatwKaQtVbefoE+waybuI1u49vTtF997p5+yL6lRzE2N0KeLKd4pcKZCtqIJWLMbcwy3WSo3Kw8FZu45f7Ff+Xo0jLzEQULWTPv7FQmtpyJr7t/vpEtP7DseKZmkr7u/gyr+Is6Dc7C1pwmfeuxc95BKjV1o83QZszstThPV0Qv7r7Gxd3XAIgIiiQhLhHRO6l2Vw/cQt9Qn90Lddv6OjJS0NmKM9szpid63fVh4F+9uLj7GmPXDmda5/mUrlmcNsOa0aiXHDMrE1aP38zJDedYcm0W7X9qme1nS6lUcmrTBWYdm4iegR4pSXIGz+nDiCrj8brnk6HvtP2/IBKL+L119gpeCpWCJJfnNK9SghIODvx38SL/HD7M0mHDKFigAN+5uuLvH0VUqEq9k25ha0638e25sOva+71ZOj47d049oGRHG1zqp1C2sAs3Pd1Z/ePPGBsYkCKXM2b5Bu6djcZAYao+p2HPOvzRaR4KuUIzRVkL5HIFrzxeIwgCBQqa07R/AwbN7k1CXBIWduZ433/JlPZzsjzf/YYnVg4WRARpr7yZhkgsQpGiWSur0U8kQmYsy5XJsb6fK7tO3Obio6cYy2S8eB0CLxwwFr6oxwQdmaAgBUcbTQn/Uk4OCLIU+AK1bz5GRtS7BHgH4Xnbm60z92JpX4ABM3si1ZOw6Y8dat+6wm7OSGVSHIrb4++lGXh53HrOr03/1LqWLSY8lnW/baViYze6jW/P9tn7OLXxPI371mPtr1uwcbai/5/dWT/pP/U5zQc0UJfg7Pv7SAYP5g2Tt3/gu/D18S2kZX5Rv9pBL0MY1+gPAl/kj6AujWc3vbI8lv7hwcrRkiLlXZDqS/B+4MuTa89IenNcEAT0ZNIsDdvfl8VDV2kEd2EBEexeeAhBEOg1qRMx4bEcXHZCZ2b5rfImhU1mpI+VnTmKFDmC+O1OxNWDt/jl3xHU61KD74uPJCoshpvH7tH+xxb81m42i6/MUAdz/Yv/xEnlTobO78eK/2WtEDan/1Lm9F+q/nvYwv40+74hXqPWAalquEuuzuDI6lNa1fslFHzJ9t9H4myTWjfUt2FDpv33H39s3cqyESPwDwujoIU5ESgRk/ra2gxrxuZpu4iJyHlHUEf+JFYcQamC9gwfOJw5O3fy95DBGBuk+j5JJRIWD+9PE8/Z8PJtcKdUKLWy4ciKmu2qMGRuX1aO28ikNhkXHSo2KkeDnrWzPT8lSY6RmaaPljY8vuxO+fplcqzZsytSMNfCLVJBD2lACWJepxCNCmPh02Wc6Pi4SNHn9rOXGu1bTl9CFG2q1a5SvkEQoVSR6k2XPv06i7T7D+XEhnPU7VyN+OgEzmy7RFxkXAZFyePrz2HrakNiXNYLRe9jH3Hn1AOUCiU9f+vI7oWHSIpPxsbZCgNjGTKjjOUPx/89x/XDdyhQ0PyDlXh1fB3k27TMio3dMjUIz2+BXW7wuPWcmb0Wk5SQTNjrcP77a586373X752YvGtshkAsL1amVCpVpqqIAMYFjLBzLUjz7xvSbXy7TPvo+HYoWsGVC3s1Uyov7blOW7O+vHzsR1xUPCP+Hoi+oR63Tz5g4aU/NYK4x1ef0XpoU1Y/XKD1tXfOP0jdLjXUf/+2dRTH1p3RWsjFpYipOrBLY0TLlgRERPDIxweRIBCDiu//6EHfqV3pO7Urjy+76wK7Lxx9uzgGt2gCQFxiIlamphmOi8VirArkTu01Oza/WMbEbaMZVfv3TNOPb5y6g9wgCYUq6901u8I2BHjnXi0WUq2CytUtlWM/t3qleXTJ/b2uIRGkSAWduvLXhCAIhHlKGbN6A2HR0aTI5aw6cpJjZzwwFIw/9/TyNS8fv2LAzF4kxCZSqlqxTK0C+k/rTsfRrfL82vfOPuLW8Xt0/7UDx/9N9cb0uO2tobUgT5ET6h+O5x1vXW2slujSMrNBEIS5QBsgGXgOfK9SqSIFQXABngJpWs3XVCrVUG3HLVjImiZ96hEdHvNBHh5fEme3XcLjtrdaiv3n1UOJj0nI1h/lQ4kJj2XR0FUUKV+IkFfhmfaxdbFh0OzerBy3Ued/8pUT9jqCmlWKcPuopiy8gbEMV7dCtBzcmHbDmxHoHcS0LvPpPakzfaakevGkIU+S09qoFxO2jGJ3yDoOrjjB8fVnCXie9QNt6KswTC1NWHxlBgbGMuKi4tn391Gt5y4RiTXaZHp6xCUmsufKFdx9Q/C9BhtP79B6TB35H0EkIH4j3W9mZERAeDh2Fm/Tz1LkcoLDYpGSdQ2dtoz85wcEQaCVYS+NYyqVikRrf8q52RB+5xyFG6Tgfi8Eg0hNURZ5shz99/B5TEOb9C47VxuOfsELoTryHsMkC0p+15I2o/5GkKhQhppgrHTM+cR8jCB6uwCuUn6cXTylUsn4JtNYfnsOO+cfoEGP2pzddilDn0VDVn40cT+P294aRuY6Ppz8HJjlBR+6c3cSKKtSqdwAD2BCumPPVSrVd2/+aR3YAcRGxhH4MvijGoLnN/y9Arlx5K2AzLVDtzmz9VI2Z+QNKUkpuN/wIux15sFdVGg0zx/4qL3PAEpVK8aMQxOwdrLK9Jwi5V2Q6n1RGb86SJWBfjct2MzKhL+O/87MoxNZPHw17X5swc55B+g/rRu9fu9EbGScOrUYQCIRI5amBlqzei1mcPn/YWAsY9y6Efy2dVS21/+z63x2LziIIMCmP3bmau4vfKKIjs9YX7T25EniklWcOO5HwCVDDBRZexLp+DJJDNJj/9VU78P+TZowY+dOQqNTUy7jEhMZsnAVcn/zD76OjZMVbYY1Y1iVX4gxCCLJ3o9Y81ckq1I/+3GmgUwf3Y7lYwbTt15d/v7xe5o0dSFaEqI5ViFrwgMi3nsuIpEo26wOcxuzbCXXdXybiCVijPVNMQp3wjDYGWPlhy94fCt43X3BjB4L6TCyJU361NNQn0yMT8oyQ0qHjs/BBz2Bq1Sq9Prh14DOWfXNDXFR8d98EWhOioOfioTYRLbO2EPtDlXxuutDoE8wrzwDeHLNg5iwGI3+YomY8Zt+YuOU7Vza+3EMp3XkMW9WOY1MDYiPTkhtUqTeqBZf+pNNf+zg1OaLAJx9s+BwbN0Zek/uQpmaJZjSYZ669mHGkYnsnHdAPXTY6wiWj1kPwJB5fdn8YhmmVibM6rlYo24o7TPfaUxr7qXzXtIG8Ss72vw+m0FtG1DSwYHtly5z6Y4nBi+LIhbEX1ZNiQ6tMUq0Ysmms3gEBtLYrQwOFja0njQLSz0LoqOTUQUWwOAdj0MrBwvaDm+mdcrvnFOTMbcxY8lPqwnWf8CcMV2pU7Y0r8PCGL10PQF3UrB0EFG/fKo1wvLDhwmMiKCovR2lawXi+SAAgwg7zKxMadKvHi8f+31Q8HXr+D3qdqnB+R1XMj1es10VLu6+/t7j6/g6aTuiGSfWn/vc0/hwVMpURQyVKoPabQY+Qi3eue1XkCfLEUsltB7WlGUj1+m8gr9QdIIquWMAkD4icxUE4S4QDfyuUqkuZnaSIAiDgcEAMt6vyFzHx6dmu6oYmRsR+G8wMeGxWZpJK+QKxtSZpFaS0vHlEBMei2NxuwxtlnYF6DetO80HNgLA3NqUod+Nw+/Za2b1/jtD31HLfuDRZfcsFyZWjt3IyrEb2eKznGn7xzO+yTTunH6YoY9EIkaqL8XY3FAr64M0TAxN6NzjF27cu828pf9hlGyJsVBCF9R95QiCgNjXBkPLSgwduwlJigwTSpEMZGU2EB4YyeC5fXn55FWGtP8+k7vQZlhTds4/mGGBwtjciMFu/yPWIIg5k7tRp2xpABysrNj6+2iajfoLqST1ageuXcPJyophLVqkjtmgATsvXeVeqAjnAsU4v/1Krj0e3+XxlWcMmtMHz9veGYQajMwMqdulBhZ25u9d06fj68XKwfK9hD10vCVtwdqxuB1SfakuuPuC+eaDO0EQTgGZublOVKlU+9/0mQjIgS1vjgUAziqVKkwQhErAPkEQyqhUKg2JMpVKtQpYBWAqWOjcNPMp6ZUNc0IX2H2ZNOxZm8OrTmVoiwiKYlStiYQHRmLnWpBlt2cjkoghXQrKqvvzSE5M5uWTV2yativH6/RyGcZ//qsoVMZJI7grV680xSoWZnfov7Qy7EVyonbqsV3GtuX4+nOE+IViTSFdUPeNYW5cgAIqB5TCmzt2Nqv1SoWSFf/bwNi1w+lTZIS6vdv49gwoOYpFl6czYEZPQvxCMTIz5MSGcwDoW6RQu0xGMROpRIKDnRleHlF4vPLn+rNnTO/dO0OfzrWqs/rn2dzwfkZesXHKdpr0rUfLwY1JTkhGqi8lNiKWS3tv4O8ZkGfX0fH1kB/SBsUSERa2OadJp6+lywxTC+M3BuNmCKKM1UWRgZHIFR/3UdLU0oT4mISPeg0dOj6EHIM7lUrVOLvjgiD0A1oDjVRvpB9VKlUSkPTmv28LgvAcKA5kr9+sQ4eOXNFhVEvsXAvy3197CQ/MvW9WepRKZaq8dDq8H7ykWOUiXD90m8iQKPQN9NgZuBqJniQ1tUGu4NK+G8zpv0zr6/Sd2pXYiNgMIixpuJRx4sy2S9gVKcgf+8YxofmMHMcztTRBniInxE8n+POt4vvUH+eSDvg89tOq/5mtl+g5oaP672W3ZnP7xH2C/ULp6ZxaIm7laEloul225AQIiozEtkDGWqXImAQMw+35ackWHBz0NerhBEFAIhblqZVYUkIyh1aezMMRdXztKFIUmFubEhny/jYgH4qplSmjVw7Wqm922ZR6MimCSGDkP4NS+75RHTe1NCHEL5RZfbRfjH4fUpK+QGNAHWp0aZk5IAhCc2A8UE+lUsWna7cGwlUqlUIQhMJAMUAn96Pjo2NoaoiNk6XWD3lfOs/v+WBubZYn/oimFiZEB0eq6+0AJHpitWlyQmwiP1abwJ8Hf+XKvptsmbmHOScncWLD+VzVNbT/sQVjG07VaLe0L0DXcW2Z2GoW0w9OwMzSRPPkTHAu5cDzd8zPdXxbPLzwhBY/NNbuey9Aq8GNSYhPoPeUTjTpXZ9Le66zevzmDN1C30mflIZZMXrpv2z5bRTiNz6Q64+fIfwlGAoiZi3+m6m/TuS213MqFS2iPu+mhxcRgUp0gvM6Pid7lxyh9+TOrPh5g1qV+1MTERjJ5HZzPtr4hUo70XNix4z3o49Qf2dqaYKZlekH+WXq+LzogrvsWQroAyffrFamWR7UBaYJgiAHFMBQlUqVuRyjjlxj7WRFmH+4znQ8E3pO7Iidiw2rftlE0EtNpbqvjQfnn/Dg/JM8GUtmLMtQQ9CgR22qNK/Ai4e+3Dp+D0jdyYsMjiYhNoHkRDkGxjLundXerqTnbx0xsTDmrxO/M7ffMh5efEIBO3Oa9atP+59aMnfAP5gXNMPayZIja05rNabnbW+6/9qBm8fu5e4F6/hqCPUPR99AO2+2hIR4tu9axRPPQ0hEInZ1Xk3kPRkyIfuabz1BxqtbKTQZPQsnO3MiouIJeylgGJfqr/jvpP9Yu389Pw8YQr0yflRzdeGyhxeb91zFKNpZlyqs47MSHRbDoeUnaP9TC/YsOvy5p/NFs3HqDhr2qs3exZrZJzp05Ac+VC2zaBbtu4HMFTd0fBBGZoaM3/Aj14/cQRBg98LD+SKXPr9wevMFzKxMv4nA7oMQMtYpWDtaEh4QiSCRAqCSpzB6xWCeXH3Gml+3ZDjv5rF7lKhShHmnJ3Flf+4yrfctPYa+iQEORayYefQ3dfvRdaeZ3n0hEzaNRGYiy1RsJSuSEpJ1D8468HnsS8kqhXG/4ZVtv00b/mHLtCHq9MrE5GTa/jYHpbtBthYDAAZKE/Ax4bUPgHEGCTD3654MKvs/andsyLGD19mV8ozylSthEKCrAdWRP/B196dp//oIgqBOZfzSEMRvPU3TZ5lkSR563qUhT5bn+FuhI3+TH/ZGBEHoAkwFSgFVVSpVpg9Ub7IkFwNiYI1Kpforp7F1ZmRfGHFR8Wz+cxdKhZI2w5phYCzTeRql48VD3889hS+Saq0qcGHXNfXf/ad1IzI4CpFEzOH4rRxZe5pn1z2QGRuyfvJ2DkSuRxAJ/NLkz1xdJz46nn8npAaL01msbm89pAlTdo1l14KDbJiSOxsU+yK2BPnogvlvnTNbLjHi7wH4PvVHIhWz8fk/bJq2k90LD6n7JKuSsDc0z1A3J9PT48dOTZkx+yKmCsv3vr5KpSI2Mpbj/54FoHHvZjy7mX2gqUPHp6R2h6oUKu2EhV2BLL1tdWRNt1/aERedQPMBDfG+7/O5p6PjPclHNXePgI7Ayqw6CIIgBv4BmgCvgJuCIBxQqVTZpmzpgrsvkHtnUz3AHlzIm3Q8Hd82glhM4fKuHF5zVq1S1nNiJzxvv2BU7SnYuljx4+LvMTCWUb5eaeJjEmhr3p+9oesQS8Q5jJ4zJaoUZeiC/kxq+xd3tdytS0OqJ6H10CZsm7n3g+eh48tGqVSyYfJ2Bv7Vi7K1SnB07Wm+n96DmPBYzm2/gqmVCd3+aEXIw3Ma5zoXtEIlSUktIsgBiUTMmieLKFDQjIFlxhDkF0yy7WsKFzZHX0+Cl08oSl9rilcpwqnNF/L+herQ8Z44lrBn/7Lj/L59DGPqTPrc03kvtNqt+wBaDGjIz2uGoVKpSIxL4vaJ+/yfvfsOj6r4Gjj+nd2UTe8klITee2/SBBVEBRUEC2JFxf7aC/7sBbsoWFBRRKoiiCJVeg2d0EMgCSGk956d94+EkJiEBEiym835PA9PsnPvzj2bS3Zz7syd88aYj1iZv4DkuFRCNh9h4YdLadGtabXG8V929nZ4+LoRfzaxRo9rq6whudNaHwYqGgXuBZzQWp8s3HceMAqo3cndyEnDsLO3Y8lX/1g6FCFsUpuezTmw6XDR49tfGMXJ/eG4+bgw4NbebFiwhVduKJgF0KRDIFPmPYWXvwcf3j+DZ3+YzJSbPrjsYzdsWZ//LXqWu5pOJikm+ZKe69fIh/EvjmbxF8tJTax8TTxhu1LiU/nuudk8PfNhvnv+Fw5sPMzAMX0Y8UBBncaII5H8uXonD1w9pMQH6sw/1+KU5VXh9EnvAE/mR33HzhV7+ef7NcwO/YobOg9j1nMPE1TPDyiY5nn/9O9Z9vXKanudQlyOzYt3MOKBoaQmpNF5cPsSdR5tgTJAt6Ed+Wjt65fdR5P2gbx60/tsX7YL30Y+THx9LCvzF2DONzPW/34A3LxdCWzToIqirpxbnx5Jm14teX/CF1WygJqoMr5KqeLTKb8tLPFWVRoCxVcKiwR6V/Qkq0/unNycMBoNFe8ohLgs+XnmEiNw4567iYd7vsSHK15h5ANXEx8ZT8iWghpdpw5GcH+HZ/hmz1Q+mPglga0v/wPO3duVt5a+wEsj3rnkxM7O3o7xL47m62d+lmWpRQlZGdn8/e1qel3flS1LdrJlyc4S2zMdHLj7vWlMmXgrriYTn//2N3t3JOCk6l203/pN/Zl17As+n/xdUeI27IGBdO3QsCixg4JpnuP79ODtD9bjjm/Vv0AhLlPE0Sjmf7CEvNx8Jr5xm80ld9oMu9cc4L07P69450qIi4zn4we+ZvYbi4gpVmqn69CO7Fi+p0qOUVlr524m7kyCJHZVoIqnZcZprXuUt7EytcIrUNYlxwpvmLXq5G7gmD6cOX6WrUulPJ4Q1SU1KY1uQzuwdu5mtFmTm5PHjZOG8tTA1/Bv7Mfrvz/L9y/PZUWxkgfHd5/kjd+f4/PJM8nTOeTpHBypeEGK85p3acLnm94mNjKBzMsoBnvtPYNZ8tUKSexEmfatC+G+d+9g27JdpbY55XgRsSWHuw/8hDJq7JK8KkzsAGIj4shMy2LDgi10GdKBm5+4njxzDl4mp1L7Ng3wA7tcyCujIyEsKDkuBaXUJScJmcYUnJum0rihN8mpmZwJy8YpIaB2LixyieURYv5TQ9XL34PTNVxuKTYijjVzNtboMW1ZTU3LrKhWeCVEAoHFHjcCoip6ktUOiRkMBq65ezBdr+5o6VCEsGlDxvVj2hOzALBzsMPVw5mQLUeJO61If5MAACAASURBVJNAyJajjA+czKhHr6PjgLZFz1EGA588MoPNB5bS5VrFDePrYWoXTaZD5Qqpv7/8VdKS0tmzZj+/nJpOm94tKx2vUoqGLesTfjjykl6nqFsyUjLw9HMvc5u9csAtrQGuyQ0rLIFwXl5ePpsWb2fWsWlMXf0aCz5cwkOtXmDjnqOlVh78/u9/MWV6ldOTEJaltUZfwl+3OTqbxj3N/PPxS3z37CMseOP/ePOpkWR6RVdjlNZJKUWT9oGcPiSfP6JG7ARaKqWaKqUcgPHA0oqeZLUjdw5ODmz/azeb/9hh6VCEsGnarDHn5qLz85n8+T2s/Gkdm36/8HtnVmaeufpNvts3lbuaPQZAUJuGbN3/N/PfmkQDb28Anh+ruWvqV0Rtz8VO2V/0mFkZ2Uxo/igA/87fzLSt7wJwjWFshfHeNPk61v4qVzDFxf31zWrGPHMjP746t8r6/Oi+6QS2bkCDlvUJ2XIUpRSJxx25Z+pXvD5xDG5OTkxbvJydW8/hpMqaiSOEdTi5/zSdBrWrVJ3UHK9YPnz4EQyGC+MBQ7t1YnrjVaRU7nqezVAGRVpShqXDEFfIGhZUUUrdDEwD/IC/lFJ7tdbXKaUaUFDy4HqtdZ5S6jFgBQWlEH7QWlc4n9pqk7tbnryedn1bs+wbuSldiOoUGxGHfxM/Io5E0eO6zjzS/YVS+2SmZZEYnYxPAy/ioxLJSMukRTOvosQOCq5o/m/CLdx96Ffc0sq/Fy+wdQPSUy58OB7YcJj72z/Fna+OYZV5Idv+2sWUG8su49JvVE9ysnII3Xvq8l+wqBNSE9OIOHqGLkM6FK0wXBUijkYRcfTCrBhvhwCGjriBB17/joyMdAwJnjhLYies3Lr5W3jqm4cqldwZHMz4e3qWand1cSTlP20zdk8lvZzkJ7BNwxKPlVIYjIYqq9WrFLTr24qX5jzJnLcXEX74TJX0W1xQ20ZEHKl8v2OeuZH6Tevx1ZM/Ys63goxCWE0pBK31YqDUUt9a6yjg+mKP/wb+vpS+rTa5mz91CSYXR0uHIYTNCz8SRcMW/oQc3cvsH77nmslXEbkrmoFj+/L9S78y+vHryc7Ipl6QL98f+ITMtEzy8vPx8nEr1Ze7kxPKePF3zaF3DWT/+pIXnsIPn+G9Oz/nvTs/59X5T7M0ZTYTmj1KclzBnw6OTg7c+n83EHM6juXfr626F19TLvEeD1E1Vs/ewOTP7q3S5K44v0Y+jH/pZua8tQhDtBeuVLziphDWQmuNh6970ftsecwJLvy2aRu3Depf1Jabl0fEmSSMuJbYNz0pg2evfr1UH41aNeC1hc9w9+u3FbUpg8LZzYlNv2/nwMbDpZ5zqcxmM2lJ6TRsGcDzPz3GY71eKnvH4u/Bl/jefP0DQ5k1ZV6lY4oOi6HzoPY07RBEqNTGEzXEapO7/Lx80pNl6FuI6nYu+hzRdjt59YWBtA3yY9nevbj18MZoNPLJhjdJTUijVfdmnDkRzQPtn8bOzkheXj7tJ7iSnZuLo/2FKZjTlqzAmHTxP3D9GvlcdJW2t8d9yoTXxvLl9veY1PkZsjNyePiTicx5+zfizkjhXVF59o72mFwcMdoZq2x04Dzfht6Me2E03zzzMzlZsoKdqH1ysyq3IJVLrjefz15NVm4utw3sx4mos7z83Vxyw734b6VTZVAMmzAQRycHtC54vzcYDZw9eY4n+r1CVnpWyf2V4s4pt9Lnxh64eDjz2UPl1nOukMGgCGzdkJS4VJZ8tfyy+ym/fwNZGdlkXMIiYJt+386WJTtl1M7KWMPIXXWy2uROCFEzzAFR/Pz0Q7g6Faz692T9+sxcvprX7nsLV/OFRSHmRRZ86OYV/pHc0L0r98z4gQn9ehLk58PM5esJ3hF30XuNzNqMwQSdhrRn5U/ryt1v9psLuWbiIF6d/zTzpy5h69Lg2pHYlXcVWEbrLKLD8OZM++wd3DoYSEjKIDvSDae8shdZuRSdB7en98hufPvcbEnsRK0VExFHXm7FS7oqpXCKDmLGl3uY8csGdLYdzqn1MKkLf0I6uzsz4NaC8lunDkYUlbdJPJd80QsrWmt+eXMRADc8fC33vDWe6LAY/vnh0mdomM2aLUt28O4dVVMKobhmnRpz4+TrWPTxn5celyR2VsVapmVWJ6tdLVMIcXFNOwZhZ1/w4epgcmD04yPw8i99X0RFGtV3L0rszpt4zWAMvqkl2rLSs0s8vmHCCNJ3+/Hmhxu5/+VF7FmZj1NyycSuTe+WLIyeyQ2TryHLP4KgqzLZvmoO33z5DpnGVMzaTLKKJUnFYf5PApSakM67d3zOoLF9CV6x75Jfl6jbsnQGMYnB/PHWEyx862lWfvYiPYa5kmm8+BS0izE5O3LHK7cQ0LSeJHaiVhsyvj+dBrQrUeP0YpRSuOZ74RofhFtaA4zFEru2vVty/7u3c2J3GM8M/h8n9oQRdyaBuDMJlzRivuzrlcyaMo+s9CxufuL6cvfLJI3s+pHYt4oh3TuSPF29JXFcPV24ZuJgZjw1izPHz1brsYSoCjJyJ0Qt1KB5AA99eDfrF25h+fdr6TigDW16tSQ7M4flM9dUuh+lFOYyplCmZWWh80puOLLjOG8ufYHXbvoAgJzMHDJTs3DDr6Ce13/6eeKrB2jbpxVLvvqHY3HbmfXm/TQPKEj+8m/IZ8Qr7+LikMEzN12N2az5Zslako44YyocWfFr5M1j0+5n+pM/Yra2y2wyQmf9AhL44tH/w6Fw2rDRaOSjhydy7aH3IPzSRu98GngzZHw/POt58Me05bVjFFmIi2jcPpA3xnx0ScmXo5NDmfXxUhPTORsWU2X3lOXl5IGCEQ8MLfV5lmWXQvsBDnw6+VmcTSai4uOZ+O508o7Wx8HJgaYdG+Ngcqj8hZdK3H838qFrmP/+Yqu/mNP16g7EhMdx5kTdK1FxqaztT4qqJsmdELVQVGg0s99aROieMAB2rdrPoa3HyEzLquCZJTVu14iEhHD2hobRpXnTovb3f1uCMd6nRMI2deJXLMuYc6GhWGkvg8HAC7Mfx2BUKKXwqe/FiT2neKT785i1mcD+GUWJ3fmnNm3kxdePPlpUBHdkz+7c8PIHZIe4oZTi75lr+O2TZaQmpl3SaxICwNXdHnfnkjXs7IxGvD2dSQ6/0Obs5sR19w3Bzcu1qF5dRkomGSkZ+Dbywc7ejoToRNbM2UTiuTq27ruwWfaO9mizrnC/zoPbc9Pk4cSEx+Ll78mZE2f59Z3fGTlpGB5+7uxcvodRj45gxv/NqrLYzobF0HlIBxq3bcSm37eTmnDhM8ChUSoznnqpqCxDAx8fpv/f/dz9/I8ENOlPk/aBLEv/BaUU0x6bydLpK64olnpBvji7OZEUe/kj/jXBzcuVu6aMJTcnl+O7w9iz5gC7V++3dFhWqS5My5TkTohaKmTzkRKPLymxK7wq2bpHU0K25fL4u3No29aXZkEBROdlEbzsHE7Ku8RTzGYzucWuXNo52NGiWzMMCty83WjYoj4f3P1FQSzp2cRFxhccCo2bs6lEXzuPH+eGXr2KEjsoGEUcN7QPf+bHMebem9m79qD1JnYyQmf1khNyOZeYiL/XhftGM7OzSUrP5Zq7BtK4fSC52blkZ+aw5pcNJUbjXDyccXZ3Jj4qQe6XETZp5ax/ue+9O0iJS2HBh6VrIne9ugN9buxBSnwq79/1Obk5BffmdRrUjnvfHs/q2RtIPJfM1XdcxacPfVOlo1qhe09xfNdJTh0Mx6e+V4nkLsDPrUS9PYCWDRvg5Am5OXn8/vlfzHh6Fne8fAuPf/kAj3/5AC9f/w47/9lb8YHLeF/vN6ony75ZdcWvqbqlJqbx6o3vcdvzowhesY8Bt/amy5D2bPxtO8d3n7R0eKKGSXInRB3l4etO865NWTFrHSYaceJcPq+ETWHOK4txytpU5nMij0fTb1RPtizZiZOriVufGok2a4z2BvJyc0vU/zrPqIwcPXWG/Px8jMaC+zvsjUYyckr/MWA2QIsuzZj9xsIyp/8IUVkOcfW49/0ZfP/Cw9T39iY+JYUnp8+if+8RhB0MZ/UvG8p9bnpyhqzWLGza6UORzHzhF7pf04mHP5lIZloWRqOBc6dj8Qv0JTUhjRlPzyr1vP3rD5Wojbf4i0sqv1VpO/7eTe+R3Rg8vj8ndp9k0+IdAMQkpKK1LnFhMCI2lqxUoNhI5K/v/s6v7/7OG4uf562lL3JXs0eLLjhWloPJgaYdg/hzRu2ot5yZlsUvby5i8mf3MO2x77F3tGfYI/1xb2Vi59yDGJQss3GejNwJIWzSqMeGM/+DJUWPjcqIXZ4D/kG+JfYzOTuiDAUfpM7uJo4Gh+LkaiIjNZMP7p5W2O7Mq/OfLvdYGSfdueX1j5ly9y008Pbiz6272XrkCGP69i1K+HLz8pi/Yhu5h+uX+OAW4nLYKwcyQny59enpuHvZkZqShzHGh3Obd1s6NCGsxq5V+9m16sL0Pf/GfqQmpF3Scv/VISk2hRWz1gEwctIweo3oyo7le8iMcOHl7+fw9r23YzQaSc3IYPKn3+OQ4AeG0p8b/7t5Ks98/whzw7/mt0+XUb+ZP171Pfls0jfkZudy5ng0Y5+9keUz15CSUHKmyOjHh/Pbp39VeRmV6pSfl8+JvadoP7gle8PWYI53oU+zABKHmzm6LQX75EtfdM0WSXInhLA5BoMBR2dH4qMuTEWr39Sfhi0C8Gl4YRqbq6cLv4Z/zcHCKaCZqVk8M/MRAJLOJVf6eE5mN1L3OfPYlN9RDvkYkjwwGP24/sWp3Dy0B2azmcVrgskK9cYkiZ2oIvbKAfuERuQngDNIgXEbla2zyCQNZ9xwUI6WDqdWO3c61tIhlPLXt6sZ9/woGrQIYOfyPdRr3YJ7v/yJnOR0XPx90GcaYa/KL+nw8f0zmPf+H8w6+gVxZ+I5ezKGb/Z+VGKfB96/i2sMY0u0+QX6En44slpeU3VaPnMN9a7O4493n8LTtaDI/OjevXjBfR7bF6Vhrx0sHKGobpLcCVHHmFxMTP7snlLTaX46Po1vn/+ZUY+OYNnXqzl9KIK0pHSO7Qrl5RHvXLRPXYnLYEZlxC0zADIp+CPbDPlHXUi5tjlLpi/HWQViqqgTcdkatqxPXGS8THcVNkNrTZZ/JFf1bsxVnbqxOvggwbvOYIptIKP/Nmb+1CX0GtGVMc/cyJKvVnD2oAFww6trQ64Z24Jl36zCnJdf6n68884cP1sqeStuacpsRj8+gj+mFRQ/923oXWtXxdVa45SXU5TYnffodUOJTV5PxMq6PeVcFlQRQtic4fcNYcGHS4k8duH+ODsHO3Jz81j40Z8c2HiE91e8SsTRM3x07/QSq2JWtYYt6uNp8sZZuVXfQQR3vTYGg8GA0c7Ij6/OtXQ4QlSJdPdoPnrqFvq3bwvADb16sHjzdqZ+sRHXrHoWjk5UtR3L97Bj+Z4SbU3aBxJ2oGD52/x8Mwbj5SX1U256nxsfuZar7xzAjr92s2vlPs6ePHfFMVuCRmMo4+KGQSn8An2x61gweycjNRMHkz2nD9W+0ckrZevJndxdKUQd49vQu0RiBwV1hXIKR3SObD/O7YEPseDrRfgNyeXn76aSFxRJpjG1rO4um7O7M7c8NZLfP/urSvstogwX/tVxuVm5+Df2469vrX/VNyEqy6eBoSixO290v164BJQ/RU/YlrW/bmLw+P74N/a7on72rQvh7XGf8kSfl8lMy+LtZS+RnVE7ZzkYlIFjJ+NIySg5Qjd1/lJOrUnk7T9f4us9H9JrRFdadmvGfe/cbqFIRXWRkTsh6pCC1b8a0/2aTiVuooeC5d/Py1RpxMTs5OunH8Lezo68cflM/uw7Dq1Pw0m7/rdbVDlTYS5mwmtj+Om1+bXqZvXaav7UJRXvJISNkBmZdYfZbObbZ3/m4U/vIScjGwfTpd9P5t/Yj+sfHEZ+Xj7KoDAYDLx+y4cc2Hi4GiKuGTq8HqNe/oQJN/angYcHc1ZvIWx/Nk7pdtzZ5BGC2jYi/HAkPg288QqoW4usyLRMIYRNcXI1kZ6cwdmTMSXaDQYDidFJdLumE7tX7cdQP5Evn3gee7uCtwg7o5EvHr+P645/AGdKJ3eVuecOwN3Hje7XdqZlt6YEr9hbvXXspBadEDYtPiqfbYeP0adtq6K2P7ftJPWsHTLRu+7Izcnjqyd+oE2vlgwZ35/73rkds1kTdiCczYt3kJebR++R3eg5vCu7Vu5j37qQotVAOw1sR7drOvHLmwuLavnZAgfliHN8G5LtmzH9zUW4442Tci/afn6hmPb9W3N05wlLhWkxktwJIWxGclwKnzz4NeNfHM2sKfOK2qfv+oCDm48U1S/y9DDh5Fhy1TmTgwOeHk5knCndrzbrElfLDQYDPYZ3wd3blXqNfbF3sAcgLSmdXav28+/csuvoCSFEZbmk1OfZTxdy9VUtGdCpFSt3HGDr9ghcMxvKyqh1jDnfzKGtRzm09WhRW9s+rbj3ndsxGBSHth7ju+dn07RjEJM+uhtzvpnk2BTumjKGJ/q9YlOJHRQUX2/epQn/zt2Mlyo9ZdWngTdpiWn0H9WL9+763AIRiuokyZ0QdUxeTh6OzhcSNydXE807N+F/o6eSV/gBlxCfTVJaWonVtlIyMoiPz8SpnH6Lr0434fWxHNx4mMPbjrF+wRab++AUQlieUgqnc4GsW5TI34vW4II7TqqRJHYCgMPbjnF42zHuf+9OugxpT+jeUxzZcYIjO04w+bN7adIhkGXfrqLz4HYMub0/K2et48SeMEuHXSWC2jZk69LgUvfXn/f0Nw9xLjyW1b+sr+HIrIOtj9zJSgNC1DET/jeWZTNWFD0eeucAtNYl6hsZzvky8f2viE0uqGUXm5zMxPe+xBDtW6o/ALNZl0ju7Ozt2LVqP2dOREtiZ6U8/dwZ9dhwXD1dLB2KEFfEUTnhpfykxp0okzabWfzFcsY+eyMNWwQAMP2pH1kwdQku7s6kJabzw8tzGf/C6HJLKdQ2Cz/6kyYdApn82b30uLZziW1uXq4cDT7BtEdnsvOfvRaK0HLO33NXFf+slYzcCVHHGO0MnDkRXfR47DM3kRSbUmIfR2Uiaa9i9FPTcPOwIzU5D7tYXxxV2ZXozGYznvUuzOeXBQ2sX9u+rWjTqyWJ55LZsHCrpcMRQohqcXRnKCMeGMrpQ5H0v7kXSTEprPp5PYe3Hyf9zYW8tuhZNi3ewd5/D9K6VwsObztm6ZAvS5chHeh7Uw+y0rNJTUzD3ceNlT+tY+RD1xC8ch9QcMvEnVNuZd77f1g4WlGdJLkTog7x8vck/mxiibaAZvWY+eKcUvs6KEcc4hqRHwfOcNGpTnk5eRiMRt7562X+nbuJ47ttY2qLLdu6NJg9aw6SlZ5l6VCEEKLabP5jB0Y7A+Z8M5sW76DTwHZM+nAC+Xn5RIWe4+3xn5ISn0qrni3469vVlg73stg72tN7ZDdmPD0LpRRPf/cw0yZ/x5MzJrG02EydcS+MYu2cjSTFJFswWsuz5lG3qiDJnRB1iNa61FLRudl5OLmWPSJ3KSZ1eoYZu6eitWb9gi1X3J+ofpLYCSHqgl2r9nPLUyPZtHgH+zccYv+GQ6X2SU9Kt0BkV87J1cQtT40sWhBNa80nD8wAIHTfKTJSMlFKMfTOAaTEp3Js10lLhmtxUgpBCGFThk0YyIof/y3RFnE0ilueHEmT9oGV6uPA4b3s27MBk9GI2d7EiGvH4uXtTU5mDh/e8xWh+05VQ+RCCCHE5UlPziAnK5dW3ZuVm9zk5uShta7hyK7ciAeGsun37Zw+FFmi3WAw0KF/G5RB4eE7iB3L9xCy+YiFohQ1SZI7IeoQNy/XEtMxDAYDLbo04e3xn3BsZ8VX8xLMkQwc4MuMu8dhMBiIS0nh7vffxN/cjesmDJHETgghhFVa8cNarr1nsE2NXJmcHfEL9C2V2AHc/OT1LPp0Wa29h7A6ycidEMIm2DvYkZudW6LNXPgOt35B5RbUcGwbz/Nj7y967Ovuzjv3j+X9uVuLisIKIYQQ1iYpNoV6QX4opUqN0KlauArYVTf3ouPAdsz/oPTiKIPH9SM2Ml4Su3LYenJXa9Z8HXrnAO54+Raad2lSK38JhbC0O165le1/7S7Vnp2Zw7vLX6lUH/W83Uq1dW3ejNTMeOZ/sOSKYxRCCCGqy1/fruL+9+4o0WZnb8fIScNqzZTFoLaNeOiju8nLzWfG07NIiE4qsf2u18bQoHmArIJch9Wa5K7L1R34+7vVNO0QxIuzn8DRyaHiJwkhAHB2cyIjNZPju0tPR/nu+dml6uCU5+x/SiYAbDl8hITQ7KJRQCGEEMIahR0IZ8ffe3jgg7u47bmb6Hp1B56Z+QjHgkPLvPhpTQaO6cP9791Jh6vaMGvKPLYt21VqH896Hqz44V/cvF0tEGHtIHXurMju1Qe49f9uZNaUeQwe119G74S4BFkZ2eWuiDly0jDM+WZuePhaln29svRzdQZ5nomY8xSG0w68OPMX3pw4Dgd7e06dO8fL3yzAJSPooqUShBBCCGuwf8MhjgWHEtDMn9Y9mvHpQ9+Qk5Vj6bAu6rbnbiLiSBTfv1S6bBFAp4Ht6HNjd2Ij4nFyNbHky39qOMLaxZoTs6pQa5K7f+duImz/ae55azxr5mwgKyPb0iEJUWtMfGMc/87dVOa2ekF+GIwG7nvn9lLJXab7OfoPqs/jo8eQlJ7O27MXs3pFJFt3T6Vjn5Yc3BKBIaoRBlVrJgEIIYSo47Iysjl1MJxTB8MtHcpFuXq6cPfrt/Hv3E0c3n683P1639Cdb5+bXYORCWtWq/4iOxUSwfcvzeHfeZstHYoQtUp+Xj6Rx86WuW3mS3NQSvHBhGkl2nN0Ft36+PDBA3fRyNeXDo0bM/eVJ/FvrrCPbESfTqOwP+uHUdWaa0RCCCFErWBnb8e9b4/n59cXXDSx6zigLeGHImowstqtLkzLrFXJnRDi8qQmpuFd37PMbfUCfQDYtWpfifYsp0Qm3TC0RJtSiv6dW6JczGSmyeqYQtQG5xeMGDimj6VDEUJU0vgXR7Pwoz9Jq6C4+lW39GbVzxtqKCrbYOvJnVxyF8LGefl74urpQkx4XNnbA7ww55t5759XS7Rv3bmJ6KR4OjQuuX+eg4GPVr7Bv7NlJS4hrpTBYMDL34P4s4nVdoz+N/fCbNYMuX0AYQfCuWbiYNCa4BX72L/hULUdVwhx+fJy83D3cSUmPK7MBctcPV24/eVbOBUSIQuaiRIkuRPCxo18aBiLPv6z3O1Hd56gSYdAnhv6Rol2szZj3zaKge3a4mBvD8DZhARWrzmIXc4aQveeqs6whbB5BoOBR6fdR0ZKJoe2HmXr0uBqOc76BVt46OOJhGw5gpOrCXtHe3Iyc2jaKUiSOyGs1IKpS5m6+jUC2zTknx/W8uOrczEYDQS1bURQmwZ0ubojs6bMIyU+1dKh1jq2ngtLcieEjUs6l0xA03qEHSj7xvEnpz/ISyPeKdVuUAbSj3gx/LmptG9en6ycHI4cj8c+qgGOzo4YjDKrW4gr4ezuhKPJAaOdkRN7TlXrsb555qei74/tKl0SRQhhXcxmM6+MfJdnvp/M8plr8KznwePT7mfT4u1EHI1i42/bSxVjFxU7f8+dLZPkTggbFxV6Dk8/9zK3LYyeSfDKvQSv2FvmdhMuEOZCyMl8FCZMKhAUJMUkSx0dIa5QWlI6H90/3dJhCCGskFIK/yb12PT7Nia8NhY7Bzu+evKHUkXLr9TEN8aRkpDK4s//rtJ+heXIpXchbFzP4V3Yv+FwmdvcfNz46X8LKuzDqIxF5Q6Mdkba9G5JbGR8lcYphKhbDEYD7fu1xs5erjML8V/jXhjFvW+Nx2hnZNpjM/l00tdVnthBwYJrWWlZVd6vNZMFVYQQtZq7jxvDJgzk6jsGEBsRR+TRKBZ8uBSz2Ux+Xj6NWgZw5CLLLBfXb1RPelzXhVU/rSM6LKaaIxdC2LK7X7+Nk/tPM2lcP7LSsljw4dIKVwYUoq5Y+tUKmnQ4xJEdJzDnV18m8ftnf1Vb39ZIpmVWQCn1OvAgEFvY9LLW+u/CbS8B9wP5wBNa6xVXciwhxOVp0MyfuDPxfPbwNzRqWZ9WPZrz3YGPqRfky8ZFW1n9y8YK+zAYDDw49S72rQvhi8nf1UDUQghbZ3JxJPifvWxYuBWf+l6MeGAofoE+TH/yR0uHJoTFZaRmcmjrMUuHIWqhqpiW+anWukvhv/OJXTtgPNAeGA5MV0oZq+BYQohLtHd9CD++Oo+zoefY+c9e5rz9G44ujpjzNe//p3B5eeo39yfsQDjblu2q5miFEHWBh687DVvWJyO1oF5m/NlEfvtkGQ4mBwtHJkTd0WtEVx6cOoHmnZtYOpQaJdMyL88oYJ7WOhsIU0qdAHoBUhhLiJqmC0beitfB8Q/y447Gj5S9u9ZkkEo++bjhidHOyNA7B/DPD2trKmIhhI275+3xNOvYmL439eCuV8cQsvUoWenZLPt6paVDE6LO0FqjzWay0uvePXe2rCqSu8eUUncDwcAzWutEoCGwrdg+kYVtpSilJgGTAEw4V0E4QojicnNyUQYFxd7M8vPyGf3YcOwd7fjlzUWkJKQBkE0Gji3juWtwd9xdnFh58AitWw5g69LgcougCyHEpZr16jwWf7aMoLaN+PyRb4mNTCDxXNUvFiFEbXTDQ9fg28gHZ3enap2mvPOfvez8p+zVskXtVWFyp5RaDQSUsekVYAbwFqALv34M3AeoMvYvsxiH1vpb4FsAd+UtBTuEqGJxkQkEtW1Yos7dva2fZMbuqUWPpz81CwD7ZvEse/95HAuLlt85ZCC3vv4xyXt8MFzCzOq7pozBYDSgVMFbQUZqJsmxKRjtjcScjiXyxNpvpgAAHr1JREFU2FnOnY6toBchhK1KjkshOS6F8CNRlg5FCKvTvEsTNizaRv/RvSwdis2RBVUArfWwynSklPoOWFb4MBIILLa5ESDv4EJYQPCKvQy6rV+J5O5s2DlGe03kl7Dp2DsUJHJZOpPrerQpSuygoM7OYzdfy4sHV+KRV6/Sx9Ra88e05aTEpwLg7OaEu68b+bn51Avypfu1nXFyNfHbp8sq6EkIIYSoW354eS5NOwXxzbM/WzoUm2Tryd0VLaiilKpf7OHNwMHC75cC45VSjkqppkBLYMeVHEsIcXmyMnJwdncqc9usKXPpemN7gm4w0m20E5HJ53hnwQKyc3OL9rmcGlR5OXk4uZqKHmekZhIdFkNsZDwhW44SdyYBk4vjpb8YIYQQwsalJqaxf/0hcrNzK95ZiP+40nvupiqlulAw5fIU8BCA1jpEKbUAOATkAY9qrfOv8FhCiMuQkZKBk6sJk4up1E3Tq2ZvYN3u+fz2+tM4OxYkW6fOFSR4b955J1prFuzchWuuT9mTrcvx+2d/cccrt5KTlcPpQ5FEHjtLwtlEjHZGxr84mj1rDjDn7d+q8mUKIYQQNq1d31b0vaknaUnpzP/gD0uHUyvJtMwKaK0nXGTbO8A7V9K/EKJqLPt6FZM/u4dPHvy6RHuafTwv33R1UWIH0MTfn7z8fOat38jGsFO06zyYyLXbSU/OqPTxcnPy+Ol/8zE5O9KodQPa9mnJqMnDSUlI5Z3xn5GamFZlr00IIYSoCxxMDmizmfzcPEuHUqtJcieEqPWiT8UQvHIfg27rx/oFWy5ssM+jecPS6yV5mNx49+ON/LBqBp8/9B03PHztZV0lzMrI5sSeMEL3nkIpRdiBcKtI7EY9Npz8PDMrZ60jJyvH0uEIIYQQFdr770H2/nuw4h1FnVYVRcyFELXAhoVbsXe049qJg4vaHDI8mPXP+hL7aa3Zsu8E9WiIvZ090WExRJ2I5sGpE/Bp4H1Zx9Za888Pazm688SVvIQqoZSiQfMAOg1sR4uuTUptb9SqARPfHMfEN8Zx9+u3Meb/bqBV92Y1H6gQQgghqpwUMRdC2IzVszdw1c29ikbwHJUTWzdH8Y7rbzw+egSpmZm89uMC4o7b46wu3GS38bdt7F9/iGETBuLl78GeNQfQGk7uP01AEz+iT8WSFJNswVdWeVprZjw9q9ztJhdH7B3tiY9KYOWsdSil6H5tZ4beNZBf3lpEaoLlRx6FEEJYN2d3Zya+cRvZmTn88PKvlg5HFJJ77oQQNmfT4h3cNWUMNz9xPfFRCYTuPcXf84/x16pPMecrHJN8cVYFI3TeDbx4+OOJZKRmEncmgVU/rSMnK5cW3ZpiMBi47t4hnA6J4MZHruPvmWsI2XzEwq/uyp3YE8aJPWH4Bfoy8c1xJJ1LZtXsDexff4jbnhtFxJEzZGfmlJzeKoQQQhRz3b2DWfXzeobeNdDSoYj/kOROCGFzfnlrEe4+bnjX96LzkA6MfOgaQjYf5eT+05xNPle039SJX+Id4ImHjxt+Qb688cfzOLs7s/mPHTi5OZGblUPrni3Yty6EI9uPW/AVVb3YiDimP/kjbt6u3Pjwtdg72rN92S5uenQ4WRnZktwJIYQoV/1m/rTv25p18zdbOhRRxyittaVjKOKuvHVvNdTSYQhR5yilaNm9Gc07NyawTUN2rz7AqYPhGO2MpCdnkJaUDkCrHs1p3qUJy2eusXDEQgghhPVSSuHs7nRJK01bg9V60S6tdQ9Lx1FdfH176FGjgqukrx9+UJf9s1JKjQVeB9oCvbTWZQallDoFpAL5QF5ljicjd0IItNYcCw7lWHAoAH1v6sHISdfg39iPMyeisXMwos0ag9HAvPcWWzja2mngmD606NqUPWsPsmfNAUuHI4QQohpprWtdYldXWMm0zIPALcA3ldh3iNY6rrIdS3InhChl69Jg2vVtjVKKAxsOsX/DIUuHVKvd9txN9B/dCzdvN5q0D8JoNBK8cq+lwxJCCCGEBWitD0PBCG9Vk+ROCFGm71+ag529HXlSLPWKrZmzieiwGPLzzTz4wQTGPnsjcVEJnDoYbunQhBBCiDqlCkfufJVSxadTfqu1/rbKei+ggZVKKQ18U5n+JbkTQpRLEruqER+VwIZF2wAwGAz0u6kHbXu3kOROCCGEqEFVXAoh7mL3wCmlVgMBZWx6RWu9pJLH6K+1jlJK1QNWKaWOaK03XOwJktwJIUQNij+biKOzI216tWT592stHY4QQgghqoHWelgV9BFV+DVGKbUY6AVcNLkzXOlBhRBCVJ69gx3arHH3dcM7wNPS4QghhBB1itlcNf+qm1LKRSnldv574FoKFmK5KEnuhBCihjRq1YA7XrmV1MQ00JAQnWTpkIQQQog64/y0TEsnd0qpm5VSkUBf4C+l1IrC9gZKqb8Ld/MHNiml9gE7gL+01v9U1LdMyxRCiBrQtGMQj35+HzERcbh4uvDNsz9bOiQhhBBCWIDWejFQqrZU4TTM6wu/Pwl0vtS+JbkTQtgcn/petOrZnGadGmMwFkxQsLO3Y/4Hf5CRmmmRmMIOhPPpQ9/g7uPGiT1h5GbnWiQOIYQQoi6zkjp31UaSOyGETeg0qB1X3dyb9JQM4qMSObbzBMH/7CU3p2DFz2F3DcTDz91iyR3AmeNnOXP8rMWOL4QQQtR1tp7cyT13QgibcGxnKOnJGZw6GMGyr1dybNdJcnPyMNoZad6lCe36tSY/L9/SYQohhBBCVBsZuRNC1Dh7R3t8GniRn5tPbGR8lfSZlZHNT/+bT7dhnXj4k4lkpmUBkJ+bz6mD4cx9bzGxEXFVciwhhBBC1D5VXOfOKklyJ4SoNkopGrcPpEP/1vgF+tL16g4Er9xHbnYu8VGJuHg449PQm9WzN1RZQe/dq/ezf/0h8vPy0VpXSZ9CCCGEsA2S3AkhRCU5OjnQulcL2vZuiZObE9qsORUSwbZlu4g7k0Crf14hNjKe5TPXFD3Hzt6OoXcN4Jq7B7Fh4VaO7jxxxXHk5eZdcR9CCCGEELWNJHdCiCpx3b1D8G3ozaGtx1g6fUXRtMjiXhr+Di//+iQdr2rL1Hu+BAoSsRU//ovBYGDg2D4MHtePf+du4tiukzX9EoQQQghhw+rCtExZUEUIccWad2mCs7sTc97+jT1rDpSZ2Hn4utNxYFv2/htC045BfLv/4xLbzWYz6+Zv4dd3fqf7tZdc1kUIIYQQokLWUMS8OsnInRDiivg08OKFnx5n65/BvPDz43gHeGK0N5bcSUNWehaxZxI4FxbD3Pd+J3Tf6TL7639zLzYs3FoDkQshinN2d8a7vieRR6MsHYrNMBgNeNXzwNXThbTkDDLTsshKz8Kcb8V/GQohajVJ7oQQl8Xe0Z7rHxxKv1G9yM7K4XRIBBt/20b44TPkZOVcdr8NWgTwzw9rqzBSIcR53gGe1G8eQMMWAdQL8sVod+FCTHpyOiMnXcPauZtQSqGUIu5MPCf3nSbsQDhZGdkWjNy6uHm74uXviXeAJ14BBV9dPJxL7Zefl48538xVN/fmr+9WY3JxxMnVVOLnDpS5+JNSCoC8nLyipLDgazZZ6dml2jLTssjNzq2eFyyEjagL0zIluRNCXJYJr41hw8KtOJgc6DeqJ2vnbrriPn0aeBMflVAF0QlRNxntjPg39qNBc3/qN/fHy9+zxPaE6CTOhkZzYONhYsLjStV+7HNDD2a/sbDosW9Db5p1bsKox4ZjcjEBkJOVw6mDEYTuO0VMuO2UF3E0OeDdwKtEwubu64bBUPIOFq01qQlpJEQnkRidxLHgUBKjk8hIzSyz33qBvrTq0Zy/v1t9WXHZ2dsVJYUXvprw8HPHv4lfUbvJxYSDyb7oeeeTw/ISR7PZXJAoFksOy0ogU+JTpUZoFTAYDJhtPauoJWz9NEhyJ4S4JM7uztz69EgG3daPNr1bsvOfvbwy8r0q6Xvg2D6sm7elSvoSwlaZnB2p39yfBs0DqN/cH2c3J6DgD/b8vHzOnY4l6kQ0m37fQeK5pCs6VtyZBOLOJLDj791FbY5ODjRuH0i3YZ2oF+RblETEhMcSuu80p0MiyM68/NH7mmQwGug2rBOdBrVj0Ni+zH5jIQnRSUQeO8uBDYdJiU+1eEmVvNw80pLySEtKr9J+DQYDjs4OmFxMJRJHJ1cT3gGemFxMuHg603N41ypZxbgu8wv0oWXXZmz/ezdJMcnsXXuQUyERlg5L2ChJ7oQQldJpYDt6DO9CVloWa37ZWOLqflXxDvC64j9GhbAFHr7uRaNvAU3rYe9QMCKjtSYrPYuzJ2OICo1mz5oD5Y4YVZfszByOBYdyLDi0RLtfoC/NOzem27COODo5ApCVkU3YgXDC9p8mNjK+RuO8mBZdm9L3ph7Y2duxa9U+fnxlLu36tGL1LxssHVqNMZvNZKYVjNQlnit7H3tHe/rd1LNa3u/rkg5XtcFoZ2TWlHl4+XvSeXA7Bo3rB0BcZDx71x7kzIloC0dZd8jInRDCqo17YXSJqTjVoVWP5qQlpvPhvV9V20IAQW0aEnHkTLX0LYS1MRgM+AX6FCZwAfg08CoaAQNIjkvhbOg5jgWfZOOibeTmWH/txtiIOGIj4ti2bFdRm8nZkSYdAuk5oit+jXyK2qNPxXBy32lOhUTU2H1ifo18GHRbXzz8PDixJ4z5Hyy5ovuDhbgcieeSWDf/wgwVv0BfugxpzzUTBwNw5vhZ9v4bQmyE7Ux5tiZyz50Qwuo5mOyr/arqLU+NxLOeR7Wu8Nb/5l4s+fKfautfiJrmYHIgoGm9ohE4d2+3oil+ZrOZ2Ih4zoaeY8ffu4mPSrT49L/qkJWRzZEdJziy48K0PqUU/o39aNa5MT2Hd8He0R4UZKZmEnYgnJP7ThN/NrFKju/s5sSAW3vTqHVD4iLjWT17A0mxKVXStxBVITYijlU/ry963KB5AD2Hd6FekC8Apw6Gs/ffEJJiki0VoqhlJLkTQlgFZ3fnGp9eJsSVcvV0KRp9q9+sHg4mh6JtOVk5RIfFEBV6jpDNR0lNTLNgpNZDa030qRiiT8WwZcnOonYnVxNNOwbRd1RPfBp4Fe4MUaHRnNx3mvDDkZUawTTaGel+bWc6DmhLRkoGm37fzopZ66rp1QhRtaJCo4kKvTBFs0n7QAbd1hcvf0+0WXNiTxj71x+S95MrICN3QghRzdr2bsnhbccsHYYQpSil8GngVbR4iV+gT4nVE9OS0jkbeo5TB8PZujRYpvldgcy0LA5tPcahrRfeC5RS1G/mT7POjelzY3fsHAr+bElPziBs/2lO7g8vuk+3Vfdm9LmxBwajgV0r9/HDy7/a5GioqFtOhUQULb5iMBho3qUJ1907GBdPF/Lz8jm2M5QDGw+TmZZl4UhrB5mWKYQQNaDniK7Mffd3S4chLpFSCv8mfrTo0oTG7QPxC/Ql/kwCqIqfa+28A7xo0iGQ3av3Ex+VyNnQaPauPUhsRLwsZ16DtNZFIxmbft9e1O7s7kyzTkEMHNMHT38POg1qx5Jpy5n73mKp9SZsltls5vjukxzffRIoGKVu3bM5Nz06HCdXEzlZORzeeoyQLcfkQlMdJsmdEMKiDEYDBqOhViwYUZfZO9rTpH0gzbs0wb+JX0GjhrMnzxG69xTb/9rN8PuvJqBJPb574RfLBlsFel3fDWVQskqglcpIyeDgpiMc3HQEgI/Wvs6GRdssHJUQNSs/L7/EaLe9oz3t+rZizDM34GByIDMti4MbD3N0Zyh5ufIZe56tX5+T5E4IYVHdhnZk9+r9lg5DFONT34umnRrTtGMQbfu05HRIJLnZuYQdDGf36v02VbhaCCFsRW52LvvWhbBvXQgAJhcTHa5qw/iXRmNnb0daUjr714VwYs+pOj0DwdZfuiR3QgiL6jiwHbOmzLN0GHWSydmRxu0DadoxCP/GfkXt8VEJnNx/mr++WUXv67vx0//mWzBKIYQQlyMrPYvgFXsJXrEXKFgAqtOgdkX3pibFJLP33xBOh0TI/ak2RJI7IYTFODo5kJudKx8q1UwpRUDTejTr1JjG7RoVLD1PwTL1p0MiZDROiGqUk51Lu76t+Wjt65YOpYgyKI4Gh5JZwQrFRjujTJm3IWlJ6WxZsrNolVrPeh50HtyeQWP7Yu9ox7z3/4CqqUJitWRBFSGEqEZ9buzB1j+DLR2GTXH1dKFpxyCadWqMp78H6IJFKc6ePEfYgXB2LN8jC04IUYOSYpIZ1+BBS4dRQufB7Rkyvn+FyV1+Xj4mZ0erSkxrIxcPZ6u85y0pJpn1C7awHhh0Wz+8AzxtPrkDSe6EEKLatOjalPULtlg6jFrLy9+TrkM70Kp7czLTC5bBTktM5+T+06xfuFWK3gohymXnYFepBYNmI4sKXakOV7Xh+geGWjoMUUdIcieEsAg3b1dSE1ItHUatYTAYaNy+Ee37tykq8JwYnYTJ1UR+vpmfXpP74oQQQoiLkWmZQghRTQbc2keWLr8IJ1cTbXq3pE2vFjg4OaDNuqhQdnxUQtF+/W/uhYePmwUjFUIIIWoPSe6EEKIa1G/mT3RYjKXDsBr+jf1o1681QW0agoKstCwObzvO75/9RXamFKMVQgghRMUkuRNC1Di/QF9iI+ru6ox29nY079KEdv1a4eHrjtaamNOxhGw+yrp5m2X1UCGEEKKayMidEEJUsYFj+rDmlw2WDqPGuHm70q5vK1p2a4adgx15uXmE7jnFml82khIv9x0KIYQQNUHuuauAUmo+0LrwoSeQpLXuopRqAhwGjhZu26a1fvhKjiWEsB0NmvujdUGdO1ucchjYugHt+rWmfjN/AFLiUzm89Rjz3v/DKpfDFkIIIYRtuKLkTms97vz3SqmPgeLrbodqrbtcSf9CCNt0aOsxBt3WFydXEw5ODpf03Kz0bLLSs8hMyyr4Pi2LzMK2rMK289vMNXB5ztHJgVY9mtO2T0uc3Z3RWhN5NIp960JY8eO/1X58IYQQQlSejNxVglJKAbcBV19pXwaDATuHujdbNDc7V+6zEXXGmjkbL+t5SikcnRwwuZowuTji5GrC5GLCydWEh69b4WPHojZlUJXuNzc7tygpLPhaLIEs1ubo5EDbPq1o2jEIg9FATmYOR3eeYNk3q8lIybis1yWEEEKI6ifTMitvAHBOa328WFtTpdQeIAV4VWtd5l9zSqlJwCQAE86Mfe4mlAJtrjuJjruvO8eCQ6WYsxAV0FqTlZFNVkZ2lfdt72CHycWEydWEk4tjUQLp5e9R0F6YTF7/wFA+uPtLNv+xA3O+jX9CCCGEEKJWqTC5U0qtBgLK2PSK1npJ4fe3A3OLbTsLBGmt45VS3YE/lFLttdYp/+1Ea/0t8C2Au/LWdvZG5r67uEamU1mLBs0DaNunpaXDEKJOy83JIzcnjdTEtIvud9XNvQndd6pmghJCCCFElbL1FKPC5E5rPexi25VSdsAtQPdiz8kGsgu/36WUCgVaAcFXFK0QQgghhBBCXCZbT+4MVdDHMOCI1jryfINSyk8pZSz8vhnQEjhZBccSQgghhBBCCFGGqrjnbjwlp2QCDATeVErlAfnAw1rrhCo4lhBCCCGEEEJcMllQpRK01veU0fYb8NuV9i2EEEIIIYQQVcXWk7uqmJYphBBCCCGEEMLC6l5BOSGEEEIIIUSdI9MyhRBCCCGEEMJG2HpyJ9MyhRBCCCGEEMIGyMidEEIIIYQQok6w9ZE7Se6EEEIIIYQQNk/uuRNCCCGEEEIIG2HryZ3ccyeEEEIIIYQQNkBG7oQQQgghhBA2T6ZlCiGEEEIIIYSNsPXkTqZlCiGEEEIIIYQNkOROCCGEEEIIUSeYzVXz70oopT5USh1RSu1XSi1WSnmWs99wpdRRpdQJpdSLlelbkjshhBBCCCGEzTt/z52lkztgFdBBa90JOAa89N8dlFJG4CtgBNAOuF0p1a6ijiW5E0IIIYQQQogaorVeqbXOK3y4DWhUxm69gBNa65Na6xxgHjCqor5lQRUhhBBCCCFEnVCFC6r4KqWCiz3+Vmv97WX0cx8wv4z2hkBEsceRQO+KOpPkTgghhBBCCGHzqrgUQpzWukd5G5VSq4GAMja9orVeUrjPK0AeMKesLspo0xUFJcmdEEIIIYQQQlQhrfWwi21XSk0EbgCGaq3LStoigcBijxsBURUdV5Xdl2W4K2/9xZTpzH13MWZbL0JRjLO7M+NfHE1udq6lQxG1UOdB7av9GC4ezrh4OBMTHlftx7J2Hn7uJMemWDqMIkYHI86uJlIT0qulfzdvV1IT0iq1r8HOgDZrtNl6Plcul73JHt+G3pwNPWfpUGqUh687yXHW8/+7sjx83UiOS72iPtx93Ui5wj5qC4PRgNHOKH931BCD0cChLUeZ+VJZgzPWYdBt/Qjbf5ofDn++62KjUbWd0dhDOzsHV7xjJaSlqcv+WSmlhgOfAIO01rHl7GNHwWIrQ4EzwE7gDq11yMX6lpE7K5CRksEPL/9q6TBELTWbhZYOQQghhBCiVrCS8aMvAUdglVIKYJvW+mGlVANgptb6eq11nlLqMWAFYAR+qCixA0nuhBBCCCGEEKLGaK1blNMeBVxf7PHfwN+X0rckd0IIIYQQQgibV8ULqlglSe6EEEIIIYQQdYKtJ3dSxFwIIYQQQgghbICM3AkhhBBCCCFsnkzLFEIIIYQQQggbIcmdBUx4fSzmfBv/yQshhBBCCGEFmrQL5FhwqKXDEFXA6pK7OW//ZukQhBBCCCGEEDbI1kfulNba0jEUUUrFAqctHYeF+AJxlg5ClCLnxXrJubFOcl6sk5wX6yTnxXrV1XPTWGvtZ+kgqotS6h8Kzm1ViNNaD6+ivqqMVSV3dZlSKlhr3cPScYiS5LxYLzk31knOi3WS82Kd5LxYLzk3oraSUghCCCGEEEIIYQMkuRNCCCGEEEIIGyDJnfX41tIBiDLJebFecm6sk5wX6yTnxTrJebFecm5ErST33AkhhBBCCCGEDZCROyGEEEIIIYSwAZLcCSGEEEIIIYQNkOSuhimlxiqlQpRSZqVUj2LtTZRSmUqpvYX/vi62rbtS6oBS6oRS6gullLJM9LatvHNTuO2lwp//UaXUdcXa5dzUIKXU60qpM8V+T64vtq3McyRqhlJqeOHP/oRS6kVLx1PXKaVOFb437VVKBRe2eSulVimljhd+9bJ0nLZOKfWDUipGKXWwWFu550Hex2pGOedFPl+ETZDkruYdBG4BNpSxLVRr3aXw38PF2mcAk4CWhf+srmCijSjz3Cil2gHjgfYU/OynK6WMhZvl3NS8T4v9nvwNFZ4jUc0Kf9ZfASOAdsDthedEWNaQwt+T8xerXgTWaK1bAmsKH4vqNYvSnwtlngd5H6tRsyj781o+X0StJ8ldDdNaH9ZaH63s/kqp+oC71nqrLlj95mdgdLUFWIdd5NyMAuZprbO11mHACaCXnBurUuY5snBMdUkv4ITW+qTWOgeYR8E5EdZlFPBT4fc/Ie9X1U5rvQFI+E9zeedB3sdqSDnnpTxyXkStIsmddWmqlNqjlFqvlBpQ2NYQiCy2T2Rhm6g5DYGIYo/PnwM5N5bxmFJqf+G0mvPTmco7R6JmyM/f+mhgpVJql1JqUmGbv9b6LEDh13oWi65uK+88yO+R5cnni6j17CwdgC1SSq0GAsrY9IrWekk5TzsLBGmt45VS3YE/lFLtgbLu4ZL6FZfpMs9NeedAzk01uNg5omAa7FsU/JzfAj4G7kPOhaXJz9/69NdaRyml6gGrlFJHLB2QqJD8HlmWfL4ImyDJXTXQWg+7jOdkA9mF3+9SSoUCrSi4QtSo2K6NgKiqiLMuupxzQ8E5CCz2+Pw5kHNTDSp7jpRS3wHLCh+Wd45EzZCfv5XRWkcVfo1RSi2mYBrZOaVUfa312cJp5TEWDbLuKu88yO+RBWmtz53/Xj5fRG0m0zKthFLK7/wNukqpZhQsznGycMpGqlKqT+FKjHcD5Y0wieqxFBivlHJUSjWl4NzskHNT8wr/EDrvZgoWwYFyztH/t3fHKpFEQRiFz48iiJgpxgab7xPIRBobGE9goLL7DsJGi6m5oYKJIIj6DJOYaCziY4zUBj1mDibuNN45HzR00A0FRdel6NvVs45vjo2AH0k2kyzRDR+47jmmuZVkJcnq+zmwTfesXAPDyWVDrFd9mZYH61iPXF/UCt/czViSXeAUWAdukjxU1Q6wBfxJMgbegMOqev/Y94hustMycDs59MWm5aaqHpNcAk/AGPhVVW+T28zNbJ0k+Um3JeYZOAD4JEf6z6pqnOQ3cA8sAGdV9dhzWPNsA7ia/JllETivqrskI+AyyT7wAuz1GONcSHIBDIC1JK/AMfCXD/JgHZudKXkZuL6oBemG/EmSJEmSvjO3ZUqSJElSA2zuJEmSJKkBNneSJEmS1ACbO0mSJElqgM2dJEmSJDXA5k6SJEmSGmBzJ0mSJEkN+Af6jsLenCURFwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig_4, ax_4 = plt.subplots(1, figsize=(20, 7))\n", + "\n", + "gdf_model.plot(ax=ax_4, column='od550du', cmap='viridis', vmin=0, vmax=3)\n", + "\n", + "gdf_earth.plot(ax=ax_4, facecolor=\"none\", edgecolor='white', lw=0.5)\n", + "\n", + "gdf_diff.plot(ax=ax_4, column='absolute_difference', cmap='bwr', edgecolor='black', legend=True, lw=0.5)\n", + "\n", + "ax_4.margins(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-2.1082139034511727e-08" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf_diff['absolute_difference'].min()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.1416417144971547e-08" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf_diff['absolute_difference'].max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.1.2. Relative difference" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + " \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", + "
geometryprovidentianesobsabsolute_differencerelative_difference
FID
0POINT (109.62880 40.85170)0.5047570.504757NaN-6.329158e-09-1.253901e-06
1POINT (-28.02917 39.09109)0.0075630.007563NaN3.951382e-115.224647e-07
2POINT (-149.88000 70.49950)0.0134670.013467NaN-6.039905e-11-4.484809e-07
3POINT (-97.48562 36.60518)0.0110340.011034NaN2.067994e-101.874285e-06
4POINT (40.19450 -2.99600)0.0029790.002979NaN-3.650107e-11-1.225091e-06
.....................
361POINT (-114.37625 62.45130)0.0144620.014462NaN1.836392e-101.269820e-06
362POINT (126.93479 37.56443)0.1844040.1844040.1528315.078100e-092.753794e-06
363POINT (-0.88235 41.63340)0.0184860.018486NaN-8.104343e-11-4.384152e-07
364POINT (8.99023 13.77668)0.9009090.900909NaN-2.038381e-08-2.262583e-06
365POINT (36.77500 55.69500)0.0131430.013143NaN1.533717e-101.166937e-06
\n", + "

366 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " geometry providentia nes obs \\\n", + "FID \n", + "0 POINT (109.62880 40.85170) 0.504757 0.504757 NaN \n", + "1 POINT (-28.02917 39.09109) 0.007563 0.007563 NaN \n", + "2 POINT (-149.88000 70.49950) 0.013467 0.013467 NaN \n", + "3 POINT (-97.48562 36.60518) 0.011034 0.011034 NaN \n", + "4 POINT (40.19450 -2.99600) 0.002979 0.002979 NaN \n", + ".. ... ... ... ... \n", + "361 POINT (-114.37625 62.45130) 0.014462 0.014462 NaN \n", + "362 POINT (126.93479 37.56443) 0.184404 0.184404 0.152831 \n", + "363 POINT (-0.88235 41.63340) 0.018486 0.018486 NaN \n", + "364 POINT (8.99023 13.77668) 0.900909 0.900909 NaN \n", + "365 POINT (36.77500 55.69500) 0.013143 0.013143 NaN \n", + "\n", + " absolute_difference relative_difference \n", + "FID \n", + "0 -6.329158e-09 -1.253901e-06 \n", + "1 3.951382e-11 5.224647e-07 \n", + "2 -6.039905e-11 -4.484809e-07 \n", + "3 2.067994e-10 1.874285e-06 \n", + "4 -3.650107e-11 -1.225091e-06 \n", + ".. ... ... \n", + "361 1.836392e-10 1.269820e-06 \n", + "362 5.078100e-09 2.753794e-06 \n", + "363 -8.104343e-11 -4.384152e-07 \n", + "364 -2.038381e-08 -2.262583e-06 \n", + "365 1.533717e-10 1.166937e-06 \n", + "\n", + "[366 rows x 6 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf_diff['relative_difference'] = (gdf_diff['nes'] - gdf_diff['providentia'])*100/gdf_diff['nes']\n", + "gdf_diff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig_4, ax_4 = plt.subplots(1, figsize=(20, 7))\n", + "\n", + "gdf_model.plot(ax=ax_4, column='od550du', cmap='viridis', vmin=0, vmax=3)\n", + "\n", + "gdf_earth.plot(ax=ax_4, facecolor=\"none\", edgecolor='white', lw=0.5)\n", + "\n", + "gdf_diff.plot(ax=ax_4, column='relative_difference', cmap='bwr', edgecolor='black', legend=True, lw=0.5, vmax = 7)\n", + "\n", + "ax_4.axhline(y=0, color='black')\n", + "\n", + "ax_4.margins(0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_diff['relative_difference'].min()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_diff['relative_difference'].max()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_diff.loc[gdf_diff['relative_difference'] == gdf_diff['relative_difference'].max()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.1.3. Scatter plots between interpolated values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(gdf_diff['providentia'], gdf_diff['nes'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.1.4. Scatter plots between interpolated values and observations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(gdf_diff['nes'], gdf_diff['obs'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(gdf_diff['providentia'], gdf_diff['obs'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6.2. Numeric comparison" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.2.1. Add model data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_eval = copy.deepcopy(gdf_model)\n", + "gdf_eval.rename(columns={'od550du': 'model'}, inplace=True)\n", + "gdf_eval" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.2.2. Add observations data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_eval = gdf_eval.sjoin(data_obs.shapefile.rename(columns={'od550aero': 'obs'}))\n", + "gdf_eval = gdf_eval.drop(columns=['index_right'])\n", + "gdf_eval" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.2.3. Add NES data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_eval = gdf_eval.sjoin(gdf_nes.rename(columns={'od550du': 'nes'}))\n", + "gdf_eval = gdf_eval.drop(columns=['index_right'])\n", + "gdf_eval" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.2.4. Add Providentia Interpolation data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_eval = gdf_eval.sjoin(gdf_prv_after.rename(columns={'od550aero': 'providentia'}))\n", + "gdf_eval = gdf_eval.drop(columns=['index_right'])\n", + "gdf_eval" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.2.5. Calculate relative difference between NES and Providentia" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gdf_eval['nes-prov'] = (gdf_eval['nes'] - gdf_eval['providentia'])*100 / gdf_eval['nes']\n", + "gdf_eval" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Difference is higher now because we have used spatial joins (nearest 1 neighbour vs nearest 4)\n", + "gdf_eval['nes-prov'].max()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Difference is higher now because we have used spatial joins (nearest 1 neighbour vs nearest 4)\n", + "gdf_eval['nes-prov'].min()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 6.2.6. Scatter plot between model and interpolated values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(gdf_eval['providentia'], gdf_eval['model'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(gdf_eval['nes'], gdf_eval['model'])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb b/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..59ad844841041dadb951c235ae440d8270d26228 --- /dev/null +++ b/tutorials/5.Geospatial/5.1.Create_Shapefiles.ipynb @@ -0,0 +1,1537 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to create shapefiles" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from nes import *\n", + "import datetime" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. From grids that have already been created" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Original path: /gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc\n", + "# Regular lat-lon grid from MONARCH\n", + "grid_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/franco_interp.nc'\n", + "grid = open_netcdf(path=grid_path, info=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rank 000: Loading sconcno2 var (1/1)\n", + "Rank 000: Loaded sconcno2 var ((25, 1, 115, 165))\n" + ] + } + ], + "source": [ + "grid.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "grid.to_shapefile(path='regular_shp', \n", + " time=datetime.datetime(2019, 1, 1, 10, 0), \n", + " lev=0, var_list=['sconcno2'])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
geometrysconcno2
FID
0POLYGON ((-11.85000 33.85000, -11.75000 33.850...0.000288
1POLYGON ((-11.75000 33.85000, -11.65000 33.850...0.000271
2POLYGON ((-11.65000 33.85000, -11.55000 33.850...0.000258
3POLYGON ((-11.55000 33.85000, -11.45000 33.850...0.000254
4POLYGON ((-11.45000 33.85000, -11.35000 33.850...0.000262
.........
18970POLYGON ((4.15000 45.25000, 4.25000 45.25000, ...0.003521
18971POLYGON ((4.25000 45.25000, 4.35000 45.25000, ...0.003924
18972POLYGON ((4.35000 45.25000, 4.45000 45.25000, ...0.004454
18973POLYGON ((4.45000 45.25000, 4.55000 45.25000, ...0.004619
18974POLYGON ((4.55000 45.25000, 4.65000 45.25000, ...0.004712
\n", + "

18975 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " geometry sconcno2\n", + "FID \n", + "0 POLYGON ((-11.85000 33.85000, -11.75000 33.850... 0.000288\n", + "1 POLYGON ((-11.75000 33.85000, -11.65000 33.850... 0.000271\n", + "2 POLYGON ((-11.65000 33.85000, -11.55000 33.850... 0.000258\n", + "3 POLYGON ((-11.55000 33.85000, -11.45000 33.850... 0.000254\n", + "4 POLYGON ((-11.45000 33.85000, -11.35000 33.850... 0.000262\n", + "... ... ...\n", + "18970 POLYGON ((4.15000 45.25000, 4.25000 45.25000, ... 0.003521\n", + "18971 POLYGON ((4.25000 45.25000, 4.35000 45.25000, ... 0.003924\n", + "18972 POLYGON ((4.35000 45.25000, 4.45000 45.25000, ... 0.004454\n", + "18973 POLYGON ((4.45000 45.25000, 4.55000 45.25000, ... 0.004619\n", + "18974 POLYGON ((4.55000 45.25000, 4.65000 45.25000, ... 0.004712\n", + "\n", + "[18975 rows x 2 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "grid.shapefile.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "grid.shapefile[:10].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. From grids created from scratch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Regular lat-lon grid" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "lat_orig = 41.1\n", + "lon_orig = 1.8\n", + "inc_lat = 0.1\n", + "inc_lon = 0.1\n", + "n_lat = 50\n", + "n_lon = 100\n", + "regular_grid = create_nes(comm=None, info=False, projection='regular',\n", + " lat_orig=lat_orig, lon_orig=lon_orig, inc_lat=inc_lat, inc_lon=inc_lon, \n", + " n_lat=n_lat, n_lon=n_lon)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geometry
FID
0POLYGON ((1.80000 41.10000, 1.90000 41.10000, ...
1POLYGON ((1.90000 41.10000, 2.00000 41.10000, ...
2POLYGON ((2.00000 41.10000, 2.10000 41.10000, ...
3POLYGON ((2.10000 41.10000, 2.20000 41.10000, ...
4POLYGON ((2.20000 41.10000, 2.30000 41.10000, ...
......
9995POLYGON ((11.30000 51.00000, 11.40000 51.00000...
9996POLYGON ((11.40000 51.00000, 11.50000 51.00000...
9997POLYGON ((11.50000 51.00000, 11.60000 51.00000...
9998POLYGON ((11.60000 51.00000, 11.70000 51.00000...
9999POLYGON ((11.70000 51.00000, 11.80000 51.00000...
\n", + "

10000 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " geometry\n", + "FID \n", + "0 POLYGON ((1.80000 41.10000, 1.90000 41.10000, ...\n", + "1 POLYGON ((1.90000 41.10000, 2.00000 41.10000, ...\n", + "2 POLYGON ((2.00000 41.10000, 2.10000 41.10000, ...\n", + "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", + "\n", + "[10000 rows x 1 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regular_grid.create_shapefile()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geometry
FID
0POLYGON ((1.80000 41.10000, 1.90000 41.10000, ...
1POLYGON ((1.90000 41.10000, 2.00000 41.10000, ...
2POLYGON ((2.00000 41.10000, 2.10000 41.10000, ...
3POLYGON ((2.10000 41.10000, 2.20000 41.10000, ...
4POLYGON ((2.20000 41.10000, 2.30000 41.10000, ...
......
9995POLYGON ((11.30000 51.00000, 11.40000 51.00000...
9996POLYGON ((11.40000 51.00000, 11.50000 51.00000...
9997POLYGON ((11.50000 51.00000, 11.60000 51.00000...
9998POLYGON ((11.60000 51.00000, 11.70000 51.00000...
9999POLYGON ((11.70000 51.00000, 11.80000 51.00000...
\n", + "

10000 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " geometry\n", + "FID \n", + "0 POLYGON ((1.80000 41.10000, 1.90000 41.10000, ...\n", + "1 POLYGON ((1.90000 41.10000, 2.00000 41.10000, ...\n", + "2 POLYGON ((2.00000 41.10000, 2.10000 41.10000, ...\n", + "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", + "\n", + "[10000 rows x 1 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regular_grid.shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "regular_grid.shapefile.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "regular_grid.shapefile[:10].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rotated grid" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "centre_lat = 51\n", + "centre_lon = 10\n", + "west_boundary = -35\n", + "south_boundary = -27\n", + "inc_rlat = 0.2\n", + "inc_rlon = 0.2\n", + "rotated_grid = create_nes(comm=None, info=False, projection='rotated',\n", + " centre_lat=centre_lat, centre_lon=centre_lon,\n", + " west_boundary=west_boundary, south_boundary=south_boundary,\n", + " inc_rlat=inc_rlat, inc_rlon=inc_rlon)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geometry
FID
0POLYGON ((-22.21497 16.22040, -22.05071 16.303...
1POLYGON ((-22.05071 16.30307, -21.88618 16.385...
2POLYGON ((-21.88618 16.38536, -21.72137 16.467...
3POLYGON ((-21.72137 16.46727, -21.55629 16.548...
4POLYGON ((-21.55629 16.54881, -21.39094 16.629...
......
95116POLYGON ((87.25127 59.16191, 87.43401 59.01025...
95117POLYGON ((87.43401 59.01025, 87.61561 58.85850...
95118POLYGON ((87.61561 58.85850, 87.79608 58.70663...
95119POLYGON ((87.79608 58.70663, 87.97545 58.55466...
95120POLYGON ((87.97545 58.55466, 88.15372 58.40259...
\n", + "

95121 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " geometry\n", + "FID \n", + "0 POLYGON ((-22.21497 16.22040, -22.05071 16.303...\n", + "1 POLYGON ((-22.05071 16.30307, -21.88618 16.385...\n", + "2 POLYGON ((-21.88618 16.38536, -21.72137 16.467...\n", + "3 POLYGON ((-21.72137 16.46727, -21.55629 16.548...\n", + "4 POLYGON ((-21.55629 16.54881, -21.39094 16.629...\n", + "... ...\n", + "95116 POLYGON ((87.25127 59.16191, 87.43401 59.01025...\n", + "95117 POLYGON ((87.43401 59.01025, 87.61561 58.85850...\n", + "95118 POLYGON ((87.61561 58.85850, 87.79608 58.70663...\n", + "95119 POLYGON ((87.79608 58.70663, 87.97545 58.55466...\n", + "95120 POLYGON ((87.97545 58.55466, 88.15372 58.40259...\n", + "\n", + "[95121 rows x 1 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rotated_grid.create_shapefile()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geometry
FID
0POLYGON ((-22.21497 16.22040, -22.05071 16.303...
1POLYGON ((-22.05071 16.30307, -21.88618 16.385...
2POLYGON ((-21.88618 16.38536, -21.72137 16.467...
3POLYGON ((-21.72137 16.46727, -21.55629 16.548...
4POLYGON ((-21.55629 16.54881, -21.39094 16.629...
......
95116POLYGON ((87.25127 59.16191, 87.43401 59.01025...
95117POLYGON ((87.43401 59.01025, 87.61561 58.85850...
95118POLYGON ((87.61561 58.85850, 87.79608 58.70663...
95119POLYGON ((87.79608 58.70663, 87.97545 58.55466...
95120POLYGON ((87.97545 58.55466, 88.15372 58.40259...
\n", + "

95121 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " geometry\n", + "FID \n", + "0 POLYGON ((-22.21497 16.22040, -22.05071 16.303...\n", + "1 POLYGON ((-22.05071 16.30307, -21.88618 16.385...\n", + "2 POLYGON ((-21.88618 16.38536, -21.72137 16.467...\n", + "3 POLYGON ((-21.72137 16.46727, -21.55629 16.548...\n", + "4 POLYGON ((-21.55629 16.54881, -21.39094 16.629...\n", + "... ...\n", + "95116 POLYGON ((87.25127 59.16191, 87.43401 59.01025...\n", + "95117 POLYGON ((87.43401 59.01025, 87.61561 58.85850...\n", + "95118 POLYGON ((87.61561 58.85850, 87.79608 58.70663...\n", + "95119 POLYGON ((87.79608 58.70663, 87.97545 58.55466...\n", + "95120 POLYGON ((87.97545 58.55466, 88.15372 58.40259...\n", + "\n", + "[95121 rows x 1 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rotated_grid.shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "rotated_grid.shapefile.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "rotated_grid.shapefile[:10].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LCC grid" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "lat_1 = 37\n", + "lat_2 = 43\n", + "lon_0 = -3\n", + "lat_0 = 40\n", + "nx = 100\n", + "ny = 200\n", + "inc_x = 4000\n", + "inc_y = 4000\n", + "x_0 = -807847.688\n", + "y_0 = -797137.125\n", + "lcc_grid = create_nes(comm=None, info=False, projection='lcc',\n", + " lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, \n", + " nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geometry
FID
0POLYGON ((-11.58393 32.47507, -11.54169 32.478...
1POLYGON ((-11.54169 32.47851, -11.49944 32.481...
2POLYGON ((-11.49944 32.48192, -11.45719 32.485...
3POLYGON ((-11.45719 32.48533, -11.41494 32.488...
4POLYGON ((-11.41494 32.48871, -11.37268 32.492...
......
19995POLYGON ((-8.03489 39.88059, -7.98792 39.88262...
19996POLYGON ((-7.98792 39.88262, -7.94094 39.88464...
19997POLYGON ((-7.94094 39.88464, -7.89397 39.88663...
19998POLYGON ((-7.89397 39.88663, -7.84698 39.88860...
19999POLYGON ((-7.84698 39.88860, -7.80000 39.89055...
\n", + "

20000 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " geometry\n", + "FID \n", + "0 POLYGON ((-11.58393 32.47507, -11.54169 32.478...\n", + "1 POLYGON ((-11.54169 32.47851, -11.49944 32.481...\n", + "2 POLYGON ((-11.49944 32.48192, -11.45719 32.485...\n", + "3 POLYGON ((-11.45719 32.48533, -11.41494 32.488...\n", + "4 POLYGON ((-11.41494 32.48871, -11.37268 32.492...\n", + "... ...\n", + "19995 POLYGON ((-8.03489 39.88059, -7.98792 39.88262...\n", + "19996 POLYGON ((-7.98792 39.88262, -7.94094 39.88464...\n", + "19997 POLYGON ((-7.94094 39.88464, -7.89397 39.88663...\n", + "19998 POLYGON ((-7.89397 39.88663, -7.84698 39.88860...\n", + "19999 POLYGON ((-7.84698 39.88860, -7.80000 39.89055...\n", + "\n", + "[20000 rows x 1 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lcc_grid.create_shapefile()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geometry
FID
0POLYGON ((-11.58393 32.47507, -11.54169 32.478...
1POLYGON ((-11.54169 32.47851, -11.49944 32.481...
2POLYGON ((-11.49944 32.48192, -11.45719 32.485...
3POLYGON ((-11.45719 32.48533, -11.41494 32.488...
4POLYGON ((-11.41494 32.48871, -11.37268 32.492...
......
19995POLYGON ((-8.03489 39.88059, -7.98792 39.88262...
19996POLYGON ((-7.98792 39.88262, -7.94094 39.88464...
19997POLYGON ((-7.94094 39.88464, -7.89397 39.88663...
19998POLYGON ((-7.89397 39.88663, -7.84698 39.88860...
19999POLYGON ((-7.84698 39.88860, -7.80000 39.89055...
\n", + "

20000 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " geometry\n", + "FID \n", + "0 POLYGON ((-11.58393 32.47507, -11.54169 32.478...\n", + "1 POLYGON ((-11.54169 32.47851, -11.49944 32.481...\n", + "2 POLYGON ((-11.49944 32.48192, -11.45719 32.485...\n", + "3 POLYGON ((-11.45719 32.48533, -11.41494 32.488...\n", + "4 POLYGON ((-11.41494 32.48871, -11.37268 32.492...\n", + "... ...\n", + "19995 POLYGON ((-8.03489 39.88059, -7.98792 39.88262...\n", + "19996 POLYGON ((-7.98792 39.88262, -7.94094 39.88464...\n", + "19997 POLYGON ((-7.94094 39.88464, -7.89397 39.88663...\n", + "19998 POLYGON ((-7.89397 39.88663, -7.84698 39.88860...\n", + "19999 POLYGON ((-7.84698 39.88860, -7.80000 39.89055...\n", + "\n", + "[20000 rows x 1 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lcc_grid.shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lcc_grid.shapefile.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "lcc_grid.shapefile[:10].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Mercator grid" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "lat_ts = -1.5\n", + "lon_0 = -18.0\n", + "nx = 100\n", + "ny = 50\n", + "inc_x = 50000\n", + "inc_y = 50000\n", + "x_0 = -126017.5\n", + "y_0 = -5407460.0\n", + "mercator_grid = create_nes(comm=None, info=False, projection='mercator',\n", + " lat_ts=lat_ts, lon_0=lon_0, nx=nx, ny=ny, \n", + " inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geometry
FID
0POLYGON ((-19.13242 -43.82824, -18.68311 -43.8...
1POLYGON ((-18.68311 -43.82824, -18.23380 -43.8...
2POLYGON ((-18.23380 -43.82824, -17.78449 -43.8...
3POLYGON ((-17.78449 -43.82824, -17.33518 -43.8...
4POLYGON ((-17.33518 -43.82824, -16.88587 -43.8...
......
4995POLYGON ((23.55209 -25.82217, 24.00140 -25.822...
4996POLYGON ((24.00140 -25.82217, 24.45071 -25.822...
4997POLYGON ((24.45071 -25.82217, 24.90002 -25.822...
4998POLYGON ((24.90002 -25.82217, 25.34933 -25.822...
4999POLYGON ((25.34933 -25.82217, 25.79864 -25.822...
\n", + "

5000 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " geometry\n", + "FID \n", + "0 POLYGON ((-19.13242 -43.82824, -18.68311 -43.8...\n", + "1 POLYGON ((-18.68311 -43.82824, -18.23380 -43.8...\n", + "2 POLYGON ((-18.23380 -43.82824, -17.78449 -43.8...\n", + "3 POLYGON ((-17.78449 -43.82824, -17.33518 -43.8...\n", + "4 POLYGON ((-17.33518 -43.82824, -16.88587 -43.8...\n", + "... ...\n", + "4995 POLYGON ((23.55209 -25.82217, 24.00140 -25.822...\n", + "4996 POLYGON ((24.00140 -25.82217, 24.45071 -25.822...\n", + "4997 POLYGON ((24.45071 -25.82217, 24.90002 -25.822...\n", + "4998 POLYGON ((24.90002 -25.82217, 25.34933 -25.822...\n", + "4999 POLYGON ((25.34933 -25.82217, 25.79864 -25.822...\n", + "\n", + "[5000 rows x 1 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mercator_grid.create_shapefile()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geometry
FID
0POLYGON ((-19.13242 -43.82824, -18.68311 -43.8...
1POLYGON ((-18.68311 -43.82824, -18.23380 -43.8...
2POLYGON ((-18.23380 -43.82824, -17.78449 -43.8...
3POLYGON ((-17.78449 -43.82824, -17.33518 -43.8...
4POLYGON ((-17.33518 -43.82824, -16.88587 -43.8...
......
4995POLYGON ((23.55209 -25.82217, 24.00140 -25.822...
4996POLYGON ((24.00140 -25.82217, 24.45071 -25.822...
4997POLYGON ((24.45071 -25.82217, 24.90002 -25.822...
4998POLYGON ((24.90002 -25.82217, 25.34933 -25.822...
4999POLYGON ((25.34933 -25.82217, 25.79864 -25.822...
\n", + "

5000 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " geometry\n", + "FID \n", + "0 POLYGON ((-19.13242 -43.82824, -18.68311 -43.8...\n", + "1 POLYGON ((-18.68311 -43.82824, -18.23380 -43.8...\n", + "2 POLYGON ((-18.23380 -43.82824, -17.78449 -43.8...\n", + "3 POLYGON ((-17.78449 -43.82824, -17.33518 -43.8...\n", + "4 POLYGON ((-17.33518 -43.82824, -16.88587 -43.8...\n", + "... ...\n", + "4995 POLYGON ((23.55209 -25.82217, 24.00140 -25.822...\n", + "4996 POLYGON ((24.00140 -25.82217, 24.45071 -25.822...\n", + "4997 POLYGON ((24.45071 -25.82217, 24.90002 -25.822...\n", + "4998 POLYGON ((24.90002 -25.82217, 25.34933 -25.822...\n", + "4999 POLYGON ((25.34933 -25.82217, 25.79864 -25.822...\n", + "\n", + "[5000 rows x 1 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mercator_grid.shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mercator_grid.shapefile.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAABDCAYAAABp2fnbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAH40lEQVR4nO3dX4xcZRnH8e8PV6nUG+O2QeyfWRNKSikhdNhqAmpMWxNJIDVBqqbGGKwGvTGSyp8bE0JCDNgrI1YCaSoBDUQr1tKkvSBGENytu/2LAXRbt1poATVppXbbx4s5S89uzzu7OzM7Z2f7+ySbzJz3vGeefTK7T95z5syjiMDMzKzIJWUHYGZmM5eLhJmZJblImJlZkouEmZkluUiYmVmSi4SZmSW1pEhIuktSSOrOnvdKGsh+BiWtTcz7gaSjuX0/nxu7R9Jrkv4i6XOtiNPMzKamq9kDSFoIrAaO5DbvB6oRMSLpo8CgpGcjYqTgEJsi4qFxx7waWAcsA64AdklaEhFnm43XzMwmr+kiAWwCNgLbRjdExKnc+Bxgqnfs3Qo8FRGngb9Jeg3oBV6sN6m7uzsqlcoUX8rM7OLW399/IiLmFY01VSQk3QIcjYhBSePHVgKPAYuB9YlVBMB3JH0V6AO+FxHvAB8D/pjbZzjbVlelUqGvr2/qv4iZ2UVM0uHU2IRFQtIu4PKCofuAe4E1RfMi4iVgmaSlwBZJOyLi3XG7/QS4n9pK437gYeDrgLhQ4WpE0gZgA8CiRYsm+nXqqty9van5ZmZlGXrw5mk57oRFIiJWFW2XtBzooXa9AWABsEdSb0Qcy80/JOkkcA211UL+2G/kjvcz4LfZ02FgYW7XBcA/EvFtBjYDVKtVfxGVmVkLNfzppojYFxHzI6ISERVq/9ivj4hjknokdQFIWgxcBQyNP0Z2UXvUWmoXvAF+A6yTdKmkHuBK4OVGYzUzs8a04sJ1kRuBuyWdAc4Bd0bECQBJjwKPREQf8ENJ11E7lTQEfBMgIg5I+iVwEBgBvu1PNpmZtV/LikS2mhh9vBXYmtjvjtzj9XWO9wDwQKviMzOzqfMd12ZmluQiYWZmSS4SZmaW5CJhZmZJLhJmZpbkImFmZkkuEmZmluQiYWZmSS4SZmaW5CJhZmZJLhJmZpbkImFmZkkuEmZmluQiYWZmSS4SZmaW1JIiIekuSSGpO3veK2kg+xmUtDYx7xe5/YYkDWTbK5L+mxt7pBVxmpnZ1DTddEjSQmA1cCS3eT9QjYiRrEXpoKRnI2IkPzcibs8d52Hg37nh1yPiumbjMzOzxrViJbEJ2EitBSkAEXEqVxDm5MeKSBLwReDJFsRjZmYt0lSRkHQLcDQiBgvGVko6AOwDvjV+FTHOTcAbEfFqbluPpD9Lel7STXVi2CCpT1Lf8ePHG/1VzMyswISnmyTtAi4vGLoPuBdYUzQvIl4ClklaCmyRtCMi3k28zJcYu4r4J7AoIt6StAL4taRlEfGfgtfZDGwGqFardVcsZmY2NRMWiYhYVbRd0nKgh9r1BoAFwB5JvRFxLDf/kKSTwDVAX8FxuoAvACtyc04Dp7PH/ZJeB5YUzTczs+nT8IXriNgHzB99LmmI2sXqE5J6gL9nF64XA1cBQ4lDrQJeiYjh3LHmAW9HxFlJHweuBP7aaKxmZtaY6bpP4kZqK4wB4FfAnRFxAkDSo5KquX3XceEF608BeyUNAk9Tu6bx9jTFamZmCU1/BHZURFRyj7cCWxP73THu+dcK9nkGeKZVsZmZWWN8x7WZmSW1bCUxGww9eHPZIZiZzSheSZiZWZIiZs+tBZKOA4en+WW6gRPT/BqdwrkYy/kYy/k4b6bnYnFEzCsamFVFoh0k9UVEdeI9Zz/nYiznYyzn47xOzoVPN5mZWZKLhJmZJblITN3msgOYQZyLsZyPsZyP8zo2F74mYWZmSV5JmJlZkovEJEi6TdIBSefy3zsl6QOSHpe0L2vT+pkSw2ybOvl4v6QtWT4OSbqnzDjbpU4+vpJrwTuQjc/qboupXGRj10p6MRvfJ2lOWXG2S533Rse0aPYd15Ozn9rXmf903PZvAETEcknzgR2SboiIc+0OsM1S+bgNuDTLx2XAQUlPRsRQuwNss8J8RMQTwBPw3lfrb4uIgfaH11aFuchaAvwcWB8Rg5I+ApwpIb52S/2tQIe0aHaRmISIOASQ9c3IuxrYne3zpqR/AVXg5bYG2GZ18hHA3OwfwgeB/wEXNIqaberkI298Y61ZqU4u1gB7R7tYRsRbbQ6tFJN8b8xoPt3UnEHgVkldWQ+NFcDCkmMq09PASWqdBY8AD/kr3t9zOxdBkahjCRCSdkraI2lj2QHNAJNq0Vw2ryQy9dq0RsS2xLTHgKXUOuYdBl4A6vXy7hgN5qMXOAtcAXwY+L2kXRHR8Q2jGszH6NyVwKmI2D8twbVZg7nootZn5gbgFLBbUn9E7J6mMNumwXxMukVz2VwkMqk2rRPMGQG+O/pc0gvAq62MqyyN5AP4MvBcRJwB3pT0B2qn3zq+SDSYj1FFjbU6VoO5GAaezzUf+x1wPdnp2k7W4P+OjmnR7NNNTZB0maS52ePVwEhEHCw5rDIdAT6rmrnAJ4BXSo6pVJIuoXZB/6myYynZTuDa7G+mC/g0cNH+rUiaJ+l92eMZ3aLZRWISJK2VNAx8EtguaWc2NB/YI+kQ8H1gfVkxtlOdfPwY+BC1T3T8CXg8IvaWFGbb1MkH1FrxDs+GU26TkcpFRLwD/Ija+2IA2BMR28uLtD3qvDc6pkWz77g2M7MkryTMzCzJRcLMzJJcJMzMLMlFwszMklwkzMwsyUXCzMySXCTMzCzJRcLMzJL+D5tmy+hioWQyAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mercator_grid.shapefile[:10].plot()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb b/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8ebefeef0f2536b9f35f135f3a3cdc3085aa81d7 --- /dev/null +++ b/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb @@ -0,0 +1,738 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to make spatial joins" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from nes import *\n", + "import geopandas as gpd\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "pd.options.mode.chained_assignment = None" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "shapefile_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/timezones_2021c/timezones_2021c.shp'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. From grids that have already been created" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc\n", + "# Rotated grid from MONARCH\n", + "original_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "grid = open_netcdf(original_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spatial join" + ] + }, + { + "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:2666: UserWarning: Shapefile does not exist. It will be created now.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "# Method can be centroid, nearest and intersection\n", + "grid.spatial_join(shapefile_path, method='centroid')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
geometrytzid
FID
0POLYGON ((-22.21497 16.22040, -22.05072 16.303...NaN
1POLYGON ((-22.05072 16.30307, -21.88617 16.385...NaN
2POLYGON ((-21.88617 16.38537, -21.72137 16.467...NaN
3POLYGON ((-21.72137 16.46728, -21.55630 16.548...NaN
4POLYGON ((-21.55630 16.54881, -21.39093 16.629...NaN
.........
95116POLYGON ((87.25128 59.16191, 87.43402 59.01026...Asia/Tomsk
95117POLYGON ((87.43402 59.01025, 87.61560 58.85850...Asia/Tomsk
95118POLYGON ((87.61560 58.85850, 87.79608 58.70663...Asia/Tomsk
95119POLYGON ((87.79608 58.70663, 87.97543 58.55466...Asia/Tomsk
95120POLYGON ((87.97543 58.55466, 88.15372 58.40260...Asia/Krasnoyarsk
\n", + "

95121 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " geometry tzid\n", + "FID \n", + "0 POLYGON ((-22.21497 16.22040, -22.05072 16.303... NaN\n", + "1 POLYGON ((-22.05072 16.30307, -21.88617 16.385... NaN\n", + "2 POLYGON ((-21.88617 16.38537, -21.72137 16.467... NaN\n", + "3 POLYGON ((-21.72137 16.46728, -21.55630 16.548... NaN\n", + "4 POLYGON ((-21.55630 16.54881, -21.39093 16.629... NaN\n", + "... ... ...\n", + "95116 POLYGON ((87.25128 59.16191, 87.43402 59.01026... Asia/Tomsk\n", + "95117 POLYGON ((87.43402 59.01025, 87.61560 58.85850... Asia/Tomsk\n", + "95118 POLYGON ((87.61560 58.85850, 87.79608 58.70663... Asia/Tomsk\n", + "95119 POLYGON ((87.79608 58.70663, 87.97543 58.55466... Asia/Tomsk\n", + "95120 POLYGON ((87.97543 58.55466, 88.15372 58.40260... Asia/Krasnoyarsk\n", + "\n", + "[95121 rows x 2 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.shapefile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add data of last timestep" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "grid.keep_vars('O3')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "grid.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "grid.shapefile['O3'] = grid.variables['O3']['data'][-1, -1, :].ravel()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + " \n", + "
geometrytzidO3
FID
0POLYGON ((-22.21497 16.22040, -22.05072 16.303...NaN0.025778
1POLYGON ((-22.05072 16.30307, -21.88617 16.385...NaN0.025487
2POLYGON ((-21.88617 16.38537, -21.72137 16.467...NaN0.025579
3POLYGON ((-21.72137 16.46728, -21.55630 16.548...NaN0.025752
4POLYGON ((-21.55630 16.54881, -21.39093 16.629...NaN0.025959
............
95116POLYGON ((87.25128 59.16191, 87.43402 59.01026...Asia/Tomsk0.032334
95117POLYGON ((87.43402 59.01025, 87.61560 58.85850...Asia/Tomsk0.032313
95118POLYGON ((87.61560 58.85850, 87.79608 58.70663...Asia/Tomsk0.032317
95119POLYGON ((87.79608 58.70663, 87.97543 58.55466...Asia/Tomsk0.032341
95120POLYGON ((87.97543 58.55466, 88.15372 58.40260...Asia/Krasnoyarsk0.032367
\n", + "

95121 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " geometry tzid \\\n", + "FID \n", + "0 POLYGON ((-22.21497 16.22040, -22.05072 16.303... NaN \n", + "1 POLYGON ((-22.05072 16.30307, -21.88617 16.385... NaN \n", + "2 POLYGON ((-21.88617 16.38537, -21.72137 16.467... NaN \n", + "3 POLYGON ((-21.72137 16.46728, -21.55630 16.548... NaN \n", + "4 POLYGON ((-21.55630 16.54881, -21.39093 16.629... NaN \n", + "... ... ... \n", + "95116 POLYGON ((87.25128 59.16191, 87.43402 59.01026... Asia/Tomsk \n", + "95117 POLYGON ((87.43402 59.01025, 87.61560 58.85850... Asia/Tomsk \n", + "95118 POLYGON ((87.61560 58.85850, 87.79608 58.70663... Asia/Tomsk \n", + "95119 POLYGON ((87.79608 58.70663, 87.97543 58.55466... Asia/Tomsk \n", + "95120 POLYGON ((87.97543 58.55466, 88.15372 58.40260... Asia/Krasnoyarsk \n", + "\n", + " O3 \n", + "FID \n", + "0 0.025778 \n", + "1 0.025487 \n", + "2 0.025579 \n", + "3 0.025752 \n", + "4 0.025959 \n", + "... ... \n", + "95116 0.032334 \n", + "95117 0.032313 \n", + "95118 0.032317 \n", + "95119 0.032341 \n", + "95120 0.032367 \n", + "\n", + "[95121 rows x 3 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.shapefile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot timezones in grid domain" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, figsize=(20, 7))\n", + "\n", + "gdf_grid = grid.shapefile\n", + "gdf_grid.plot(ax=ax, column='tzid', cmap='seismic', legend=True, legend_kwds={'ncol': 5})\n", + "leg = ax.get_legend()\n", + "leg.set_bbox_to_anchor((1.05, -0.05))\n", + "\n", + "ax.margins(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot ozone for available timezones in grid domain" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, figsize=(20, 7))\n", + "\n", + "gdf_grid = grid.shapefile.dropna()\n", + "gdf_grid.plot(ax=ax, column='O3', cmap='jet', legend=True)\n", + "\n", + "ax.margins(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot ozone in Spain, Portugal and France" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAGfCAYAAACtEPAuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOy9eYxl2X3f9/nd9e2v9qX3dXbOcDjNmSEpcrhIMiVRZBRRiiEokWTHtGATMOAYlpgAgRLBAKE4sIzEET0xhNhQZElGwIg2qA1yRIqShuRwOPvS09N79VLrq7e/u538cW9VvbvMTM+we7q6+3waF9XvvLuc+6re/Z7z+/3O7ydKKTQajUaj0dweGDe7AxqNRqPRaK4fWtg1Go1Go7mN0MKu0Wg0Gs1thBZ2jUaj0WhuI7SwazQajUZzG6GFXaPRaDSa2wjrWnYSkbNABwiBQCl1QkT+F+AnAQ94A/glpVTrRnVUo9FoNBrN2/NOZuyfUEq9Xyl1Inn9Z8ADSqkHgZPAl6577zQajUaj0bwj3rUpXin1p0qpIHn5FLDv+nRJo9FoNBrNu+WaTPGAAv5URBTwr5VST2be/zvA7xcdKCJfAL4AUK1WH7nnnnvebV81Go1Gcx353ve+t6qUmr3Z/bhRHBNR/et0rsvwJ0qpT1+n091QrlXYP6KUuiQic8CficirSqlvAojI/wAEwP9ddGAyCHgS4MSJE+rpp5++Dt3WaDQazQ+KiJy72X24kfSBv3+dzvVrMHOdTnXDuSZhV0pdSn4ui8hXgUeBb4rILwCfAT6ldNJ5jUaj0ewihGufvd5OvK2PXUSqIlLf+j/wo8CLIvJp4FeAzyp13awdGo1Go9FofgCuZTAzD3xVRLb2/12l1B+LyCnAJTbNAzyllPrlG9ZTjUaj0WjeAQLYN7sTN4G3FXal1GngoYL2YzekRxqNRqPRXAe0KV6j0Wg0Gs0tz504mNFoNBrNHYA2xWs0Go1GcxuhTfEajUaj0Whuee7EwYxGo9Fo7gC0KV6j0Wg0mtsIbYrXaDQajUZzy3MnDmY0Go1GcwegTfEajUaj0dxGaFO8RqPRaDSaW547cTCj0dy29ANoebCncrN7otHcfLQpXqPR7GouevDMAK4EO5sM4NnTcHUYb90A5kpw9b+82b3VaG4+Wtg1Gs2u5o868IWldNvxCF5fTbetDGEYQsl87/qm0Wh2D1rYNZpbhMNOvu2q5NsUcL4HdzVueJc0ml3PnShyd+I9azS3JEXC3hZo2ND20+3n+1rYNRptitdoNLuaA068jCUifmBNGTApsL8JngeWAgIIPLi6Cizc1O5qNJqbhBZ2jWYXESi47MMVH5aW4HIPLneTrQcf7MLZJVhdhbUQ1oCpu+E7Gd/7D08DD9yMO9Bodg936jr2O/GeNZpdxXIAf+s0XApgNYhn5ACNP4e2l973QYGrV9NtRUFy5zdvSFc1mluKO9UUrxPUaDQ3mSkTXhzGAh+NtS8U+Mjr5YITqHzT+db16p1Go7nV0DN2jeYmY0nsPz+dmZ1P1oHMUjarYPrhjR1XthUzNYVVCflPvmJFKZZVxIpSrEQRT5aruFIQSq/R3IZoU7xGo7lpHC0QdjfJHmcKzFZhogKNiZBPNAKcRoDRCIjqPrUJj082WvRqA0ZuHB6/rgx+tnM8d51fUxGHRS9w19wZ3KmmeC3sGs0u4NGqYihgGIpQYIiiUhdmDhusWXBF4AowMTFkZe5s6thJhIB2qs2XiGkR1lTaTn8xijhsaGHXaG4EIvJp4F8CJvBvlFJfzrwvyfs/DvSBX1RKPTP2vgk8DSwppT6TtP0a8PeAlWS3/14p9fW36ocWdo1mF9AsK/5yFKXaTjiwmlmffnlo5760GyhmMRiSPn5WFGsZ//uSSu+j0dzOvJem+ESU/xXwI8BF4Lsi8jWl1Mtju/0YcDzZHgN+K/m5xT8CXgGyETb/Qin1z6+1L1rYNZpdwNGCb2J2tg1wcWhzTEGQcZNPY7PEKNU2aSiI4h2bxOveL9Pmu/RoM9ze9jLBD3PP9boVjWbX8B6b4h8FTimlTgOIyO8BnwPGhf1zwL9TSingKRGZEJFFpdRlEdkH/ATwz4B//IN0RAu7RrMLOGrlA9ouRSAoFDvvhQgz2FzFp4nBBAZ1hP0Ii1gIoIjwCJkvnWaRK3jSJZIQgE2m+U0upa7zPvZqYddo3p4ZEXl67PWTSqknx17vBS6Mvb5Iejb+ZvvsBS4Dvwn8U6BecO0vish/Q2ym/++UUhtv1VEt7BrNLuBI8k00gFlDMWNGTJgBM6UQw/CwrAGGNSCyuuxVPc7KVcJknVsIRBziJbqpc84bFkPSC9o98qb4DXo34pY0mpvOdTbFryqlTrzN5bJkzW6F+4jIZ4BlpdT3ROTjmfd/C/j15Fy/DvyvwN95q45qYddodgF1Q/jFvX/DsrGKkp1nwQKTnKGT2vcQ9W1R38IqEOxRwTOkT5BrW6f/brut0exq3mNT/EVg/9jrfZAxj735Pp8HPisiPw6UgIaI/I5S6ueVUtspqUTk/wT+09t1RAu7RrNLmDADrmYEu1LwFTUK8kqpAsFu51qgPeaHF6CCQx2LK7xASAePNh6bjGhzjJ+iwsw7vg+NZrfwHgv7d4HjInIYWAL+NvBzmX2+RmxW/z1iM/2mUuoy8KVkI5mx/xOl1M8nrxeTfQB+Cnjx7TqihV2j2SUsUuM10q4zs2DWHRSkmvOIF8G7GExgUcdgmoh7KVHCw2GIQx+LNhNcweQqBmtIMiB4loP0WU6dc4ETWtg1mmtEKRWIyBeBPyFe7vbbSqmXROSXk/e/AnydeKnbKeLlbr90Daf+DRF5P7Ep/izw99/uAC3sGs0uYYFKri0iwgDqODSwKWNSw+JBJhEUIQE+PhYtPsFJQobbxwpwkBdQhKlz1rEZbS+JjbGp5q49YP263JdGczN5L0UuWV/+9UzbV8b+r4B/+Dbn+AvgL8Ze/9fvtB9a2DWam0iXAS26tOhRYcijVDDwgRERfVwi6nhEKFpAC/Ao0c0ExZnAvsxyNwW4TDBkLdVu0cgJu4Wb61v2OI3mVkMA+3qpXN7btWvRwq7R3ASe4RS/zR/jj82mDzDFJun6q1WaRJlcFRsMcRGiMZN8CFSp0ckE2tk0cgJtFlgGTNLZ6CzKjNhgk1fx2MTf9r0bLNNjSJcRPUb0qDHN3+KL7+j+NRrNjUMLu0ZzE2hQSYk6QJtBbr8+bUyaqSj4CGhSZiMTzV6mmhN2oQYINhVsalhUgAnqPIzCJEQIUChqlNmHzwCfLiN8WrzKJX4vdT6Lezg1Zu4HGGaW2Wk0uwURsPSMXaPRvBfM0cy1bdKnjkkwJvgKxSRlVjMiXqeER0CVEiUcbCyqjDhAhRCFR8gQnxYRy2wQbS+HG3EUm1bGMrDAEbpcSbV5GdN+3J824KTaBoXx9xrNzUcE7DuwNIIWdo3mJtCgiovNiJ1k8AqhSZ0hHiWqWJRRlPGYowoMEHrABhEDhHOpZW8Bn0RYyojzHppjor61Z9pSAMUi7jHILayL2ADmM+fz8BliU3rLex4RYGJgFSzX02g01w8t7BrNe4RHxFV8riTbAe6hg08fgw4G60CLPTxLuvLLR4EXaKXajhQEu3kFX+dhwdK4Qeb8AKOCJDVD2gXeeJ8a04CFSQmhhMKhQ5+pjLD/H3yHl1ihh0cPn4CIL/FRHmVv7qwazY3gupribyGu6ZZF5CzQIY7RCZRSJ0RkCvh94BDx2rqffbv8tRrNncDXfY9XooiLUcRFFf/cX13hOUmnbv0ojbF16/Gs+kjhbDZvSwwK1rf3Co7tEuWO7jPEBQTBpoZNFZMaZaYJcYiw8bEYYdLDZBNFF0WLiD4Ki32sZvzqJ/CZylynxYhLGZ9/u8AyoNHcKK5rVPwtxDu55U8opVbHXv8q8OdKqS+LyK8mr3/luvZOo7kF+d+8Ed8I05E2+5SRyxJtFXz9zIIIneLUsPmZ+AYk4XFlSpSwcLGwafIQATYDDAbAeSJgHy381FmmUfQZEA8y4oQ3DeZYzSytqxTk8tosmPE3C6wKWtg1mhvPDzKW+Rzw8eT//5Z4Qb0Wds0dz1HD4BtZN3Zkk51Q+wUz8RCf7I4doImdJKixsLFwMTmGyQihh9BGcQqFm4lYFxTzWASZAcNeHFTGJF+hnAj7DqVMoByAW9Dvdua6oIVdswsQigxetz3XKuwK+FMRUcC/TkrVzW/lr01qyc4VHSgiXwC+AHDgwIHr0GWN5p2jiPC5gs0scoOzRx838k+SQZT/qnURKsqiqVzKysUMXYywyn2my0CZbIYGq5HwMgbdWjl1rIOiQReVmbnfjZUq9KIQGpRZz5jOK5RoZ6q6uQVC7BQ8IuwCC0KbAT4RXTx6eHTwMDC5m5kkt/3WMTpwTvMecp3Lu90qXOstf0QpdSkR7z8TkVev9QLJIOBJgBMnTuTthxrNdUIxwuc8AWfxOUufFkNO4nEBj4sofI7xB1R46Ib246gRi1cTYTYyaPiC2zW5vzTPcGDTHtisdm2ei2xeqz2QOrZmKsLZdKCcoJhTwmis6puHMIXNWmbW3aREPyPiVdycsBeJuJUMeARwKSUL6arsYR+Cg8IiwAKqzFFniNBH0SXiKXr8Nv9P6nx3M8NLmaVwTsF1NRrN9eWahF0pdSn5uSwiXwUeBa5uVZ0RkUXIVJDQaG4QHmfweT35GYu4MGDE0zC2tCvkMfq8nDp2yOvXRdh9pbgQwkVPcXrV4MIAzifbyLMxnp/gSl+2F59N1mDj3nQGOVPAqqXzXnRDYQ/CxthMXCHMYXIhY06fKBD2Ci5kRNzGoYJLFRcHFwsHE5cFmnhYDDDpYXCJEifZxwayvSDuAzR4LpMz/iHKvJQpVrNY9BkVLKtrJ757jeY9Qc/YixGRKmAopTrJ/38U+J+Jy8/9AvDl5Ocf3siOajRbrPAlBvxVqq3CByCzXtskbb6GWNjfLUuh4mfWfc6FiitRfLWZAFafTc9CbYQok0RuowtNEzbHtC5UcMCE8xn9mxeDDZVunFAmFyQt7A3lsE9CKolUm5i4VDhAnT5Cmzi3/EUcnmUydewj1Hk2s4TuYVxWMz52v6DOu1e4hC6/36CwlKz2sWveY7SwFzIPfFVEtvb/XaXUH4vId4E/EJG/C5wHfubGdVOj2cHh3pywq8K65XkRGXLqXV932oBv+yolYasWNCxFO9jxO/vA/jpcyCRk22OnhR1gxsgL+0xkclQUdQxKkYERGkz0LO6iRjswWAuEZd9kfbrPM256dv5JLJ7LzIqPFPjEwwJx9grEeVTQ1i8Q7F6hsO9YEwQoY2Eqgxfx2SSiTUQHRZuI/1ZqueM1Gs27422FXSl1GvK2S6XUGvCpG9EpjeatcLkv16YKlltBG4dDmDQxcIgTtL77hM8lEQ6acCYjxHsr0M6I+FwtL+xTwKID0zZULXANmAzBFej5sOHD8gg8qfJiZsb/xEzANzKmbT/MB6J5BSJelKSmX2Am7xW0dTJtDgZgsJcqLiY2FgYGFiYLuAQIPvFiuSEKwaKDokdEhPCGwA9Hy7nfwn9FhbrowDrNdUZHxWs0twbjwi7UsFnAoIbwMQKEEUP6bBLQo0qLOH/SDiFtzEzFtGvlbks4E6aFcrKikLYw48CMBQ0DFmyo1WBkQEfBWgi2A5c9uBzCll5+zIG/ydRQifKTX7xAct/WbpB/YnUKhL1TIOxdQkzi2uw1rO067/ESNYMQwUcIECZo0EkS1AzYihvIz/knCQkyrTZ2Mpvf6VcTYS2z3wYRdR0xr7neaB+7RrM7iegTcpqANwg4jccpmhwGziNcBC4CcIEPEmSisG0W8ZP3txhyiiofeEd9CFBcxuf+UNHtG5g9IdgU+utCPRLsU7ASsF3l/GPvg29m4sSO5jO5MioQ8W6BUWGzQNjX/LwQtpUwLzY1TMqYWElu9hlMPGCwnUVO0cakNSa4BiERZiLZO8JbQpJZvySfBUxi0s7M5htYrGeC+RqYOUtAo0DY14nQi2E1muuDFnbNrucqJ4gyiy7K7CPK1BkvM0cnI+wms9ck7IqIDi02WWOTdVqscZZF/ogGS3gsJzHex87s51t/mA5E+9AR8DJi3C/wDLSHkF3ttVEg9mv5HDUMfeFYRWgIlBGsSDBGBnt6Dbqh0AqFlUB4NhJm5gKizMR9hoBRRkwnMOmM+cYjYAKLtYyhfBKLy7lldXlhr2HnhL1SYAetFVgVNgjpZvzunZHFum/QUdCOoBPBf1GFB5z88RpNIXrGrtHsTmzuZ5QRdmEWeCPVVsLJZCYHNaakBjVs5hjwOhf5vxiyxIhLDFlik/v5XuboER/jae5KtXmzfchEmG+k87wAsLIJ2cqsV7rkhH09gEMOTJhQMcAWIIK9JnSCWPhXPDjdFUqewzBj/562bDaybZisZER3qkCcJ7HpZAIMJzBzwl7HzB1bLRDsOhYz2FQwKWHgIjQwmaKCkVgBFBFKelRQ+IQMku1fAN/O9PnuzUX+spse4ey14IF8MjyN5s3RPnaNZvdh8wAj/r9UmypIdeowxGISi1kUTQJK9LGpcYSAZSI2CdikS4U1vpo61mISqGfOdwEywr45k1fxiy3IJrNbWofGNEy7MOFA1QbLAOXEgXKtEawMYdUDNQNnMzP+vRVYypjyFy04k5nhz4rBhkrb8yeVyYrkzeRZca4VPPEqSZ64OiZ1DGoY7MFgAhcThZGIc5OAKj4jfIb4DPCYoMQVVunBdk67Kou8nsk1f1BKnMkMKIoeRLaR91O08vF9Go0mgxZ2za7HJp2dzWAWmCfiCXxKDBF6DOlR58Xt0qGxvMxS5Xhm7briUu4aBmeAB1NtwnksJBUQttkYUncVXU+Yq8JMFRouVBuxv3wYwKYHKwOoT8CZEbFTOhHu4wG8nhkbzNuwlhH2GSsv7FNmXtgnCszaFZUWbFGxsO8XhyoGJQwshEkUVUoEKEaEDAiYYUCTFoo4zU0X2EONNzLifIIqFzOJa8yC4LeiyZJVEMxnFrQZRcJeEJOg0bwp2hSv0exOXD7KJF/B4igWRzFoMGKVb/MTqf2EHrCf8cCvVfrchYMaW9sdsYLJfsIx07vQpUaDbuKjF4QKVR5HCDAoE2AxIqLPS8dHPHW6xNUAriZ695ADz62Q4qgF5zNL6ScdyKRop1kQDF4vaMvWRm8aMBkZPGQqygi2ElBCY1jhbtukE8GGEtaVYuSOOGv2Yczc/VFMns8I9j4qOYmVgsFDrlQdFEhznA4335ZX58L9zCizD2xG8YCnG8VbJwSvDeuXoTuC3ij++eBe+MkHc6fU3GloYddo3lsU6k1EI43JHBU+n2pzmcFmEn8stamiR41JumMzSYXCZD9Bzh8/xxBwmMGgjuBwiGlalBnRpc8Gih4HeZ2XMrXL3PIjeFEpdb5Ggd+3UjC7LBdMYbOHNkxFzVY8XBfKlsK2FMpSNA3hAWXSimDVj0WuP3D4fmZm/yk75GkZbxQCVVRJrihxTZ6iZDZF+xVlCAgTEbcQqsmyujoRD+DioLABi4gyQz6JhSIiIiQipFZTPFY2GBoBIyNgICGvrs+z78V0jMNHLsFffT193V98XAu75s5FC7vmPcPj64Q8T8RrhLxGyOs0eQGDhXd1virHaPHdVFud2rawC0KZJiGT2MwR4jAiYsgQkxk6XAKuJBso9rDO+dT5auTD1ienO2T98VI0Xc0IrhD72e+tQ90G1wLDhGY15OHZgE0zYtmM6Ap0bYPn/PTI4PHA5sVRWqCLlssNAyMXpNdXeRPAsEDYs5Hz8W3stMXr3U1sDI5Tx0nM+kL8iTxEnZCIgAAPnyZdDrKMPyb7Ngt0M2VeDzCfS/Z7zFRcMdP9Ds38kMIveIq181VkNXcqOnhOo7lxDPnnhDyVagt5FoNPv6vzVTlGSz1DiWlKqk5ZWdQAw9yDxyYj1lC02eAoncyStylmc+dzCmqKl+iSfTKUplvAnvS9+bGvfb4KzQqUS9AswYdr0FWwHsHVADbK8Mq4nkbwfgOed9Pz3U6UF9iOkW9rF0yTW54B1XTbZkguwK+LYgqbOiZlDNzEdnGCGgpFSIhPSJmAeaCLR4CiTbw87Uom1/whSrS4nGpzUSlRB/ALBktxvfj048gjJLvuLzDzN+wVVOHVwq4BtCleo7nRWDycE/aA57DfpbAf8BfYP3gaY0woLro/zcnMrG5UYDgOMsVOAIxMxTIAk1XicglQx6VJCXuxwxOPKFRVMagqWhVF34b2WSu1iv6gCecyPvblgnXry0PJLY1bKRD2bKQ7xElxxr/FJVFIYHAvJhUMbCWIEuxQsadUYZCkd90kZBkFDFNe9ilM/IxgT+LQypgfugXi3L3GvPIjPLKDpSEeeWEPyI5GvAJhH2aeYiLxQGt5Oc4n0O9DrwejkUenc4l+P6DfDxgMAqanS/z0Tx/LnVOjuZXRwq55zzB5ONcW8uz2/5XqQfQqSrUwrLcvQ2AZdxFmM50Fp8A9nGrrsZmdrDLMJLcBMFhniuO4lLAwMQgJ6DNHjQ36dBjRYYQ5MeSb73skZbR2IjBQRGPm7aVBUpZ1bMdLHtgW+GNtVwaCDak7uaJi3/uWnDYFJsyI/bWQkqmwrQixIgIzpOs7rCnFGooO8CLgtCqkveyKozUPf8xnYKCYRVI+9BYBdYQo1eZhkK6d18bHJB0w18bPBfh1CwrxDBiSNSmE+DSxqCTr312EGgZVbEziqHlBYToj7D0DIkMRGArfiHAHwv7HawwU9KN4u9SF+fn0dY8f3+T11/9jqu2DH5zTwn47o2fsGs2NxUqEXdQcplrAjGqYQY8w/BFU9CqoJUCBzGHUr779CY37iGd+OzPZWvgyhjpONFbitMcG05SI8HGZwaGBSQmTA4QM8OngsYbPJWwqDDKJajzuSZUvDY0Re0zFUrgj4p4Be0uwNGYCDhTst+HCmMJGxG3nkjZHFPOO4m7bxzB8GuaQqtHDNbuIYdAyWozMNqHEsn+28TFOZvzgzY3JXIW4BQPOjymxQpjC5OrYDDpCmMRmNbViQGhiszHWpoAGNptjQ48gaduauQtgY7JAAxPBxsLCwMSgRIhK/sVhcQFzxDP3+N+IkIgmbRTxb7MPeBgsZVwmjmnzvUbavDEhBhfHJ/IC/YIn23CYj3Ds9d59USDNLYAWdo3mOqIUKjwH0UsQvgLhy0j4Mo2wgRmeA85t7xq4TRg3CKtlVHQZMRbf8hIiJTCOQ/TqdpuBxQL7CVGUEBwG2LQYMscmr+GzhM8SAFUO0+NM6pxVJhllhH0Wh1Zm5rnf9lkK00IxnxF2WxRHXJhyoGorHEsRWRH7G+vcW2rRd3r0rAEI/BBLXMr4p6vcw6VMitxZUZzPZporqOk+aQjnM+b8prK4KtmsclZK2AGauERAFYtSUuu9jskomceHKDwUNSKWGdAnoEdAG0Ufk9VMpb1DeLQybXVKdDIxDWVs+qnBQ4SLkSod6yWWgvHb7Vl5F0CvwO8+GOQbe70C34hGc4ujhV3zg6EUBGchfBWC5yF4GYKXIHgFSvsgOrm9qwDCA0B6Ni7GYVT0bPq04bNvKuwKj4g3CDmFIY9jhLMQdhD/CgRXOFZ5g7b1XOqYkB8lu0grDrVL42bDyYGJAj/xrNVnwnCYNxVTVkTVDGgsWjiTNm0jYtWEFQFVC3ghSF/35xptVuzVVJuRM2JDuSDhS1MCUOmvbc2IyAaZ1cYC9ASYFpgKStzrxEVdTAwEYQYHkzJ94mC6TRR+Kru+AgIeAk5mBhn3UeJqgThnKeFCRtjLODlhr2ClhB3i1LWpmvACdSRJoRPjm1AyFcMxC0rXjH3tamxs0+2+ubB7XsRgEDEcRgyHikHoMhzByIOhB4EX0ltbx/MiRqN4azQsPv/5+dw5NbsMHRWv0bwF/mUYvgjhaRh9G7wXwXsFVBeaH4Lgb9L7yyxwMt1mTOYXQatM5BhA9CzwY6mmEf+eIb9OxHm2PL614HGM7l+m9nN9cn/ZdmFSlLxwWmP72VQp00QY8D6mMAmJ8PDo0Z58g+enmgxhO4/dPdYiT+WyvuWXlHmhk4tQ9woGFHZB0F9FRts3V0eYBuYdj4+JRckIsYwAMX3cTo27WmVanrDmwaoSOnWXZ5yt2Xmcu/0JTJ7J1VzPPxacgqdjUZtbeGy+zS0YAJQL9qtgbheWcTCoYGIZ0FNCSYjXwgtEexV+KEg8ekQJGJ+EUR/CEIIAgsAmCGr0+z6jUcBoFBJFQ0zzr1KlcidnSmzMPpLqx95Jj6W/Tg8W77+/qoV9t6NN8RpNQtiF0YswfB68K9D9z4mgJwFnE0+A/430MVFBhpaoaKicjfYuI6qEMj8EOCjxUKyh1Ms52RXKRJna6oEzykmE621AOd1mZjKsAUSMKLOITR0LFyGu8jbNNB5rBKzgs4LDFMuZIjSOrJMNZQ/tEdmgsH6YF/bNMC/i3bG7cLBoUmICj4epUCLAZYhFH894jZFRo0+XIDGrTzn7eSlzviOdg5zspPsSeUW/j6Ia6Pk2ozBdbFFb/pFiYeFiUcLBxcbGokkVFxsjsePE13BpIERE+IT4BNTZoMYGPt52QF+lVOIsI0aw7SBZlw/F6XvHmH8Jrl5Jt9Xrm3Q6adeDkbmNQb8gkj8oyAMw1PltNbsTLeyaHXrfgou/AP4ZtgW49Cj0vpPez8/WUAP8jbzJK1zd0QiZANkPUgXrY0AfwstxwFx4itBKZ4Yryk9qkk8l5ttXshqOOzwXh5EDBnUs5jBwmOF9GHgoukSsAmcIaI2ncsdknn7m4gZLZIvBxJXX09H3bSu/hG41JPW5TAqo0OYhHGoElPGwGTBDmwXOASsougCUOQLJ/7c+ElcepM3+1DUCPLL56yKnIBp9lP+6F3mYg4LENRGx/FaxkgxysVDfy1Qi+kJEHN2/iMUQxYCILiF9hNXt+4gAjwcpc4p0Dt4jLHAm01bHTiR8h1JRfnw7/wdTqbOVe2ibatXJCXu1atDp7Ij0cFAk7PlB0agoO5Bmd6Fn7Jo7HnsP+KfTbf6Z/H7DN6CUaRudgoqBkgki+yC+NcXQLANAR6cAACAASURBVNH0yxCeQ9Qq0AJeIVa6nYe1BGfALcPY2nKlTqFUD5GdWafBYeL8ZjsDi9C8jDL3ITSBJkQ29sCnqapEcga4nGzPEXGUIOPrNZkkHGsLWE7yyO/4fxWXsLkvlWjFYxkbwR8bBHSsEUctFZdgNSNsI0LMkPtLywzNAW2zjychPSJm+bOdjxhoYzGfCeQL2CBns2cFMsI+YEBW2AdOPkNLe5AWJ1tBhMFhcSlj4GBgIpSB+5nCJ/6N9IgYYtJnRGdMVF0sXhgbeAA8QpUXMr7zokp8RW4Qs0CwrYL98vYOKFsRuURC+RAKSqUCF0A5LewoKLmK4WinPwNPz9hvSQTtY9fc4ThHwJyDcMzsHK6AtReCpbG2TbDvA2WCmgBfYNDm3P4Fus7rQDvZoLFuYajxQLEAzONxpHyCECFyBKXGDcoRKnoeMT8EgFIDlDqJqz4F0TJm6GMEGxjBEmxOQP/F1K1Y+z+CZ6YTzpRYoE964OIwz2BMUAVFhZlUpjpBMUWVFn0aVKng4mDgo2gh9PFpMaItPtbMBKcy/vwP2i1Wx3K39zCoUKU/Vg2mR4BNFX+szWMDk72ptfoRyxiZdeZt+my5BVyESUxm3REfnvQp2RG2HSK2j+0E1COHDooWEW3gIgYXJS289yIF9deNXG55r8CsUrR4rMgCUERR3QCjoK0g4J3SWGR8yVBUTJjeD0c6guuCUwLLgenGIfbsmcE0TUQsRAxc12Q4jFfqR5EiiiKsiRfo930CPyDwfQLfR334MYa+iR8YeIHgOkVuDI3m5qOFXZOm8jh0vrbzWlwo3Q/hIcAEvw/eRVibgNZfpw61gp/KVTTx7T244WvpRpnOXVbUFAoBFhGZR1Sd0P8dAv9/RKmToC4AilL4UcRPB8vh3p8Nusby6ngZG71DLbsbNvVtO4HgJMVlZijRSCRlREiHfXj0WWfI+vZ81OceXs1IWdFytEmc3JKyMo2UsMf9m0oJuwA1GmyyhkOJMlVcyjxGGYiw8DAZodjkGNBiA3/LEmLB2l3CcEx8TQUdVU8NO9YKggo3CgS7U9DWLzh2VNDm5VrS8ZMCie9dmE/yz9sYWBg0MHmAaYwkQU1cJKaDiyIiSJLeBqzdu87ssRJ9EyKJLQ3+Xzc53U8PDB5avYvnvpf+K7jrrrOcPJmO+F9cDLl8Ob1f9a6P0xvsnM8uGmFodhfaFK+54xleAOt94G5C4MFgGYZngRF0/yq9bynrc4ZSx8/VFh1YpZzpVGGAeQyMaZASSnyMsIrplxEusRVnHrqKSNIiHhkFljWnYFY3jHIBdDYRFpM4TGFTxUQQXGz2ErFCxArCMgF7WefV1LFlHspdo8mIbG/qonLxAa5yclVOTerAZQyEKlXKVLCoM8kkCiOpkR5Qx8ViSJikefWAWSa4xEpqTm1T2hH1hFkMLoxJaCgwpQxWx8S3C9SV0B/LSLeKokE609xGQYR+Z6zNBGpYWJgcprotziZCHcX9zBISbQfGucBeSNLTeCgUDjaKC6mguGn2s8yF1HXnsHJ56rFDupnP3SrnByO2m/9bcZyC6H4331ZyoTcWRuH7EEX54DvNLuMOVLk78JY1REMYvgSDV2Ht29B9Pt6CDai/H7z0mnL89YJz5NvK65e20qoDIJTo2DUs94fpmhNsWC4rZkgZg4f73wRO7eys7sPIzKclXM/9haqCfO7Y4w95AXsee2hRUh/EUIKoAcJ6bMY209YDgxMMeG3rSADMjN8YoEQ+YLBCl2xkvC0+W8ZiAWYwcHF4iCo2sVk/IMJnBos1ugySHOtdFnFZZyl1vkMZfz/EFdayVAu+ylOQkUSYQljNtE1j0B8TaQPhIC4KRRkTN1n3XsEiRJJBR5xNwCKiQ8iAkHUU4DHIWCIOYBFkku+YVOlkPtOgsDzstdVudwqcAFYlv59VYD4vFvYC376ryI7QRl5c8Eej2U1oYb/dCddg+H0YPgfd78PwWRi+xlZSUNY6pKaY/dfj2qLjD9TBKTAtUGMPT/8USNymDJeocphIpoFH6KPYpEebTcQaETZqqLF5mKVMHsYi5ZFVp8mmh5XgTK4vEecAQckMGIsoo4GyHayFx8BcB7kIxhVs4zvYUdq8amQqsgGozDI2AClos1gmlsodHNbZyxxNTEoIFmAaAx4RoS0ha/gMBTpEXMjI6SKKTqYQjVf4dSxaP54XrCJtqSWfmwE0MZjAYBGYwMJBxQlciChjUAe6hGwS0UUxwuYMHuO/j0OELGd87zNEiUk+FrzNZDaeziEf5dLvDAssANkqcABRgbAXVYM3JR/fb2SXS1As7K5r0ZywKbkmbsnEdkwW9xg4joVlmZimgWka3PfhgAgH1wHHATdviNHsNnTwnOa24so/gO5/hGCsXKk6BN7ZsZ3aUD4Kg7GlZmEPasdhOFYdOxpA5S7U8A3C0mG65SOsVvZgu7BRuUqr0kZJPOv0iehup2wBhU+VA6m2QEIi4yhGNDZ7liEYRyAaD24TMB4DEZS4REZIKC1Cc4iSUzBWyMUszSHRztomI1pFWECxPna2S4jah5KdGXnEEsJkMvDY4gLCIoKJyzQ2dQxcDnOQkACPET26eCyxzgLjtotpqXJS0uKxVvD43yhY8d0v2M8vEDYr6auDQ5UyZRxcQh5kEnN7Tj1kyBIeHn0GqERqTeMBXs6c80HqvJbxhNcKotHrmDlhb2ClZvshsfWgOybSHSKqiYd8iy5BzkUzKhD2LbE3EJxkHXwJOEADG8FKNt/c5LOOgy0BBkEs9J+Y4YfuahC6Ct+N8F1F/elJFu+dZaSEAQZDJYi6l01SSY1p9F7hhRfSA8Mnnwx48MGCfA2a3ct77GMXkU8D/5J4OPFvlFJfzrwvyfs/ThwZ9ItKqWfG3jeBp4ElpdRnkrYp4PeBQ8BZ4GeVUgWmyx20sN+uRJ20qAPYezPCDpQX08IOYC0Q2W061Xu5VDnOyeph/GqTZ8trDI0d8fksLULSpu0yjZSIA5Ro5toG5gGqibArmScy9uPbB1FyBN8cMDLW8I0VauIS8e3UsWb0EFm9U+a+lLADGGofoaRdBhZ78Ley4SnBYooqD6AkSNKsjhDa9NlDh9NEY/XJ1piil3IX9KhgpoRtgz4WUymJukh2rh971/dn2lqENAATmzINHCoY1Jnj/iRHe4TPCJs+93COaMxEX+IDnMmYwGtM0Mt8UDY+2SlMUQyYUyDsRSluqwXToTp2StgVME0FhaKEhY2JjcHM9gr4OAuegWKBSaKkEryPR5ku0/RSKwNcIpzEAhIm27R1F471/e3rBYB1+CM8uy/9iJs+XeZylL5js+ApaNv5ex0M9PI2zZuTiPK/An6E+Gv/XRH5mlLq5bHdfgw4nmyPAb+V/NziHxGvCW6Mtf0q8OdKqS+LyK8mr3/lrfqihf12pfwRaP9Ous3IzwgjWwhq99OuHeFKbS+na5O8UX2E/+CmpehHGDIkLZItJqhnzmcV+kQBDMpM4zAFVDjnDKnYJptmC1+GQMAeDEy+nzpW5dLPgJJ8XnVlVHNtRlQnNC0MtQdhBiMqUw9mADCDZazwAsJVNmt7Gcr3MvdxIOfLnaSSEXZYwOL0mLBHKBYxU0FrPYQj2LTwKWEwhUMDg8PsRxBC4pnpCJ8hC0lZ0xDoUCOgOlYwB6DBTErU48+pS94w3CdrpDcKhL3IZ120ptzBZAKTWlLr3UVYJKKZ7B0RDz4m8akQMiBgQECfAJeAVTqp6AWLdYZj92EiOJnPV1HLleYNCmb2qiDVTpF5Hid/r1JgqjXtfKMW9luQ93bG/ihwSil1GkBEfg/4HDAu7J8D/p1SSgFPiciEiCwqpS6LyD7gJ4B/BvzjzDEfT/7/b4G/QAv7HUr5I/m2qA3uwyD1OHn2cJmrpuI3Hv351G4VemTnmOdwmMmcbgmLezJtijXKzGEzBVTxENq4LLOHgJCtNe5Ds8FcJi3YEJ+sPIcMc/NEJV5Gdiwi08G0TrAdlq96lPo2Jd9DOMVWoF5UeoJIfSt9dGjmsqi6hTPRvNhNobZXxpvAFA6TCIvYsR87SY26F4tzdOjj0yFOsbPAiLVMdPc92DAmdl36NLBT4tannftMQjbI/s4iumSFPR4QxG02wiQGFULej0UJsFEYRDQJOYHFiJA+IV0CLEhq0u/QwOX5zD2cwOJiJgDRLUhSU6KUEvYQhYODN+YWGJFPtOMXLKCLCsReCmrBq2sUdsPSM/bbhuuncjMi8vTY6yeVUk+Ovd5LOl71IunZ+Jvts5c4i9ZvAv8UcvOleaXUZYBkADD3dh3Vwn674t4PzhOxTXIwgM4F6L8A1TqEO77Dhd4blNRPMRxLoNLnMhUOpUzMpxD2ZB66a5Sx+QBDJlmnzhIOFxCO80qS/iS+jllQzuQyfeaS7OxbdFnLCbvPlW1frDCPsEAkk9jyUWBApFaAi4TGRezgldSxwvvIpUwJuzkRN6Ps6nZwCgSlyoAmTSpUsHEwEDYY0KNMhxEbDOnRZ4Eaz2UC4xaJcpXLypQhI4pl6vRS0imUmaA7lmo1wKNGDW9MPAPWto3b8eI5mwqKxyjh4uEwwqSPQY+QOYb08RLhqzLPCxnLyH00eDkjjEWyVpRUpiiHvF1YHCYv9i6l1N9YQICJSTj2F+QVCHaUpNW1MHCSUrOueNxvRLgoXCJcCZnYA088bmI4gK0QW9E8a/DRZQsVQRhAGMHk5CIPNWfwlRBg4IcGfk371+9wVpVSJ97i/aJYyuxIsnAfEfkMsKyU+p6IfPzddnALLey3K2LARhnW/jjdXjoOvR2zs6A4ELictMdnQYpD2LycPEzrWCxis8g9DPDZQHEJn7NEBMxwORPxXWeOzTGfeohPk0VaY9HmQwJsFvHH9vPoYHAAwUGYQuESEhIxS8CpZKnbBshr1JXHeAraUM6iMBPD9lbj+fzHEl7OC3u4VUbWwGAWkxkUFg0eISLEZ8iIFhYdNhE2x8KsykxzLiM0RqE7Iv9VMwti2a0C14NDHYs2JWq4yaCijkEcbjciThXT5iCv45O+5z4PZUzZFdqZScSQAdlF/6OCyPOirHLZbHRQnC7WKLx/O3nPwMXFxWGSElUqmMk/A8HAIiJEJQlqQkKmCQm3E9R4OLR4kFXUWL8nzEs45p+m7/XYx/l/D6RrDjz6u3X+8hvp/j3x6TLPrac/k25Rph3N7ua9jYq/SDrf8z7IBBe9+T6fBz4rIj9ObFJriMjvKKV+Hrg6Zq5fhIJlOxm0sN/OTHw4L+xGPoH2oV6LkxMmhjKYi6rsGYZ4coXJSok+G/STmfc6h3ghs/a4VFBH3GSK7N9zhVpK2F2qCPdSZZEIE58RAzbpoQh4lvHqHQ77UeOBYRIhcgilxmboMgLjAERnx666CTIPaqz+e9QCuQ+RGqgyohSOP8BWJUJZQlhKIuUvscK+9EfHZcgtmWuRWrxPsal4VCB2PlZSWKVMnRIlbEqEHOEg8Vx1iE+fKoLPKiT56zxghgW6pNPo1jiS8zRXqdMei42I6FPGYjAm03HcQNqM3y2Q8aJMc1u58h2EGhZVTFxs7mECC9lOCVvG4xhzSeHbEI8ABx8XIcDHSz61Beq0M6vvyyzQysR3lDFTn3OAmxJ1IOebBzAlb4lRpYKKQwVNQy3stx7vrY/9u8BxETkMLAF/G/i5zD5fA76Y+N8fAzYTM/uXko1kxv5PElHfOuYXgC8nP//w7Tqihf12ZqLAz75Vmc1sgnMIVJPHz77MA/4VFq88hRXEZuln7vt7fOvuD6QOLQpQ8gqGw/2x2V+JOlUmKVFngbsZMaLNJh36tKjnouqjTMW0+LoT+TaZzj18lTmPRGdBamDsBZkEZiBsxfEFwVUkvIplKIjSFeuEB5AxoTBZwebexCO+xQUM9hONCYjPVbLC3mXIVqmSBibTWJQRHmICI0mFOsLHwqdBjygJKusCC0yxmRFsxd7c/ReVQjELBlllSrQzbRM4KWH3CahibEfQuwgWiiO4VBBcBDsJY5ymnIhzxIgAm4AyCo+QTQI2gTkqnMy4GD6Mw9nMROMopVwgXNHM3i6I23dwU8LuM0oGETt/FGGByd4ocLFEBYlsivwOg/zpNJptlFKBiHwR+BNiO8FvK6VeEpFfTt7/CvB14qVup4ijW3/pGk79ZeAPROTvAueBn3m7A7Sw3840H4uTyNjzSSGXMix74O2D9kXgOQAm3QkmS+kH8dEL/xkywr7BJlkX0VZi0yolJqlTxsZDUecIbTZZZ8A6q8xgs5rJPtbGyPnUR+SXX4UFntxQSlhyFJgBKYFEhFYdI5gDtQzb+ek/BsN0sBySXXwGVjRNaKZnihWm2EwJe8gEddYT0RIMyhjcRx0TSfq9tSRNsUEPn5AecaBhlMmaZ+LkzPb9AhN4UbGV6BrN3W6SOLdElTIlXBxMIg7i4uJhJXXel1hnjS4jetuCucYhVsb6U8JkPTOgiHDxMvcwKuivX2Cyv9Z7sArFPu3vVsQDgPQsPl/OVjIz9hJg1iMWZsGx4822oGrBB46AKfFmCFj5X41mt/Mer2NXSn2dWLzH274y9n8F/MO3OcdfEEe+b71eAz71Tvqhhf12xqrCxuNw8Vswnqr04LH0fqMWTB6CwdntpmbnDaajEmvGzoOwTZcZ5ilj0cDGIsRjQECddTr0ktmQhcFRVlIBTy3WIDOjusKQo5kud2kzOfbaZA6fKhV+iAghYojPGgPTpyznYGwpmGmeiEV9HCkysxaIR1RilDE+lHEZMI3DBAY1Imz2MIlFhT59OnRp0cOmxbmci2IWf+z+NwmYxWI4NkNdIcjM9aGNn/OyDwvM4gERJmUcmkmFdJcSZea5D8FDGAAdhCUWeDoVe1Aj4nzGF28xTTeVogWa2KmBxpCQEgbDMSHvFPRtUFgcJk9YIOy5AAhiYbdxcLCxcbCxmKBKlRImRuKHV9gYiVUpRIiICLibDgYjhCHCkI6MOFZ+BI8wHoAIGNY0V15IDzGnDXjmu+l+/Njxgu5qdj8685zmtmPm/Ymwj+EsksrTDuDuSQk7GLy/73O51qTJkDLrWJzjPDVez8xjDzLB+lhLQESDWTbG/OQBHk2m2BzzlXYYYTNJQJcS81hMEntrmwzZpM8qEUMMzrLARcZTiSqGOQEMzJUCA3VBgqaxKHglDTAWMcIa2B8nxMAjYESbIVUus0bsR28l1z3EZa6mTtcoEKMJHFYyZt9Jylwe+5wGRFQza+PXGXAQmxJVSpRxcChj0uQYsUc7DpQr00K2a83HwyWL+/H4fpLuJcZhNh1QCDgFwluQfZVawROxicVwbFbcJcBJct1t0c5cz0KIMJmljpMkqLEwcYAjHEj2isPiKsBhqkSMiBgSMGCeq7ikVzxUuYchp1N3YjJHN2Pun+FqEjGf3Lt4ucFIUM1PxcOC2flQm+I1twha2G939nwYnv3f021BZhZrN4BJmPsYGFFc4MU7w4nBt3m2tpbadYr35y5RLojutpmAzDr1OpNY2JQpYyLJw7tBixeS4Lg4QM6hymCsHnqEj8k+wrHZeSgtYAbGIvJD4wIKN71+WZ0HYwbMBZAGGDYRNqPGQ4TGFSJjAziDxxSXM35hKzODje8r76MtFZjPi7O0lbDpM4FLDZsyJvswCekT4eMxYECPRXwGxNkAPWIzdolV1JggDWjkPOpBJvNcTL6/VkFwn1t4D9lsAcIcNi5CGYMSsaHbwSBIotbjWPWAERX6hPTxGRGxjk+YST5jUiLIDDAnmKbPadIULanLDzrMguVzBuWUsNsFv9OgUiDsBUsABoN8m2aXo8u2vjXZHLYi8n7gK8RuqgD4B0qp77zVOTQ3gT0fTr+u7gXPhdoT0B3A1UuwehE6p+CudCDb1PppmE1XL6uyTDZ/QjafuYNFSIM57iLAoYfBKh4+igFnUiu8p9iTyiEekw+WE+Yhk4FN2I9KhF2YRGQPyppDoiGEXlytzluCsgXRi/E0Nop9475VY1z0TM5AygkABlfJZ27LC4MxdkcNXJq4TAIfoIGRhJp5DJlmwAZXiWB7lf8eKlzibOp8Nk0GY3nw45Km04zGLAUebao4qFQA2UZO7lRBVTorEdg4dW2FEi4RfT5AFRefEiMcBgxZYpYBET0C2oQMcHiYQVLEdWsoVuIgFzODihIuvbGBSJswF08xQOXiKYoqvBVhFAyczIIgu3jwsPM7s2hnHELgFwh7oGfstwda2N+WbA7b3wD+J6XUHyVr736DnbR3mt1C4yAs/iRcvQBXzkJvCWQJmhMwGJuhnjkF95Tjgi8JU1e/i9z1o0mBlxiD14E4qO7/Z+/NYyzJ0uu+372xvj1f7ll7VXdVL9X7Nj3T010amrYkiqIs2BYIg6Y3aCxaNCyZhAz6Dy8QZNCEDFmwBcu0TMCCAcuySNuCTEs2IA9HBDkbac5w2NMz03vtuWe+NdbrP25kZtyI29M9ZE9Pd1WcwkPli3wRL97LzHfift/5znGRrNJmiTltVsmKkbUR2+wgeft4OEtj2eIsHlmDT+q/lhltBCEOp3BYQOCjlIufjSG7ieAucBfiz8D0t8yd5WUo+cgLcqQ6Sy5Ogm4cdnHVJVJRLt2/i+CR0hiVwGHCGhcJCHBwioTxlAfIGDMiLdalOae4WakArNS8+0BYVpmiRoHgMjCIHcBjibgkSEw5wKNbmL52cWghaeOxhCRFMEcyBm6wzms4JcKb8zk6lePP+JO8WbmQ8S0/L1t1ootnEPuEjD6CrESpE/LaJZxNKGijersxjodHq7Cn8XFwCcjwWChEebob/xkCUhwoLinlQOC+cEieKdJUkaU5w9DlkdN9kkTnrscRqLqPUYOPOxpif2+8h4et4oTkB9QH8Rt8XDAN4I1SxroCFh+EmyV3xCwD7zJE3zje5KZjBvkK+3KTDkMGWYeFJKEXHOLJ7xLwbUTx4f1P+bPMSyXwPXZw6BkCultEteCTQ8Y1aosQDLhSEJNCMdYfwExRfPeYLhRPILI/MPZVri3SpD67L/NF8spVRsAKigyHZSQDckJWucSYOVOmTDhgxC57DA13NI+A/cpzTKmzgE0EZ1OLZ9aSco+QVTy6uAQ4xURBlwUEUxSHKHbxkWR819hXB7okpWOtkVcI26kNxYFLvfZsywIIrIEx5purELQJGJWqJCMyFtAOdDq3zcelxQIXkDjIwoO+jU+f8whSBAmCGI8xAxR63T9BMSVkgdZRwE+BjAeYYIYcfYc5u6XXEfqSb/yGef4PP9zhtdfMbfvfM0+rQYOPDz7otYzNw/YvAf9ECPHX0VLWz9h2FEJ8Hvg8wLlz52wPafCDxsXPwO/9A3ObV18VEi1oAxvvAkRD2IeXXt8lDL+Al598qrlnf4ptaX6ArtE1HNhyMpbpc7ckXhuR0mbIlD0Eki5DfHqscxWYk3NIzF0ivk3OzUoW9xK9ytotFqNad185lj5zXj6OALmBky2RuS+T45KLlJRD5qxxiwMo9fsjznCn1O8HGNBjq1QqT4joslzMr2scMEIQVHLJ673tCTktWnRoE+Lj4+CRcppLKGZkjEnYo8uEpCIg83mEmK8YzyHZICtPQAAOS2QlvUPGNvpPtlyJ2aPadnAYUx0Xk5aLEw89CtfBpY1LiMMCAT3cY3rPUQzxGRMdy+LmZOS0GJOhdfMRGTEtft84fofTOHyp8tqfY166gBHYV/HSUp5vg2F3E7frFytxXN/W9Ng/oWhU8XV8Dw/bnwH+slLqV4UQfw74H4Afre5fmOT/MsBzzz33wRpoDT5cXPx0fdt8BP1T0DsDsqXdN77uwbfGoE4MUnqdz8IVc6nSi1O2K785iySVDjj0CdlCskSXPgFtFIv4BDiM2WbKJlM2WcNhWllVabvZE4KK2UHQM3rGc3mrUKSffAhnzm1cJDinQK5qs5q8B+7zkG9DfhPyW+TZLofSXO17XK29TbbVaBe/5NyusUDbIPaUjAEt5qQMCOng0UbSYhWIyZgTM8FjC8n1Yu2pbwOWUPyucfyUeu5DvUMNwmJSIxkaxC7IkCyRly5OtJ7gPKD70l6RyH6eVXwEHjkuWVE9CciK15Awo80uYaGFPwq46XCOb1cmEh4m5N1KpSAgNER/E2tVI63VMKouc/p11WGz8m1VLhBzB4IAolIPPYrqx5/XdYgNPu5oSvHviZeweNgCfxrddwf4X4G/84M5xQZ/ZJx9Bs48BetX4czT+rZ0CX7uEqhSB2Vho97Q/PZduGJu6s1uUuWPAVssscISLkNm9Nkm5W1SNguttP7AX+Uch5VerrCI5fT8enXleY6UEzIWoocQLyFyEJlApDNEsgPJAKIbcLTSln3omaVmNzYV+wC+ZTTOpiAvk0ybNl3atGnTwyvEchExM1rMuFl0qObFbZmQw1LZWyJYQhrViQMiVirPGZHUyC23dJ9tRCboIVlEsoCgiyQA+ihmaN1rhGLMJRwybiKKC5Scd9iunInkKtuVlX3LZt1a26JjX+vbQiYlYp+RIXAM4rZZ9NpcEG3deGF5ziNiDxAERfys95AkmStcV+F50OslPPcchKEm/TCExx6zPGWDBh9DvC+xK6WsHrZCiG8B19AOOT8ClcZeg48PXB/+w/+vvv3UI3CzFBW8fxtW1uCwRLyvvgE/0TaUQ93JLYbtT9EZSdo7Izq3ruNF/zunfsxc5cx5FlURjEWWFVlkWXlmxZWDJMRnHZceLi0CXAQ7wA0Eb+FOesjZl82dvasQlUg6PwS5bgjo3OgtUIsgTs7H4120XOQELhMW2CCghYNbBJFknCJgxiEpI2ZAnxZbFY/zPhdqr6tN1yD2HEWbBcal4vAhE1YL89kjzJnUiD0jw+U0Dj0cQiQuDh08nkKv/Q+BPQSQsYVi62gwAI+nSTF/JxweM3LeBTsI1ozz0MesNkBsJuq2jPc6yboWZo+PKgAAIABJREFU5wGXtmHlm5DiMCg0Fz6SAI8uCzyK5EgSp5BIWjyIIDvuxUsyOgyAOYoImLPGQRGio89yDuzt/mNu3Th5zm7XZzT6BcvravCJQrNi/77x54G/KYRw0X8bn/9wTqnBR4YHXjSJHWDxwgmxey1YvgjbF2A+hpsH8Pq7uHtv8PjTCsbmvLGjniMTJ6Tl8yqSP2asRveLcaMyRiT0GdBisSDQDB9FhxaKO4iijOzwNBLTbCfz2shq79MZUoPYAKMcneByipRbOKwjWMKlzSKXSYmIGTNnjzk3GNE2hsa6nGNUGe/KLeQmLcXhqhUqaD/9MrELBB0uIEiLaXcHB8USGZIR2ixnC4838Cte+5JnSfg9Y5uwps3ZCLVrrIMFOT49olL5XNvimm2BjDlVI+DyijssLjdCPM6zUBjUCASSDiEhITna2CgjxycBtkmISJkxIuVSRecQcJY+v2Nsy3mJCd8wtkmuEVW0CTpE2GyxhB3z/GczW0WgwScSTY/9e6PsYauU+k3g2Q//lBp8ZHjgRfjir+ivh6fh3FOwdgmUhP07sPU2jF+F31qGd79o7uuvQ8VIpBWdYhyWy8wzhgzZKfVyhRKc4TyByGgxxWcTl99FsA2FolkBCct43DaoMeGwRkeZl9TX+07lL1mEoNbAfQHtsZZDPkam54i9TXQKonYsm7HI2GgBTPFZIi4pxCN2qK5aY4s5TGapTkgc+vRo0yLExQMWyRiwQs6YjH0S9ov+vJZl5xytsiPykrNaZukzK4u6HWvZ2ubTXjcaCmgfE7ss9OurrODg4uIWynWfhxmSF8QckeGhGOIwJSEl44CYiIB3KiOAQxyuV4yMLjNnUokCFrRRpUmDzGK8Iz5g5SAkpXohErTNj8IsUyRJhufdh6zQ4BOP+7BI0eAYz/wErF6Cc09CryiZv/r/wv/9X5uPs61eEosoad9jvK6/dvMlwniFZ2YxchaxdPAGy7tfJ4i2ef2z/yIj31xp+pwjLvmXx2zTZWCMZc25TVCxF0m8nYKOfHDOAMuQ9sD9LCRjiO5CdBuCCTimf5KTn6Ya49WizbjyuroM2S0Re8KEFovMSkQzZQ89+BbQo0tIQAefh1iHIgom5pA+d8mLFWRW3FwuMC6tNEXxrFUIlilHMSumCBZQJbJUFULUqJvU6GcOkQyR9AviHBLyIgpRUHSMxwYpPnOmpCRAzojciFF18Xi3QqobCEaVbZmFZG3LKWG5wJD0DOe6zOqyZ3ekryIkpmqiG3bqv8/TacJg0BD7JxpNKb7BfYfBmr6VcfE5EBJUifA2r9clx3u7+rfHX4D2eXD6LH5L0X9jgeDG67jjYhzuyh4k/8zYtTXzGFUq0tpsxQwmkZwxiF0xReaPIXOJk7aRETjTMexcgsnb6ArCm+Ctg6qI46L9muDPi2e19NPAUrYOS0Tj0yZkgT5nichRCCJyZkSskHDAHoeMOIRiMtvMEZ9Z9ASp5TlzS8leHdtGnECwahA7aobkApIWQrWRyoM8BG+IJr6jmfeYjISsVK1IWWG3MmqW8HAtHKaFb6y7UxJ8HGIjMKa+era/znrlQFlNe8wfXsYMI1RICW08xDKSEKF8pPIRqkVHPl5ExQiEEjzKXSJHt3wEKZKUV596HmfukWUuSeKSpg7zucdgUDuVBp8kNMTeoAHQ6hWiutIo2NYNuLQOYR86KyAdSKYwPgV7tzgKSAmWThMsmkp2DvdqCSPtg8OqRo3yyk2ogIA1/HyNbhbipVO8dBMvegs5BibmTDP5FYyVd3IHgj5kpbL05HqN2P24vroNmNBiA5cB0CLDIWaA5DKHjImIgYglJG9XPJnOYMbBRiT06TAvrS5HjGq0NbesNKvGNQKfnAU8HkHSRuAiAD/2cBIXmR8gsm1kvkfm9Y2RRYVP5AZG0p19VVwnY8+yyq6azwD0CNgpragnRFRn4OeW1XOKPJ7g1wExLoIxizwKhTROoWfvHdbIyVAkZCR4cQehtoucgRlOcofhDVPAmA1fIO2ZlZpF/zHckkd9Duzd/Mt87Wumt8NkYhuga9Dg44+G2BvU8eSPwdJZOPsEnH1Sl+r/0c/Da/8Yys5epys5ljs3YX0IcUmRvvMmnDFnzVu7b8E5B6k6hNkaYdQmTGChdwqXGzi8huBVwuhF/Emlt+9Zcsj8ZZh9p7LtHMxOyI1kX4fB5NsoMUC5p4tc9qtESOYkTBiT4PI2CToVTr+OPn22Kitv2x+ObyHLNn2D2CPmdGkTl0gwJqHPOj4tvCJ9PEDQ4SKKAxS7KO7isYJXEYx5yQs4kbkNMQR14iqgY1w3UJQDfcxwH/24enn7/cb99H6wQIDOfffwcPBwyAgBgUKRo/AQ+AyIyIrctoQEmDJnWuqZnyPkoDJhsI4ixvQdEOoA2Dq+BMpl/fxFbjv/mOpP0Lf4xTeGNPcABI14rkEDAH7yl+rbzj1fEHsJnVXYqUw5ti+axJ5MofUYIEAsQCII7oy4+pUx/v6JpW3eXmL8abMXnDp79aKsYxFIOfXytnJXoXMV/AH4HspPOBwuM/JfI3VGHBH3dfqFGO4IEbCG0ce3BL8oC+HZZqYDuvQZ0iYsxHI5A2IUW8BeMYL2OiHyeNRMr1CXyTA9TVPSWiE/l7L2rLpfbkKySGYEy+yiyybl17FfPLaDSx+HNhKHVa4gkFB4vY8IGXCOWZHeNiUmwOUuI8p97g599kqE3cElqVjtRpaKgC2nXVlU/IiOqY2TVXUEkNWrITqhz9QwBJ26jqQh9nsATSm+QYPvgfMv1Lepygew3wH/Iiy1IZMwGcPeTbjbh52TYBYB+N3HjV3ldAehzqDEiTgsdd5FIc1xLVmasRct8M5oe9zla+CkIA+Am6hhSu6YK7zE//GC1E/QYqFC7HPaLBRiOI2ILXTinCptO4qY9enToU1AD8GjrOAwQ3FIzg7r5BxWXOTaXGLEN41tHktEJTV+xG4RmVM2ahnXctMzWSf7quIbJE6+DFIhVQehAmTu4NNGqhEynyHyQ1B7jDsSHYWr2xSKDjcqz+pwincqqnTPGlPrGcQ+JSFAGMY6Mwux2/zzc8tHlaJtPlKkKBEiVOncLMTuqzkeQ1xcPHw8PC5eyXjxRUGrJWi1tCFNr1fbtUGDTwQaYm/wwXDu+ZOvpQMrV2D9cXjkj8PaY/rrxYvwrV+FX/1XzH2zh+rHExa3ueQMqb9ZeswMnPMoNdcGM7KHQiAWlhHqhnbNE98FOYWKSx3xuNbb97K0VpYLLEKtDr1jYtfRpguc4wKgkFDYqc54kG3SkoZeskbC60YnOcMyU2+dIR8YxK7IcVgmM6JaK854qoWSDjhPoAUEPiAQ2QIieQaRjSDbg2yXXj6H3Jxvz7yroKrl7UdRJbZ02QVOG4+RllEzW7UzsAbBeIxLVYJpUbAvIzboWuARkNMh4CwCv7h5pDLGUwsgtFO8AlSoEPkMVAIqQSQ+3jdAJBEkM4hnPL20yTPP/3fmk/5ccWtw7+E+ZLn78CU3+EOhtwY//T/D6kOw/ii4ltIowIbF2mBsma2OKys14eGMFqH3AnLuI6cxzuEu2foCuL8FpXhSx3kI0lsnSv3spvaEVyckK+KbFmI3++QAHnGxeltGMSChjccqPm0OmTBhCqScYsR2RbW/hGdMqo85qFF2bBHG2axgzRlyiccCPivAOhIfgdKGNekiqE3ItxDsI/LXEbFp3iLE8zAzqwSqWl2B4j0zN7kMSUpz5Q6bVIldMKP65grLa/KL1PQ2Hi08QhyWcUjIcIvivgMssYEiIycjK9oNLdokzEgLt7iUNlFp1A9g1V0m5avGtk60BHFJX6D6iK3K719qKdk3uDfRlOIbNHgfPPOT7/+Y4UUIhzAvrS633uJ44SoD6J6HtAML1yCbQ7QF83cJ3tmD2W8bh8uH18iruSbuMqSlOXgBuOcgOXHRE+lddKrwISBQYoNM9Yj5USZ0OSBkG0HMKm8ZkS4RF5HcrsS8eBZhXMgCs5IhTMycLh2SkghtymGlMK4psMMFPELcItU9wKPNCrCDYgvBbXwCkiK97Yg2RT4EVVJ0i7v1doXNpEbZQlP8Gh279Axilxzg4JGR4OIT0CZAcYGlwkFOa9c94FEWyEiJSYhIaJPhEhf/4ADo0uZGpfLwANcNsx2XLlQek1nL8169UiCrvyy2vrttBr5Bg3sHDbE3+PCx8Qy880UYnIf2CkgfhhIO3oTxdZh+B3begQsZqNKad1YPZmEyrY2pKcetf8w7i6h8ldQ7S+QuMfG67IQb3HKm3JEJsdDEN2aD6Dj8BFoWEoysDmY2e9h6klqHVVLGtGjj4+CT0cZHsFd43N8pHOTerRzrSeKi7y5KW+unMTRX2SLTKXYlH3yUxaQmm9Xr5coDcRboI1QHlM9CskTH8ZEqQqoJDiMuui4jsVUSDG6yUxldW+cMb1dIdNHShvAtHzlhJQhmTkq1vZ1YsuyUre8ug0rfPdfphXlJCdes2O8fNKr4Bg0+JJz+LLz5G7D1OhzNC7c/BaNSsGsWQfggzE5Wn0zfga75ISwObmEGjLXJXI+k9QqR12XmSsbenLFzmRvOUSE8Aw5wuMqtysqvz4CtkovcjBEBfaJS3/jQ4tQWF7asDh49+rQJGOAwZAOPMS47SG6zQMS8OpLGeSPUNmWzKFKX4mYtK0tlMXSpCcagGOM7IvZAiwrdJ/XXBFrkGIfAC5BMIDmAeAenHUFkjpV1zjiknmko5PCiMQWgOEBWEukSS8vBVp63BcH4BAaxTy1WvCl5fSLAOFaopwH8IaQPahth6YHwtJhTpdp4SQjwF2lwn6ApxTdo8CFh+WHIKh/Oql7KxlmFklEIKofgIsxeBW8VglPg9oj8x0ncMbHcIRGb5OJOUSwu25pWHPQ4Cvsw0bII13oMSsSuafMUG4QIQlI8JoTc4QHeATaPCavHo8RUU/PO1F8mA8OtXZHiskJaEsYlbNV05eWkNZSHwzJKDHD5FEL5CCUQeYJSfUQ6K/LmD4AbMJWQnFQFhFyAfL/yBPV5LpGJWqEgqHxMCBQdgiLXTiNmTnVpZPOyrwbj+Dh06CFQeHi4OLhI1oriu360KoJmH0av3WNyIlSRZqeYFDP4E/DPQvK6+aTiLMSlC5i8nm3foMG9hIbYG3z4OGUR0M0sfc3Eg/CsDpSRLU3srR64NyDfBLWJSGDqvUTsnMx0S27jcNlY5ebcpl4ur6+CXXIkDj36dGjRwqGNz1k8JFvFcSKWWGFWWmXnSCQRyphvr5fxc8sq2xau4rB4TOyCFg4DfM4ij4NVMqQCP98oAnK2EWzhp138qalDILsG6RuVJ1gwiJ18vwjDKY+C1c9fpPXVcqiovbVt/GNi13atilV6hDj4OHgIWkieYKl4VzJSMvpkXAAiImJiMjICHA5417j4WWaXuPT+9ukT8nXjHBQPQDUtUFo+0pyKirLpsd9fuA9Z7j58yQ1+4Fh8UFu6RsUHsxCQCrjwp2HxKiw+qv9P78DX/hSUXcbaL+v89BLcuEdc+WwO2WDCiTlOziYuDxyPnwlcHBJWuYCPi0MKjHHZQvBdymXwHo+wz9cNSnYrvuyKHJ81YyQtZguvEkpTL6m7CDxCHiryxDVpe7QLx/VNYAvYwuc8GScEneMjOTSOnwsLKUlLuIrTqW9zV02yz7ZrhC3SCFhAsIBQPYRqsZgJYnkFeZxzPicnpkdEwpSkIPicdeYkx3WGAcu8VTmFAcvsVxLehEVL4NIyiD2mbiCT2VoV0mIDKytVmnwKShVjcg3uaTQ99gYNPiQIAf/cf64Na1avwsqj+usqIovIa3pQG/P2Zll9dI0WmhKWcVkBOiyxxowDYvaZs82E7+DRLshHQ4+QmYSQWnq6deNUcBhSnpfPifBYL9p4AxzCIs70cQQjFDvk3MXnNklptakAyYtkmK59omqgL2IEy4VLnUYm6yN72MR+snz+DjhL4D0AzopeuePqExE5pBPIxpDu4+5Ncae7lNsc7ZXT4L1tvGuSy0wrtrRtQoOAY8tYXPIBE96cSpUjsvTwU1ts7QchdhnqNoRTFz82aHAvoCH2Bj8YfOovvv9jgmUIT8O8ZC4zertO7KMDnMXLCIbkhKTk5PQZsUpGBEUYS48+h5ie8SErjEsl9YhtHLrFfkfb6iXpk9WgJGARnwEBi4Q8jkMMHKK4S4gkqXiYQ4v8uHIAOXUyVpaLCVHzlQOtHDwh9lxsov9sUyAEsQK0wH0RlA9KaH2D6oK4DOkepDvAJogHYFLxlffOQFKagxfU3/+0XiXwrSl4AeWI2LmF2GMLGecWYpfFSUg8XEJcAlpcKfruPgIHqXw89VJR0FAIlZGHj8HqXwLZ1ZazsgunOiDauooh281K/X5CI55r0OCHgP5TJ8TuLUJwRnvQE+uxpPgu7s532Tm/SJngBNIgZ72tvhr0Kp7gAkWbFUackFnEiD5nCAiL/nCEx5wBHopbiOKxbZ4j4sumPTnnas/psE5aEgVq9zhTBa+sKvijLwSCIQ6LyPwsMl9A5hKRpchsChMB8+8gsgPgOsgIZqZ5C+GnYF7x8Rf1KoTuxZeIPd6unauf1r3yfUtpvCqyU+T0CBEIAlwCXLoE9NjAQRx3AXxSznKKvEhtS4lokaOYo0oXCpI5ael3wGUJkXy58nouQPgT9dfZ4P5EQ+wNGvwQsPA52LsBoxtwsAPswvB5mJ84ijmApx4mEWXyeofqgLttZOwomMWlTYtFQlq06LFBgstOkSZ3h5xLJCWyF8dq6/Lx6ytXZV1tVjNpYxzWyQotv2QBVIDPU0gCpAJBhJM7tNMBMr+LYB94Cy/uIme/YR4uewKyEtlmW3qsS5XINq+P7NVs5gBktUWSH6fgHcFLdoAeDl1curi0EGRc5DQu4JAhSYnJaBGSERXZbRN2WWOHESkwARJaRJVQnTYDplSU7CygKhcP1Uz21FKeV5ZRxQYN7jc0xN7gh4vwHGybamfSeu8zSJZJSj7yigN8Thv+6Ql7dDlFQAcfiUtEyCGrbCFLK72QTzPHVJY7LBrErpgUXu0nBJdS1wSomm+6i6CHz6PIIh1NC84C9Hz9HQTXges4uQel/YW6jJObxjXKJoyTZhUCoQphXKmlkVt68aqyypZ9vWJvPa6nEmSgZ72dNoh9UDPIJ/iziDOMEaX2QYjgTnEhlRc3l1c4rMStVlfxM6LaWF9sueCwXTBVWxWpxbNeqYbYG1TQiOcaNPiIMXyyvm0e134zw8hl7IPWu6/issQyl8iYIBiTc5eMb9OmRWaorlcNUgctequiKtYCcFmpEPsWHh0kXVwWcWjj0CLgSWCEYBvFHXx2yTFLxA6fJuPNkw0CdEb6iW48F5VyOqCwZIdaYmpxljSxi0AL5OQitC4AnjaoyTPI2uA9CukBZDuQHkJ3Dvnvm3pC91koZbw7gFTnUeKE2F32qf6QHEv/PKx8qqbkdPEMkd3ckt1mi26tNv/1+bTQeoMOgvZ76BQa3LdoSvENGvwQ0HsQ3I5WZh9h/zasBBCcA28ZpE9n6jLtnSXjBvAuincJ6XFYMojRf8PrBrEnbOLTRpVywG1iNkpEI1UPj1VC1ghEC4cEyQjJJhCiKPnU04IS+erht3rJ3pbVLlg0iB1xwIm/vYaSR6/F1daxLELeB+8VyAuhXDIFluFgoEmbG/rmr0NcspoNz4JjrqjJLRMBok6OkiFZ6XVKdoHVymPqfXfPYsXbKtTzuvfuI/EZsoaLh1P8c4EejxW6iQzI8MlocQ6d4j5DMaE1XkDmN9E58Lvg9KExlmtwn6Mh9gY/XAgJp/44zO+C44KaQrIJoQvqu5BoAZg3fYSs0j+1EYmszJ8DOJwmLY2WpdzCUau4LOKqNk4OTg49sYTM38XhOvAqyv0cifNbxrEEL1b07DMEq6hS8ljOdo3Oqv1ifbBW0fYOEGoVqYZItYjIIkSmIJ3ruNGdlULUdkvfghB2v2Yeq3etIPUS3EWT2OPN2tggWb2cbR0/Uz0yUX7ENkfELvHx6eADa5wq6FlfykQo7ZhHeiyMa+PgkhTJd4o5B6S8YbyvQzaY8hXjHNY4S16ZQBCiUmlRlgCcBvcvmhV7gwY/JPRXYefXzG3eIxB/6/iuP3od1BkQJwSprGNkJ+zjqCE+y4T5Mk7ew8snONkmbvoOygNUebZ8gcwxSUEo28q7riyXrJIZxH4HBx+IC4X7EkL5eOp5ZO4hc4VM58i0hZz2EekO4uiiRZjCQQDSPobwLa+r1K3COLcSpZJH4Ax1RvsRssOKSU3h6OFdAtnTM+/CJ4xP44QDIC3aAxMu4BSK/1mxZ8xe0eY46rsLPsfdijahzanCglZjToJAGt74KXntnbaNxSnRMk+/6bE3KKMh9gYNfkjoP1Hfpsx6qlAJvjpNLN4+3pZzHal6BKzi08UDgjxnKe/j5G8jizK3l7+CTL5gHC/nKqo0365V6Do29fgU8nrPm+r8uZJIVhBcReYdpHKQeYqbgkzfQqZHYrkpIjMNXRAvQloR5FnK4PjrMC9ddGR364/JLStvUfSkhaOjbt0F7RugYsA5Yd+gC9kI0n1IdyGYQvameSjxL5DVdAMLRnlez7D7lcfUzXPcCkErdFJeXJpqiEktxC5rnXdVfb8aYm/QoCH2Bh8DDCwCuqSyOnM36M3OkHqLeGmCH+3hR9eZLWco8fvHDxNqCZmbxKeEJeyEVm2NKzlFXnZTUzdABQh6SNaQeR+Z9/TKOxsjsk1kfhvhHEJmkp5UzyGyt0tbdlB0irCSI1RCWfSJ1eEumPfzQ62MVzF4K3oV7gyh+8qJQU0yh6gNk2WIdoC7+naqC+OKSc1yZVQum1KFzOon5tIxaFuxB5UwHmFRrkvLytsjNIg9IqGLxKFV3EJQHUIeR+AikAgEWXgVx/9zILogevp/lesWT4MG0KjiGzT4oWDwBFom7kDnAeg9Au2HgRSyXZ3Mld5muH8bcnOm21GfIRUngjAldoAlyivvjM3637YqK7gd4JQ2g1EDRC6R6RyR7NLyJDJ/jSN3OyWWyZ3KKlvVVfbINjWRuNyAvJxmZ8mfP1LsyyHIFRB9UOsQXIMsg3QO8QHkARx8E21xexO8OzCunNfSZ+q2vdKSsueuQHLr5H56UPtkkGld8e7WkvKmRQL9Cd07xPTo4ePjFfltbUIuch69Vlfk5AzZpYcgJyJjjmRMh0NEaebdVxfpqC+YT+n/yyB/uv6aGjSAphTfoMEPDW4XfvRb0Ll44nGebsMf/KL5uGRau/qWaVizdRfiDEqVSupcBzoglhFqBVQbMe8i82cR0TYivqnL5a1LkHzROJbynjePrbapKtdRtl6/ZfEtF4uxsh7INWABnB7kOaRp4dkuYTeEfA+OZvRb12C7YlITPmPej7frJjWJLcXM8icvhxxduOj9durEnkQIhgj6CLpobfsQeLZ4vRkQs8EyMbvkTEgZobhOxKoxYBhwiZuYpf4zCCaVbdX3LxWqJiVQ2PQGDRrc32iIvcHHA72HzPvuMrgbkN4+2Ta/BRWjNCeJS8QuEGodmZ1B5D1kKhDxBBltQeYjk+/CUV9dDorxshKUZWZcWXre8rSZQKdKxjCIwr89APFp7d+eK0hnkC7CqF/EpR71gq9AVPK3l8N6v9xWEXArb4QAwjWYlexh0z1qyEoD605f2/jK09DqAj7kEvKcdFmh3BFKjFFyn8SLCv+4k4AYwRrTSh694mliw8HvgOpYnC3a1h660zUmITLbZEHjNNfge+EjXrELIf4E8DfRS5C/o5T6xcr3RfH9HwOmwL+hlPpdocc7vog2a3CBf6CU+k+Kff5T4M9z4qn9Hymlfv17nUdD7A0+vmg9AaMSsUc3odsDFMgzoBZxRn1a4+eR423k4buI7B1YOQ+5ufImfPF4dA7QynLPtE4lNY1sAN2vrUCwhhISQR+Uj1A5pDkiuQnpbYTaBPkmzCoZ6d4r9Qx0dwFjOZvvaZvXvLTatgnCbD1kfxGiLfCXwRsWYrnzJwY1aQRRCPPTukSfHQKH4J2FkakRyB48SzlOVyfbVd+HuhLfrRn9HCAQRo59ZgnAUZaPIknHIPbEIsTLOSBnhGJMzhjFGHc6QSYHOrEun+gLpdV/t7Zvg/sEH1GPXQjhAH8L+OfRZhJfFUL8Q6XUq6WH/UngcnH7FPDfFv9HwI8opcZCCA/4TSHE/6WU+lKx399QSv31D3ouDbE3+Piic00LxIJHIHgYwkfgxi/B7v8DvAaAFD7S0QYmx4gPLb/Z1X4w4JyuEPt1cF1A6u/JJcgG4LyiySHbheQ2TqYgrSS6icchefvkfn6TI7uaY6i6KM3a8/bXYV66KMi2tf7AWylW2D1wBjC4pg1mkhkkB5AtwGHEcd8dwF+AuCTSGz4Cs3KFAU38FYh8gJInxC7zLcAcnxMWonUsHnIBYZH0pnEUk+vgEhDgEyDxGHIJB6ew4RW0GNFhvSjzJ7hKIXgYmBUjd2Pm6vfY5aLxjOu3LsPBl0pbBKz8hUZQ1+AHjReA15VSbwIIIf4e8GeAMrH/GeDvKqUU8CUhxIIQYkMpdRuO1aNecbNlHH8gNMTe4OOLtV/QtzK8/8W8r2LwL0D89sm22Y0qBxUjXiWIrhazeSEQamFaMgJHQPx14C3gLYSzDm5F5Fb1XAftu25gDs46ZKV9laU0LgBc8NbAWdTnlS+DOA1ZBPEIJrsw82B8B4ogGXpPw7ZZBmd4un78cMUk9vn7lOePTivrGHwvOUCoJZQov4+z4nsdHLo4tFG0GfJIQc4gyDnHGhMSMlJSIhwy2igypmRMmaGz6FO+bqzlJX3ikiFNJgKUMsNihLCs/p3qx5rSK3en+kvR4J7Hh1uKXxZClJ2hflkp9cul+6fBCEu4gV6N8z6POQ3cLlb8vwM8CPwtpVS5jPazQoh/EOVoAAAgAElEQVSfBr4G/JxStg+TEzTE3uCThfZj9W3emkns8bbuVas5OLpkT7wA6rN6NT+9DfEWLE0hqsR++s9jXChnd8DrQNmsJre4mwnLn5K7qveXQ20HK5YgOK0Pn8aQjmAq4CDDWGV3Xobdf1Y5r7MwK30eJBZbXNuonFdJmptvQSjNFkNa0hYIB9whIh4ivKcQhJD7CCVYyBdBjBBqhuAQxT7SGSFKEwgpC0z4hjEQMOU8d0t+/QGtWr88RVpO36xm5CJCL2RO9hXVCzZAScvPIhs1xH4/4sMl9m2l1HPv82xVVFfd7/kYpVQGPCWEWAD+NyHEY0qpb6LL9X+1eNxfBf5L4N/6XifaEHuDTxbaj1c2SHCWofUsiI5Wnccj2A5h57fhyEpWBtBNMNJOknrMazUKFtBl+bQkcMtuYTiliCUgBPfFulhu7w1T4S5WtWXuEfzL1P7236t/Xib22GZSYxHZOaG+6AiWwB/qCYRWX1cocgVpAvMWuA/olX28C2zjHsxg/nvGoYLWE+RFCwRAsVorxwtrTrvZ5IyZ4VT67jGq1iyx9d11KaZ8UWNJeHMsTdXc9rNu0OBDxQ3gbOn+GYxxkw/2GKXUvhDiC8CfAL6plDr+YxdC/PfAP3q/E2mIvcEnC92n4ex/pufcWw9D6wps/h/wjZ80H9f/rHk/j8A/B3EpFjW6qxeAxuMsim2xps3kxVCr5BV6xZvegeQ2qB0drjJ51dzPu2aK4AC8CrGntln2+iq0poLP5xCuF733ITgdcJdg8Zo2qInn2q0uDWCeai9+is+H9kOwVwqy6a7BsHKhkGQ1WYJUbXJjvbELqqdjYwvY+u5+5cJFAT4hUanvbif2AIdhEX8bIPFBpAgiwCmiXVu0WAYyRDFyl7tdkFcKXcQM8pm+iLPIGRrc4/hoVfFfBS4LIS6iy28/Cfyrlcf8Q3RZ/e+hy/QHSqnbQogVIClIvQX8KPBfAJR68AB/Fvjm+51IQ+wNPllwh3DuPza3da7WH2db9bprFWK/CX5Lj7k5y/r7eRfcghyTAx2iIhyYmb1deo9AUtqW36YOSy/eqfTi85FWr6elPnh2CG4f/FVwBzorXSzD4GUtlIsPYb4NcgH2X+O4hB+swWaFoLvr9XPwKyXpyRYMJUY1I4qhGvueh0alQpAi6ZOXZsml4awncejSQbDEGi7+cTxMG1EY2WQoMkIyVlhCMSdnQs6EPvt4pQoBQCQfIeM7pWdYx8PMsJf5izD9jrGtWbHfv1AfkSpeKZUKIX4W+CdoLf6vKKX+QAjxF4rv/23g19Gjbq+jx93+zWL3DeB/LPrsEvj7SqmjlfkvCSGeQl8Tvw38O+93Lg2xN/jko3NFl5tVSUiVWT7IxUCX8uUAcCCLwXVg+nVNlGyDe1f/X0ZuS2ar2LyqPX3cckCLspinSE8TtbembWBFR8ewRoeQTiHah8NxQd6lXn73ZbhV6bv3HzTvR5sgXTOKNbHM5rvVRLRcj8jFpdc9q75/LjILkc4DCNVBqBCRu3T8JZSaIkgRak4mJAM5IOcQxaiYY59xq1IyfxCf/VIFckCLFczxwAxRK6iIaiY79Z+zcixVF9vvQ4MGHzKK+fJfr2z726WvFfAXLft9A3j6PY75r32/5/GBib24kvgacFMp9ePFtn8P+Fl0Msb/qZT6K9/vCTRo8EeG9KH9IExe0wKp4IxeBQ+uQZZAvKdHvGZzmP2+ue/Ki1pYdYR0G/y+KZCrxqECNkMVvFMQHWiCd9Z17731ip7ESyL9PSVhf4a+8H5b79f+FOxWRXyB2TOvlvShTtAoaK3ApDz7b5uBL5YwwQIEi+D1dMKenIOUIHN9gfCWA9MRTPZgdkDw5JSg9S3jUPn5Z8lk6T1Vi+QF+R5V7QNLH7xqRzuxlPDrJrb1dD1F/X3JHcuezYr9voQSkN2Hy9fv5yX/+8C30H6aCCE+h57Je0IpFQkhVr/Xzg0a/EDR/5weC5tuwvRbHP+qpiWCjiyhK7b5dv8MzEv98viWaXIhu9qr3XsB8rAg7jmkA9i7UVwIHADfhnkHshL5DCyNXsdyDsE6zE7S54yo1SMIi8C2s6bbEMFQ9+W9Liyua2/8PIJ0DLkH+x5k+xwH0Tz4HERfK7FpCO9WCDlKa3nuouLMJ9gDdRrECbl6jKjPwJtr8ZQMQYAqufWkFoc6UfvICpFqtfheiFABwlmAzouAU1zECNj6Mtz8sg64SSf6/+f/LrgWsWSDewcNsb83hBBngD8F/DXgPyg2/wzwi0ppv0ullC3jskGDjwb+ii5Fl9E6A6MSQY+v10XvmUWoJofaztZd1YlhytHLz3hLi9DiHWjfgmlFw9K7pkfYyghPwaTkeBdZ3O2sY2qLJrGnW9oy1lvUVQkRAAMYvATxDGaHMNnWc/k7pdE5gHXPbCd0urqSYbwP1YuLOXh9SEoXRnNLSl7uGhc9AoVDn4yTCxHfSuwOHiE+IR4+Lj498qJvDxKBi0vC51AoclIyEjr08FhFMUUxRjCmvwkiL4kQo8dhs1KZCRVsVtwIn/5vGmJvcE/ig17L/FfAX8H867wCvCyE+GvomZOfV0p9tbqjEOLzwOcBzp0790c72wYN3gs9i4DONzPdSQ60e1uypVdy/mlQbWi/DLnQI2rzHZh6cHAbKJW0+1dhWl7FW8RyylIC9ocY1eLotna3K+sB1FwL3/zlYtY60PPnvUCruaMdmG3CdQ/mpVL46hOw8w3z+RxLiyBchWmJ6G0WtanloyBYNIl9Vt9PpA54CwgGuvdOC8nDpORkuCQ4pAzZ4BQJKQkxEREeKUEx254Wtw43yHmrdPSL7GKKAdtc5ejncnw9JCrkLG3Z9Jarp9QWktPgXoISkDofluOgLePg44n3JXYhxI8Dm0qp3xFC/LHKvkPgReB54O8LIS4V4oBjFM48vwzw3HPP/aEt8ho0+J7oPlrfJhwIT2t1udPVf+VeC5Jvw+QGjN+Fg21wZxiz5P5a/VhOxegl3dEK97L3e24hTCfUo2jBulb009KrxHgE8RhmO5o8d0vjaABnr8Hel8xjdVdN57jZDh8I/tAkdpu5TaR0V0K4emzOG0B+AVqnwHEBAZ4D3hLkU92zzg/wZwluvgmcVEve7D3HZkkY55JRvQzKbH4BFRm+tX+OqFl/K9kqV/5BWASDtqpIQ+z3PJQQZO6HVYu3VPc+pvggr/gl4CeEED+GngTtCyH+J/Sg/a8VRP4VIUQOLAOWWmODBj9gdK/A8GUtTksjmG3DLKqXpc+9DJO3T+5nU2hvmCvw2ObqVtVnA/4pXQKXXfA3tFK+WxLLzQ4gd2FzAmXF98rTsFeyg3UtjmhZ3SqVsDIqN92k5kdvU/A7fWitaYL3uuC2IH9Uq+HzRKvxlQ+rA63kF8VFxmwRRhXP9TWBsXKxPJ+vpEGkKSP0R0N5Wx2q0sBXjKgyck5ez/QQ1YF7mye/LIJxOuC09f+Nd3yDexTvS+xKqV8AfgGgWLH/vFLqp4rZvB8BviCEuIKWCW+/54EaNPhBQvpwuAWHpbnn0OLEpixLN3/dJPb59QqfOKBcaD8NdHVmehxBNIDdTZjvoh3uvqtDXcqxq4OKsQzUzWbSUT2sJbaouIPqmBqw9AggtKOcCHQp//xLkM51JWC+C54L+V2tDzg6td1VmJQ0CcuPAwfm6/aqZX2lg3HKwTlZnURDVS3MZQQERCVhXGwpa+YEpaeXCDw8VhF4SAIEPiLvE+QvIpRAKBBKoeI2TAKtG8hiSFI92ZDNCoOaCXTPw2f+ae05G9z7yGxOhPc4/ig1il8BfkUI8U10jeJfr5bhGzT4SNF/1CT2+Q1dCs9KRBtZCNPp6dny4DS4i+jClA+zXb3yH9/UWfCH5qgXK9cKUi+hdQomb57ct5W9bTmS4VqF2CfQvqRL4rINuDBbgJXn9Xz7ZEef27aCO6Xe/9Jp6FTT2ywXM+1Fk9j3LSN9rmU/uWAQu7DY8gaFfkDi4tHGo8U6q6TkuEgkgg7Q5zJa+56iiMiI8FklZwqMURzSBvKSEC/IHqI7qszz770E279tbpt7ZlhPM8d+X0IhyD6q3NaPEb4vYldKfQH4QvF1DPzUh39KDRr8ITG4Cjd+rbRBQeccHBYuZE5Lr7b7nwJCSDM95z3xYXuGNoMqsPAU7Je80qcVsgRTAHeEYMkk9vkt6uXyFNpntbjP6aL/DPuQ9nT5fnwX9t+Gfm6GtXQ+Ddcr+tRqeX5/EzqV50st5fmgYiu3aym2lefBpQ/+Ikqeg3YPJUOU45G7AePwCqlQpCIlETFIFx+XjIiUiJQ9JGscljQEOT3AFP4pVsgxJxv0JcAJsWfC0ue0rcjcLiQlPULW9NMb3D+4Dyf8Gtyz6D+q+6bhGQhWtVpaLEDcgcM7ML4NW1+DYdssIS8/Wz9Wte+dHoK/pEfdjpDYxHIt3cMN13VPV7ahE+rS+nwE0y3YPYT96xjpjavX4MbXzGOF6zArjXHZxHlepb+cJRAuaXX/ESJLzzloQTiE9lBfHAQtVC/Urm0yQ8kIDts4/jlt8BOPIL5DtPgAWevrx4dRSG55F0GcXIDkPESG2QbxKnPrM2KqQbcZ0uI0Z4rsMmFpr0hLZcFpm8SeNiv2+xEKQdqs2Bs0+ASj9xjsuZC9C0f+4evX4FYlt7xzFg5LISgzSxCLbTyqtaGJXXgQbugM9iPBXhLr1fbEg80pUFq1L1TG0qwdK8u2YNk8t8yigncq5+n4MHgQOuvgd7X5jd+C3md1SyIZw3wf1h1olVLngKx3HijNzvuPwNT0YReJa5jUCPJibv2kjeBQv5BwMYVqcyKqlYxEh8Saz1cxEMoMxbunfQbcAMIHtb5B+Prnkw4gvKSfQwht3/vlnzkxqEmn8OC/Def+pdq5Nri3kN2HNHf/veIG9y4GlyGvzJLboky9ynz79Ba0KqK3LNNBL3IABBBnkPThYFev/lVx8TBoa5I4wopljKsaujLdBL/S+88ts9dG1UCAVLD8qFa2OwEgtYnMg88UQrkdPQ4nczj4g9Lr7YBfKUXnl+vPlw/MOFq/7tQnovoFj0PPIHbBCCpE7lTU7QJJi5UifCvAwQN8fF4sUtsEihyXAB9VONLNESqCNxchGSPyGNiF1gz2KyE9/mMwKhkIBedh8x3zMauVBMAG9xyaHnuDBp90OAH0L8FByeltbukdi0LtHSzr+XJnoFXU030Y78Pebdi9BeO3zP3OXoODSrxy57T5fJFlVS0tf2btDRiVjp8nMHxEE7UM9ERZvgD+49oqd7IJ2R2YSDgoieXOPg6HFZc1WVn3JhNod81ydFK/4BFZG1XmY6/+WsS8asLj4jFEIZC0EYSkDBlwlhypJ/8An4A1BqTMSZiRMmWJbRLK1ZIL7Ff67i2uoPjK8X3lAHGMUOVeu0XrICuK/swy297MsTe4R9EQe4N7C4uPVoh2H4ZXwR8Ani6ZHwZwo69V5UcTmpc/BbdLQSzRWBuzlJPSbPaz/tC8P75JTSynMuidL8RyHc1Obg/mA03a+3dh5w2d8FbGlVfgboW0O0vmxcXIorpXlvnscAXGJWK3hcMkvh5xE4vAAOH0UBsdvdYWgMhQ+YCZ9zwZEzIxQjEmEj6HJfsKRYdvVmbSLxIwqtjUSMyxv4zIsrayrLacnjYIOn5CizhQVrr1uUVn0BD7PY9mxd6gwb2Alc/CwY42jtm/pdXeg65Zml64rL9fRnWVm6cwOA+HpfLt3BLEIsMiXW5Di9bcjh6di0ZaLDcpxHLvvIPRvz7/MrxTUt2nEQR9k9xtordWRc0+ugvdyoVEUlnBShdap/Xon9vR5+y2YHkFPak6BTVCTjzEOAbuFDdAXoRZqbKQPEkkbxiHdyofnIIJLh5pKY8+s2gIqh31lGktMy9HUrtMcTsVYrdccAnX/Fp6WozohLoiIv26sU2DexINsTdo8ElHsA6v/6a5rbMKB2+f3B9d14KqsogttZRzWysnxN5a0SNUG5/R4qws1cSd+DBPisCW4rHDJ2Hr66XjWM7T5nrWXTeJfWpJo/NKK1Hp6te2cQbdg/chl5B3ofNMcWGxC9MdWM5hszSHLx0YKMouciI/Vbde9fpQqmJLyznZ/NtCQsYlYk8tIayqQuxJSXQnCPSKPm/h8pBObVMeQrmoboDwLxaPzEG1oBPodkYeQRZB2oZkoPUPeQLswbiia2g/bjnzBg0++WiIvcG9hRWLZ7xbYdZsDr3TcFiaTZ8eQO+MLlm7R77yA/Avw8FNONiCO1vQX9JEeQT5dF3lHlTFcnd1dnpaFudZRH1hyY/ebWktwOpT2lteepDnWsDXfRjGO7C/DXu3tEbgTom01x+A/I3Kwaul6QzcZZ0/f4R0XnsYnvneyck2VAhZWkg7LJzmfFr4BHi02OACLgKJlsZ5zGjzMIoExRzFlA4RObsIpsAewWzAYPd3zINHT8GoVO1wz8LudfMxvbMQV0x33I5J7E0p/p5HM+7WoMG9gOWHqfe4j1bjArqndGZ5eAqGD0I8hek27NyB0TY6AqHAg6/AVqlfD3r/MrGPLGnFsvJBIoDeBuy9dfL9XMHq41rhLnx9PxzA4AqMtmCyB5NXIe1DXFrF91+Am6+Zx69eSBxsVVNS9fGrcBZNYk/GdWI/SouTbfCGSLePp84WOec6HCani8+zpCRkhThOEjLmEL3cnxEx5yxfJeMk8l1yjojfrbxVHqIUtpFLmzCuUkLPPyBBuy2M8fqkIfZ7HbrHfv/R3P33ihvc2/DasPaEXt16fe3xngXgPgC7N05CYR7qwjuVfO72sib5I8wtArOgYqsyug2DSt55nkDvHISLhS+8C2kXVF9fPBzcgfh1OKyozh+9BpvfqZzTqknsWUUbALoaUMbsEJa6pt98Uu1DCxCrEOYgeyBaKNFG9ddBpigZoZggZYCctkBNgSlwkwTfyGtLeIFdzNfiVQr0E+oCt9xSxJd0yEtCvEzahHGVjy2bcY+wXchU3qdmxd7gHkVD7A3uQZyG13795G5VlAZ2k5jBhknsttW4KCJMO6v/P3tvGiNZmp3nPd/dYt8j97XWrqre9226asQhxxqSJk0SkmX+sCRYkA2ZAL0QlgQD8i8bNARR8g/CNG0LoAxDokTAFm0NTXOGM8NuzvR0d3V1V3XtW1ZWZuUamRl7xN0+//giK+69cdk9A9HNrup4G4HqiLxx40Zkxvd+55z3vEdF/lYO8lmlou81obULtQZsBkxyAKbegtVA3b1TAyujPOEPEdOCRqIYvm/HtO8FveANC7ITMHVEqcX1hNITWGlYeEl5ptt7yminCzQD/d9Cx18K190xqyDDrWK6n8ULJCU0RjcbVkQsZ+OhYeAHWtM8ZMw01bAPgKvFOc2pPnf0LGiDaW25jPrdCFMRv15Sbn7qA1L/TP17kFpQpQ0jA8nq6LnHeOwwFs+NMcbjgKnTcCVA7P2GmmXeChB1NybKC/quGwkV4c2+qsjRA3od6JvQt6AdmJ9+/CXYCNjBJmPMZvQYiVlhGnYCtfB2zCAWLVDjTpYhXYETx1RbmqmB8CCZgdkTarPQ3YPuOtiTsBVw3KvMQz6sZldN4UMI6QEVCETf0hglVs1LhbrQNA4g0rpmxvSWG+SwB053Okl8TNIsopFUk9sw0bEwyTNo5EfToFd+Gan18UUPqXVJtkyMtATRBAa/x82I3W/xVdj7YfgCnvwHMPW1kesa4/HFuN1tjDEeF0ydHn2sMBsm9vqGEobl5iBVUS1gVhFKz0F9C/Y2VW/5NLAbIN/ZU6o1LQgj4jbX21ORvB3YPMQ54KUDPfCpovKZX3hFpdaFUL7vZh6Ki0qA5+wp57vUEhwEWucqz0AtogWIXlN9h1Fz9jhr2zIhYtdH09W6FyzEJ9DRSbGIRmpgAWtik6bKDM6gYt5HDhrXdBw69PHo0McibAKU5wQewxnwvpbCToYzJ1I/NqreNzJhYo8b0DOe8DbGlwRjYh/j8cNUQBmvGVCYVyrpxTy4EtpN2NsCDNi8xcOpbsuvwMpH4XNlK2Fi338w2t8Vx4+5GagNiF3TAQ9mnlGELwxwPDCLYB1R11I7gLUP4VgKnEDqO/k6NMN+7aRKYWK3Y9riROSr7fZHZ767o/3fwisj9Tkgj/AzIItQqqqatfCAPpaTxElVgAOgATTY5CROYENgc4QPCG8KnsOjTzNwzGj9XEaWJCm6qGVqSNRSG53lPqIziNtIjQfBfOkgYayKH2OMxwKTp2HmFWjsQO0+NFfAWITLEbHcsdPhNrFWTP062irXbUC1rFLeh+h3ITujet3NnBLsGTlwUtDYVdF/6wZsRVLtJ8/CRsS2Njs9VM8DdGPIyAqnvelsjR7jB8hPMyFTheIxoK/KDJqu6u68qjoDenXo7qHfMKA9EBiCKgVUwzV2jRIQtta1SIf60HXaEBngEu1b7zNKvjJ2Ec4RHFYjxWh73Ygw7pDYha7c/vRUfIvhGI85xqr4McZ4PJApws6qUp8fwokhyFTEDnZ/TRnHBGegHxJkIqvS+ckSFKrQaUCnDQc1WGmo1H7QMvXUWVgJiOV6dUjlw453MREzyTIE09ONGiN2bPrgayt0pR1IlaFSVpkEQwPNhVwWsifURDh7D8SGyiK0PuRhz5mch/Vo3T2qOO+ClgV/+PlpIwp7sEiG4nONJiqtHzh1hNh7jGoRlJxOIMgiyAIZfJED2UeKJD4GvsiT8M8pAaTvKbOgYglMH2RPif00HXxr4ErXGNxiMhtjjPEYYkzsYzyemDkTJvbGg9Fjon7iyRxMPqFU5tKAvgNtC9oVeFADBq1oz74Bt74/fJ6ZgETEyS6ub7w0Eyb2XoxYTh/UxnULctNKLDd9RJG58FUkaqVgagp6O8AG9DegdBL2A61yiSehP6i7H9ajRdSoJyZDEVd318oRYh8dqGJFlhJBnTQJklhYmJgYeOhUeRLQkIP/MlhAC0kPnw77FLnHs6FzTWpluqxw2IQ+7RkU174XvoDk69APTHMzyqNWs964ve3LhrF4bowxHifMnIZrfzy839xUUbeuQ3FuIFYrwOzrUD+A7XXYrqmI71bANKU8PdpvHt0QOH2YmBlE7QPYMT7vmUDrWn4CEhk4/poicQn0e0BWpc3bu3Cwqm6lGegEzl15CXqR9LsVUcZ1aqPzU/zI1132lDjPCWw2nLi+8YKaPa8VQcujuSVMXmP4Ah46FfpU6OHRxaaL5D5BX3uPY3h0CZvrLPIAj+F7ERwbefnoTHbbiBHGRTUFcUK5cY39S4kxsY8xxuOC+edg6RVVW3VcaDagZ8DNC7A2iJQXTo4awiQjQ1b2NyGVAjsQpcaRX25ySOxCqPTwzNPKFU6YSixnFKG4rMbC7uyoGxnoBSLJ/BuK1INIVMPE7sRE+lEtQGcH8tGyQjAa19Rc+vIJlQUwU2AYkMqB+aaaD++3wT2AdlZ9foN+dS1Tw5kPb1zavMpapJ89gU4/YDfbxRwxxFPp9iCxj2YDRMQOz9bjhr5EFI3SVmQfVMePVfFjfEkwJvYxHk+UjsKF98KPLb8Rvr8dMwwm6lgmUT3gG4F2slYd0kVlaJMqKIvTZAFKOuzXYHtDDVxJuGFiPXUWtlYi1zkNGwHVfTOGfLTIZqMXZ5wzIDbdUiK+ZAkmKqBJ0AVo3iBjcRLcfXBq4O/C5Dy0A50A4hi0Ij7z4kT4cro1opNtEjH18gzmZxJ79DyC0XS5iCxTtm4jtRToWeRAGCcTefTiy6oLQujKN8A3Qbig+aqMIWvwvZ9RjnNeWw2IefNfQSFmvsAYjwXGqfgxxnicMB/Ty25GUuj9LszMwV5gGMxh9KxpUJyG/CRUFiA9C90+7B3AnSZ0DwiJsV44B5fDvudMzsDBZ6TnsxEBX60GpehBA/VcoqiG1CQKUMoosZzwVEo9nYTFsup1FwNVe/oUdK4NjeSsJ6EbyVCISL+7EzPfPRINC99GyCmkGEboSVpEVX5pdIJna6IzCQg0DFIYpPAQg7GvSXwsXHIUSQ8q8D4eHj2KdHkWBweHPo7RJ/H6EYJ9hkcOfIq774evW58FL6Ct6LwKGxHTGmcsqHucMR4CM8YYjxNKM5AuqKlth4i2O2kaTB2D3JRqibI1aAEcgfvrcPsB8ABeL8CHEbHWQl4p4w9hx6SH8xNhYm/HzHNPDiLWVA6KU5Avw/KxYTrfboNMQCYF3gH0D5SGrHAEGgH1fOIFcPfCxi16pO4eR2Ijten90RR2TKO+Jgt4ogGk0SiRBkocA5J4JHCwKFPBp0p/kGDfoUOVBfr0H57RYpo99lAy/S4GEpOV0GuVeI79gH88gEYaPxDde6PetCAiLXBxvvNjv/gxHkOMiX2Mxxfzp+HGu5Crqla1REFNbGv3YGcX1tZUi/S7gUhbE1CwwkTtxvRNl2fDxB5nBxucupafUJa1J15X6XJfQq+rprsl8tBqQGtg3pKdDav4i6+AiNSerRKhtrheHGlHpqD1t2OGp0cYUctA4qgqIYgMSAvsIuhnlVbB7kG/TbN/gkZKQ+IAkg00/inPhE51hgyXGJYWmmgcGeldD0dTLj0sNGTQrz5uYxEldi1GzR/tAhBxxB6TRRnjscK4j32MMR5V+D6sXIXVa7B6He5dA3cKtvJwcxfYBes69JyIeUv0PBIm52DtzvCxVoyvfCaSL+92YeGMqrXrFji+UpFnlqG2ocxy1nYgX1WmNYc49Vp4gwDK4jZI7I0DKIQPQY+QVm9nVAV/+OaECdYEmCVVe0eobAW+2lhUngJZB78Gsg03HNgLqNcrz0D9YvjU8skBqQ8umVGTHDOyadiLSYn6MWNgDNI4gQ1BmOQHb2lkWEyMG51IgMgpkx2RAkpQfUO1FBpplaVJzYw+b4zHBuMa+xhjPMroNOE/fCr82Olz0Aimy/swuQCb94eP9WLEalHtTKgAACAASURBVMVqmNh3NtXQleqsImYrBVZFudvt7cODDVi9C7N5NUP9EC+/ARsrkXNPhok9bhiNGZGY7e+MEvuhSCw5AVZZWdWm04Cv0uh+T/nfZwZ1dx6A8wASx6EXmOhmPA98Ej53IiLWcyIbD8Cw9dAgtjSjPgFRyu6hYZDADUTtccRukR9U3lNoJNHIUuIUarnSkGgY7GJRReIi8XBSBq2FJ5GajS+6+FqHwpaFZjfBG3zGpgU/eWPk9cYY43HDmNjHeDyQLSji3Q0QjBOTZq1Mhol9fxhpymQKpubw8lP4T53F6fm4+236aztM+Bbizgoc1n/PvgmfRFT3lRloB0iwGRfpRxj6IM4OdjB6NDeliDmRhamkcllz+9BvKgMdXYKzqW5twDgKncCGJPFiTN098vpOg0g3GSQiKfzeqDbA6IVT3xZ7WAjsQdpclwIdwZxIkkTHQsNAY4In0OgNxHEekCDBCRxsbPrY9FjlGDWGyv8ZTFqRGvs8bRyGzn66NoOTuBM6RopIpOaP292+bBhH7GOM8ahj6XSY2OujbWEyk4O5o3j5Cp6ewu5J/MUZ+itrODc34cYtLGaw3347/MSXT0Ar0PJWjyHtXCQ9vxvTlmYMlOPZEhQmlcBv8SlV03YctTHwDbA86K+rG0BpWXneHyKRgmiK2oiQtl0fDZu1iKDM3hsldjOyLNgNNePdyKjMgJHG6OSAr+Ki46DRx6fcX2RFJqj5GnU0KqbDDTNIyD5vUaPJsAthinkO2Ay9XDKyLLkxI2BlxLTGi/GdV8N3gk8aC+W+jBir4scY41HGkTNw/tsAyNIUMlfBe2oZty+x9zvY97fx7/p0/+QOMIzukseP460NycVvjKae/UI5vDxsxkTaeoBsChU1GW7mGGCALaHVhX4SvAxs7qsbwNJyuL99IsmIYMwsQ1At3h+9xod2tIfo1aJt4gEVvAZmRVmvJo+DsNR1SlT2Y/IF5a/vHEB/DzIa9FaUIr8PTe8YFyJkuuEnuROY8d6Vo2l2EbkgJ2bCmxFZiOOmwPmR3YgbN1AmSuz+mNjH+HJgTOxjPDZw51/C5jTu7bVBinuL7vxx3FvDmrIeFb0BWqWCFzjG29wcOcY1kyG6kY0GnDmNTGXxjRSeC5qbw8wcVzX3BzWgBqeOwv1AiviFF6EbIZhcOUzsewejvexR0m5vEZmpAhhKKJeYUKp5IwuZwUG+B14PSCknO2cP/B2wd6A9A06gLc97HfYiPflmKWRjm2zvE73IonAhQOxtOSLBxx8h9lFC1iKKxj52iMYFGh4pLKYQJNFIILAwZBqkjhj8pnwrB+mzA3MaFwhoD8b4UkCOp7uNMcYjjqkl3PNXQw/p1UqI2P0HMcNgkuGFXm5tITIZSCTQpqehWKSRKeE++ybt/QbNjS06a9ucyTjI20NTlMQrz2Ou3AqfO18mmB2gFvGdB+UZH8TW1iix+7ryg09NqrY9Mw1FU7VwyS54DUgakHJAPFDGLB7gz0MvMMHNeA3c3UjdvRQmdm3U1hU9fI3Jdo3oReaFTXBUayOmA80jNZDQpTBJkiBDjtzAXU5HoiMpUiaLCzj42HhAif7Ah97Bo4lNIjKtbcG+AQEHPM1/C7zIqF7ZJmZHNMZjis+7xi6E+MvA/4DqUflfpJS/Hvm5GPz8p4EO8DeklB8KIZLAn6C+QAbwe1LK/2bwnDLwu8AyKm33V6WUMaYYQ4yJfYzHBtoTo25zWipC2js7iHxeRdyAKJXw0mnEm2/iGgaObdM7OEAzTboXL8Ke8k6znn2W+scfh87ll8uIgPuqtx0zLS0RyYVvbowq3IUGiTSUplSdPpGGk0nlKOd1wN4HQ4DZALfBw5JzYRm6K8PzpAujNXWjBASIPc6QRYtsLESMfkAL17RTrW3gBBZJLNIkSHBM1HmVMroUSCkQniSRLNJD0AGaSDZY5CAghBNAbiCmO8Q0E1yLiOXK9ENpexc9UmUHyBIkdiliWuBkG6iMPj7GGP+WEELowG8CP4X60r0vhPh9KWWgVYZvACcGt1eB/3Hwbx/4CSllSwhhAu8IIf5ASvku8PeAb0spf10I8fcG9//up13LmNjHeGygTU4iKhVkICoWrg2pFNrCArJcxk0ksFMpWvfv01pfp7+/T+7+fdxPwi1f5ddeC933tkZr6m4yFUoRu2sPkBM6wgsa2gzSyvkiVKYhm1dtcY4N/T4064P56B3YvatuACdPQj3QmlUaLSEo0l4Z3ndiCNmIkHb/YFQsFyRtLafu554G0uBZ4GjQLYL1uhqG022Rqrc5w3UICNtc+xf4Tju8pMxmwQ747/ciLy5RuvjwbPZRQk6QCBG7jU5m5KhwuUKKGGOhsYDuS4fPMWJ/BbglpbwDIIT4F8DPA0Fi/3ngn0kpJfCuEKIohJiRUm7AQ/MGc3CTged8dfD/vwN8lzGxj/FlgvyJr9NfX6cnodVs0XN8at0u3BiSZOHNN6lfvvzwfm99feSLICItX+7mJloqhd8dpqkdXw5pKptFm57BPz2H9HykD36nh+ibWH4O7h+oG8CbT8Ld4euTCHusA6MK9/ZB1Io9xuc9Js2vWUoYZ02AWQS9CNllkIPJb14fOnnYnYNuDdwmyCvweya4AdHa189C+wfDlwZ0eRRPDIm9oI1mBwto7ASIuoM14glkRYhdMkrIZnTCW9xiLVIhzaEUILWqelxLKsMa532EfwNkR5G8Axzsgt8Fv6P+nfvPITU6PnaMRw9/zl7xVSHEB4H7vy2l/O3A/Tkg0EvLGioa5zOOmQM2BhH/eeA48JtSysPBBlMD4kdKuSGEmPysCx0T+xiPFdaTWdb/eFhX1dLpkQluwgj/2Tv7+yRKJbz9ITH5brjFSghB6swZfNdF5HL4uk4jm0F/4gzO5hZ+rQYHN9ELWeTHFx4+zzj9BFa0nz0d8XCvxaTwZSSF39wazSBLE6wppZg3csob3XhKjSz1uiptL02VHXAGg2EA3AK4AQtc6y1oBgbhCCBfgb2AiLAfQ7Z+Dk8fRsB5bfR9ZCPE3o6Z8GYFkuoCgUCSJ4eFhY6JjkkOQZIqEh0fgU2TCZ4AbBQ7d0F66P7eICrv4FtdmtPDcbMAucZvgBsoqfSfgZWIq17lF8bEPkYcdqWUL33Kz+MmFkSVJn/mMVJKD3hOCFEE/g8hxFNSyk9ijv9MjIl9jMcK6dPhOrvf6WDNzGBvDMVhfn9UiW3NztKt1zFnZjCmpnALBYyzZ3G6Xbq1Gu0HD0joOnvnzz98TnFxkfTqaug8Mh1OELsPNhiBFgm9dzdhylBDXw7RF5CdgVRZec7rCZg2QLRBtsCrgelDawvsQJkgPQv9gEDQiNncG5UwsYuYHvBcMUzs7R7RorbhpkI2tnkxrIungJIGM9IgIwQJBAaCBAXmOY6HomMljjNpU6SDRwePBpN8EBG4vYHDCjscpulfw+Z5/jR0jCZnETJYMolpgRNWeGWN84/3Y8SDYzyS+JxV8WvAQuD+PIxYMn7mMVLKAyHEd4G/jLKF3DpM1wshZoAYg4wwfuR3PEgTfACsSyl/NvD4rwH/EJiQUsaEHmOM8fkhc3pUQJeYng4Tu22Tee45RC6HJwR2r0e7UGDz5k389XVYXydRrZLYDf856xH1fP3BA9B1CNTUPaGHE3/1BnK+gGgEiNT2IJWF6rSqvSdSMJsCuwn9FrR3QTrABnQ2lHYWYPYEdAMmOd786AdglMPE7sW470XT/H7cONmAraymQU/C5DHlLa+nQFhM7eeplI5iyB6m16LAZb5R/Zi+1sEdpOg9nuSTQNDSBg4I9+CfpMIOw6E7LjZR5boWSac2YpYuKSIbJn90RvzINLu4Tc2Y2B8rfI419veBE0KII6j02F8DfjlyzO8DvzKov78K1AeEPQE4A1JPAT8J/PeB5/x14NcH//7rz7qQH2cr86vAVeBhHlEIsYBSAK7+WU8aY4zPE4fErmUyJBYWEKUS/uQkMpmkub/P/v37eJcvk7HtUHp++tw5/MBEt/7uLul8Hi9gViNkOKsmXRdtYQH//rBk5vX7ahnJ5tCmp9HKJeRCBeG0VT27faAibacFG7fgcL9RfBbuB1LE+xUoR96cFiHkYNR9CCPi8+7FTX1LKZ95o6IEeFoVcl9RLXUOYDswWwL7uJoa19mD7gO4Ge7vr74lwRo69NnaLu3Mvx86JhHJRO5jMB25HD1Sdf9RetsbMYu1HFEFjhK0/LOIXVhqWIyWBHsX6p+ocobXU6Wc6ldGzjXGGEFIKV0hxK8Af4jKZf1TKeVlIcR/Mvj5bwHfRLW63UJt2f/m4OkzwO8MAmgN+JdSyv978LNfB/6lEOI/QnHtX/msa/mRiF0IMQ/8DPDfAv9F4Ef/GPiv+BF2EF8aeLaa7jXG54J+vc7uhx+yf+0aB1evsn/1Ku2jR9m/cweuqQll02+9xeYPfhB6Xmpuju76sK7sxaTnE7OzdALE7reGXuPCMEjOziJOnya5vIymadDvo1sW2aUiWvMA9pqwB8y+Cld/ODyxODL6RqwIIdd2R4k9aiNn70QPUHX2xJwSyhlZ1cpmLQ0Hw7hNcNJw4KMyetvAKvwwQoIT56AW6Mlv7o226XW10GNFf2Xkckw8gvn62ujcWESk7NiPS6EHnpeQBh4JdLGIiuyTgIWtpxHiq4CGFBq+1EnWuwjfB89BeDZC5qF3DPz+wLBHwIQGwkbV6+uw+s9h/e8MXzw5Cz8b0CCM8cjg8+5jl1J+E0Xewcd+K/D/EvhPY553EXj+zzhnDfjaj3MdP2rE/k9QBP5Q9yKE+DlUWv5j1XMfDyHE3wb+NsDi4uKPc22PBroHcP8dWP0TWH0bti/Bf7kDZtTLc4z/P7D6zW/yrV8OZ7uKp06F7jsxw1iS09MhYu8P+tWDMCcmyLouqUqZVCpJMplAf+EF3N1dnPV1WF1FX1pCvP32w7jULRfRkpEo2YgYouzEmOTIyFdxZxueMMAP1N09E1JHFWlrKRAGflUgEy2k2QCrhlaXaA/WQa7zsDusnQEv0OZlzhDW9HRVHb8f+Jyi/Ovaam580Mo2ksE36ZCWOp1Am5keIfYeGjmSGGgkMUigkyDJMvOAjo+Oh0azdpKWY9HxDJqOwa1Snw9ST1BHxxtsBH7C/B3lKodylXvaSGNxKfCZ6sw9iJQj03noBswHtDRER75qkTcfl9If45HAeAjMnwEhxM8C21LK80JthxFCpIH/Gvj6Zz1/0A7w2wAvvfRSjBfVI4bOzoDEvwf3vwdGHlbfCR+z9gM48hN/Mdf3JUMprqZeDoe6nRiLWC2jRG56Mklmfp7M5CSpmRl838duNGhvbbGQ0KgG0uVS01jpG8hAyt4RIrRs+HsHyCfyiGaAAP3In32/D9MTahzrIWwJ1UVlL5vMqEEsiybIfXAPwN5WaePunVCG2T+zSKgSZsWIwaxq2MZW2qPHZCphYpcx50mVwsTedJGiAqIAWhYp0ryBTxcTAxcdG5N7lOkj6eDRwqNFCZMGbbocvpVn+E6kpn5ra5YddxgwVHMae5FhMDopPIbvy4vuRoSHFAmEjKmjH8LvjG5ionGKNyb2MR4t/CgR+5vAzwkhfhqV88oD/xtwBDiM1ueBD4UQr0gpR1fRRxntLUXi974Lq98FIwVbAR/tzMzoc1b/5PMhds+Dq5dg6Sjk8p99/GOI4qlTCE1D+sOoSzfDtdbe9jb548cx83mMbBYPSOXznJ6bQz54gLh1C27d4ubUFN2AEU3ziWNUA+cRvk9qdpbOysrw3LY9alA6MQNBYm91IJVRjxeKkExBMQ+tPeg0oLGtRorKVWisDruznn4GmoFWrH6MuYpfAm1I7FKPOcYsQvde4DkxYrl0XpUNkgVIlpXN7dHXQDcBDXwfnk4r4Z3WAu0AZ86lUeozTOlDnqs0qT+k4CmW6BHWAiTIAMHrtImWGXK6DBG7642m8DXSIWJ3Y9L8aFmlbXiImNhCDFz+hicOY0zsjzTG091iIKX8+8DfBxhE7L8mpfyl4DFCiBXgpcdCFd/ZgbXvwv3vwPbHsPL98M8XzoXvtzcgPw+NgG3n6qCPunYXVt6BO2/DU78Ap7/xb3dtmxvw8Qfw/ttw4Ydw8Tx02vDbvwc/80uf/fzHEEYySe7IERq3b2Ok02QXFjByOebOncPv9+nXarTv36dnmqx/ONyQJV94gdz6eig4y87MhIi9tl8nWg1PTlRDxN7e26MIYFnos7PokxW8k7MYS9OqjtuvQ9KGVBtat4beUk++DtcDdf+9DKNWapG6e29/xKRGeBmCs1akGSOWM3Kq7m4UQWSBorp1PGj3od6CbgWuW2rUK3WY16EZ8b1/81VwhhsNrTda4khHwl0vZjKbFVl2ZAyxZ3WfIMPaXtykuLBBjxci7YT6uZUFr6TMaYSFZxTxCm8hNQNf6EhNZ9eYwhHgaDqOMMi4FsZsEVeTeLrE031O4qKNu4MfOYyHwIwBH/8WfPvvENrV55egEYh2eqO1WApL0FiH0jEVwfcd+O+OwN7K8BjN+PGI3XHg0odw+SJ891tw/l1YW4VMduCQFagLfvjuF4LY7VqN1scf0/zoI+b+1t/CyH8+WYTZr32Nfq9He32d3vXruK5L//bt0DGZUomgL9vO1hZzkfOksmEi3V6P1MILRUozk5hnXyeVsUgZHmnZJT81g1bfRMgV5d1uZOBKYPhI1C8ehnPZH77YJiO7CDei8u5sRYhdIPpppHEa4eXATSB6JuwvqBGx9Trs1eAJHfYDBjUiB29HSHl2AgIlBpoxG4TI9YjuqCo/FXGNcxiNds1IBOUHxHImgiw6haSNLzWSuiSp+1RNj4IwEahvpw88kG8hxCl6SJQMrssJLuLTQ+ICkmmtjXCGG5RmZYL1Uvj3+qc8TyvQgvc8OXTC/gPH6aFFN1pjjPEFxY9F7FLK76J8aqOPL//5XM5fMCaeZSRVV1gME3vtmkrHezaUT6ppW3oOKKj2JQaLSOmp8Hnuvs2norYNF74PH7wP734PLp2HXg9e/Ap8P1DDb7fg1Am4G+hnvvDD0fMB3sYG9nvv4bz/Pu7Nm5R/93c//Rp+DPTu3KF14QLtjz6iv73N1je/SX9tmLXIv/gipXPnPuUMf36wikXaASFcY3WVpK4jA/3lZiQ9v7u+jkilkAGL2ISuk6hUyE1Pky0WyZgGRg70/W3EzgNE84Aj2Rbc/QEE3VMLlVDrHF60pt6FySrUAwktPyLY6vUgU1U97KBarGwTCk+qiFuzAKGKYf2a2mD2ttFu2miN8EQ7zuehFygFhEvTIJtgpZTv+yG0SETcrEFBV+NeH74PPbSx0Np7ECHp9KAfPSEt0lgU0NBEEQuBicTAp4TNPKA9HA/TQmMHlzb+IMLfnK9wFY3D7Yfwk1x0w0WPs8IM9b9PIPBohY6RItIP7332/HcPMZK89YnRJYzxhcdYPDcGTL+sDDic4OIwWBU1A8pPQGrgPX3vT2H9KnAV8guq3zeIbMT/c+sydPYhXVIkcPcKXP0Q3vmWIvTVwYZg7hm4Eqir1kaHj1CdChP7xfPIgwPcjy7gvv8e7ns/xHU8Ov/690NP8/7RP0KfjzE1+RT4vk/nyhVaFy7Q+vBDmhcuYGgaje985+ExxvQ0/YhArfnxxz8ysTutFvXLlzm4dInlX/5ljHT6s58UQDnqNuc4ZJaWaN0bbsiCgjcrl6MwN0d6YQHTttEcB/b3Sfoep+s1qA9je+P104g7AeJsxdSwq1Oh59CNqWGXpsLE7tows6zGuqYyYGhwOgX+hnKVs7fArEPtcvg8E09A8/rwvhcjDMtWw8TejyGl3ATUgvYTEctYX0KmDM0dpKZDroJNHll+DdvK07MydM0UdZmjKwRtBC3gmf5NvtH/39EHUbiPwf+c+xswGATjAWVmOeCjhy8lyGET/h0m8Aim4nsxk9qkNENCt26MW6eMTKXTvNHPYpTYR+HHtOCN8WhgTOxfdmgGzL0FK3+g/r/4hJpDPfEC7F6HzcEiO38urCBu3IfsNLQC5BYcj2kkoHIS/s/fgI8uwCc/gMYelGbgdsRytByZ4nXvlhLGhVTWwJHjMDkDmoTaJo2zr+NdvTY85qlnRt6e/d57pD6F2H3XpX31KgfvvUfjww+pnz9P58YNzGYTGfBOjxK2u7mJUS7jBlrGWpERpwC+59G8cYODS5fYv3iRfq3G5h/+Ia2VlYcRb/Hpp6m+Gp2b8OmIU8Ynp6bA98lOTZFKp0mlUkw8+yz6xgba9jZcu0a6UMD74TDb4R87OnrNuWJ4WdiJGbSSLYbv79XASkB1BsoVyKahWIJKHnp1aG2D2AF7BXZXhs979lXYD3xu0c0iqC6MIOyYiW7pyPV0O9HAWm0oHFsp3a0sslSEXzwHeQkFB/IdPpl/jt38Dp1cHwToPMMVwoNeyqRpBx47IfyHpA6g4ZIgST8g5fcjBOzTephiP4SFS3AMXWck7TBqSNOJEc/JiH2v5sf4FUg1H95Ax0DHxCTHEho6QmpoaAjRi3f5HmOMLyDGxH4I34PNDyF/CrLbsHENdi4Dl2FqGZwAUfdjZtyXjwyJPZFXvctzX1WL/Op1WL0EW2WVZj/E/gZMzsJ2oObnRKI9KZFnnoZ+H5nOIA/qyN09jK1bg9S/gnHsrRCxyzu3RuxO7fffJ/WLv6h+7rp0L1+m/vHH1N99l8b587QuXiTz4ovs/WnYgzt74gS9m8MMgRvTF55aWqIZIPb2jRts/fEfc3DxIgcff0z94kVIp9l5Z1hWSC8u0ol4rR988smPRex+r0fW95l//XWSloXe7yNqNabTaZz79+HQFS6ZRPb7oZS5H7GItVfvk9S0UJrcN6wwJ26sQ2FwTHkSJqZU61r+HOgOeE01Pz3lgFwBVlTSRzsHKwGxnB0T+XsRM/b29og/O1pEg9+vhVXcwlSkPfGkyj5pCfBS0MrCfg9qDdjcg0oCPtkEBn+zP3kK/vq10KnbxRfpaEEiHM1EWKRCxN6JOruhRq5+GrEL5GB0a7DW7hAU1HUGHvEmkEWQBQQZZplER0dDI4PE4afwMHDQcdG5WQG/cAxbA1sT6HqKNNfx6T+8fcP/f1Bu2QqG9yxW7/3QNeqZXxvdHI3xhcef83S3RwZfXmKXEnYuwf0fwM1/o5Ts/TqUTilSDyK3APWV4f3aNUXc7kAYlK6q2+JZqG/Czk1ovAPyJGwEZmq7McK7+aUQscuNu3DkJG55in7Xp3t3jYxnYn4QIFsBcqmAaA7FS4YZThaKTgfj2EncGzdACPTlZdzVVe7/6q/Sef99Oh99hOx28V56ifoHw0XNien5NicnQ8TeC6S4EQJrcRFjYYF0Pk+v3aaxtsbu++9z9+tfD9W5K5FIv7O6ipHJ4LaHJFf/JH6Ykd/p4Fy9inP5Mu7mJv133sG5cgX37l3wfQqDcsDhq3mTkeEnvR7W3Bx2oBbvuG74C+A4iOUF5NpgM6Dr+JoBz78AhQwkBYge5HzYvgzOoM0rmVelmSAmp6ER+CyjJih2V5VrWoEMQFRn1q8rsaQ7KA0ZGeUmV3lBGSAZBug+3FuG+gEc1KC5CyUHPg6k8K0MXItsJKbOhO+vjmYHEjK6IDaJyvKNyP22GF1Ek5gPpWkCDQ+dNJMDexoLDYPTFPBxB3Ts4HKNZWxMWhg00EWd8+bzdEVAE0GZuxwE7uvsP4zifcDnyVSFfYZ/r0V0ZgmXtyRmZKsR08Mvxy1vjyLGqvgvA/bvwMq34N63VV96ZxvmzsK9gIJ5/zoki+E0aGSEJ6mySs/3mrC/Bnt3QX4Cu3fDx1WmwsS+dx3MBDhDCpbpJJx6Ad/M4dWaeFduUk/YeH86FNuZ1Wo46SiBxeNweThpzKgPU/piahoWFzEmZ9AzGbzbt+HuXZxajZ1mMxS1Wplwj1X3zh31nADZSl0t1sI0SR49ijExAakUrc1N6nfv4t67R2Fujs3vh1sDc8eO0Qqo073OaMSXWV4OzUavX7mCfeEC7uXLOJ98gnv5Mm6/T/db33p43ebTT+NcuhQ6T3pmJlTn79XrI+PLrampELH3Dw4whECfncWcnsQsZEnOFNBb0+jdXbT6GiK3pXzDWwxb1crPgxMQnvXCgi31xibDxB7XO16YHBK7boCtweQzakBMwgTLh7QB/hqwDbIOuQY0Az4KPrAyDc3Aa0Xr0XZbnbMbuM6ou9q9XYgkxBO+DEWpPnVgYngKdJIkKFMlgTFoMOsj/HMIX6ppdX6fdKqEZ2boYtPDJo9GmgehjWiFNk2G4ssqT9EPEDKAJWTI/T3akd7HGwx9lYFjoj70MSl9oR9KAAbPGT1mdNc1xhhfXDzexN6vw50/UGS++m0VdSenoR1YBJ1oSlTCxEm4/97wofYBzL0BUoP9VSU6yrZVf/ohDu6qqL0TEEgZkQXCteHMm9D3wTVgbRt5ZYP+R+EMgfnCs3iBXunutZtkIjPFpZVVy3Aqjb9wgn6qSP8r83Su3cBe34T1TYpvvYV2YTgbXDQaWMvL2IFziwjZCilJHT1K++ZNUseOYZTLeJkMPPEErTt3aFy/Dtevk335ZfYD5OpsjYr8ElNTIWLvPRhmJoSmkVlcpHL0KBPVKolej8TWFomPP2bnhRdC59HPng29dzcwdOUQyVx4ynf7/v0QseuFAqmZGRJfeZOEaZCw+1i9NuWlBJq9rlLsG8DSWVgPpGG3NqODxiARaXuqxwgcrcjUcbuuSjqlMuTTkNahnAPfAGMX2ILqJqQj2aLki9AOTnSL2UTkKmFij1NwF6thYvfCEjHRd5FU8AV42gR9UcGjTIoJXEwcNLpofIMPsNjFYgOTPTTO0ePdh+cxKKLv3gidu1/+JfYDavUePlF5pB7ZhsmYqDmJEbK6GZXTgUkCO0DCUWK3Y2v10cEwo5+fpDcusT+iGIvnj/NJngAAIABJREFUHjfsXIL/6z8IP1Y6Fib2/WugmeAHFpJkDhbfUBOw6vdg5yq4R2A/EJH3YyZrVY/BaoDYW3dg4ijk5pQZyMo92Dfge8M6u9A0yOagNaxbG5G/Q7/egGePwr27yIUlvOk5OmTpZE7SunILLivBlfHCC9iBqLW/tRUdG0JydjZE7P7qKloqRfroUaxKBc3zcLJZdq5coT2IpI1MBtHthmrPUeV6Z2UFPZnE6w0XVTFoMbNKJYrLSxRKeYyTJ0ltbpJcWUFbWUEsLtL+k2HGxANEpYKsDVPU0QVVHhygVav4gbGqCSlBCFJzc2Rmpsjms5gYeDs7uA8e4O3uUuw0SX4QTpmLIyWwA9FYdN3f24VjOegGdAUiEu0ebKn2sWwJihNq5OnkBMy/AXoTtB1gDbp9YPjZM3UOdgOZh/Y2o4wX+Q26MfqOdGSj4UY0EIYF8/NQzEIxA6UE7lyJzk//OzhlB6fSw660+MfFf8CKNvwefAVBl/cJbhS+wkVkoKYuInGzq8foL/zwh9qN0Z1HzV/i2suSEZL2RmJ2MLFwsTGxMLHQsSgwh46BhoaOTh6JGIyeEUiEOEmSXwRSCNIIPY2WthCklCsdKdCWR15rjC8+xu1ujyNmX1ORU1A5HGUJpw0zrw1+ZkJ9DeqrsH0zfFxhPkzsu9fUFLdg+4yZhInTYE1Aswcrt2Dfhc1AZL8ctqAVvo924gT+hWF6Vd9RaXWRy2EcP47I56kXsjRX93AvrcClFUShgGw0QpGsHolau7dvk0qnIRCVJyyLxOnTaNUqnpR0t7YoaJpKfQ+O0d58M1Qbd9tt8keO0L07fP9+L5Ka9Dyyp07hd9qU5mco5SzK2STFJ6ZJ727C6j6sws7c09hXh+1jIjA97eH7mJ/HDRC7rEc2UYZB4pmnEHYXM5fENByMlMHJ+QR6fw3uqZTu7dln6V8ctg72G81R+9epWagHyLIZ16o2C93rYCVhYg4KWXj5HCQl6G2gBhhQvwUMshL5N8EdlCYOf0XJSegFhpJExsDS21NDSUJp+8ii5ASzAwLMCkyWQb4CmQSkNEjpcDoJySaYu2Duw6QH/aF+wS08xfZCuK6eiRBnZ2TuGkjKhJv4IyQtPKRWQPjD31k6YsnajonG1bx1gUESkxQJLKrMY6CjIzAQlNjnDH0selj0MNljmR5i4Dov6VBCo8swo1PgJG2G3y0JJLlLMLWui1exxH8cvaAxxnhk8XgTu2bA/Fm482+Gjx3cVmRfPgVGGhoPlN3kyvfCz01VoBsQNnnRtHoPpp5X9fdEGVot2DyATwa97YeYPQObAeX39k1lPhJY1LVCTqUV5xbw5hboCR3DzOFevIgzSKVrX/kK7v5wIZb1OsaRI0o8dvhYtIbt+4iXXkJ6Hn1dp7WzQ//ggPrVsKFJ5eWXcTcCbXcxArrE9HSI2J2dHQpnzpCtVEgDib09Fs5USX1yWbVw7QLFSdgNT9cyy8VQLCZj0uocOtYZBsbSIuZEgcTPncMy+pj2Dmb9HmLJgw9+yMPcbL4K/TCBJIr5kCN598HGyORRPxtpZ9uvw9IJqJQhlwTLgyezYNRB2wRxG5I63LmhWOLwzyL/IqE8cbs5qmhPToSJ3RudF445BX31OUujgm+lkeXX8BNpXEvHTfjkei2EtgZiG8QuTHdgPlA6kjq0D/3ZHp449DJGv0Z005CLEG4LQaSggE8hxHkyrr9bLyHR8fUSvp6nICXHmcZEouOh4zDByYEqvYdPlxJtkqwjBhsFgzLJiMf8AsfpMSwteTxHLRK3GyF3f5AxCXtBauBVd3hMzO9hjMcG44j9ccTyT8K9P4LyabCK0K4BWVgL1FGNEZNuqJ6A+wFi37sNegKqp9R5WnVwC3D1u4EnCTVMoxOIQqMKrk4dlo8j76/A8km8wgR9PcVWdhL7xn24oYiu/OKLodS33Ij0uwPG7GyI2P1GA/OVV3BSKVr1OrXbt+kBnUD7mkgmR9rgRCSt7t29i55OPxS7Gfk8yUqZ5b90lrx0ye1tkV1bYdPL4l+5Mnz942fDF3iwDeWqSmcfXnPkOyZrNfTJSUQigTkzjZlJk5wskP7qcYzdewjvDqzfgSNVNaP8EHoklmzsqgEr9YBK2gwf09vchFIabBttdg5tehI5PQFffwv8pirR9O7CIuDfPJwGqlrZ+oHNjh0zdtWM5NAbO0Gd2eCY4bZCaia+piEnX8RN53EySfoZDa2Uop8sYVs1fN3GwsHjTug02Z0ywg3MB9ciEbDwwJwAJzA9ToY/C723DXIuJLTL0yP4B3uAJAfo6KRIkSKBy/PkOY5GdXCbI0sOnSoaZTTK/L9L/5BdhhmvaVxy/EHo9XukcALELSg+JHUgNNxliPBypVzrIgY0kQ2MH0vsydBmYEzsjy/G7W6PG7Yuwe0/gvUfgmPAWsD4Y/Et2A8MuKhdDbevARimSrVXT4FVUoTs5uFm4DyzYZEXSJg9CbeG7WO0BkrfdA5mT4Ceo9fL0rm0Dt9XpOhns9jN8OIiI77l/p07aPk8/iB1LZJJKBSwzp7FtW26q6s0r1+nNjdHL9jS5YQXfdnrkTp5ku6NocDJCSjg9UIB6+hRZmdnkXt7+A8e4N67R2VznYWbH4XOZR49Qv/C8LHu5v7oHJOFhRCxm04X49ln0QoF8H28/X2qxTScfx921KbGfOlpzFpkAEl1Puzc1htN4TO3ECL2JA6pp57CKhUwDQOj3yE1bWDceB/h3YOde3BMh413w+fJL8FBQJHd9sLfFL8FySr0AtcTsWSVzV3kkSN4qQp2pkQvnaaXnaT+/DT1jE0j1cbQkpRZIdgfPsFpnIBPuc/o+5R6IawFEDEkaFTCxB51XBMaOsdxMXAo06PABC5nsdDporzkmhznJoSu4T+jyM+Pvl4AViTO92IEawbpELFH6VfSHxBw8O83vEDHEbuILGlerI9csCBjETvx7S8K0oabk4APUrXsMfWbUPybf9FX9khi3O72qKO5AXf+SPUXf/TPwi5w+Ymw8YsbiXC8PlSfg+1PoHIKEhXVytrT4U7A3nX5LdgJEM7udZXuD4qDsgNqy1WhcgzcBFQn4L3z8LGq9Ynn3oDOkMi1Vgvr2EnsANnazWZoGdNnZzGffZZ+q0VrZ4fGzZtkbt9Gux6wFwWyCwshYm+trY1khK3JSbo3bqDnclhHj2KXSiRef53++jqt1VW4cIFyNkvzB8pQRQB7t+6yEDmPWSyEErGdayvIOYGQEqlpMLOEM7OAbeXpNtq07q1jf3SDbLSu/rVwpO/cWSUVzZmnIo5re4GItTqjXPhmFuCZAiQbINfJsMbU2+FUv/7vvo4IllVqMU5yqWqY2OsNiDgEk51G+jYUZvELJezqBI1TP0+7KGkWezQKDXxtiS5bqD+mOkUWOWDoZeDQRSODH4hOowptj53RKaJ6OkxxIlCiESYYk7ipI8h0FddKYVsWteQC30n8NdbMHCtGjnU9yUkheMBQf/JVPCRvh2jYiNCyTYwbXgSJyLAUJ6amrkfUDnGRtU4aN0D+cmBDo5FGI4mgyBTzmGgYSHQkBVcw509jSAdD2phuk0TLQvd66F4H3etgTf9PaKkXlVDuCxfNCfCjwtyYvvoxxvgUPPrE/sN/Ahf+V9gZCIMmng6TOkD5hBrHeoi9a0rZLOVgkMs0aHno3IL9gEFKphoelCEjkYfdhukn4cFlKMxA8QjIDHSOwKW7qEIzcOxV8AKK8s1wfy6oGvYhsQvLwhUC46tfxe52ad29i72+jnn8OLWAgrx96xaFdBo/UFu3rIhhyP37ZMplvHab5PHjaJUKTrHI/tGj7N29ixxYv56eng6Z0/gRYVf/oI48PY94MOw1NqVacEQ6jXbkCF65THMqSf/uOt1rd/Ev30V/c5ZWxMlOm5vDD2w+fNcPk9deHXl0ChH0yXclTM5DdRoyGRAuFJdg5Zpy8NvfgDNZZQw0CNIEe5BMqgErh68lrPBrra3BZFjzgBbIO2Sq4KVh8k0wDdBs4ID2qWnswm3gHnAPG4/wFgtM8gNiV/BieqENJrADxO5FCE7SQ6OIzwGCJIJJGtYsmvwZWlqBAz3LgZZhufgRXbNJ32iAAIsqTe7DgIjrlPkXHAudOxFJW7fQR7ooDHK4gev70Yg9vAlzA61i2sCwNUkFDQZqdYMESbI8g8BHjWGxMdAAH0kTQYskBzgENnRovBipw+f6L6P3A74U/lHEbriUgS9gRD3wRUHMRiMqshzjx8K4xv4oonF/SOoAu1dGlfDB+mJhCbKLINJw9z24ex24DlNPQz/SI1w9Ppy2BbAXWCCK84pYxATcq8Mna8CGSuGvRf6Q0uGYWd9cR0xNI7cUkYpyBbOQwzh3jl6tRvPGDeT58xROnMAOOL7Rikyu8jys48fpBZTf+sEBCEH6yBHSMzMYmgaWxc73vocctK8lTp2idie82JmLiyFi78f0pbvTcxjdDt7CMt10joaweLC8TP3ePRic+8lXX8U/P6y7y52dkfNos7MhYndqe6GsgpiaQp58DuF3AV/pIoweiDWorfFw/qrxNHQCn0mtE9KICSTa4jz+jWGWxWs74T/6bhdmToBpKs93w4REAY6egs46OLuqtm+mwO8+zBlrzIbek849YCn0WNSVzYlJq2sP5XwaBmU8Mvi8RZ8cbdI0sBBYbLFLcxC55VPPcC21GzrPX6UZSttH9ewpRkWKZuTr30QbIfboqNIfhdgXeI0cMyTIPbwlSWOSxxhE6iv8XfZD/e9HkFwOJcWTLOFyM/BOoml1H9UfONzYyhHnuxhxnx9Xv/+CINpKCcR37I/xo2Dc7vao4shPwbu/MbwvPZg8NTQZSVdVL+rMV2DnLjxQERZLZ9W0tUPsXFXWm0EP7+DM7MI8lJah/CysXlYtVffWYPZl2BxGsbgOHDsDVwK1+EaY3OTCMtqRp+nt7tO5v0ZvZQXtw4/YXV8PHWdMT4eI3Y3MGAcQpRJGtUry6FGsVAqt3aZbLOLfuYN/5w42YJ47FxriYt++jWZZ+IGJZ24qvKT37twhPTeHNTODkclAt8sdq8Dmyh6sDNLJuo5nGKGIwkkmQ18j7+5dZX0aeH05EOuJfB5taQm3WiU7W0VrHCDW7yEOthSJXgtEXs3yaI93MTLs5P4WROa4aPNVReymibawAFYWXjgHjguNhjKmyWRgNaAfSL8IWsQoxpgDe7hB0LouQYm9TgODPG6AXLUAJQk0QCPHMXRSCHQkHhqSFBU8doEHNEjzh6FJZx7HST0kdYgXe2lUIsQeJkGLXRJo9AMkoUWS/PvAElnS5MmQI02OCZJYfJUERSzKpCMbmjhMcopJTn3qMUakP8GnO1JyEJF0fZwjnCCDDBF79IiY9sUvErH3r0Lnj9W6haf+TXyVUN1f/njTDscY49En9qVzSq1+OL7SSEFmWvm2NzahdgMa34bcEtQDxBl1nPNdmHkW7g9sWvOzoCdh4U3YXoEHa+q2/BbUAuepXVf2nMH52tUh4UhdB0PDfu0v0Wt0aF6/jfvRCnp+kdbbw4Eo/vo65tRUyMHNj8zslgcHpI4fR5gm1sQEOA6GYZDf3YWgWcvJk3T3AyYikTY46TgUTp9mP9D21nVdSi+9hJXJoHW7ePfuoefz9D744CFFGC+9FP7MPI/UiRO0rw1JsN3thhKxwnGwTp5EOg7m1BRaIoGbzdI4FPkN3OsqTy4i1oZtgbIbiVIO9mCqouaEH8KKrOL31uDpSWXnahbANUg4eRJrS2iba4itO2qyWmRmN8aT4ftbOxC2GwBZDt3V26MGRRk5gysqGGTQ0QCLAkdxOKBPDYf7+FRDUW+BI/gBsZzBfeAMwYVdi6gkHJpEG619SkCg9TEQqQosDEqcIk0XCx1jUK+GGTRaSBp43Mbnt/j1mO71P3/okXS9F0vsn+1GBxlgJ3CMSrNLkVbjlbUsIrWoPCo0Q5lOaXGCur8gdH8AW78SfqxtEFJHZv7K53pJjxPGqvhHFWYKnvhFRdrtJqxfgfuXoRmpq5UWw4KonWth4VtmAvJzsJiG7XuwsQobD9Rs61Yg7Skii0K/oZTwawPhm5lApi3cZ87h7LboXboOty7Tf8ai/+GwBzcuRZ1aXg4Re3d9HXNiAvPoUUgmsff20AsFWu+8gzMgZW9mZiSQTQ3EcYdwA05zAGaxSO7/Y+9NYyRLs/O857tbxI19ycg9s3Ktvae36uqZ6q6qIXtmJA1J0ZT1g7JNWyRgWYAF2LAEGrT/GBAML5IXATYkU7Z/EKItw5AhEDIBm+J4OBpyemZ63yrXyn3fM/a4y+cfNzLjuzeS3dXDnp7uYh6g0B2RcW/c2L73O+e8530nJkj39qLX6/hra4jZWYz9/bZ1RhDG1auh4/wLZs6tnp7QYFL58JDCM88gc7mAf3hwQKpQwP/+95FLS4Gy3ORkiOAH4JX6MVRg37nAMCc/3AH2dBHycXj4ANISrDLIDdjOwjud1oxevAcbKsO9AqO9cKDMk7ciQLa9AUMRUqRjAzpYA6D3IhoFYk4Rza8hvCM0d51Mqs6x+PD8PdS5ToXw52yRCwF7K0KX02iSIEEtRKgLL0wtjjhj8yWwSWLj4pBqd60lHhDDZhiPI3xOkdTZJkGnaeNzB4uFSKn6FIds14zmZx96JGP3qGBiopNBI4NOBkP2ovM8Ag2BQJM2aC9xNocoaOGTwkNH0kBSRbPA1aqgvH+JjIZQgdKIqFH+LOMC0xyEHuHzXJbi/yxxyYr/skb+K/CD/71ze/9xYMBSUfrEfmS3r+kw8fNQb8DeJqwugLcAOx+FH9c7FQb2w4jRi2XD0NVgwd85hg9mYOEHlJfqoVlxI5MJLaHe3BwimUQqo2ZmPE58ehqjvx/P86ivr+N6HmXFMzxz/37o6RtbW2h9ffjKhkDN77R4HLuvj6HnnkOvVBCbm8iVFTg5YfP73z/Pxl2C0r+r9NlFhLTj7+xglko47U2J2duLmc9TePgQv9mksbnJ6eIi25kMnqIjn4y4uvnLywjLQiqtgFbMDn0Z5eIqDAHZIvQPQzYLg3koyGCEsHkA2geQ3ey8AAgkgzdUbfULJFh7B8LAfqKUtjUtUJrrnwBk0Nat12AvTmCrtw6sB/n4iA1KWdzyRSiR9unevBmRbViTVtePMI1NjSoxbFIksTAZYQIdH40mkir9VGiywZncq8YAZUW8RSePF/FOL9JE/XbULhjzOqT1uQB7jm9gcx2DDDpZdDLokfemIX+Tlvwd5Z4rtJTqhgQkz+GF+AMXgKBIgFT4DfILVIq/cAmO1i4uyXOX8eni6QD2q9+E3/+t8H35yTCwHy1B/3OgZ4O56tVHYDdhVunjbs0EAjPqjLQeZg5TP4Hp++BqsH8Ij2fA24fvdIhAgib61Rt4SqlbK4eJU8LziN28ie84kM3SKpdpVioczc+D0lfvuXMHV/E5b+2GldwAGBuDnZ1ze9Z4Msng/ftYe3voCwuIDz6gmkxSaW8QBCAuIMfpo6MhYPeUUTBjaAhjeBhRKlHb3qa6ssLp7i4sL1OLOK1ZY2PU3+1wDJqVSggqhOcRm5ykoVQV6i2P5K1b0FtAJAVCnCJ7GoidGWgdBNXW4ftwoIwfnmxCMRluqySjnuYXKNslM1AsweAg5NPQl4Trd6G5B9V18NfgsA9WFT2C5AtQ7GRRQY4/CHR4D6Yf0SLgAI1BVEPdjia6hkUeSQaTu/jEcdBo4TJGnTSb5710myvUmAt1zU3GaCoafk67+H8WAaiHS7o5qqjAXv5TgH28+x37zMNiAKur3xEO0aUT2A3I3eX6C5zZhB0B9gv67j+ruDBj1xQsF0hftjN42f4nEMLsPu4yuuKSPPdljsHnIVGEmtJ/9UUw5pYcDNjkax9BIgfbCkmqFumT+j70XYUVZUEv78Hw82Blgj7v6iOI+fCeov+uit20wxgohYCdhTm03l60sTHceJzawQEimeT0u9/tPMY00WIx/KbSH43YqjYWFtDaI256LkdyagpvYAD/hReoLyzgLy3BygqFeDykEW/GI3PDjx+j2TZ+vQNGXptAZw4PYw4Po9s2tVSK8sICzsYGbGyQfPiQQ8W/vba83CWRSza8IIec1jQN48oouZvXEJMD2H4d+3QLy95GWAu0Zb+DGIn09CObIySQHYV95X02FCEWw4RSH3zzxQDPbAe0YxgwIbnHeW/2wABfgq9ApxVpcOzuds2yC7eINDrAbkWqAwYF0tzAQeATp4lOjRRNpqhxio8H7JGnEgKkFH0hglwzygkAzEh228Lv0sE3yOMqVYM8J0ChfXwg5TJFjDQ+KVxsmhjsnT/mZx/h75Gk22AmOh7mi4v68BGu/88yY9/561D/ThuoXdBug5/hnDiHB3kzKDwIAEkz+3u0/N88P4XGDVL6Oxed/TIicQnsX+bQNJj6eXj8R1CcDkDm5ABW50GRtmSwD7aVaeOtR2DEwFWK5FYKBr8CsTwcHcPMDGTSUFbtWCM92co+jIzB2vL5XSZNWgODyLExWkKjsrFJzYpR/1FH0zvx4ovh8zgOidu3qXzQ6RG3zsRcNI345CRmfz+pVApzZgaxtIR44w1wXarvKD9030efmsJTxuCM0+6KQWpyktbBAekro6TsOEnbppnLIdfXkevruEDz6lUchYhHM9yTdcvlQBRH6b97bdKfnslgj40Rz+covHib2OEW5uYyWnUZrFH4UKmWnBgwbobFg8yIjt3uxgUWqoXgM8uMBP+vZeHrL4G/B84ayEXoT8PyOx3fDy9yXs+FzCgcK5r+0Wx2ZxNumiCV62umwJhAeEVwbOxKkpTdh88RLjvANpvk2WCTM3b2ICa1SIk8RoEGnUpM1DGtwQlmxCvdiMyg13Hab42BSb7N0B8LeA7YVIlTosqv8AEeR3htkJxkA08hpQn+GnCNL0IIcpHboMkBEAkEaTTSCG6hM4ZGBkEKTfRg6f8miFSgdC9SkMkGWTvJ4LaWu/D5Ppfw9sBVqkiiBn50DFJ2m1VdxmV8ing6gB3g+rfhrf8z0Cc/i56JoN9+Fm6kBOc2YfR5cBoBk7pchu0jWHwv/Li+58PAftItMMPoCNKXeL0jtGo+tc0jNtc2Ya2jK249eEBdyeIb892GMLFCgQptUJyehmyW+IsvUpubo9wu0+cfPkRTNOJZWOhi5vuRUTCxuIheLJKYGMfOJkm0aohkBrnwQSDuAvg9JRrH4Tllu1SiqhLxLtCsjw0NIV2X1MgItm2TjlmkJ4YxNtcRc8F7mfrac2hLSmZdiXwWjgulKdhSqh+tqCBQE6ZfhLgNhgZeNeif7leCcUUIHPYKTULAnIqIkZTDM+BAQJ5Ugb3Vvj4rCbkhSBXAykK1Cidl2NtBN1yIPYa2lrumGbQGJkIEy+hceO2CUTWTTAjYo8YqPi4WGVxqxMhjkcTGZogpDBwENQyabWn6bUTbYe4P+AYzClFvECfUo4ZAJU7daNTpZvv/rMIUv4gu3mvryGcRosubrzsE3avaF8mpLVpCFxf0z0Ol+Mv4s8Zlxv5ljsmvd9+XHwoD++6joGeeH4LsCNSaQBpm/5BzRzahdRu5aJEf49EaFAcCffncMFRd6hWD0x+0Z+TbYY2NhbzPaYX1uv3TU2JTUzQXFrCGhrCvXEFks5Snp6kuLNB4Mxi9K0xP45U7ZchaoxGWDalUsKamaC10QNFxXWK3bqEVi+A4eBsb3CxoiMedMnrrxVdCWmja/h56fz+e0me3tPCq2FxbI3XjBlYuh2FZyNNT+pJJrK0taIO+iMcx9XBJ1E9muhXfopEqAQuBPWr/WADI9x9CrAL6FshNcBzYfrNzTM/L4UXQaUBqCCoK6z4WSX+O1kA3Oo59RgzSJZi6CykbYh5YErxeqO+COxcIuM29COvKc58MQ2/npvBdTFnCEZ33z46AdIVKRPIFtHZZ3SSHQQFJjjzP4iNw8GjSpMAhdVYR7dJ6gtvU6VR/XEBwhEoeS0bGw5oXiLVYJCPAflG5+2cTQuTQ+Rlm1z+ViCy58iLG+yftRC5R/0njctztyx7FMeiZhH1FxMVvL2SpnsCtzdPh1INHPwCWg7+NPRM+j/Rh6BrMK+5vB+2suzQG+RFouHBgwff+iLPZYatnuOuS4sPDIWA/d2LTNGLT02h9fSSzWXKVCrR72H5vL9UIQc7o7w8R6o6Xl7vAwZqYCBTbNI3W/j6nm5sUlpdDhCvv2tcwlHaBvt9tz2qNjFA/A3ZNwxSC1Cuv4Oo6teNjjh4/Zsw0qf/gB+ew0XzppRCFSTYaiOvjyGXFec6JLGDb2zCSD8C7fyCwSO3LQnoMyivADBytwlAdkJ21LBEhVDUvIBMm+sPAfiZSkuuH3n7oTUMyEXiVx3fA2IBUDbZU61OjW7QsFinhHxyEgB3A8nI4Wud9jZ1nwIIEKZIkydKPh8BD0MRlg34+IIfTfpEZLEqq9S8wQixUoncvAOlApKbz3MlIdaB+QbVAFZEVaLQukL29jJ8wpITqPwfcoIUjnaBVFD+bbBF4ehY3/hogzsvvlVgeKZ1gCAOB0FII8c3z04ou28DLuIxwPD3ADgE7fn8xmG3vvQlmCtLXYWMGNs5028OjV6w/ChbspkKoibUXu55R6B0NxtaaOszO0dkQhMfO9P11tIEBfKVUbbZL7CIWw7p2DfJ5zOvXOXjrLdzZWZidZeyVV7CVDFnb3SU+PExDyWijQjWtWg3t1VfxdZ16tcrp4iKJWi1kzwogikWkwmx3NSs8M722BIlEQLLTNBgdQwwN0rIfcHp0xN7iIv4bb1BrNJDKNYhImf90fb1bebu3HxRgd08rWDe/AvlcYBVaOYLJGKy8BeVVKAN9d6G83DlHswb2MNSV7N6M8htWgyqM195maAbYJbjyMiTiEHPA9uEvpcDahjPg274DR0q4JgebAAAgAElEQVT23aVGdkHfPeLgxs4m3FB633qaZCWDlryN5ehYzSaev4/e00JjHdEGzUf86+wppfccveegDlCmSR8GvkKokxGynHuBopog33l9QJJTIIWBTgqbJCb9ZLAAEw+NJkkEUKRFlSZlGnS3Wi7jJw0J238lfFf8HjT+5Pymk7vLQU9YUXKW+yGtgwlS+HR4NxaTXxh64xc9Lt3dnoa4+Ysw/8NApGauvWjnb4UrV82I1rXrwsSz8PjNIKMrTQSKc8Uh2F8N/kEAFGqUu0vJ1vQ4ja0tSCbRrl1DpFJ4X/kKpzMzyDaRLX7vHq5SVj9cW2Mocp70yEgI2J1mk/S9e6DrNLe3qS0ssO+61L7fUa67aAxOjI2FgL21d3TOPZODw7j9I9QSBQ5W19mfX8SZeYydLrLz4x+HzpMcH6ei9PRbkY1GbWsLkc8j2yQ7bXiYVqGE98pD6rU65bUNGu/O8uJIE7GhHHsj4t++ux114Qz0AVRgPysV6zHIXwmEakaz4B6DuwvNVeitgt+Z/aclIGKOQzzCene7Z85JR/ruXj3oxaf6gykJDKjrcLgDu1twekjJ2oTEH3VOaxVZ7wmn9YnIz84nOiYnsMlSpfPZRRTuaXGCSSD0YpBHJ4VPHIMefHx8Ggyxwy/wHppyng1epM6xsmW4RVkB8y9Sj/1LHyKQEQ7P1ofL7FGtCDiTHw7fE47LUvynicse+5c9xu/ByrvhvlW2B1Rc2H4UiMq06kFZd/AapAcgdQgrS7CyHWRmxYhNqBVBnIMlyPfA0T4ymUZeuYZIlajeukVlZgbeCixaa6VSSIhF18NfstPVVUZ6evDPJGEti2ShQOvBA5qNBiePH7P2xhv05XK4CrFNRoCqsbiIadtIdXwtGeRjolCAiQnK2QyN+Euczi7ivL8O76/TevCAvbc7M+fOBepy9sBACNjLe3tBAVfXscfHMXp7cQsFvLU16ktLuOvrWP39nChjcQCyfwyxudy5oxlZoLbWYNoGRwE6PwWZKUiUAnDWgcQYyFWgTeprvQwbCpBXTiO68hK0EfAVYp4ZeW5nPSA2SQesIiQGQR8GLwWNJpwewl4VlpQxOQC+ElgFn8VpK/TcRusATY7hi06GbUcW5sCXPPxTNEljUiVODpMkHhY5ngMcfOp4nNCkr90T3wV2STCBh/qet9AiPfMYCeohI5coA/8Cn/vL+MlDmCAvMKI5iwt67J+HpO9lPN3xdAF7Mh+w3FeUEmtLyUCMGAzeDMq7KwuwPBv0VcdvwYbCMvdlIBO7oGSuZaVEmcjA4FUcbxDn3Xncj2Zh/g3ciWMqH4Vn2hMTE5wo8rGqAAyAkcvh3L1LvVpl5/CQ9bk50ouLODNhE5L45CSVNzuvqxlhr0vPC4h4s7NB/75YpJxKUR0Zobq2Bm2Rm77JSZyDjuCN6Xmh87jb28RKJZrKNeumiWZZpCcmSJRKGEJgxWIBU39hARYWSD58SEsRpfEikrEAbqEfSwX2feU1CBFUScavAw74rUDXXTiQWgAWzpREQc+AqyyIsQhb+nCz2zBGFoNznN206gj7GugF8K2AlDcqwHkf5AFwAId5eEcZyTPi3TP7RqQJcXgK/eG7LFmkoQB77Fx4JkWCNDESXCeLjoOgjscpVQZ5fM6QaHEDsBSyHIDOFVwFuP2un/MhRMbiYhFBFy9CsPNwaFIlRodP4ON3GcZcxpOGSZisEQVt5XssA0lgDRONWBvgBULqaCLdPlYEZMtL7H+iuJxjf1rixjc6wC5EICU7/XNwfAILH8Hm23A7A48VctLqDCTSUFeyG10ZVIqnIJmFW9+AtQ2Yn4WFN/CnH+C+3zmP/ngBPZ/HU+a+rVg40/drNYoPHmBIidzcxFlcZL1SYV7xWT+anyebSOApAjMyFabLlefnSeo6Vn8/8dFRTNPES6Woz85Sa1uoGj09uPvh0S6tvx8Ulzhtu5tAl5yYwOzpCY4H0HVKUsLMDP7MDC0CHXlf8Tl33PBomre1hZ7L4SkbkPoZrKQycGUMBnpg9D4YR+Aug78OxhVYVLgCuxZdTEF7BMpKlmxEREkqewH4e6cQ6wN7EF8MIlMP8LUyvrYNjQ1i34v0k5+5E1YoS0bIZm4DMv1worxnMvIT2t0JPFwAqecgNkC+2YdnZLH8JpZ3woG2w2LcwGePOnvUAZMsVUWIJhnZHRzhdynCGaRCkOHih5YwyRGCoZB5itkGaA2TGBk0EuS5CsTwMXDQ+GN+hyrb1DmhwSk3+RZ3+FUu4xOi/mPY/TuAA7IVVH+McZAngfiMdAELRPZcjMba8hj8344RijjS0C/9CXhKCyj/EHzFmlqvd5E2L+PiuGTFPy1x81sw+31wZGDMMvshFC2Y7+hoR21U8TwYuQZz7TKmFQdMuPYQDg9g6RHsvAEDd2GmA+RGZbtrL25PT1NRRGg0xyH96qs4vk9leZnyxgZXBgaot8vUArCOwoIl0vNITU9zomTAjXIZLR4ndXWadKFAymng1Vs4b70NGxtIQPva10Jlf3d/H2twkNZmZ5bei4yvydVVErduQT5PQwhO9vYwbZttRZ8+nsvR74TB0yyVQJlvr52V55WIjY3hHR6SGBogkYpj9KTgXxuC1gac2ZHYA1BVANaMfCXXV2AiLM6ClY8803FAdMv1QSYBKRf6TDDeBG0H2EEmE7iGsmGwQeo2wlPAO2qPGb9g3j3b2wF2TQcMGHkBEkmIaWA2qV4fw42t4RunwDLFah9G+bvnp3DMOn48LAJjk6ahALsekUbdD+xzQu+DHlHrcXDQMdApopFDI0mJEoImGk10KnxIkXcYpdzOFHuw0VlGtTd9gQNOzkiiQP2yPP9k4Z9C/Xvh+6xSIJZ0HtcCoG+HwAkrHgJfrMH7y/gyxtMH7JP34MM3g3nms8hE+uXrs2ArGbqmBwv2Vx5C5SQA8h9/Jyi5q/acqTB0advzwbnPVN2EwC4VcR48wGm1OF1YoPnDH1JPJPAqnUXbj8jEirm5Ln90PZcj1ttLZmKCuGVhVitMWz7aXEeXvXr3fqiQKi6YDbdGRkLA7voesXv38A2D5tERtYUFNjWNPYWI1xeRhG0cH2MNDdFSyusiwhWobWyQv3mDeLFA3NKJlY9IDuQwNt+BxXb20RyF5yMl+sRgGNijcp+NGlhnmwEN4iNgFqD4MMiKnANwt+G1I0DJcux74HQqMMI9CX/bBZAehmNFmTAsMwDWRtC+yQ0EXI1kArK5QIHO2AexAZkdKCvZlA9u/Ba+3gFDGWHTp91FoupuVmRbJCNjZy4SgxIaom2cYmORwuY6GjUEJ2iUaQEu+0CwKckxjIfa1nmZsvJcpzSJbpO0yIbhsu/+hCEuMM8Rn7DEiovm2C/r7J9lXLLin4aw4nDtHnzwnc599UiG7ntw+27QV23UYWkWVh7DZrivzchVmFH67NWwcYoA9Jfv4lRb1OsNKrPzeMurbH/4YegxqelpTt7uVAzqJyehPbloNsnfvo3TaJAeGMD0fVKaRmJ3F3Z3kQSYo10fD42QmREnK7m2hlEonJvGGIUCqYFe8q89JN2qkFpfRF9+izd2XKRSOk/n8yE6WOWCDYKpALuWTOKZJpkHD3A9j9reHsePH3Mv7qG918mKRc+r4ZPMrcHdBDjKqJYWmQ1vtEE+MxAICdlJ0FKgp6C6EvwrlKAWJuahDYCvbhDCvWXRWu+Wo80U4Ljt4Z0cwDMSUPw6nq3hxhs49iHZzSqissz5mGPvQzhWORwXmOk4OXxl3+PprVCn25IHWNKmJTrVAg2DGAniZLFIoJFkmBIuPjVaVKgTJ4ZPIInsARbP4dAZnfJJEiXDiUgfI8UB0NO5fHwsLFqhXU1UrvYS2J8oLgL2riU2CtoXecN/ErBfsuKfNC577E9TPPONMLDvzED/OOQGwQGWFqHqwntK2WylDLkMVJVFLBYpzW7Pw/QzAUmv3ICZOep1h73vdM4jT8td5iqxSMXgaG6OUjKJNTEBhQKNapX+TIb973wHd2EBF/B7eohAHl7/ILoC7Pqe0us1TcypKfpvTmHXD0gfr2PvrUJzDd4JG0bYE1epKWV0O0Kgq25skMzlaB0fY6RS5CYmcAcHady7x+72NttLS9hvvMGVk/BoVKNQIrHYOa86agcEpMTMGBwo1rgtoPQMxHNBBdI5gbiE+ibIraBC3HoAZUXjv97NC4ABUGewI0xkIY9BjOCJLA0xSEXLYT2bQDwnaWR28Y0mJjvEVW8BQKanERVlUsCNpPXNvYCPoZT0tUY8tInwtOA7JUUBVxuiofXSK69yKgR1JFUcyqTZRwcqQAWTMlF1AL/LwzxqeFJFkEaGZtzDHI8kO0SrBQkStGihoZHAxiDHMM9ikyVOluwnuLBdRjtEHOwXaW/5QWhIMwVcCfg+AG4OIR6AbN8WCegbCmO1b4M13L5PgpcHXdHN0Hq4jMv4uHhKgf01+Gd2wIAXSdjYBJEHVcAlHZGq9CUMX4NZJUM/2oGBiSBzrDkwPwe6BX/cAfJETxi8hOuSvH2bsgKm8vQUPZ0mefUqWipF4+iIiudRUexOc1/9aug8zf198iMjtJTxsxra+VIvSr0YPSXir43h7O7Smp/HffSI0Ru9JOc7WRxbj7uY3Mn+3hCwm20LVyORID05SSyfp5hKwaNHeMvLiPfeoxGP867CHaidnBAfGqKhlOcrIuw2LleWkZqG8P3gGkZGIT0FAyXQauBsQmwXjmcITWUVbgXAfhatSFZT34CEDapNqps6b01KrYSrJ3Fj36RhWNQMl7Jxwqz+Kgd0zvtsMUZB8fJ22CWOhVqT9zMZNJVj17hgzjs+CNVF0GKQGELUMpD7Oi46Lg7HOvxu/iFVpeyaIs66ci12ZBvnUMeiQEsB76hTuke9K7czKOIowC7RgDiCHiBPghRXGUAQsN0dHPo4wqZJkyoCic0o3+Df636dl/HxoRtgvRm6y0tNgexMY2gbDxHLah9+HD5aCh3DM1eh0vl9Yj6EpuImaUx+hhf9dMfnnbELIf4i8A8IBnP/Zynlfxn5u2j//dsEactfl1K+JYQYAX6HYKbGB35bSvkP2sf8Z8C/S2fO9j+RUv7+x13H0wnsE3egnIE/UX5ktyJiKMuzUMhBRRm5shLQMxRk964fjMBtV6Gq6M0PhCVo7eVZRGR+PJ7NUs/nSU1PY8fj6MfHrDWblJVxtcEH4eupq5ry7TDOgF3XsaemaCRSZO5+DW91FX99A7Z28W4/R1PZINQOqmGIqJ4GznOrnfMnLcG+aRKfnMQulbCkZCEWY//RI8rtc/U+eICztHQOHMYF8+1mBNgPjk8Dsm48jpiYgIECDCXB2YDmErgrkLoCNWVhc+Nt0wt1fC3S9a0cRarDEuwrgShNfAjMJHXRx0Hvz1M2j3C0KrDdLnJ2zpvARN2GHSMiCl5+2z+98175mfCiIJunULiGTOWRyTh+wkUXSTS/CvoOiMe4iSvsoHxnBDjcRB17siKLTb2rwQ9pkhwoM+c1kiFgdzgmThaNIpDFJ4FPHp8JWng0qXPIOEuh56qxSPizHEXSonL+WVe/QHrxX6q4sBQfAZWuKvtPUoq/jE8TnxcrXgihA/8j8E0C9ZQfCyF+T0qplCj5S8B0+9/LwD9s/9cF/nYb5NPAm0KIP1CO/e+klH//Sa/l6QR2TYObX4N/9c879x1FRpt8GfTQNxaC/xomNFrnmu3nMXEH3lf6ubVIhu442Ndv0VhdQ5+awo3F0HSdoaMjhJLhpq9f50SZTXcjLPPm9jaxwUGam5sYmQzpqSlivb3En3sOd34eOTtLdWEeJxn2Wbdy2ZBu2enMMqUIV5DBwcD5bXAYTIMcGr6moc3MBLa0gHbrVkg2tuw4oZa02NrCLhSoH3Zm4F3bxsxmSY2Pk8xm0XUwfmkSyksI+VHwVc2/DEsKueywHO51ew1IX2nrw589WeSHWKvC2AsQS4EhQRyBmYfKDMhtaIHn3eMwprLYHUyGcZTyfDxSuj6gyQTR6AF2EQzj08PpSB9G/C/QyDaoZY5o2SeMNHdQ7YDjp69CpdMeMFvdbPoUKQ4VYNci/IgKtS4udKZNcktiEUOnShqTb1EHyjiUqVNiHYlPUPIoU6DAibKpiNEXOatDAovax/bUL4H9J4snAfZof/wnIc9d9ti/oHEXWJBSPgYQQvxT4JcBFdh/GfgdKaUEXhdC5IQQA1LKLdq9RCllWQjxCBiKHPvE8cTA3t6NvAFsSCl/UQjx94BfIqhbLgK/LqU8/rhzfK5x57UwsG/OQ64H6hUYvwHJTKCTfvoj+PD14DFCQCobMOPPIhXpdK/NgJ1Axm28sSmamoVrpFh7+53AFAQwUinGdD0Yo2tHrrc3BOwVJUOP9feTGh8nVSrBzAxyfh7x1lvo9TpNxeYVz0dMTiHfV3zWG+FZ69bOPv6z19GSSUilAg38mA1vr8JewBq3LAvNDy8OPcUiKg1sf2uLqK3N4I0bOPU6xXSaXLNJQdeRJyfhHv6v9SHU7FuLMNY21iBaSUz0BcBupoIevJ2Gmw/AOAFtDViBlBOU7s9OHQ9r/seOlyDSkzYphIDdUgBLoCFIEpMvYiDQpYsmT9jTJlkRZ8TCGtl8jUw+QqoUg6F5d2mEF2KzuQaRfngKk8PQPQ5xbJKksbARxDAYoorgFJ8DHF6gTpNH57o8R/SxFTmLSY6Wcp+IAImvjNB1riUWAnY/AuzVS7LcTxZRS1bg4zN2Eaw5RjzYzGpBXx6RBKNI0FvS8EUMoQ9wJlAj9d7LgbgnjM9YK75HCKGydn9bSvnbyu0hCJXD1gmycT7hMUMoBCEhxBjwPKDIafK3hBD/NgEG/20pZXhGOhKf5hX/BwTepmf54B8AvyWldIUQ/xXwW8B//CnO99ONF18L/isEDF8NdOD1JPz4D2GmzVC/Mh0+Rraz+Eeq4ly7rZHOwug10OIcHuuc/sH/BwsBkMvnnw+dxq1U0K9dw5vtEL6SSoaempggOziIffMmzswM3sYGcnsb68EDWnNz57/95vw8Ih5HKkIwXi4X+lHr62vErl7FHuzD1nzs/U1ETw4+er3zoCvh9oFotTCmruM+6gBWIUKgOy2XSdy9C7ZNo17ndH2d5w0DrS2VC6ANDXUVkL3MMMaJskWIbDw42ge7FwwbUgNgxiCeAW8YauvQ+ACqB5COVFjMgQDYzyJCjjNrW2hyAF90gEzDRiOBRR8WKTRcEhjYrBJjHo0GaX8YqTip6XI0tPhWOSZaAJGiEEq8fD38GnX3GE0OI4WHTj+SAgnyxBjnFJM9dHaJsX8+nucADkXSHCibDy+SAdYv7KlnQ8DenQ8eE80kkxiYbWMYmzgCjUGmEAj8y2zwJw4p4hC7DsJtC9C4rNrXqGmpQL9feIwe5yjoRiBWIyToLRiNOOplG+B3KoNHxRh7ic5naGJ07Y0v4+L4jHvs+1LKOx/z94tKLdEf1Mc+RgiRAv4Z8B9KeZ49/EPg77Yf93eB/wb4jY+70CcCdiHEMPALwH8O/EcAUsr/V3nI68BffZJzfW4xdgPufBve/hF8OAvMwov3oaUAwuo8lIpwqpTXz5zd4kkYvR6Uf4eeg3ffhcWgtK499zD0celzcwjDCI2QeX19MDsLmoY1PU1vNoX/8ssYi4tojx/D48d4X/1qqEfd3N8Pf+quS+zGDRpKD73p+WTu3MHIJjFqJxir8xR1Ce8rZJvbo+H3YmsBIhUEs68H98yCPp2mYFkMPXyI22xyur5OeX2d/cFByko7oTo2FsqJ/Y0NtEwG/7ST4Tl+KvylOj6A0VuQL0AS0A6h14ajN4LRNYCerwZGL2cvvrwFQ23luLMQkcpJ1LRFz5F1riINB1N6WP4BmnaEq88h2iNikhhW1JRD5EKfZUyGM2KXJjpFPKU774mk8hp7aZpptNR9PMPCNX1co8wS99lQaiAVivzL86MkOdyuwm2SWAjYW5GfZ4Val4ueFiHd+fhY9KCTRZDCwyZJLw0CBn4FD0hzhNt2YW+SxEQSJnA5tDAvLC1fxp8awgBmQt+nlvYSda3zmQZtE7fzXZeXPfanKNaBEeX2MCgM2U94jBDCJAD135VS/l9nD5BSni8kQoh/DPyLT7qQJ83Y/3vgN4nWOjvxG8D/cdEfhBB/A/gbAKOjoxc95KcXiUKQIZ7F1mr47xIYmYYPD4Ie+5UbAZAPPgPzj2CnTXYbfDHoybcjVg8zo0W1SvLGDSqPHiEMg9S1a+jFHMUHd4ivzKHtz8L+LKfxoZDEqxExcqnOzZGOZOjWyBB2KUMiKUhWNol5C4jHkXGv67cDZ7Sz2I90RFp1GJ2CpYXAPGZ0Eq2vSO2VVyhvbVFeWoLvfY91w8BrKn3gfJjEdri93fUFMMfGaLad6/SeHlqtJPYLD4NMpLkFlSUYTYDzYcAT8gAjQmRsXOCpbo6Cp/Tmz8r7eg7io4H6nD0MnIC/Af4evc46ruxMBEhewNM7n1ugwDaGr5TnZWS4PcYaUAzdpzOOQS86CTQER3qBde1XORQNmsLBxOCr9ndCxwh+LnLek9B5T3AYQMdVyFNWV0k8HBKfDINtLfEEEpM98mzzDPtYbGHQg0ka1b63iYekrGwYIo72VKBrtLJGmWzkfbiMT4rujVDU0EV21dCfoMd+gQPcZTx5fI6s+B8D00KIcWAD+FXg34g85vcIyur/lKBMfyKl3Gqz5f8X4JGU8r9VD1B68AC/AoqH758SnwjsQohfBHallG8KIb5+wd//U4IW4O9edHy7B/HbAHfu3Pl8v6Evvwb/4p90bm+uQN8A7Lffo9FpyPTBtTuBdvzsezD3PohMYOd6FrmwyEdsZQZMMxC4MQzMa9cYvDqFlk2QnJ9FX/wQrbJPohIWL0mMX+FUVW/bi2SdQpB69R4x4ZGUdZLbixj2Eaz9MGwo1tsfBvKoCcrsIhS1gDDXPwIDI/h6CV9ayIUFePcRbgU231L79x7Fa9fY/ajD1WhG7FkPl5cZa08AGAMDxMZGiA0PoGdixLbXMPa24fEspML+0iSvwLGi7e5FFrPqKmhWYPxy/l70QPIGxAqBzGzchEIvaLtwxhQvj0FruXOIY4S+0cLvNqLRKYWA3Tv72cscuj9I0s0xEL+KrlUQ7CFZxyaPQ9CC8IGGeJUt0WmtOLgIikglq09EFmyDQ1RglwiyJEIZuoFGH1mSmMQRJPGJc5UGDlXqnFJlmx4O2Id27zzONV5XVOo0unffNnHKqC2KSNsFeQGwVy6B/VNHN7BrUWDvcmC9IGOPoH93/n4J9E8an+e4W7st/beA/4eAXPG/Sik/FEL8zfbf/xHw+wSjbgsE426/3j78FeDXgPeFEGekpbOxtv9aCPEcwQe/DJ88i/okGfsrwF8WQnybgM+cEUL8EynlvyWE+HeAXwRea7P8vljx8mvh28V+uHEHTg5geQ6W5sH1YFcZTZISJq7Be4qTVl3pYeo6cmSc1PMT+FvbyNlZ+PBDkpkU2judcTZ/Zwc5PYLY7PAk7LgRoiXJrS3yL98lnbbJVI9Jrc6iZz34ccfPm9ULCDmjV8LAftoGulQaJqYCoZ0BA1behsoaPF5DDt5HftgBbXt1AWFZIW35bLGImjsf7+4SB+yhITLDwyTjcUpFk+Ti2xjlLdjZwu99Fe99hZPw/jLyhRjCVVoeeniojJraExaQGoL8NMJwIN4AYxuKEnxl40EGtAipy+wNAbvWqBNWZt1ByD6kaIOn1DFkAY1n0WUM3W+hN8FeSKM1A+tTAPulFg7BpiBQZw9/BhabQCl8LQxACNhrob8KtrC5QRabBHE0YhSoYVGjjMsRLYJe+xxVgmy9jEk0b49FKgx6RHp2H7gSubKoo5uITAcc4TEAaOgkSGATw730Zf/UIYSG1L4GyDY4a9heFl974fwx0iIwdTlHax3yXnCMAITkJD2Aaw134Nt8lgF+nZC722V8IaMNxL8fue8fKf8vgX//guO+z5/Sg5FS/tqnvY5PBHYp5W8REONoZ+x/pw3qf5GALPdQSln7mFP87KJ/BF79BSiXYW0dHj+G/kP4UBFwWXsM/SU4UlJiW1k8hQBcai9/nZOTKvsfzOK8/ojBh70IZS69NbfQpVjqD42gq8DeKJP66svE7Bixw13Mx3PYVhPxtrqJiBBpdrZhsh/2FSBPte1DR8ZgaAhieiBqsTYfgPkK0PsKVBS2tBNxeXMcEpPXqSoEupSUJPv6KIyOkk4kiFWrjPb3oykjgPFfuY9R7gCYOI2Mdjle4J9+qCjFtdrfVzMFuXHI9OCN9OBnD/DTy2CuYO4MIk5/0DnGLYa9MPxT0HrBV7YeRkS7v7ZLIHxuozGK8AvYbgnh76N5O2juCph7+FKZo5dZtGYYxCwvh6N1sn0/Ysiis4qgH6lkvi4FdHSgH49BPHL4vMAxCXaIs4bJLGPUlWzrL9NiSdnqVSIA3MIhQ5yGAt5G5CcrqKF61DaRxEjQVDYWMXSyZEgQx8IggctreMQpE+MEk31GWECyjmhfX4JbwFe4jE8X4vgdkB0ypUz14NLhyOjeNJSVjbtuw9Uw+XI3/8tUtE6yUeIePfyVn95FP8Uh+fzm2L9I8WeZA/gfCPQq/yBoD/C6lPJvfiZX9VlGbhj+7/+pc/sCIRiGpsLALh149n6gNjc7Dz98xPpojMrbnbGuerUaVlk7OEBcH0cqkq+uaeO/eI+6r1NZ2aD6+nsMFxNQUTy0M7nw1+5xRIUKoP8KsnIKw9N48Ry+jGGNZoP+9Vz78X2T4V6cF/4yi8PFTvugHZnhQYxkgnRPmpRWR4oWYmcHdjotBOv69ZCHfLMVESndWgLDCLcutB5IH0N+JCAhmjbcHAV/FUSwyLnjI6hTHzIWKWM2N+myi9OGwsCu+RC7EVQEhIGQNex9EK0VRJvp7hXvIRU9deu0iY0AACAASURBVOlXwvti/QRpFBBuZxNktSyqSpLuUlH2GBYG/fRwgxYGHjp1PH5ED+/y3HkubDPEdyNSrL0YrCjZcnTM7PiCkniCVAjYoz1bQZ1++ogTQ8fCx0Bg0cThCMEBkleoYbBEi2A2NUGawdAkDZiYOMqmwyGs13AZTxjCCgF7d489WtjsLsV3HXNhH/4yniw+03G3L018qlcspfwu8N32/0/9FK7ns497Pw+/qwD75gZcHYEdZZTQtOH2vaB8troEP34damkod7KpdDEbmgguz82R0Np97LMYn8IcGkS3BMbOKs7OMss/Cveb5eQ04t3OyJhfq4WBfW8XJocCoBweB92i5SdpPGrC+20b11QSsy8y+tQ7AKvKc+2EM2mhgXj1HkKTCNtF1DcZLziI9bfOq72+YSFME6mAv+wJ61LXtw5C41/CaSFefAkSOqLfQqTKULBhYwcO2huEkyz0nYQAVXMH8A0F2K3IWJyzHbjryVPAAmMM5AiYGZC1tuHLIXiPztdGAQhjDJWQJHwz3LL0d7tGi4kNggrsDRcjOQyUcMlQJk4BHY99PHbx2GAfkw06G548pVCBu8UBRJQAspEFux4B9n3cELAbGKTJYmFhYbb7tWkMvsIJPge4eJi8GTHnfo00Kxxw1ottRJ6nQpOo2rgWMYtxI/Pyl/GkEWl7yE/osXdZtnLZQr+MP3M8/VuZr/1cl1Y6Q5NQ7IN4Ana3YGkpkI9V4+o1eLPTO07Vwkxz9/QU7e5dMAxamkZ1ZQXDrZF+1GEkW4CWy+Efd451E+nQMusuLGIKAVfGA4U4TYIegz/6DqwEwKhdeybgApxFpQovTgfjemehR1aMcgO+cg8sAxoHsDePMezDu/+K8/apHSlluy2SE1epzHZG5xxN62SrQtBq+fDqPSiaEDsG/zHGFQNWlfaGF57rp3ICZn8A1menatioWOKbO4AAcxisgaBEKeNQmYf6Csg5GOgDU9HMvqgPrPeCt9y57bbCa63cgHM9+CK+NkKzNIlfGMCxHRrxE45iCd5HEOwYgqGwl1hGKlu7aGbtR3rqHg3SGJSVMn70mAomg6RIo2MjMXEpMEqdKlUq1KkBDnuKHW2Kq3wU+tk2SaJRVTYzRgRcquihe2q0EBhI5dpEpG/rXmbsP1mIKJ8hHFLIQMhG6MF4nNCRmkHQd9I5YzqY9LfvE4iu0tVlPGlcurs9rVEswY1nodmA3j4onwSmIu9FbD8LvXColHnT4R9TcmUWY2AA/coVHNPkeHWV/Xgc53udfu2xHh6UEkD82hS1H3aeq35SxtR1xNWraMO96GYdeVpFzH8IR+2+2rPhcTB9Zb6r3O1n+9DPZE3jCbBMePgQtDqcLrX17S1YUlTT9EgqsL8YjPm5nVwzPVgKgF0IEmNXMIoJCn/tAfH4MTH3MZo7A1fH4WiJ8xRVixD8jiJjhQDaMCgZrqj4iNQzCC+H1gJRP4JsEeQa5yX61qtQU4iNjdOI+mkZjP7Aj/08IougcwjxK0itF6ml8DSPfb2HE20VR1SBJqW4hs+754eY2ERH3jQG8BQZ2SQRxT9OiZYC+rBIYpDDIIkkwQkF6vic0OSIHD5aW5e+3v4XJ99mvXeeWQ2fMkQc1AvoIWCP/qxP0boy9BiBelmMIjEKpBgkz31MejDoIdZFwbuMJ4qBdv9cWCAs+nf/C/pO/iXCbyF8B6Hlglaf7PzmFntHUX3Zj4lxpNR/9MsU/s8Ul8D+tMbDb8I//nuw0ga53v7ux4xOhoG9uodMZ/Anr+PocRorm5zEEhy/3lF0Sw8MhHCmuryGfLYfodip2rkkNcsifv0a8VKBuFsn9lISrfEI9tqs79FXwtcSIaSJRgNt8jr+bHD9YnAIP1FAf+5BULp/PA/f/x7c1sFR2OjpEqAAey1CdHNbgQjP4xnoHYShUUayPQxkniNz8hijsQwnu2A3oKEAR7I/APazaEZkS2sHkClBtc1byA6BHIDCQxB1cLfQK1voqwvh45LXwVWuUY8M/VY3ume5jAFwtwPmujUKWgaReAhaC9hDsE056YDofLZNvo2jsM1dvLCaHysIekPkOJ8iqj58ol0tsIiTbhPTYpQAB48aTU7Is8E8BzgEA3p5BjlV5g6OEPQgkMrCHY9QMKNKcE2OiAJ7Rrl6HRAYjJMkDSRxyVLjCnk0KsAJkn2e5++Tjdi3XsZnEGZYE05DA1ed5rioX65H7o/22C/jMj5d/PkA9pfuB8B+FrvbMD4Gm8ud+zQdrFhbRz6L3Ntn70Qg/7DDWM8+fIhakD9aWIh0N8EZHMM8PcaduE7VzlL3TUZzAm3l/YCtDoi/MA0bqu97BMDW5iAeh0YjyNQnp4lNX4WpHHp5Ge10A44T8FbYO5y+a7CuzotHloTdRTCsIMMfnIB0GrQ0mPtQ3oSdTTLWbdhR9A9qNegZhz0FyGWE6HaWoWs6FMeCjVNfHsx1iC+DthG4ulXUcr0RVBnUzrQWmZvWIxItrX0QpeCaRS8QQxoZSJ2AWAWxCJoNTuf6gwGhCaQyu25GvvZNqqE8X8PBJketXY42SFCnhMWrNMlQwaJBiuvMIZV2wAH3KCvfkOiURIOwDK6LJEGSqlLij7Lem8r7I9CIYzHs9+FLG9dPUPNsUobANFpUaVCmSQ8nlBRxKoEGbIQ2Cc3LHvrnE1H9eNkN7ALtE8D7kjz3k4ZEXLLin9q4+6BLUpX+EdhagSvXINsXOGrWBbwVMN8FYE7fofWjThk9WQlnpvX9fcyxMdztbRI3r5PqzXKYTDG36eMvBefR4zF60+GfrUz3I5Tsj7JSSk6kYPwqFAbgZAO25qD2CFMvwIai/767CIlk4Hx2FrHIvPjRDugmDE5BoQcML6j3Lv4JHL8VpJFX7kN5P3zeKCkw1R8G9nI5OG/vJJR6oCCgbxwS74G+CCyCeADHijmMG6kWSBfMKXCUrF1Gvo5aE5IvgZYISpfOLjgpcN+BMwez9H1QRoPw14mGLntxRQfYjcjsd5MDbGLoDAI9+CTIMYpLD1VOKFNlgRLbnECbWx7HpRTp8SexQ8BuRJT0K5HyPRACdhMTnTglRhEkcLCoEqfBAAdI9vDxgA8PH7LodbK6X88vs6U87yFWaMpe4mOSwVGu9xLYP6eIArv40zJ25SHdnfnP9JL+PMVnbALzpYk/H684nYVbL8B7P4bSQFB2t3LgFeBHM5yXq0d7gyy5HVYhGVqa7bk5hK4jdJ3czev09OcZyCdJvbmJdvwOHEN56Bp+s3OU12jiPXsbfaaTRcrTzgZDZnvw0j3od0cQW5vweA7efAu+8QCWFWDcjUgO+z6MTcJHHac3qj7k+2HgCiTi0DqEfAoajzhfx0v3w+epR0hSzToMTMCGKtoTC0r2g6XA1bSnFlSDNaXMn38eWgpgauHZbxpLAVlIKveLErAQZODGGPhpEPeheQS1FfA/CO7zVRvRyPW75QgR+RgpigjZeV26n8RtF0UEPcQQZHgOHb9dnt5ijykcjqAtTFNnmgNF5tmNZNsNGhgkcZWSvt21QIcJdXVa9JEhgYmNhomLjU4DixPqHNJEI8PrCs0uhiQ6EtWjeyx6nZ9uwwsNILKH0VVkNyLA3roE9s8ntB6wFSdCmYTSWSslEKUpHWQIxDslSEkufYeh+K8S6NYJbAY//+u+jC91/PkAdoBv/dVgBGxlCda2AlLdfgTURifhoNMDNc+81zUN48Z17L4SX80b5Gb/BKP6XmBW+8IrQa+6HamtefR0Gq/cAaNGrni+VMvBIZpuAq//VaofrdP442Vgn5FvPI8+rwBlNeKbtrEExYilbKkAN27BYBESLUjU4WgbjrfPVVeZvA3bisOfEdn9Hy6CboDXBlxNh6lpmBqAAQ0K+zBaBaFsgACsUtiIRUQ80GTUk9wBawr8JpiDAeO9lYHmMDTWgffBOurOuGMjUFcsib1I28LZ6lby1IaQMoFnDNLSsziyANzGZxPJEfAeZsTO1GK8Dexnt8PqbM0LGPgWxRCwW/gkSJEiSQKDGA3SCEwO0NgGdtjgPkccnwvJ9nCLTSXb9qihKgU08SlgUlWuJ6M5qD/dUz/8Bmy3O+1nmZ6OSZxB4uTaZLkestzsej2X8VMItwbHiiBN/Dr4YRvgzHoOPKXJN/4bEP/G53SBT39ckuee5rj5XADqZ3GwB1cmYEXJTIXydoxNYI4XSRuvoM+9j7b+EaxD6lsP0JpKSXVLtdYF4ftkrk1z9EYwq26PXaGWSuM8/wrHC0vUH23Aow1ypRK+ohXv2Jnw129pOXz9Eph4JtBTT9rQPISCC3wYbPZPgTKQyQTs8bOwI+X5ZkSfPleC6zcg3YTiMSQXoFCHfcVIpNbTPatljoSB3Qtn6NLfAPsGwiqAoYM4huMMHHyfc9Z78SVwFSBvrYOVAF/JdI0wUYxmJ0OXCCCGa76Kq9u0dEFdr9DUhqhobxEIrO6TRSeOKk+7i6CEVEDZjLDpDcqh2zWO0bDw8UiQJUEaCxOTIh4OTcrk2SVBZ0RSw6YZlvMhSQzVSDmq296iQkQCiAzxELCn/n/23jxGsuxK7/vde98Se0RmRu5LZdbWXVW973uTTYoekWOPLYw9g4HHsmFJEKyBF3mRZPgfAzI8gBdYf8gSxpYByfbYFmzDGkD0DCR6yBmuTbIXNrura8uqzMp9z4yM9S3Xf7yofPe+bA7Zw16GXfEB2dUR+bZY8n33nPOd76gu99T/VQFOLHgMxRBdqjSosM+r/BZlpigyjJ/pUR/gE8SpGe0/PRU/qKl/dBi0u33W8cxLp5zXmJ5OiX18IiHMX3oBWregtYhoLOLxONowqtHHdkqW9WWoj8NO34xlep6Z+TkmRn1q24v4h0uEd5q890M7glULCxaxt/ebtthqYwNefiypuQtgawUqDlz/ZrpNL5Oi08DoWbhrpPADgzjyNRiqwBc+DyMNqNwGZwWcM7BrEHmU8WTv7CTz7I0+dIQhT5cFtFDE1VeJcprQ2yX071Be7UHwrfTa/Fex0No4HW17Z5LSwcl5kq+odifQhRnC0hiNypdpei0a7i6R7FLFIzCI27WmIkKHg1NCNsUEIamhT/YPweWYOjPkcPGIUBwT4NDkJrrfthfyMHu8a+xjHyWmjUednrFIyGfa13SmDt/iCMkIMclQmCFyjJGnhodCoImp59/itdxbFORtXHlAmRKT/NA6zihfosAnPE1xgNMQmW/WBw19ERni+QCB3QADfBjcP8SeL8Djz8LrfWIslqFaTmrZjZVkEMy1DXhoFFop4YrJiiVdia/dQJl/h1Nz8OCjydzx1Vuwc4eRkRrcTMnVOdrBmz9D785SepyMOUzz+h2GHr4CU3Xw2nB8C4o+/Mgg8qlMN/LOGsyMQsOInHP9oZzSgclzMFyEhRcgtwbiDojvQ/2ynd52MjeW9gfY2nozEGygZRFdOk9UGMYtv5AMbJF3QHyX5rCtctfeOCIw3PDczEiB9t1EoW9E6NoZRxcdovwIYV4S5osE+Wli5xB4H801NjlLjGnbmR1EaovUumyTRMHpokxRI9I5HKZQ1HBRIC4i2EOzCnyHNnXupbM14PJUhojtskabw1PrlBxVi9g9wxQmUcALxplBU6RLniNylBnlJpJNBBrBr7PKslHvH1MhVd48eXxMCzJz5gN2YNDO9unjJGJXyf9LH6gBzolBDWIcZKn//wrUT5qOPcCHhUYQxYOI/bONP/cvJjXk/X249h688ToUMrXg+jk4Mojdy8w2d3z0i19CxM1EKb63DL15+JHRyrV0/ZShTHF+xiL23uEhxccfwx+tkuse4d+9BvMt2DbqcWMP2edeXOTUYKeR+YTYy3WYOQuzFVh4BNQNENeA61AsQWSklp1MW5netx+Hh5CfQfd20dXzRLURuuOjtMoxncIqiHXyoWJ0P7XGFfSQ+jyxSFXukecjzY41kUb8GgH5WaKhy4S5gF4hpFvYQfhFQvEm9N3WFFMIo74t0HhM0mHROJZNpzGZ7AhVnPg8ThyiIo0KjgldQeBsIvrRd8RDNNWWRdUew31r2P7blrFmjU6p64/I4RGfkL8kxxAxOaBMSI4dxlnnMVZw2ERRwmUtUwaYJc+GsUAKM69vD2XZ52hiHGqWDWzPmtM3wKeG0X8HRv9af5jUAJ84NIThgNg/23jkafjP/0b6eGcHnj+XRNr3EGW9nrfgmeeJhEO0tEy0tETxhfOoa0bq+sieu06nBWcuwa00PVysOPSefBK/XMLb38NZuslQpYvYNEoDpSm7rayXIdyDPZibg70VmDoH0xNwdhgenQO5DOxAbho65hxyDYWz0Eid1U71t/duoaUPpXni2hhRVdIZKtGqfDvpD2cZyTN0SOvhbbWGJocwyE3peobYWwkVihw4CyBH0GfPQ34HvNugljmqXqKt0uyGy7h1aRHrOOQxo3CXkkWpUZ9IFSM4TCAoUWOSpBVgBbhDueGieka7oHyZ0BASClbI1gV8ahaxZ7uNexwh8SgwTI4COQQSny5HBBzRYYddxniXbRJlexMHzQ+MwkCDkDySthFtlzLtTq3MdW0juIANSa3/ehXuKZ+5AT41CPnTtxlggI8Y9xexP/U8+D50jTp5bcom9o1tWHgKVBHW1xE3r9MLRoneTtvKosOMHGP9BhQr0DRq8WNThF6RY1lkb3mb3vvrjC9ex8Ijl+GGkRJvZW4COzcSxbrnw/mLSTR+vgjRPokL2g2oPAFdw8K1swpePTFzuQeZSVV3ttG5WSjOQN6F/B7NYU3s3ACShUUsXrN6bk2DFwBEjHYWEGG6eJGRA7KKis+g4jIqluCdg/BOMls9JikDxOlrdiNoG29mfCrS1EhmiI2+fw+XHJcQlPvNYBqXEpotwnsz1ZkgMjzWQ1W0PjMZHlgaNckBUl8kFmmGxu0TsMAjR50cikkeQNFDcoRgkxrvIoz0+hZf5oh0ceNnovpkZvtZ65k6irsGsfuZPY76mQKBoIZHDckkC/hoPLq4NJnkK9R4CZc64j4UCw0wwAdBa0EU3l80B/cbsedy8OTz8O2vp88dh3DuCkzUQR7A/lW4WoHDlBjV1BCREfD23ruDZ4q14xgeuAJRANUCdLfZ67W4+XuGH72A8eEqHKZp5ahStz+AOxuJ2Lk6mvSS53z4kobg23Cvplp7BXaNtPrh4mmLs8K8Texaw9ATUCiD30S7d6F2gDk2VcpXLPIU2m4Hi1lBMGQpyUN3FkcU0KpEJLsQRlT2twGj5h9XQBiCIW2r9J3wyAqUkwi9gD6ZM36WFg/Q5gp7KLZpM0wN15hxLXAYwXapk9QzxG4TpgxWsSHIsYCOe3ixjxsG9GRIwQO4jeAWgl3Cfppf938UdWLDAz8roHM4wrQIDdnOVMOhiuIuAR6CSWCBHWZoMsUWE9xlmjUqvE2RW6gTncA0oXFejy/iZbIdAwxwvyMh9vtvoXt/ETvAS59H375JOL1A57hH5711Rp+6g+XXceYc/CglRkc2LMlUvLSGvnIOUalBqQTH2zDqw7vfSdrOgHIuYzargXPn4Y1UvRy2guQDmJ6DqVlAwHkFW+/Dep8cL74MBwYxtjPGIt0DGJqD9j0Sk5Crw9TLUIghtw7+XWvimQC0vJRE0X2oWJ2YuCSwB7lIXcXnKWIRENMjZJ1WXuCGqYhLxB+QAnbPQC8lYWJpWWG7wRJJLDpDxBxNhmlTZY1DdvoGL1NMsUtq8OPSwVweaEIUk0TGQiU7DCZQrf62BVCzIEfwuiVUcIzs7SK7y1QKuxClWonj4udpesZoWdbJTKFBniJ2W/Ws2IG+D5xEUqLMc0jydBiiQZkdzvFNhvnnlLmJQFPlEjlSrYWmxF4mjldULWIPjP8fYIAB7m/cH8TeasCP/hDe+AN6t2+y/fYKvJ3Wi/W/cA6xa6Tjy5mb6N41KBSQ585DbQj294lHSqhFQzDn2zd8r7OFPzdLdzklhl6xhCcE4uw59MQkDaHI1cdgdTn5AXjwMfvaG5nWl70bUHAh7tfm3QqUH4byQiKQ69yAwi5U0n5qIhIDGW20sWmbhGVwYHwbSijG8eNHkTRALyNYoSM7tEmzED1RsmhOyx20rCNic5BLzb7+sEWYe5SWM8WBqrLtKG5SpnEScUdMU2HHSP2H2D3yhxwxjAPG85JRi9hjLXF5EKVLKC1QtOkWpoHVvqgQ/KPLiMAohWRI283a4NLEYYHQ6kS3Fcye7pATCwhqBBRpUSDHHAcE7NIlBn6V/42Wka6fYwJpZEvCjKpfcNxfQKSZGnlqzOpALDfAAKegGUTsn0l89/fgv/zVk9GkrnQR+Ty6nd48AzWFZ/Q0091AC4GevkgvN87xahPvXED8jlFnf+Blu5J59/qpEaili7N0766Qv3CB/MQEYS5PuzxEeO0mXEtu7PXHJm2pVJyph99dw3KULIzD9KVkQIregN4tcI5hz1DTH9yBjBEc7lnoGf3tke5PBHVBnkeFQwjxIrHYIGaJSPwYN1LEpMp3pQMr2u6IjVO+NTizJ2UALeuEzhBd75doeZIj75hjV3DjxKUuIfOk79xMpdsE28Luq4+J8JigxwqKEj7jSEYo8hSSI+Aurl7FCezeboRt1KJVDWEazMV2T7kT3CUb+buMoInxqONRwI3ylMMrqGgPJ7rLptrgG4Un+lsHwCFb9GgZOR+d+XB6KKua0vsAlzvFsEXsov8eSco4TOKcGkc0wAADaC0IgwGxf/Zw5opFtiIO8B5+iO7raQq5ezfA8yGuTdPMnWVrOcTZDOh98xqQRHejX3jVshYJ3l/FM4PRbhtmL8PKVZi8ANUJxhtV/B9Ukdevw/XrUCgQdjMTvqbO4G4ZwrQNo9dbOUAezrwGThN6ixAtQ/EMHPX72wUQZtLzrW2QkxAbx71HpmoOnOm+B/tl6N5EcBXBVeLqBWJhOMGJktWqLbWt/o/EMTBDojyfQsgZeoUpdN4ndFaJ1SY9drmr7OvzmLZ6u7OjSoOML3uTXYoUKFCjQJ4cITUOcFlCchW4is+zRKQZlFCEp7/cYtrOWqiMK1h0aIw/L6P0BKXuRZwwwu10cNs7dMc6RF4quMhFT+N2/+jkcTW+DjxhHbZK3iL2XmY51CG03oGAA5LFjUYyjWSWEi8AeRym+z8zOEygTs2xHWCAAe53fPaJffIcjM7Bdloz9mcrdF8HUanQu3SJqwd5orUz7Px4iXtDQB565hnuKcQBus2WFbtF7y6if2kE0diF6fMwOgWFKqyuwY3rwHVytfPIA6MPvtXCfeABgmvX0uPip/FpvpC02z37KrhH0L2WKMhzeWgY0WeUSc+3bibGFpar1SyIY3DO90m9DO4I6GWIlhHRvbOmix4VTRI7Roki45cuuQW6ihJ1FFNoCgRuSCQ0WuwDB3gqD9qI8rkB2H3zJYbZM4hdWW1kghjNBPPkAZcGkjWK9AgMUxafB8AwbYkyFrBa7JGkLVIiF2IIbZwqVgFSzoEcg7gAXQVdF5rr0N0C3md0SEEvdZeLh75gdURGsmHlFwrxKq72CERK5MXMn1mLHD6J6C/HMJoRqvwaLjMnPz5TSCYGCvcBBvi5IIijzz7NZXF/vOJHPg9f+4eJOc3MJYRT4rsPPcR7771H/L3vAfDiWbsFqZPLWW9O89oN8kKA1sgzZ1Dn5tCXyojV1+H4ZvIz9ww0jLGdh7cQ1Qr6MCUXd2zshNhlrUqQ8+BffgXyu9C7BvpbkJuFY0MIpjNe383MpDfdhsIDoLtQmAZPQlyC7jEnanp3Djxz6E0AzoMQpgMpVOASGC86FptIPQbMo0WRUHTQFGhwnXuE6csrCKPmHIl9lEGeigNcfYlApDXgfJ8KHXIUGcdHcYYzRBzQZY2YNxlin7jfKpa4vl2ylhkBdsI+Yu0UBQoxi9bvAjmknkUEo8jeq4huD9HcRoTH0Er69NP3MjOQI9MqKLuB5ZsfyWwdHmoU2O5H6HlKFPA4yySq7wzf5ogyY3TY55g9WjR4jv/zA8Z1DjDAAD8XNElLzH2G+4PYn/gKXL0GV9+DpR+TV+9z7bZPbM4cn55OnN36ONjePrH5UOPjFK6cx58p4W6+gzxcgr0lCF6FY+PGbgrwAKE13qWLdL+bCM5kvY5fr1L81VfIiw28xg1E7+sJEZsp+nyG2I/tVi6at5PhLblJkr67FhQL0P0GcCfZJnwCK48eLINfBW3Ub6UtoFPdDir3GFIXEXETHS1x5DloYfSqY/u994gsvXbEMgqFOWo0xwgajWSKiAouBUoM02KbY271ZWW2w5/LJF0jY5I1j+nSsoV7HCD1JIIySteQsUQd53EPDhGtFQTXknn1R98xXn/WihbwJqFtXktGBd/J1vv30fIckRgmkDW6Mk9RT3NTXGCDiDaaR8hxk7S9LgDKRhtGTECXA3JkBt4MMMAAPx+0GBD7ZxYPPgffS13HVBQye/kRFr+fpoyPwvDkFu5UKji1CvVffY1C5w7+/iJ0N6HwMhwa0fJWZlJacxcm52H9TvK4WqdYnyJXfgV/dwVnZRGx8iYMGz3UQQAjD8Hmj43nMl/EzUWYrUP1HOR9cHYh9qH1Bif+J4XMnPLu4ulPV52Fk/Y0DyiDeiWxvm2v4TavE+TTqF4ADs8TGONancwQkw77diOWCIAniYRPhwoNoTngDEsEJAK5JlUc2mydxKddDnApEhgCOskQZikkMhTwApcIH08/jdROv+d+DT/IIeL0M3W6ryJbRjQeZZz84kNQQ/bzWRV/FAISnElwx5C6jh8MI6MmMtpGRCu8U75MW6xxr2dyD5/bxvV2M451G3BqaGqLnQGxDzDAAB8J7g9in5iF2XNwN42o58fKJ27j0nEIpObKFz/H6PEOtcWryOvfg4tPwrox1jUzmpTF6zBbgG5f8FYegbmH+zX9FVhfJF9YhrcNNfrSKiyMLtqmWQAAIABJREFUwaHRnuRmvNt3d2BoHmrTCf9GKzCvIfxeuo33KpgzVbrblmKd6CBJ6Yf9yN+ZAeZAlqG7C+0b4C5DO60fC0AwiSYVyTm6QmAcV1iKbUlEDniFAI8OXRpsotUFDnkX+nVvl2nr5TXZJYs8YwQWkSdVaI8xcgyTJ0eZC31v9yUEq7hxHm20eQnxrHVM7WYm8UVZUxqS2fD3iF3VwB2GykugFIgO+ArKLohVYBUJeG17oIyvi7SN96jCEWYb3HFGq9AEHIrWLPcW2wyfMoodYIABfi5oILz/Slz3B7EDPP15i9gv5EOOvvAy51SXhdWr+OvfhdqCPbM90x7FRmbqWa4Al19IppM112DvZkIS5kS2ndMDYRhZsIm90YXJB2BmHOo9qC7B2n5ixXpvN/ECVi1YZqxKWzeh5IPuokUe8hchN48IJ6F3O5l73pmFppGK7t2EbD94NEukUmJXcdBXigskc0TUcHmRLi2OWSfmkJAiXVJBoJupduvMUJaQFkWG6RjpaI8yNc7gk8MlIE/IGLuIk9fskySxTeHgFBjEroWy1jbaySwgdAOccRAeuOMgCyCqiWlOby1Z8BQPQHwrPU0wAcYCQQCIKdDpd8HX9usts4NJ7Pv9RYAAauQYQTHFK5SoUmScIuPUWGCAAQb4GBD+9E0+a7h/iP25L8K1d0B7cPM2E9/+Ll8ZykHbqF/PzNjEvpohhuYhPP8K1AXUt8C9Br02/CBtd6K9Yu8TtODsg3A9TWcT5eHsFZisQ6EJ5R149CYY5Ej5ITgw0vNdBysLHqWT4rQ3RVyZR1SGEGqxP9ntbehV4fj1dJ8wG7F2wTvfJ/gEMigQKYBpAnmehpjikBoH7BHQBlYoUiIwSFmRSV9n7F1DVhCMovt19wLD1JlCMITHEYq7OKxzQOITEAMhdYR1nC6KGSJjEI0WFUtGENMxlhQu2nEg9xwIH6IeBLvgFKHzJgT9TEbhVWgZzni9I6x1SbBB4tmbLqQEI2gju+DHwck+Ep8ax1ymTJGIPG0U+zzBt8jxDqpvPnOG/54qX2CAAQYY4KPG/UPsT7wKf/HX7efOPgDvpjVZy9Mc4OpN+NKjMFYD/wBaV+GFCA6MyW4Fu82Kxh2o1i2veWYmwHVhYhhoQLkBI/0UeI9kLogzAaFhC1ocxtKTHezBMGgUOn+RsDhJNPEwvfINYn8T2KfcfAbVTYVuqIzRSbScCMZi43l3HB3tEhYu0irUOXAnuOlVaIqEyCS7lDg8IWUAjwmL2ONMhJ5arCpyTOIz1E+m3ybHVRx2CfgSR4b5TXamepM9hjOEmrjLpcQeI5E4yHgWGdZR7RpO/Byiu4VoLyO4Cq0pCAxdRPFF+z3JpMnprp8ejaumIUqzPZoKsXyQUI7SlRVaYpxVLrJDxB498jiM8E/72yYBQ5Udy1EuyA7VGWCAAT563PsDvM9w/xD72AScfwBuGlFxLmPusbYIZ+bh4hyUOtB6Hy4J2PhGev9vZeo18bWkjS42FgXzF6AxCcPDEB6BF8H6O2nb9Y4HX/SSKDLdCUy/by85oXaKMHwRPVyjsfAKreIisdoCtihxhdioh4cOKLOsLBfRCIQR1mr/AjFtOvlpGoUcx/kxNv0Oybd/A4WgaZipxAT4TFojW2XGja3HMQIXnxlcqjjEaBQdbhGzSBtYYALNd4xj2PXvmEwLH3F/qls/m6AFSldR+lGc2McN2rjNGOdaiIj7RkLCgUmw/pLdcZvYReYrnxXUBTt9Y55uoksQdQgmIZ6EbgM6G6zlxrg11O6fZ4+YCa4bqv02HYbxLaMdnRHGBade7wADDPCRY0Ds9wFe/LxN7Ns7UB1ORqLmHNhahIUIdv+IE42YyrREra1ijbvWTZi6khC/V4etBpTysP4duDcgrTJpH6PXg8oV2E+FazQ98EE7I1A8j86ViacvQek6yETJ3i0/RyzSqE9nHMwCZyujUG+g808QOnk6uTLNfJuj3AJ78i04GW9qu75FrOMyZynUHYbBIPaImDzz+FSQxMABPoeI/lQ3DUheQBvEFpIZm5qpu8fsopgm4gili+QZx43zeLqMinaR0S3caBOnY+gXognbBlaHoOatgTe4mcVbnNEm6BbkH0kyGVompZNDBXvf79fRb8PcK2CcN9+xW/MEq5ApRxSocmjU/8PMlPXegNgHGGCAjwn3HbHr3/0HcOEyulIj3tpGlXqI22krHI+/BLtGD/mB3bfMxm2YHgW3BkzCVhtaZfij/y/d5tJD9j5H6zAyCbtG+rU/CU2XJwlnzxHUh8iPnAVnEcRuQgLumOUm58ZDVidcSNMiy1CuETuPE6sagRPSdVc5Hp6lSarKV5nRnh3u0p/3dvJcgXEO+z0DAomkTI2HUGhidpCsATeJSLvVi4wQGellj6JVIe8QW8sQwW3QZVzG8Knjaklduzhs4HAVgcaLXkSEf3iyj5aZ6FpugCpBZIyYlaM2sYv+f9yppGVNDsHQS8AhxCvAEqwNQZjOASD/rO3il6nQ5FsbmB7ygkN8fLrGQsbr5/N98pQpIcgzyl/CYxaPWfzMTPYBBhjgY4DmVLXtfsD9Qewrt+Fbv4/+ztcIRQ7eNIarf/kJuGHU2dt2zzGL78OUC+UJqJ8BJ4INB977Y7g3kWvkOXufW9dhxofASDdPnkmIfXwaHpqnd2mIncceozu0CdxC6Luc2c9EcWIedBr1uVHXEnaFHOLxFAnJ7BOJGxxVckSkk92cjJtZNuWtaeMzS7f/vMcEOUbw8dEc0GUZwTohb1uz1HLk0Fb9e8IidieT/2rpFlV9ATcawgk0bmeHfCVAiFTcp3iR6J7BDhDTtRYusTWWlYS0czPQfD99Ih4C/2kQBSCEwIN2HppJuxoyBxNdLNVdbhKOzV72zID7XstqJcwd38bsRHcZZpYxYmI8YhRtzrDHE7yO089MlPhlJvh7DDDAAJ8gNKcW5vcDPtvEvrkKf+nzsJQQsACon4VGKnjTqmxT3+07SWdVaSgR14054Iew+V046hNL6RX7PK1MG1yvB+MPw8o7MDoDD5yBB0bgL0/3zWlWke403aG0dUuLHrG6gIzSMaKCvGVtIsM2jvccGkWXLdqskKOHNshQUrG+xyrTMx6z0k95N1FUcJlDM0vAGXY5oEObWVpEpITbYjszDTzGY5YupqAsjceFLpKLYiaiSxS6xxTby+Tbf4h03KR00UdYfj7TYppZhIgdW5YnjtByHBFvgphIhrpUZkCNQLAHnaUklR4ZI2v1bNKOeHLQDqhJiIzsiZdV9WfuBO1ddGUE/Em0VwXfoR1/gXWp2SKkTcRzbHHEe4QkJT2PkRNSBwj4gB76AQYYYICPAZ9tYh+dhH27litnp4lvp6Yz8epuf3qpB+cvw3gVHp+F+HtAP0Wfs21UzSgagPYmjM/C5l0Ym4KHF+CFETi7B9UVYAXUJeimN3cnWEXpR4kMD/XAmcDvE7tG0SNPw/sVNlSVRRVxKANe4kdojNQz02AQO5iCPBDcJGnXkjgsEDGJYIp9NjnkADimjmLTiLYbNC1heECDInVCg6hkXwwmdZG8niYf5sm1L+G17+K2byHElj1JDUCdhzBduMgwbznF6swgl2TS3HBC4KJGMtDBQx13IN4ANiAswZHRpdA5zKjaV5O+dW28L3LcJna37zko/cTUp1CG/KvgheDuo9UOrYlDMDzx74qvsGq0LcQZQWEbYakXws8asUf7EG1BtAm4kH/+076iAQb4YAzEc58xSAlPvgJ/+E9OnhKOUXBZOIcujKCfeR5x8BZEbyUtZoUX4NicZGJavAHN64mivtOAch0uXIDxURgWMLQMrEHtGfCMm3l4g2w/dC6aouncI3afI2+CQPwat90h3ncgEA5Ftq2Ut+AcmrSUEOJbUa3uE4igTshFmoxyyAh3WSMkBBqM4vRJ/d4x7Nd3yD4lPGJjkeAySayPKTBNIcpRDgWFDvjBWwjeBBbg2Mhc6CNQUxAZqX9pK8NV0DOIXYCOcPTTqMhHhk1UsEqnkEOTigwd/ZLdrudk3OVaazaxixj8GegYDoJ6BPxHQFUSh7lyEYZmwFkFcRNoQSe97sSRbxptLH5qWrNqJBiCjKf8MaGlg4/YRtNFZHIfvxDY+x/g6P+BcDP5KVyEnqEpyb0IM9/8yfsPMMCnhYEq/jOKpz+fEntlCIo5eP5luH0Dbt9Kfh5/HKJUbc6RtM1gOtdASNAxeBWYfBAeH4OJq1C/BexA9BLsGs5wxzdh2DiGCMG9DEEi0tKigAyGaTlfZgOfZZq47jC7bpPU9qzHOOM0jDa4LqMWhfQ4Jo8EztPlDIeU2MBn6yS6PmKImT6pJ9CZVHMTe866JibHHJIeBQrkaVHr9ih330Twg/5Wj+AE142dlkDkk0lz95Al9r5DmxZ5cM+CruAHL6DCXWRvEcEbEA8hdBoZS54nwsiwOGTS85npar3dk159rcYT0dzoDCKeBvcQ1CrkQ4gMsZy6DNIwFtLrJCuOdGEj9ChapMRezSyGWiTXJVAUGMKhTJV/A4czuMzhcobsQJlfGHSvwvFX08fxrP376LRF8AADDPDp4bNN7HGciNUefhXWd+Ddq8j3v040ew42UrLUnYpd3b2zyoloWXqQX4BHJqF4F5yrIF6HmVdBGtPcZMZwJNwDOQ/xneQcokjHv0CzsMCWL1j39pCiwE063COQkB1y1OkYenKfYYvYD8gxRgHBRTqMsY5Lmxod2iTZgA4FLoGVNrcHt7QMcxmAgCYjnCUPlGlRZJkaXTR/fLKNKx9FmAsCfcd+vSIGZx4CwyBHFJMo3ZkHUSFSJTq1i0TOMogboJcoH7Wxatpyxuotl7FPZFx+7JplCIC7Sa96bgq8MriaqOqi1Q9BbgKbqKgCnfS1WIsPsNPyAEL3TWnSDITUJeKTL4lDXR9xhnkKaJJxN00mOUByF9FfRI3wLjLTkvgLCWfMfhxnPfhPj64dYIA/ExhE7J8RdDvwtX8M3/1/4fV/Boe7sDMC+2lUISeniG+npBzf2EeOGsfoOJD7AqwcwjvvQftt+KtVcI2+89YhWFbyt5IhIv1Z3loW6DhPcOg/yZrnsOw2GBETdIw0uuYWkjPExjevmiH2EIccFUqMI3DZocsNnkATc69RfoY6GIpxN9ObHnCcedxkjLMUiMixi8M1RugRnETjIHnaiut7aseuIosjtJxGxGm5QXvToIrgFIlVGyIH1dqHICFq4Y0TDRvqc9FDyzlEbKTwZc3m+ahnZE9KRG4eXXwJ7Ui0OkKruyjHQ5xMrQPcF8EYVqNFZC/c4myEuZ94xpsjbVU9KYE4E8SqxAHnueVcZlMINkXEpMgx2neXA+jhorBFlAEr+DzALzzUqP04zowRjvdBa0An0Xu4De6Z5LswwACfJj5hYhdC/BLwd0gSeP+j1vq3M78X/d9/mSTR929qrd8QQswC/wiYIEnZ/o7W+u/09xkG/g8SF7M7wL+mtc70/tr4mYldCKGAHwCrWutf/tOc7BOBjuG3/zIEhljq4gX4njGONLQbG/XNXfQTLyEaMbx3E966AYzAnZToWA6whm8dXoOSy70mSS18wtoLtBzBXkmzXdgEOcGKcbM/YtuaKq7pUWOUPaN26+NQYogSdWIkx0R0adE0jlOkTMcgLochTGLXlqubRBAzxXk8OghWiHmPOvv0DG96zZz1nkTWFDcIxCrJSqa/SBB1Yu9RYnmR0OnSc1bxwwina7xnwp5WJnubCMbRhghNq3Gb2BFoJKg5QjVNV43RcT5HV+7SYwvEIrPBMZhCO2c+nWIHiMjBnMuiRcb2N7wLjgRiNDlQc2hnAS26RLJHLPfRokQskogfYEfN86ZKUwcN2lTMQxIgGSI2XttngtjjDsg8+A8lQ3Okn7QMqjHQQZL90Mfw/oVk2NC9MtLZr0H5tU/10gcY4JNEnyP/LvDnSBy9vi+E+D2t9XvGZn+ehEkuAM8Cf6//bwj8h32SLwM/FEL8s/6+fxP4mtb6t4UQf7P/+G/8SdfyYSL2fw+4Cif3sw99sk8EuQJceRbeMlKvlUxtc+UO8cOPExQqNO+u037vOnPv1hGLRn97mNnnrUWb2KMesfMKkQ+B36Tr3eBQVNgwZperjBK6S4My43SNmnaZHAGjFBlCExISsk2X7RMRnGASn9Ag6yKjFrFHRtXZoYiDwyQPoDmkxxIxb1OmRpcVNPfEYFmL064V1QbcxSUhP0EFJRbQqooKtxDhXUS8Rrvao+Ol5j6RHrO/UPIOGuckNQ0g4xkiw2gmUiW0fpRA1WmrHF2nSENdIBYdYAXBNh6m7gC0nEXExt+Kk4kMA7vfX7OOFlVQs2inhpaKyBVEYpFYJII5KerEOn0tUoxbre4lvQrGBLYGTarIfuakvw+jGWJfJaZFyBIhdxDkyfM5/kwgPkz0D/FaUvoIbycliXgd4gK0vgnRBsRH4F4B8W7aExzlIMg4+DGPNXkv2mOAAT51fLIR+zPATa31IoAQ4n8HfgUwif1XgH+ktdbAd4UQNSHEpNZ6HZIIT2vdEEJcJWl5eq+/z+f6+/9D4Ot8FMQuhJgBvgL8F8BfNy7wQ53sE8MTn7eJvbVGPDtPd3KOw8MWu2+/R6VbIFpM07c9p2YnsFc2sLC2Cc7TEORhswPX3qczI+mOfvtkk1xmsEfIMi7nLHtWn4m+MnqKQ3KE5Glxk1a/jq7II6ig+6yi0ZSY4IB0mpsy4v4idQRFajxMh32OWafDuwyjCAzlu8MoXcsW1jbi6bDVT7X7OMwDo/2BrouE3CXkXYgfRvbSmfAqaFvtaoFczVjaBuCehyDxe9dqHB2dIVTjdAW02Ufnh2iKZe5lHByGkUYXgKaLYoLIMNaJ1AiOwSHa0SeLEi2rxPho52W0kMQ0iMU6yo2AdFpe7DxJrM1JfGYuBciI40rxDUxij4nxGaHDNg4FCgyhcPGpAQGaBl1+nxX+45N9fF74RIhdx6uJq969f4NdRHgreRytJu1+gaFiV69C9xvG42fBEkZmMh50EgMgbbxHMrO4GgjqBvizgk+O2KfBctFaIYnGf9o205CShxBiHngcuHezHe8TP1rrdSFERvRyGj9rxP7fAf8J5pDpn/FkQoi/AvwVgLm5uQ/a5KPHk6/B7/43MHcZyMP127y/2iZ8Kx2vKqanYTFVW7dWj2xiv3UTnj8HxXHoCLh5C77twU56DGexRXc63cXlxwjOovvpeYGmwgRtOijGOcbjLlVucwvoAl0qRJZWOqJNjXn2DWMZ1U+SCBRVJnDIM84CLTboscYWG/hEVnuaz4RF7HFGkd3hEAdwmEYxSYxHwBQtrqNJUtBjnCcyvoM9R1lHUUHGxU7ugxgDvYUWs8Rqlu7QNB01R9Pfpufs46I4tma32/3fIXvkKBMbqXbFqEXsoczjUEDLeWJVJ3CHCAov0PM2CZ0d0Fcp93axQm5xAXR6XqGz9V/7rz/WdsSZ44AxPUFeSIpE5DhkBA1sIvuflc8zdI1BN/qUD/8n1Mt+cB6zrZLosuUfgLRthREZt0Wdae/MDsqBRBQZmcSeGYkXDiL2AT5zqAshjFojv6O1/h3jscjuAJkI6qdsI4QoAf8X8O9rnTUC+dnxU4ldCPHLwJbW+odCiM992BP0X/jvADz11FPZF/nRYv19+NFX4e3fT2xfr6cOZMWLz3G4nap3w9C+kR//4H2Gnq6iJ87Ti0t0rq5S3h1CfS+NyJMbZgrnrRvwcvpY0qLADAEBgmlauDSY5iprQPIZDWU+1yOOmaFK20itVyiyzy4OLiOMIskzzBkOWeeAVVrs4rAPJ1F9TJFJGkZULzJq7C4tFEV8ZlEUiWjTZZ4GuyfXNsxZtEFwcYaYuk7DOqoMlkAPI8QQmhlCSuwXcjTlO4TiGFgmxxQd0uhPY39XE0e2JDdw8r4ySe+E2CVQR/E0EUW6aLY9h7zngUhc/JJZ79vpQUUXmAKDSIUYSvRd6dVb16F1o//cFIgJtKjRkg/SFT3a4oAOBzwhvkVgaB3yPEnXWIDpzN9wlDHcCVlHEyMy5/7IISdOujGAZFqdBZV53Ms8zkTouoHd/idBTSTtjaKU/BuNJqY+OoY4gIyOZYABPhV8tF7xO1rrp/6E368AZi/oDJya9vQTtxFCuCSk/r9qrf9vY5vNe+l6IcQkkHFIO42fJWJ/EfiXhBBfJnFYqQgh/pc/zck+NgRd+M+uwJbRfnbucXgzTbWXiq4lB2vduEFBCNTFi8QTEzQPD9k+0Oiv//Bkm9xDr9q3wOsrmLM75M4OMnwQ7YTAAj0UPea4wRL3RFd5WzrPPju4mZGeBcZoc4hHgTKTKDxmqHHEGi322KVijWft0aHKmNV/7lpSLugRkmcKn5H+40MUHVqGBiDPWbAsZ7OLgZ6VoO46q2g5DWqGWOaIZYMDPUFLXAOSHv6KeoLQUOFr7Nay01FriMc0PVaQlHCZQTCNZIwOxzTZokuOQ2NxUBF1y2xXs42gbDnXCTGO1oZi3yoS5AAHIZ9HCx8tAkICjuQkWnS45+R3zAVCY8EgMzPjswQZn3qtu5llXI+ILRwm+FghJzPEnjXF+eAIXYsqWo0QqwmknALpopVGyxDVkohwK1HAx7tJwql1Mz2G9yocGel8/9JH+YoGGOBPh0/WK/77wAUhxAJJVPHrwG9ktvk94Lf69fdngcM+hwrgHwBXtdb/7Qfs8xeB3+7/+0/4KfipxK61/lvA3wLoR+z/kdb6XxdC/Fcf9mQfG1z/9Gz1qQoYHVDFvaSequp1Chcv4gnB3vExR2+/DdeSFG31VZvIu4vb5Mw78807cKWeJFOmLsCQQ9gYZX/odRJdIbjYrUFd7iAZJz4RFmmGqLPFKjkKDFPHIY9LwB477LLJEJqGUa9vcsQQFTpGtOszbBF7InE7i6RKh5Bd2pRYo20sGEcYt8aFOhmiijJ/AT1aFHkMQQFNm1DcpVfogU7r7C5/wdonNgVUYBFj8uoPUEwhcHAYR5AnoEYHnw67wAY1JmgYC5Bs5Ntgn4lMlC+ZIjJV/qICuoAQZ0CMEFIncl6iJ3cJxTpKbOJwExsTYCy4XIat6xeZDIbOpO+TTgKBYhRFHUUBxQUETQQHiYiPpf55PkaIzJhg6aLVPMgqWhYJ1Qjt/JfpyZiuCgmlT1sGxPJeRL7J+dYyZqijjh+A0Ki7y8xiIZtgDAY19gHuL2itQyHEbwF/QLLq/5+01u8KIf5q//d/H/gqSatb3+KSf6u/+4vAbwLvCCHujeP8T7XWXyXh2H8shPi3SSKof/WnXcvP08f+oU/2seLSa7CcjifF699YlIILl8nVhhkXdfT3v4/YSVLy7ssvW4c4Pj62qK7zxnUqLxYQWsPFyzBRhCc8UP/8xPHMbX8JU2Ce4z3MTEtMhyHq7LJFjgKjjFCkiKDMEVscsINkhj3DUOaAbXw8QiNFWmLMInZBjmEuInHp0OCIFnvsY/qZDzFEYDx2GPuJc8AFHjEueZ4hJqLLNkdsUCEgNq4tlg8iDeMWL+5ZWW3zfAARmzhcQDNCjyJNQooEdHmLbv+4ilf6pJ4gzES+XWwhoyZCMk18UnrIIZkFhvtagYCWU+xPbF0ClpBcttr7IjZw+qr/e1CMExoudypT/0/jXIFDHUmRCr+BywIu8zjM0OALCG5Bf0COZJ6YOyf7xh+op/k5ELcSgVy0DNHd5EeXQTyZ9OXH28S5gNC/c7JLWz3Dsp/qLyRFRDYdL0ZAG+97VhwnsrcOe0E3UMUP8GcCn3Afe5+Iv5p57u8b/6+Bv/YB+32TD66/o7XeBb7wYa7jQxG71vrrJOr3P9XJPlZceg3+oJ/BqIxDfQieeQV+/Bb8+B0A/AvPWx3e+Uydff/aNapKgdY4lx7AOzeGfsFDtL8B+l6K/hV7hOfWYlLO7UOySZHHabKDR5EaE4DPEPRrzRrBZRpsnhzmkE0k8iTa1WiqjLNrCNccikxxDpceEatIVtmw0ugSlzKBIZrymLSIVhuJdZdJIioUeYoeR7RYYZerFIHYUIQL5jBd7CKZRxqBvRdtG98iBSh8nkEjiTikx102maXB8slx5pm33neRIfLeqYl0x7jUCTgkxxg5asSAZpqAPXqs4yFpkbreCXHRSr7Hp0oAPQQTaGOho6ha9wAHhxzncCmhkDg4eEySZNluI2kyxv9sHbVJCW29f/bkuIhlfm7s/weJij1aTtLiuechTEV7wn8VorSklPSipw+V3sUsI8Q0cfDQBrlraghjQaVVzr7riOw9qAfCTSbtqQqIIQYY4FPHwHnuFxhxBDUPLnwOdtZh9Rqsb0L3SThKo1wv71jE7hmqeG9qktoT5yiPF8itfx/ZvQrNq+C/Ci0jmlnbxvRy8fZuovTDRGIfQRGfs8xTo0mDkOsI3kfyHFusntwYO6xg+oZHBFT7E9cg6V0vUiWHD3RpsoFgi4i3TpLlIR6SMvHJMzFlRtkzFgNxv4lBkaPADJoyHV5mmyNatJFsMc8dyzveY8YSu0XYJY5A9vpXLkEs4FOiygySHTS3gDfocJnAILAcBSuZHmSFaxki7/WtdX1quORRBOSJ0byP6EfUMS/R4h1rLxNd9jPjVg4RjKCNTgHBGFpvIJlC6lHQdVDPoDkiZgOHPZQx214ya3UJxGyhaSOMyF4xR2jITYQ9aq4fsf+cCN+HwKgznfKgt0sqIuMU58RbgJ2uV9Ss69ai1BfLF4ERcGeh+BdAjYMzDnIBhv7dxG7WHYPDRbjx54H+1D3/EAZl9gEG+FTwi03sN34Xlv8p3P196O7BwRVYSdOtTNjCNW/HaB/M58mdn+Xsv/IY1dwNCnoRWIfCK3DHSCevH2GVVpeuw3wR4iZa+lC+RKXzEL38MhHXELxOkQpH3DQinGXMCCnkiDIX+4r0xMu9xgglysS0OGaNmANbgYzDAAAgAElEQVT2uHGyzx5daghSJXyPEuMcGRGn3yeRIsOUGEKgGGaMHkvADh3KLDF1coyYCJ9pOgYJq0zdvUeih1bMJG1xIkesHiXWt0DcBm6j9CVikUZ3LsMWsXuZJXOLrkGFAkFIlSs4SARNYlYpENHrj83VgMtjBFZkn2lPy/jf99iB/sIoOYiLyyWgh9QuMu7g9wq4nRjBDeAGzWKBlnrTOOaIdcyITcik70Pu4nLx5LHkDGB2xDhIzqE4i2Ie96PoY1fTf/LvM+1qIj6wHksaOPphpMjjUETik4t9JGNIOijdQDZKePuF/qKgCSO/CZN/+yefM5dJ5QefvgnlAAMMIvZfRLzxt+HAGDoyWbdHkytbuOXkFMVf+yJueQ8vehehfwAXX4IlY6Snl3HUunoNnnQgDgEBoxfQ9fNQXYP8eyDfwlPjdHj3hMgdy38AYtbI8dCJW5xEMcoYFYYJadFgDc0RuwaRH2MP1ujSJc84bSM9WqBEA0WFCQqUcBEM4xCwRIsloMwIi8YCo0GJYY6NCFkwCgYJR4BDjTyTeDh49FCUgDtE3CGSEOOASMnDYZyecV0qE0GqfjlA4FBgEkUVwSs06XLELgFdzrJMYLxvkoetY+iMAl1nLG+TeecStMJjEpchcqHACXdRwQYqXKJXPCYibV+U+mWrDOCEh5hhfpSZege9vhBy09hmySL2HL+By8t9Il9AMos41V72cyJL7DoVuWlksu5QV0CW0MInFhInmE9IOtxHRNvU8m1CQzxY1ufRxoJERc8jYmOBENl/S6fg2Ysg4h6Ex+BkW+0GGOATxoDYf8Ew/ZpN7MVMP3+wCi+/CEMSglvQukXpyigsvpFuE2Zaf1o3kvrhvabn4hBcegSGD6BwDdT7MDoObnoMr7dhGZdJbqC4RNR3nBMo6szRI+oT+QqSI7aNnuhWRnTW4ogSNdpG6thljB6HlJnCw0cQ06NJzLu0gZgJAkNg16LBOCOEBpGXqVjE3iNPkQU8Sii6uDQocBVO6tWC7NdEiDOYYwGU9i3dQaICz+Eyh2CYkBxDOH1L22u0gEPmLcFcssAw/e7txUF2ZnysN3CYwtN1XK1w4yZaBkjeQPQXKvnO48ggVfDL2LEmxWlp1/ZVmJnyxqHRRuegmMTjWSR1HM7icB6Px6w9PL7Exw5nAbynE892IftjcB8GvZuYA8klAt8ub3hHNYQ2BHO6ZH1mWuSsLjjtZHrtww9J7AC9vQGxDzDAp4BfcGL/Arz7d40nrsLUg1AYg90juPZjeKZrD3PJ1iM37IlcyAhefA3yPXCXIVyCM+fBcBTj+MhSwru9q0l9ViRpXwFUuEQHSZuYHTap0GPXsAwOMhH5cV9s1zPsZ4uMEBNSYxQfQY4AwRawTg9wWSA2xHIdNnEZIcCMpqctYi8hGeEBwOOYNod0KPPmScYqwqVAOtwmiZXPEBntZzozgU3FXZS8hKZOiKSL5gifxCUxIcuIScz2qRzDFrHHmR5600VPUkHoHIX4KZw4QMVbqOgWkh3g9ZPtQv9ZIuMcWtqqdpFR8Edi13bSi9aQ+gGkqCMpIVAoFHADzQqCaxT46/j8Jp8qnDrIfu1fA2IOdD/rIkgI3voMATl8MnkQQGn7vcn+XWiVaf79aRG79ODyfw3uEHij4I+CP/4n7zPAAB83Bqn4X0BMfS6JCCoPQpiH1UVo+PCD1Pb1VJvOdka8tL8Bjz0PBQ/UHrTfg+EeLP9x+oXYbWOVW/evw1DaRy3o4emnaAuPQ3L/P3t3GmPLvp6F/fevqjX2PO3u3r2nM8/Hdzx38J18bTDGDhYgZbAgIZGADEghIl+SfCCELwkiipCCQARQQiJBAhHEMiYkMnKMr7F9L7bvcM49857n3b17XHNV5cNa3V1V+4742r77sh5p65zqrqpVtVb1ev7v+z7v87rjoaY5952OeR1WHNe6bkqslYa7zFvTUbNgRSLVFhynwDto2BIVGHXgmtiq1PGCIjdj3e4kExCpYV3Lx/SwZ1um52rhWg7FzhXqxrmh2AWpU7OfyHLhVWuGYV4t+qxRFBuEHYPQtW3bsUdRLogslNT1DWtGpYl0ZW/2vlwsUrepYVlDY9KXf03kXcK72v2YgvlNCE8aN2dMtvOZUhSaRVkpCR7Sh5MnPgjOCs7g3Fh8OdwThjckSzWpr5zcb80HZC6fnDarjGb9PUFUScVn90teOeNrXaU4uyCaryzGktIxYz+8RVG+IuRzY2e55f98/N9kg/pT3/66nvpz3/29TDHF7ySmxP4Y4fAqN/4x13+O7iXeL0Tkm08rtCtzp1InvXOZZ55nfZW1lPabiLhRcM0KlSfh8tusnArXQnokC5+Q1RLDWqRXu+pOtO5aoQe6XpmBfuiGSFKYvZ5ZdEbXoTkrgkxLZOSK7kQokLhYOseeu5YlJ8YoQWrGpv3J/nULFs1bcEnijuAtzLla+IKP3aDgUteXTqL6oi7gjHEfdhA7L7Mi9XldRw7d0Y67ZuKvnOw9nuC2dtIuFeQaNnULi4NaRR0eG5l3zqymGUdmPdRwX3TSmx4ZR5GFPoboAsWpbmG+bAefZyViT6MDtXCBsE7WFvXH7u1hdFVwHdc53BLS01a4JHtVGp9a86qY0mSu+j1HfLa0GXTl5in6HIQlk1EOaMvjVSH/wHh4S6iZ6c5odl8Qj/bFw21xfiR07zkxkGwEzv7vvyu3M8UUU3xv8fgR+z/7w1z7R6fbC58tBSYaFTXu5bd5aoWNc1xYZPEWW4vsFqZb1Ss9uYeVqOxol8Yr1Frjuubwmk59Vm/mND0/WxFadd0QCmM9cyOzLhjqaFuTC3JN+961MxGenakQ+YG74kI0nRlpOK83iRobttSsaEvkbhh534yWvoKGwBVFRVhqz4wLjgq1+9xZXBdbErtoYFHLS1Lv431D8+4VWqE67pgROw4Bg5G6s/oF5WJSWDzEZjXUnfGChiN119T9S6GQ0aAuKrWsZSLnZIXFwaO90YW8etgSspaaTwnZQDTaFkb3hd0bjsWBAXmyqjS5LTkznnh2fK2jRmXka9l4Jf1+iNjDGaep9uZkHO15wkAeYnnIxOkM6UDI7gv5HgaMTg2cGtmn6RUmICar5ddIf+8coqeY4nuG761X/GODx4/Y25U0ZK0ysKL/dRotZhd49mmWh7wa6P5qYaeK5WZWMQ3p3WHhEo05lpaJt4mX6J2m+GsH5/QKWf62LxsPbB+HkKmuGU8Y6WrbGKuVNT100+FEKLfoudLL7rkvLrS0jfTM23DkliA256ymJTVNfdeMXNO0qOs0eu5XLPtz9zS8rF8aMjOv69CyVfPqugYOdR15iG1nRTadzqYP3sXCyXXlBiLnZQUir1vUN55E17ClrqXtKcF1wbvqElmh7zzTLt3rWHG+JSso9INVCsSeRXVRuiC4KGSLZPPS2quy/CrhtpBT7xaMaMLYWjYUhySFTfKCviEui7viUXa6Dsob4za5/KPitCYaDcXD1COW8b+TyLZJr5J88NQUJgSWvjQe9hKNhyqOej8qz04Xq7Xs00JasMyt+snklfa06mjWb1dTn2KKxwG/u17x3zd4/Ih980d5syCYG7xJXBtPlVp9aTxO8o8Gtn8Rd8b1lfwz5XMcVVzIRtdobxDXWbpIrU+ryc4vjYPlDP1Plr4cazvvlCy/Y9tmfMpAx6yzIpGRObd91dHJZLfnSy/bqVil9nWsOqPjrljdnLOWzVnTw5t4T+w1B4W54oNH2uLua1uY+JaPMWMNkbZ1kURN35I3HZu6jLwwIfUxdhyUlj7BobrnDAqpkWBD8EDskmBeoqluBuPe9sTLRoXrHLlZsqXJdUTOlhzhxjXvCbHnsSifF+WvidNEPDoQDYLkYI/JQiarXzBYvHHyueRuo60Ukde2GBSIPa+wchTGwrLkHPG8Rr8tHr0g6d0WD64L8ZcnU+ROr9J8dzzR7HuBbJ/0Mun7jC6TXhlnENK3x4R+TLhn7hEX5hAkr5ZOE8LZ0miXXFTh8kp5KS+b1qj0ussPybpE36P7nGKKKX7X8PgR+8bnCNGYyOvLzDw/HvjylX/B1Unk+snPlo+5d09p+Nnhe8yuyrOefPVFo9WW+EEivvkLY5Ifof2p8jl2rpQEdFH3rih/XhYe4iU9S5Ysu+uao8ngkJYPlU7RqUTTA3vaNnTsSDQsOGvJjFl0XZXalnhSXkitZ5WhKgO3xGalhZp+3QWZI5EVqVymYc8D+5NFwJKnFJ3aqv7xPT3B2ZLdasOaWFvNvFhfpm6kazRpi6t7RbkfvjKr3b7IGVnJlW3d2J51drxASDc0so+N08fp+5L8vrhf6EbInyydMwxuKI0TDYjOkRWGlcSF9H28QbZE/BmynNHeOJvf3mFicJNkT0iOCun29DZJceBMzugKtd+GrdroTQ7++JjM8x3CGsNKhBxmx+R6jOxmmdgrCKFcd8+jind7xbaXEfHTY1GdFmrM/RGSs+P3Kd4kNE0xxWOPqXjuMUBjibM/w+Xf4t3XyX+F5c9yWDAsuV+ZLHXrLZbnGe3L559lbdNga0Z/6ReJxnXHZv4JcTEw61TS80e3WD8rH92TzbykM3/W3uis27WvTsxS9rSsCIW4aVTxJ+/b0bSq56FE0/xkrOqRmiPXdb0h84xuwTjkUKfUCDZ0VWSpNCK06Qm5VDBroKNv1qH3mGQEGpVe644HJbV4ak/DOf0T97bIyKtiT+lpO9A1JxIb+4+P77AcMY7cKkWIuR2RFVmhpS1yVi6bDGyZk5mReyB108jXJXlTPPzFk/2zynVybSLUOxYPZoLzEyvbCcIKYV2wRTY7zuDEL48XbIM7QnKftFCWqT1dnr6a3VB2l8smi4XC85Be/u0Re2gyKgg+s/vK884RrZEWiD29Qa38OZbPeUzs0VgsaJXoY+ORrTmyBtGr48g8vcfoOqNK1L7294krtfYppnicMVXFP0ZIz3L1fzvdzitpxPff5LkZhkc05ll/Uf7EKutfpP423qb9OaLTT3y0fKfcgNW9RuMM/Xuy2af0zzzp4Py6uwuvG8Xjmd0N6yWf9VFJxcfQXQ0X9e2LNczb0rasq+HIVT07Gj7gqFCr7ip/2e67Z1ZTPulXDzJtzxmKdc15YGRNTc8pWS1WTLpHlei5b89CYfJbzYI5T0idlTrUc909yw4KQrFEu6QPr95rZltiSV5I6ceeFRnItQ31Dczre9tx20LbB+WFxc8w2it9BuMRp6ciPWFEfGmcrjaun8fp09gUDYn6u8KoLerc5VjM2PgEg9OSgLQirhzdJC7U+sNw7OxWENSJVsfEHm2M/w1/mdEvTNLl71D/KWb/su8Y0TmlxUNAtE5W6EwISxSFemmlfFRBnPwxcfzTYxFhqDH4InuvFc63zLAycS20y/az6b0psU8xxQ8AHk9if+rz/PO/dLq9/3VqTYYTs5bVJ7nwLNE1jl5n9Ks0P0P9VLkeD2+XPDlGc5fl9RVhsC2vL+utvaqzec7O6ju6rYe4quWMUcEQJnuEyG+pWTe0L1LTdtG8C3pu67pi5IHgg44KbXEj5UXJnrvm1U8MWnKZuh+So69u156ei5PWunE03LZRCjqrKf/UPXUXDewJYrPOWrag5o6a90V+w4Ettwp9giNlcdWh3RKxp+5LLJxYu0bOiL0s0zc00HVbU1u3IJibqZQmRvZL1z0It8yUCG9AdJHshjy6JI/Xx8STr8rDTcJt9aOOcFjwLah9svQaj9SOBzeUVw/dcYSbHz8bTXntWXntvDxpSuOBOJsXm3Ey4GS0wKjQHpk+67tCSIg2x+n1Y0TLZNflYYF4Q56cJZmTR7k86grxzrc0pg1hZZytOEZcFYjuUJlhL1ohLRL7Xbz43d3LFFN8P2MasT9GuPjpsdAtnZBPVOOFT7N3xNV3eONtXtgkP1WLu36PQokyGr5jbOKxi0QWXvHgh55zd2bP7cUdwshZdWkhAk0fqZFfFzsjdYBIyyWzLum7r+OynrfMmtMteMCPKh7nHTdF6if97bnMvBdEMsFAzw0PLdkutIY1KvXSHYfFWzOwrW3ByJ7EvJYtC85I3ZB5F9fNe0nmlBDrlevqViaudT0UJkQeWRI8IThjaEfHPUO7FkQlhX6mXOet3vvQrbIyPvSInkdDFi1KI9Ja3SjsEMZ9583BJ0XD0zR2Ho/KIrFq9mZUiXTzo3GaOx8SnycsYmFcSx/dZXjTYD01ik8d7aLhp8fZnxNU5glMMgjfCXKDcS9845Py/J40jqXxcNyell8mjMs6IdqQZ6eLhyi8/N05zkfrlN5bYyLPCm2Z0cJ4fvvxDtl0cMsUP2CYtrs9Rqi3efYn6WwzOOT21+h1+Y1CX+679ymaZV17iw+dmniEsGmUfcatJPa+jn4Y2jq3XIqmDx0oaoL7rovNT4icuq1JK9muI9ftuWHRmqNC5JtWIvKO6yK1ApGPrHhFkMv1ddyQSB04naddd750jswtRYn+nkOb5o3sa9k0Y1FLXeYN2aQXPfEpB4XoeSgtEUXiiqIZS98DbbNyI21bmpr6Wu675dAujlxgklo/vq6y736q3EI1cEu9QDaRBbGXJh0pQ3337DcXpYV7n8leIz81qcmirKSuz6Ld8hDYSso6Dwm1jwtq44z+8IC0weDXOP5s2p+if/qaIY3KrmyhXzqnvEKA2ZXyrx2iKRT+vDr+rKF/MukCyITZTxgV73PwmmhUdCcs9+jk+bdOxT+CUJuUEO6PU+7RFtElsg55MhYPzvwhWp8cG94km4T6tz3tFFNM8f2Px5PYYf1VfuEvnG6HimDu9bd4bo7RmFxCqMv7n6TdJb0ujN53P2v6ejhtFzt6pL59x4xZ2URxnliQ+Iienl3bunatGjoo+KhXrWN7rglqJ25xuZFFr0rFuoJtDyZx8Om40JGyx/aw0hY3dFvDM4b6VqxawaaWyBeEE9e6TzkquKSFSpTZta3YwR27JvFhdfOalsQy846kfvlkBvqhn5qQ+vF1lmPIUeXeh24KkwVIYktkU6Stb1/HXUMHzsgN/VbhqLJPQSYvReRZKEf9juebhzlqF4iXx+N0oweEG4QH3D2iU5jd3vzhyjnK9xEN+6V0fRZVXjO9I9eWx+dk8apR3DLM/6w0fF3qPbm7Fvx/aoXSQ+aBrODuF6re7I/0mVeyAt8tscPKF8ZRerQ83r7ykxx+4fT37Z+h+Ynv/rxTTPG4YNrH/pjh6R8tE/vRmzRn6E3IeZTS/jD1lKMet14X7nfZPE1vrvXfpHEqFhpbtralkz7oSE3sYzKpbQMPbNvSsF9Iiw8qi4GOa0LB9jU3sORVkUyka+R9BxquFgRz/UrH8aFyPXXorpp1mZEFG+YEzxip+0XR5PVrflivNJO8nK5Pjwnw5DXvm3dBbl3Xiociy4a6Xjc0zl4tek5W+KuoV87Z0SlOOdV3SyJRc1awLtWQYd97Ul1ctugZnYLqX2X4S1p1egv7kkIiII3uEp6aGLPUiTqyuSNR9gZh8rnUXyEtEHl9odTaLqs89lk5Ig+Dh5PLGnvKC8sOW39AJ246jDN7ccdqdE0edh1H/bN2ZYVMzdho55TYo0rWReU+s9AvfeZ5fryYmCdsyKN/hYEqyTOV7Y3y9qg6lnaKKX4AMa2xP0a48HFqbYaTb+xsxJMfojdgZp6dG9zNOSim5++VTOeW+r8uyf+oURgTVi4342UjHIjcsC2zarcQUXYr5HbohrqafFLIyQ3Ne0WYJNe7bmrpOirMum5WIts9eyWC7Ni2NGm8n7GlLpjTF/t/hAnRJj6lX1pUVIm8HOFl7mt5VawtMpK65W0fsO0qEx3B05VoeVROcksqmYMDD8zaEluXaurq6hnquMEkW7DkmQmpjxGUDU+qf3MjBycPZbBsaEkcfsQIg7Cn77Zz9klPW9xC/KpyFaBoWoBapTqdlok8zw6oPUuyQlQXokzzMBMNrp14yv/KxufHtf4JzjhTKrOEih1dqtwuGblQudPjiLwlsiGLFmXJj8hCJA0DozCUR4fycIBbuKWuI1Q8978rJJXFwZTYp5jiBxKPL7HHNS59ipu/wZnnCCm7bX7ln53uc6NRtv985y0+XaizS21km7pxTRB03LJrrjTM5UiZBHbdNVsg8szQrGfH2iSxvtsicWkxkFZS65HLihd2YM/cxLJ1wbqWkZaBnl8RXJah5uVSa105BH2UyDnQ8qFJGeDAyGWxgZ7fPDlL24dLErlBJSXdrWgMYrvmfUisYajryG0PNfQKKf+lik1ueGSISjlS7TtQF8S2cNaRBUNbHjpw5JDQ90LYlhUc9rJoS1S0PA0VIq8+1smYRPNojuZ5ebIoij4zVoSP7tC/zkphgArCYKlUvmjmyw7DaV95brH0EnlpaXYcsRev6APqfkbkCZEn5Jo6/otJn/8tUdQR6g8qx5Rr3plbYk/7V0ataJW4PK2pT/GDj6kq/jHEiz/Jtf+X25O64UzFNOStt/jU/KmlaJYxfI76Ib0zPNi2bN/ra6eRaKwsjNpx34KarETklwQkWgYeiCUOCu5wdWWHsI57pdg3uKnpPHLLls3qW7Erc1omqPm4fiEMHRiWqrJpaRrbmIxaPi42NJ6K866+rp4vnuwTVdLezYq47UD3pO4eaWFO7vMOxLYdOnBo0YFuQTBXt6lXaPuLHiHycoFr6FDdE3LremZcRyY21DceyXpo1qxBcTyrsxSIPY2WRIXT5lFcUcYPZPGL0njVMG4atRIzzU2S23iD7Krozmm2IyAPK5M55hPEm4xOn4Vm1nRY+BCzSuScaYq8KjcvVdeVlDQMiU9InNazUzsl857MrlhTUXEfWS3V5VO3f3vEvvAzzP7k2GY3mrrKTfGvAaaq+McQFz6hlIPtvEVrnm6ByOef5fB9Fp6nE/iNFvUvMrFCXa0tsjZ3coquK5MZ5+OnITUy67xcX9OikSOJ2H4ptV72gO8/Ygizbd6q3EDTBbHYy+g7FTLVfKCUG8gqY1+77hSIPYjManlRcGQ8we2yxKJRQYSXqH55l3vTa64bK+GDGWfULZnxUX3bOm7peU/PKw4L91O3pFvyjC9HqmnhryioGamr+YRDM7bl7huo25HrOx7JetHchNjHaFg1KOgFskqaexTVJu9FLI8uGUbzsvj36ydD3eShURSMwkPHJYYob5gZFsoI0ZE8XhNKg042KOYvovJrtrJcbFZsU2rFPfNYsCtzX9d5C5Z8xbExTkPqmxvAElsWtOWFzMvYcrc4BGeRArFnlefqu0ay+ugUtymmmOIHDo83sW9+iOYivUmtM8+49BxvfomtZ1ndoDPD1//l2HoW0hcVjdmWbv+6+MWfkIZjQ5ihRes6DixalRhoYtvbOpOUc0PZs/zI7VLEOLStbl2qo+2CRF1DpusLhu4bouW1EpEPKyK8YSkiT0RW1VyU2zd0xcA1c2alhQVGVYgWVc6ZTkgnMqfmCZk5G5b1XJZ6Vwcta7oFoV3LXGmJESrp4WMjmyCZ2NKu6ftR9wS3dOWClq6skB1YsuKwEIHXrOgVtuNKHb6voWl5kq6f06m1DJIP60W3CLsiN0tZg/F4tsK429AnbJAX9qltVCaYHRN5JI/OyaM1efJZWZxL4wN3o3X/sLCAe96sWiFzcWCoOFR2+Ehp5FHEzhoVhISRJbltkQ2ReZEzInPoye3LTWviU0zxXWGqin8MEcVc/Axv/Sz1WdZfIt/k6CqHb41710cvkBei+jff4uXZEx/uKBtYHm3Yrt025yJmRVpSX9OdEHlSaogfq+eLcerAnnkbBnbMuKimLWjY8+v6vqKPxqRX/Rj5I4YwNzUmZBTU1GxoWpe7b+g9uTekXiq1huWPCKnKdfcwIejE+UmkGWTOOPC+Y7vSyIelhQVAbIlCzbxW6U0fR9aRpk3Bhl2L7ttwQ99IJpJK5NLC0JQ5y/YKxN20WCL2UFiQBBFaFr0kSKS6OvKJ499YMFeP1rULXQXjgTM1p8W0/uR+T4k8izZE6el2Xlsif0ZaX5PWm9LmomHtFcP4FmFHPTskPx1A0/YsBereM1KMfXd1S/K4zIHUgdicb4aGT0isGmcu9iRak1LQw8l78Smp01GseaVuP8UUU3wbTGvsjyle/DfGNqG7X2X/12g+z2HBIe7OW7Tm6E8ixlFK8hzpv6R1kcWLNjp11xbm7Rl7jK9VFMwHbiu6eA0cWbCp544Z57TNa6g5ckvqy1LjiLy4VBxUUuv9gg96UFN3TtvLRrb1XNbxrnlN/YKpTF5JrY90S7X7cd29Ifa0YAkduTOO3ODENa88vKVhplRpzx95JA40rIht6Vr2nhn3LOlO7i041BQbHUfHcouWbBem0LXMlYg9miyLYnUz1vWtqfuYfUMP7BmiXWgpHFkoDtYzcE9bw3EqPxhKnDcqZBrG5ZTbqEuc06udkyWbulHuMDoyO7cihC+fvC91r8kK3ux56AiFNc2Cd/HyyfYDA6ulZ2IosiizO1mYXZDa+5bEHusbFXQVQbm/PlQWVb/tVPwUU0zxrwUef2Lfeo1/9idPt3tv0V6kM0nPZxkbz3H1S9RabL3EaIsX7pNcxVWzmkYFlfOue+oi+YSsRjrmbTpyS8uyBasWMXKNiXtY5LWTQS2QVuakH7mhPVHTB4maSxrO6ttx6LqOG+paepPaP+QVE5NRZXEwcEMrXxB7SpQ3ybcNo7o0nJJi7HOlY8qSLpLKcnagr+FFfeu2td3S8EbpOvq21BwvWnIsWHK/kCZumaFA7EFdTcOcdczbM++BWbf0ZbikqV44fkenlIvo2LNaqkfnYlvSQvdCbBWRyKqgKZiR2DVy08j7tusX7BaIu2GpVFTIKkLC1IPSH8eCN0V5kE3YPsWScxrGBZCWjnX/llkfnfTxf3sD2LjS255XcoZ5xVRoSuxTTPGvgGnE/hhi9RVaqxHjGpgAACAASURBVHSPiTQf19ffmXh9r1xgaYsk4+7r3PkSt/q8eJrWXOt8QTT/h2Rh/MU61LNsy4HrEi1LNi1q4tbJbPSaDxoVFPTZIz7y10RmZHqINZ1Ts2VX1z3bRgaelDsqjBxNKx9H1cmt57oZ62rOiRG5Jc5SCpPdYh8vjYuta5Y63ENJIhowkviEbaveM+OGxJrL8snYGfrmnHdQSPPPWyrVtJuVmnikbsV5LNrXck3LW2onHnKb2CsoDO7qlyjuSE9izqhAtoktw4nnfmxJ5Gk1ZwRDwX1dTX03mdx7y0eNSk5v5TJFX6dC7OXOhcxdY/u5OWzJworfh6Y7Zr2v7S1zzp/Mox+/Dz/xiP3vt0JU8Q2oEnnuwLg0dEHsgti3GNs6xRRTPIqpKv4xRQic+xzv/IPxdlRjc43ks+xe5eEVdma598bpMb/1Jj/Wcmzqkjiykp9xP4xT7ks2rJq37iG+hq9qV8arDipmLQPXxBalk6i64awZT8s80Pe+1Jt2XXCrQIj9SlTXsV+KjXuuazun5oxIhutm9OV+8WSfKHxElp+moKO8VbSRlyi2dbXlmjI/akfDNX0dwVc8KT1J+2aeNG+/oAFYNlMi9nohnk7UBHOWvWLXjCsa7plxvRCBb8pLxrD3HWkKJ97yXam2BZ3Ca47Fh11t65rm1Iz1+2MHvZtq1ii8D7Wi8xDyimGPylCbnp1Cknxc1898Ss+cI227gVZ06ChcMf52eOBZv2zoa4WjytmPUaUF8dth7EZXFzkv9oTEy2J/ROSSyIVJz/vyd3XOKaaYYorHn9jh4o/TuTOe2LXzOsN3uHyqWB7X2WfpT1LZgyGjl0jGQrQ8WvXMMHemsaTht0S+pO5DDgtf4v2Kynngtrqlk+lvNauafkhfx4HbDj10xlFp2lm9kko/qBjAdNy25pKaZcHIyDUtjHyhYOvyKoUUtEq7WciL/d+baBv4fbaN3LMrd2TbQimtv2bGncJ222KJ2FuTWDYSLFsytKJp2TUN7woumfVe4fjVyhL5rr4zYsNJqnkks2jOTiEjMeuMOS1zEi2Hlh1NxuKOMyuJHzLy5ZP9RxU1QPTI5LgykeduCmbVrKtZFqtJnJW6K3VT6o6v+NREZzE+17OPGN/MVc5Z7RD47oi95jNW3J8IBqeYYorvOaaq+McY536YXyzU2YdvV+rs6did7vpkmlYUy+5tyZ5eNKrdk0ZvWo6/bFAQX40K9djx9n01a4aT2vG4j/yHpI4M3Jn4pA9tlwbClAkuuKw4QW3fthVPaJiR6Mu9ry4Y+BeFYz5AYexrplYZirI/0W9FgqekYdmBH/fQQ117uOKmtmGhPr1o2YNCRL2iVso/HKvt58xrW5VbEFtxWebrcufNulKIiO9VUsgPDK2r6ZyY+rBszt0TX/WG82ZcEGnYx01P6RkW7jvxskHh/RtVCmVF69nx+1Twws8jQazug+JQF/Tl7knVjbwjm1zTyHlpoRTS1qosD6o+9pXBLYXlVrDwyGCXb4fvpA4/xRRT/DYwVcU/xlh+gZlNjiZp7oCzz/Hur53u017m+R+mndF/Q350U79ZiKbTrxKvEI7V3Q/VPWFwEqknWp7Xcl5u29DbckcOC45zKiNaD90pxdO52xZ8RktjUvl+16JY35dOKKI6LKRswMrQ3iRObIg8K7NmEH1K13VZ2DH2ynvZsEBR81ZtF1ql2hV1/YyeusS6eTPqehK3fMD2ZKm7Jni/sOy9qyMSTq7twMiSuocFA5w1bVftWVJ3XuRpR15yOLHTvWvFq2556+RvbmC9tGAZVax8B3ZL8XHf3bG1Tr4izs+JsllraUN9eEN98I7INQ+WXpW6elJkSHzSqCBACxVP+dYj8+OrmodI07NqnlL3pIbn1fxXEk+KS7r9KaaY4vsCU2J/zLH1Od7+u6fb842xAn5tleQe7dvsfO2k1Tt6+00+VnfsxhbZ1fBB/UIdvem8hjOCrpG3JR7qGovyxo1O5XTvWKU9rgTD0IEFz4m0pWqO3LdlT6eQUuaHSufIK09h346Eicr7KcwZec3A23iP8J7MkyWnupYzhgXntlYlkqwZmbEi2LJtw7ZFI2+7Pjlm3oztQnr4gUN18wYT4hvInDHrTiFqX9OUyVwUW3fkKXf9iC9IJgujWR9xuzT9rJzNODAq0WzPTonIh3bMeFpiQSyIPDRzGImytx2Pbm3nL5CeitmSfFkaTnvyk4rIr5pKb04ejoZ5LUtycxZ8UKZjaNtA7Fm/YIopppji+xk/OMR+7ke48o+58Pu59JPMX+T/+vx4tTZCPyGZYTQWk4V+TzR8VVY7raO38yVxmFUXibyHB/YKNfJRRfk+8p5gZmKPSu7IrOdRn/it7RhYsF8g8jlnKueoRqbjpPjYHe5JQVtN3dAbhpNrGTvfnUbHifVSD3ej8rHWdc14Ut9Ztyz5TZt+vmB4uigvOZDvOzJj2dHJxLrchpZrBSHehroluQ1d8+456117k0UPrDrvqKRLKKvSB5Wk966jE2JPzKtbtmRZzaHELZF3xTZlBae9EMoDZ/KwVIr647ShWL6OSn3hQaom+LCRRT01qZY5kdQdA3d0rcsLPgL/Wi79p5jiccZUFf+Y4/k/xgt/Yjz1jbHbXGOZ/iRyzUcsvcr909R5vLssP/M0YUvuwGy+I5r0pU8OKr3E0FWRuULP80DTyzK5YFlHB4u2C6K7ZkXV3K8QXNctdeOafc1TcjOCM/peN5yYtNRdVFSABCuKdfdqrTbWE/moB85725Ib1rxXuJfYgcTaCU3tChbN2C0Q9xltlyfkGwsTbf6Muo6BbS+7aq9Qhqg9YrNb9qUfVvr6O+4LEm3zFs2ZM7TlKbGvC5MFzKzzpRnnwRqFkkIezQvFYTChrD+Is/EdRvmmJFsXpQ1J/SWR+4Ireu76isixSU3D8KSrYXwPu6WixdC2VPcRy9spppji+xhT8dxjjKTyZRsCZz/D5X90+rPaHLVZVl9kpiHsMtq8wkn6vTq245rY0wWzmVzNpYmj2JaBkZ4VO4XhH7OVcZ69R+rut7UlInUNT8nM4Sl7Xnesdl/1pHJ1vToitDTLTCrT82Nu2fKGBbfMe78k/OpomtObUHkqc07dlQL5zlk4IfZFbRvqFiwYOLJj24LY+wVSPajY2XYriv89+6XRLT0PLHjKrIZZA223NeyUWveani7VwFUWRdVZ7lkUl/XkOaIPks2RZpp7QWuYi7I38aa0+bT9tWKP/xUKC5K+B4LmSTkk1ZdYKvkV9N3SrlgMTzHFFFN8P+EHh9i/EbY+Nyb2hSdZP8dCjVqf/NcZkNya44WxresY98X5c9JwSmAN5/QM1TwtVdc348AVx37ejYppSL/S395xS0tLLtPwpJFlYwp5Xe42bjvrCWUiL5PmqLTkDIZiR37SdRte1/ZA3b7axFQGjixYsXeiVs9taLtSaC87Y7ycaQguqWtY8ZShBw7s6li373qBZKsR+D1pySt937ak4NZX03TWc2YcmnFL21cMHZyYzEDkoxU9fbWdrFHZnrwPeUOcXySdF/U/S3dfOLouxHeJTrsS4niD5PSeo9518uREIJm4L/GK0UmmItO0rFsoudSdVXdGyyVNlyTfwiJ2iimm+D7DVDz3jRFCaOKXjBumE/yDPM//fAjhA/jrxmqxEf7jPM9//Zuf6fcAT/wo2Xlq7xtHwzVCMu53R+gfiNJXZPFpunc8POSm2AtSazIr9rzPZJJXS7muOyyI7cbbd9WtG9o344KGWYdWvOu6VIYHXrJSapXKKiKuQanuHumpG/lJN614Q80ehmYMC5ryZSu2C3XrM1oFYmdu8lEvaVozo2bgFalbDt2dzBR7WLCB3a8Q+YPKpLj7ejY1DfXMmreqbUPfvHcs+E0198U+IC0Mrcl8oFTuqs5uzyv93KmBOF8T5+fEaVvSb4i3z4kOLgu+TLzDoNA7HvWUgvr0DrU2+bj8EfRFtib98WO0LDlwpGZe24q2VZGWmm2JG9b9aXP+iCmmmOIxxJTYvyn6+Hye54chhBp+OYTwT/Df4C/kef5PQgh/EH+JijH57zXmXqR5WKixDJl/id1TskkOVw0W3hJsysIzBs7YcdHIfdyf1I5P69M9VwXJSbo2s6vpWT23NW1JbEg0HfmSzBu6GPnxCakfn6OsUu86KiTXYwMNmR93x5y35Q6l6uZ1dRxH9iuW3CkQ8aJ2idjbCIIN82bNSLS0Lbpp4KauSxquF8j7hnLX9n0HaqKT6+4YWDNjaOi8hi37XpRZ8s81J8Yssdekimu7sjNb9IgRS2EBk0fyPGj6kCSN1dJd8WhHo3vNSV09X+SgUNoY3SDUySf3kR0Sr5MWxpvG5xidmhVF6SV5siG1aCh2Vs3IQ2EyPrXto/oFi97RdKLaFFNM8Zjh2xJ7nuc5JwXU2uRfPvl3LGRe4PtwQkWIaH+ag589/Vl7YdxuHmosviR0Fj1c+ohuuIl3xI4KqVkGJ4K5cdSX62l6Ss97EsuCp+XO6mh5YB9XXXTpZH+oV+rsOzqF+nNkpKHm826b9a6hjsyyOXuF2u6SpQmxT26jEu3GqEmsWhWbMTLjQMPdifXSOUMPCkR+16GgfrJkuS3z6kQax9jZbcuCxMimkRV3veBd8372ZOrYrA/JSn7s5dR5VbUSnZQCEokL4nzG8vAV9fSe+vAtkS8oDo+RJ3KJcLzkDrtjr4F00mYYcpLzDN4rvMjGmNijJZILxBdJ1sn3SK/Zy5fs+CoT3cSilrTUtljWL4wKI2ynmGKKxwy/y6r4EMIfwF8x/kr+m3me/7eV34fJ7/+gcavQn8jz/Dcmv/vb+Cncy/P85cIx/zX+pNMvx/8yz/Of/1bX8R3V2EMIsfEYs6fxV/M8/7UQwp/FPw0h/GXjpqJPfpNj/xT+FFy4cOEb7fI7i5nPnRJ7sjGe2/78a7TfIPotcXxDtxBZpm5JbBlNvviDVNNFHV8XNNQ9Y+gpN110xz4yFwWdQv36yKCkU4+8i/XJVpBpa/phDyWu6egaWrDlXqG2O2uhROz1R1zNRlqalqzJtB2ZcVUykd8NLDjUKRDtbV110UkvetfIGXPuFqLms1bE+tpGUrs+7osyv3Ly+zkXS6NEs4oav+rPnntIXhPnT4jTFWHQcDjzgqHrBm6I3HOhd8WpvuDQWCg4WQiFEdETZAUXwPoW3QIRJxvjRVqyQhRjjtFtRvfwkPoi+T8/2b2RpopvZabc+ZBV2g+nEfsUUzzG+F20lJ3w5F/F7zNOgn4xhPCzeZ4XBpX4CTwz+fcx/LXJf+F/xv+Iv/MNTv8/5Hn+l7/Ta/mOiD3P8xQfCCEs4h+GEF42Juv/LM/z/zOE8G/ib+HHvsGxfwN/Az7ykY/k1d//jmPmR2h+lv5tjt4m/AKbmeM+8CR9oO55g0LkWXfuhNjrLmBL37K77skcmZVPSH2MfUelJPOO+84U0vV1Nee8qCO1bdtt+/qedKfQe96uCOaiCmkODcyYNWfNSMuuunfEE1rqqBtMxGbjn+zpWjA7mW4+frY3tV2dJF9qgmfUPGNkxkOp685quubyCbX1zJaq/z2hdJUj+6UHKMvvi/OnxMM1SY/o6KEkHYzr4cf30X5OHsbvfRYG8nBeyAtRcXSerJDhiNZOiT1s0LxAskQYkN8dk3rnzdM6WvMzE1KfIE1Lvez14X7R1beUnRm/T3uCtsRZsXm1iq3sFFNMMcU3wWt4N8/z9yGE8Pfw0ygS+0/j70wy4b8aQlgMIWzmeX47z/NfCiFc+l5cyHelis/zfDeE8Iv4A/j38J9OfvX38Te/Fxf0PUfzVQ6/TDohi7xH/ArpqfFIe7RiUBsTe2ROYkHLR/Tc0HVTbt3tApGPB8Kcpmwf2rFhxnBCEjVtc16T6jpy174HBkZulExkqr7j5XxRR9esebPOGGi4gytiYwY7ME58z+lNjhsYOWPJnZKJTPOE2OfVnJ9ovHO79t12yY5r3juJs9PKGNHtysy0I0clYs/zVC19TTKIJb1tcecd4Qj5aR+/uS2yQotZflY/FLzv403xqEDsYQGJPHpCHq+Tn5EMB/Sukt2hvU/2S6f7J+WpbvLKVLfhXmlOTmN42rUQmcOMuo/LxVJdPfuW3RNOvPQjuYFQEThOMcUUjwm+d+K51RDClwrbf2MSuB5ji9IkqBtOo/Fvtc8WBUXvN8afCSH8u/gS/lye5w+/1c7fiSp+DcMJqbeMo/L/zrim/lnj2ZmfV3RM+X5CiJj9FHs/d/qz9LQ/Ohe0B0G39kkDh468b+S6fqG2GrwpOH/SbjW0Y9bTDiep8kTdkkuCI0P3dN00sDTpTR+jXWnsGla2j+yaNWfeqlRiR98NNelkJjosFnrNyZ0x61qpDt84IfYz2s5hRazrgX07ZpxxpfBMdSs5qoOKec4N6QmxJ+bFFs0OP64+3B77sWe/RndTyArPZO0FBqe2ruLNMrGns/qFCHoYL4ryV6TRilEcG8ULetES4R7umRt+UNI5FTtKK8Sdlx3spGUjHP3b8ua8LLlkkKw5qs2KtHTcN7Rrz/vmDUuz0CMr8hPvgkzmqtgzpphiiscM31tV/IM8zz/yLX4fvsHPqlnq72SfKv4a/uJkv7+I/x7/wbc64DuJ2Dfxv0zqBxH+jzzPfy6EsIu/EkJI0DOpo39fYvazJWLPu33Z0icNGnXdxvtG8dftFvK1fddEZgv+6x3zNuydWKQGazasWBTb0feumlkPJynngLRCmmPP9NPQcd9dbXOWrAiCfXt2zLhRIOo1Z90pbC9X3OFmJx9fJNg0Z1Xwklk7dh26KzNyt7AQPKjUj6stbA/tqouRW7NoTe6ip7V8VW1irTrXuSikp73iki0GBWJPVpQ65fLTVHYeZrWGTXnyYbW8J8pvGNZSe41bjrWXNT+keIJR/UAJRcU7pUWD0CC0DBc+Y9hsGDQHeo0HbtZnZY6MTYTuym0ZFgSNiXXD0kJuvUDspFNin2KKKb49blCa4nXOo6Ly72SfEvI8P/nSCyH8T/i5b7E7vjNV/FfwwW/w81/Gh7/d8d8XmPsR6s8Qn6W/I7/3dQ+f4NiPNELNRwxP0q+5hou6hYh7wbzIkoHEPQ8s2tf3xZME+rAS7XaK6m7k3jXj45YsmDUUueqqRTcLffCrtlwrRI7zkxT8MWqTunsitmXOqpGXNTx019BdXZuuFUhxp3JN9x1IRLKJWG1fz4a2gA1tSzrOuq3ul0STY+etMmkFgyxeFRfXLHGlBh1NtAFhhvpTsrBotPRpw+SONLkmCte0s988PV9eLklklfetXy8b/hjdIK4Tr44/zzAzXpF3ro/T9fkb7n/0A0bR6d9KwxO6hUVM3VLJ4ja2WCL2vOT0F8sqdrhTTDHFY4LfXVX8F/FMCOEJ3MS/jZ+p7POzxmn1v2ecpt/L8/xbpuGPa/CTzT9MwbP8m+AH13ku69L/Bbr/mO7/zf4O6bhaECHJPmxUMKZpO2OvMKM8NqvuFV1r7hkYabpVUEjv6paauw7dEgrOaz075m2omVfTMrTjBdsOfOlEqLnkBcVCyUwlS3M8GLUucc6sTYfm9fRdntTk190rVL13PRAsnDjQ7eha1NCZROpDmQ0LIrllNXVHnnJZWlC+L3pCr7AgSC2WZHxpUhMXI/IwGE9Jqz1NsirPZ0S9pxhdpv8Vea2v1zpVtWcVZ77gKnk8bl1D6rZYk8kCJwtDefOjQqgTYuwxGDF43clCt/sy3fdPzpmkKyVir5kt6fXjilCRlpA3NWyo53NiT2tF/5HYs2JPTevrU0zxuOJ3URWf5/kohPBn8E+N293+dp7nr4cQ/sPJ7/86ft641e1d43a3f//4+BDC3zX2glkNIdzAn8/z/G/hL00M4XJjw9A//e2u5QeX2O9+kNEpcZv9CHunuoeks2xUcAetG6o5K/eEXTW3zbjvmuPWwXqlDLJjx3ltowkJpnrmnROhZVbkoVzDwcSkBtpeU0wstx5pDzsW3yXOWnRGx0W3Jb4mGApmbVtzXJLpujcxxznuPR86Y9bdwqtsWJQbmpeL7Fqy5453Tir3idnSc59WSKxfUcKn8aE8esIoOW+QNAxDZC7JCRPhZ7YmGp1G3VHnivFS6ril7baxdez4GoMjsWelbgvmJS5oDpbV+g8l/duS7vtC7SzDL55eRPKxct2sVvbSTwbNUktbUliaRHlTM6treF4rHWqPdjTCDtEbwmQhnER/XCP6aVNMMcUU3w0m/eU/X/nZXy/8f47/5Jsc++98k5//8e/2On5wib3+WpnYWzOKk0JrDw/15pqClwwsORD7skXHHvCRmkgimzDIwL5Z5xxOZpbnaDlvZEfbGQzMSnX9qtR4kdh6RBBZJvLEbeMhq4kV61oiHzYw9IZ8IptbcCg9GZ96aMGz9ibXEOTWzLteSBWva1lUM2Mo88Cae2565yQRveJS6RqO1EoPQU+vFKF386H5/EOSUVM83BdG264u9pxoJfPEXL+w6Inuy6NlIZtcY9YX5c/ICnPRI+fl7onyC6K8rW5FPw4ytwx9zfLBC5LOL5+eM1T82aOyOY+43BpY641o19XyLbV0STRkpb+u2b2i0X+dmZjk1072T+sf1o8KFr/5ZVNMMcUPAKaWsj9gaHyOzv96ul2fkF/jHM0nJIepO/lZWRgPYoGGT+pPIsnM0LxLdgsq8jlL+jpWrGvKNYz0fdXBRFTXrAisMuWOhMEkdRwkZp1X07Bl07abHrrsIc6KDSYxdI62LQdOndXmzZ4QO6yKtJ3RdiR3Q8MVb7l9soZZqrSwdR8R0A1tTP4/iGVqlnxQZCB3U+YNM7t3hULtP85floZj97eRPLokZKd1ePXz9E6vMRpdoLZKmJGHrlq/JR78luNUer/9E7KCfmSUNCsPZlVIWv5LzRPyxdek7VnD1kgc586/d1c4bi+sv0x6WpbKRzulJz+k90sRfp4XnOymmGKKxxtTYv8BQvNzIJdQf0naWBTvvSD0vk7vhrhHPf8xvcIktyUr7hTS2E0zgmDeWQ0L6iKpGzJXddD0ROklD+2U6u59V07GgLZckFhVFxz6ur439RH8/pOsALSt6hQsTqPS8FMW5BrOWrCj5WuGYl91eELX9Ur9eL8yTvWhHWOizDW1tMxa97LIfZn38Csi89LjWnggj58Q0tMWtlq+cUrsyKJN8QmxL8qb54VkkdAlvyYIhvFpy1otfLx0TXFWmd1eS8uGufn4HvLQInlSHi2Iap9mtEf/inTmlt3nTvUnyXBT+0FBMTO6VV4bDG6WTGpCetP4T2H8OeTuy/O+EKoWuVNMMcUU3//4wSX25EnDpZ8yiH+ZaNyq1a5/QLF9fKa3oFfgwdnJF3vDvDmbEk2xeTuTNqlFK4pKjH23NQqCua5tM5aN7KjZEDsvMe+h92xPhsqselpWuIgFccFItjrpLcjUbHpWw77Imxp+1aFToVhNg8IA1a6bgpUTAd2uPXMaBvoWLVnRsinR8mWJdwS5hmf0CzYEsY1TYkcar4sKxF5Pm3r/f3tnHiRJXt33z8uj7ur7nJ4+5r73GGZnD2B3uZcFCckKJIiwtBhFYGxjW4ogzBIQSDahMLLDOGwFhsCIEHIQWjkkYQhYBGgtvCCxsMuy1+zMzuzcPdNzdU/f1VWVmT//kdlVmbXdM81uT3dvz/tEVHRlZWbV65fd9fL3fu/3fRaIyZAKBgmqXbiTdyMT55CZM9A9AfaPaqsz7crWRCD17VLiD8/241rtUHbHKVodIANgipiqhd+0CTgNcgiCYaxL9XkVe6YCJlVrx+o5FzCSRkx0uxOMgdMEJhQZEjOLkS4w42BvR+zduNZ+LGszluxCZHtYrKcoyuubFdaKXyus38AO+OlWCOqKcX6hGG/PTX5iitEcgE2GLbj4NNHHJOcoM4lLnnIskI8zGnU4C2esPeZooY9ZzuJQJMsgQoFJzkTiNcP0sItKomQuOQrMxBTt5kfSgwyS4yoOh0kxghNbilVhhrDgcn6p3ilSDFCJFfG10MIEk3TRSjvQyhxVjkLUQrWTHsrUO545NDUk6JOj/rKTxq0IyCZgA8Upl8JMO6mJIwgnwb0DxmPFbXPjiVZxdiW59tyzLiS84HhzZPzbcX2XVHUcd24YLlyF+DKz5jy1AjxrAmN3IJEYjQRzWAzU0/liwN1Yaw5jcCB9F2K3gLsHUnsgvQ/szYQyDGjdu6KsR1awKn4tsa4Du23dix98vbbt5ydqv7BxOnA8lyy3UeYEcxzFcJxZ9teOrzJDkX6mYmPqHBuZ4CUEhyIDuHQDTVziIoYRNrC9pkgH4DX8VVUaUuNZrrKHbno4Sxc/JsUYJxmo6cxXgDQt+DVBlTnSbKVMfAqhlVF8OuighQCLaWb4WW1evMh+xmI2+Q36542yRz4elmkmTT9p38XyA5gqIkHY1z5lbYepo7ETGvQVZk4nArtVOg0mDeJhmX5srxfxtyDlMaR0Asc/QnvL1VD7fZ7UEFROAfNZ9I1ArBgytQFKsbXofieBcx7L9GIHPfiFNqygi8C+AtYprNyHELdedLqQ/JOiKMp6YH0HdrkvtiUEWcP0hvcw2jrHWP48yAkydBFEo2bBp5WuaJlbSJ6WWmAv0EGKTgrYXGWEi1yggyZGY2nruQZRmJkGcRPPlOhkFxkpYXME4VE6sDAxYZYMfZRio3SH/lhgB5d2PMZJMYiQYTuTeDyORDcRAW9hNpbuNw3VIyW8hFK9zyRpBknTikOZVHCV5rlDMC/QY1qRWOYD/yTxOWn8c2AVIYgyE94kWL2ADbIR8dMUL1pY409jeUeBo1DcA9W6ABCyIRnY0121wA4gQRsm3mkn3QZmL6RawYHCVDNSsbCCY8Ax/Px9BPykfny8uE9RlJsDrYpff1jWNhzeieXNYJePYPnPcrRvgJmEeMkGqjHZfiAHBQAAHW1JREFU1SJSC7EOGfLYDDFAwBk8nsNnB5djwboxcE9wkXSss5tnKmz0t9NWvUrH7LPkK49xtvs+yhKvvB4i3oc8S7FhYVwem1bSDOBg4ZNinBnKUdOgJvbUgjqARXLOutKwPcssvewiTQqXMSwOEzDOfM4qEIACzGcXrKsYuwfx5wvqqpDaUu+DLmBykQ1uDmQaplMw/ROIKtOd5rvCYreakcm15+Ha9hipbP252wuVDsjfFzbxMSMETQY7Xa90t4M3J6ZdJPATNwImWJutDBRFuYFoYF+fpMstUP7b2nbBSzMTm1BtTEMXmWSIIfKcw+FphHOJVjxlTmHRTBAFwRJjZGhhLhpRGwN9wXaKwRid1cM0e9/DqR5AKj+rvUfGb040QvEpJtq+ppnDMS0Ugw0UvTJpM8Vk5gxwBgMIexI2l7nc0OT1BNBU++0MFbrZTp4SOU6Q5vtYtCWyBMIW/Hl5WyHqg17vgIe7EaLAbiSPye+AfB9BuoKfOo077SKTT9R1aOx7GzzbUGFukhbjm7BhjzUEVg9kWqB4O3inIBhB/CsYPzYCry3Smyepey/BVKJdq9ERu6IoNwnrPrDj3gflR2qbhcpFLsYCe9VcpSB7SWGi1Ph3EJqpl1KeJc0tlKM5akOZJnoZj7Vg7fQ3kPey9JZO0DXzPaS4H0O9taixs4k53XSllFg3XWGOrGnBqWzDmXIoTF+mr+kfavt9q4vJ+PIsjiO01bICZS5RoJmACUBwaaeP7WS5QpbDODyDzS586pXtFhvxY4Hdoqse2IHAaifUbAmL5qq5fqr5VmbT45RSZ2nzqrjV2O/oNjRJsRoqVoJkeZ7xSoi7Pey3bizwUuBmIDgRPmRPJBsb/c7lK4l5e9OgKZ9I4wNUL4C7EbH3gbUXsQ+iKMpNhlbFr1Pc+xKb+blnyOTvJkcLueACKX5M2eohkPgc9y3MxRqfFOiqBXaApqBAi7+R3tlL9E78A1n3Mnj1IBd4VUzMs4FcTYzIM3MnkFyerLeJ7JRNdmyE9MVLSDQlYLCgKc/8KNQOLmGbffgyH7zmSNPHHKcBhzSD0ZTCGDOcZ4oxtnICYhrwQmvCD9JQQCfRn0J4U7OVOemhYPaD/zLCCUqZQa6m64G2YlUS8/SBM5HMGpix2IaDQTDNb8JPW/jpMYw1RvbccZivznc6oDlWnyDxPAlhk5e2mDStOYvBQfAwUsSzunCy70XcW8Ddhzj7cF6R7lcU5aZDq+LXIc4ujLWRwO6hnGphJn2BjcElDD+sH8IgldgIMEVTolt6xhhazSAdnqGjfIji3GNYE0/UD2iQPJXymQbPHsOQQ6xt4LeQGhtj86mLSFzyNr0JyqGUqRBg2AI8V7fJ9FKSi0AKly1kGWKOTsYZwWeCDfQwG6sar1BsWMLVOOkQisJYDCFswKOVS9wTFQpO0CUZiv7TdR81VL6XZTRxaxA4Z6NPEUhtIsh0Y1mdCKMQHAd+RqnLoXb7bMBIDjFRMPeugLSCiW6gZBLsLvDDwkUJ5kD6CRAq9iYm7U5KmTwXnatctWZA4F4eptggmasoinKzsW4DuwkuYLzvYLxvM96ylzm7vs46HexPxDnL2In1TzZTOHSTZQMO02T9F2iZ/MfYCZ3JDysfhXQaotXg4p0D6QXpQpx3IM7bYeQzMFOfZ5f0AfBi0jSp3lpgB6DaGqXrUyC7yM21YnI78XkJ4RkC+hmNTQdUqGudA8xQTQR2wxiQwWIbPh3M4iJ0ExBK6ho6mYqF6jH7KptizVsc7ziYXpCoU5x1CWgCyWGsTfhuDr/fo5x5EezwPVtOdYIXrjAQQNiMoV5wR2oAyrHe7rKxFtgNKUxhH4FU8LIu5cw0z2TuYtie779uGKDAZMwHMwxrYFcUJUnjmOYmYF0G9qD8eYLyx5m/oq7zTuZieWJPqjixiy1mBEgh7KFMJ5NU6eAJ5pd7lW0bQx6ZL9CSyxinH/Hm08VVcPaFQSnzDki/AzvzdsSO3QDkvpkI7K/sYx5dCnEhswsmmsG9DcaPIMGzON0pgtzR2v1HmjPEq8NmGuRspxilzbTimE04fgrxRjmV6cHIJYiW77XGykWFy7hspBpNOXhSwViDSDCfRfBIsQUfB582SgiT2VF8eRKi6vysuxti6/RNqgfx6ksHLb8T3z4e298W3idIDlJb8K0t+NlWPHsUzz6JbzuUpd6RL8U9CZcJ2cT2TCzIK4qi3Kysy8COtY34bZrrnUlIi81xmgICdOPJbiYkwyX6KDMG0frxLgYJalXiPsbZgXj11DSpAfDHILUrDEyZB6H1E7XdrxBAyd8Fl79Y3w7mq7htSO+AoADshyuHwX8OKYxBWz1QuVePhRot89scweJWgii1XWKUJgaxaSXAZZZxitNnsEx97j+VeSvl2Pp4iwH8KCgDZOmsBXYQSs5eMFsZt1u4aBts2pjmGYjW/TdJb8PvmZyS8NN5rNi0ueWl8O08FpuxghZMugWqW6B6Avzn8ZwOyun6FIdlTMKRuQZxH7/h06c1sCuKoqzPwC7OvZBII7+EmM0YmcRlB7Y0c9G6i0tylPn141n2RIE9xGcAiVWJe04LKQ+wtocpabsApgLlaERp1/XaFyQfa+Ga2QtN98OkBWNHoPIiyLFwudd89fj0MHS2Q6SjblXGsYO9+FaYJbAo08pWfGzAUOIiZZqYiknFVu0h0l69CC0TNFNOVLi11X1m8hRME8Z6Ax4lSpzjcGYzFznMfL/bXroTv1KZdKJZi2nIeXkpg53eS5DpoJo2YGfITc4gPBudcAd4x2vB2ypPJ9VszWTi/XKENQaRxfhYNLEbyFKhysTNWP6qKIrSwPoM7NIM1q0Q/AJowXLeRcG0UJFHgWcwgJFfT5zjNKR1S2TJAWJaSQdbIMhDqRMJIuU0axuJdRRzP+GapLfDpkegeD+4UYA8vA0qUfAyVSjshqn6CBpnUy2wA7iVLZjMEFWEGS5gYxilLtJSoDfxkbN2G+mYOEM68JgvXXdNN5ZpIu/tx/GGcbyjjKaHOZuuFxG6DSp6lYbAOUO1HtiNjQksUuZunEqAPXcBI5cZ6z8N0Q2S6w2QnY3VAkhyiZpdGiFZvD8vHJTFYZAiKbayAYeLWBzHcJnDsQY4Sd19RVGUm5N1GdgBrPS/Bykg9psRcXD5EyrUdeMbg4Bf62AuFNlFJjhI2/QR3PIL0ci9GEufA/7LYTW8iWRU/RGongQ32cq1hgi0/VbytbY7YSbexzw56g9ox7S/GT/n4eVOMufCaGwtukuy3anfEHjHLTuKkzbG2kYqSNEZbMU1R7F5CtufJD1XvzHIeSchXahtezGpXIBpRmu3Pyk6wbRRnDuIW7qCW3oJMT8Cu0RdGCcNJgMSrjep2ucxuEitMn4YJBOqyQFSPoeYfix6sIICtl/By3QSyFHgEA6HyLCDILrhEGZx6aca6fTNMU6VWdyGJjaKoig3E+s3sLu/kth2eGNiO82LhOps4NBMnm1s4Ldp5W5StIaZ/Op/oC6lNgXOdvCiVLcYSG2F8i8AGzIH61rpS6X1Tjhbv9nAcqH7TZAOQI4TtFyl3BkrHgsuEl8sbsekcAHKkXSsRYo8/ZSdLOO5tzBln8OXaWzzAu3BKeYDr2+dIq75nvVfwjL3EESV7x4XcdiAIaCZbpoQXPL4HCPgGL4cI3d1GokvhbP6IQjT/0IZm6348/P64oE9AH5UQGe1Q/YWCDwwc4h3lsJ0DoJ6kaGV2V9riwuQppsS9dUDeVoZjwnwTjJCO1uu53lFUW4Kbk6FmnUb2BtxuBWhiIlaqKboYJAP0cRBiuxDGkRZw5PeCJW/jm331AO7PQTZ90LrpyH7NrCbf3mj2u6GjnvAtcLGKqnjYJ0K9xmwxgOIFda7wYtgumt9xwNOYDGIhUORXgqAT5oyh4BLlASmHAd/vqWrzCAMYGqBthJJx4Y66oJPnh4qVHDooUqavYyQ5fs1Lfpx3ko1pj3vuf24icDeUwvsAHbQiW+fRkw7TjBA4LRjSzv4Z8BcADObnMYwQwkX2UEzfuzSuBQSOvpZ0kxgUaSHFgaxtQGroig1bk6x+JsmsAs2Wf4NQhcp3oVNX4MW2wK48cCehdROyPwGZN4F7o7XblTzLcBTUIlS6iWgqRn8cFrAKl9G2IiJ+oxbTJNiPx4TOGzBo8A25rD4fwjhyH6aN1GO3aGG0rH1RjVGNoCpV8b7Vh+etHDF3spJuwtDnis8D9Fn9uMmGsykyCRm3ituM25czUdyGGnCd7cz53ZglZtoLTdje6eB0+DcB1582V9DIA7cpKaA7ySyFGlc8uwny06y7GQL+8gxhNOoRa8oinKTctMEdoAcD/9yJ6TeAcHvQ+oBcO8N54OXEysFhdtgKhboUpuh9Iv6IV4/vjOLbbZhVzOIbGU49RJE89876IFE4M0m2qGYBunYKq1YvJEZaWdMfE5nD/C01OsNbqOQOH6KfCJkOglNPphNpUi5tzCR3sRIqoNqqojvjILMAcP0zw1R8M7FzkhqxmMl3w+vXNPRN9KDb5qY5T1M4HKFMnn2cJBPoiiKcn00Fa804uyFwudv7GcU70wGdmnC2E2Q24nJZXFLOdKlcYSfApBq74FUfVlZiXSint9pqGT3SJHmDgxChQtcshyGqTJ/Y5DiMvHOa+OYROnZVQzxkj4HD583M0oPJ2liMlfAyT1b278BSWi/TTmzyT5swSWShFX4xhmgmh6glOlhIvterjrjlKxpLAzDsULHgHMoiqIsDU3FK6tB8SBYOSjuglyBoJgiyE6DhMFegtuRWPFY09wZyNVz05OUYoHdwsWjwAECPEqcY4rzeMRbllYgtvrc4gSwq7Z9gVk2I4DBJs0U3WT4fVzuwOUgPu18lj8hiArwHMpsxSKIbLxEiaFYQd6kPZqshA/Ogr0DnFvBeQPG3s+L7Z+mYk8Co8AkZVxqveEZJiw0CN9/mnMYDPJKCSBFURQFDeyrT9tdsLkK8nMARLpqxXEARo5HYSwMpMXZZ5HWAxgJEGw88qS5B5jG4ziGnzDF9tqSMA/I11q6htKxKfZRifrHwxhFCkwxTZEmWmnGppNhAk5TIcBwOx+hEInZWEAnLVycl57FUKCdyWjk7RFEneZCKVlHugiyB7CtLeAcCB9Wvboh1JD/r1AblVdJ00e5VvFfIUcbs1GdgE+ZEpfJ0bUs7lcUZT2jqXhlNXC2gFWoNT+R4BJIT1gxDlGXsyHwTwEOtr2J7uB2ZuwpZjjDNCcJgCBa3y5All5mqGuyO/RTqa3ThxwdVJkix0YyNLGVJs4wR4kLXOECU9zOSeriMee5RGdMpW4DHbXADoTLA7lMhjzdDNHK/TSzgTx7cOmgYdr+FWTYTDm2hM2lNRbYIUszgkMzm2lmM7YWyimKsiQ0sCurgQi4d0Dl+/WXGMBwAXDA2oFJb0HKbVA5At4hrOAgU/ax2vE+3UhMuCbTUEAHTVjkSTGESxaHDAFlDIeoAA69lGLz2I0lgue5zK3srG330cnznGAjnQzRy2466eO3aXmVo+gMW5jgsWjLIcNGMmwmz07y7CTLDtzr3R0oiqIogAb2tUHqYBTYLXBuxVj78W2Dx0sgh0lXO7DK9QY0TeUZzrv100ukEwVvLnM4tFCglwwBWXyqXEGiUbjLvZhYdXsuNvoGsBoK8M6TLHi7n9t5B3fgLLT2/1XQzFtw6SbHbrLswHrFrYWiKMqrRYvnlNUg81vg3A7p+8FqQ4K/w6t+ubbbd8YTF6qpdBoK9cg+yRxF04Nj+nCDADEXKLjP1PYLm/BiS+IszhLH5RTx1nEe42xikCE2MEQfQ/Qljs8ucyo8z63kuXVZ31NRFEVT8crq4e4NHxGWHICoMh3Ac46Twq4JxRRnniPX8R6agyIt/jjN1RewnLMQpeMNNpgWkFCjLeA0QjOmprF+CottBJGGW5ppdnI7PQzSxya66cfWPw1FUZTXJfrtvQYRaUFkK8ZE8+jWLGTuIJxzBys4wZ3Tz4N5qXZO1dkTK6DzsRnE50i0HWAzgMcRQHDZwQZ+gzTbKHIbmXijd0VRlHWDrmNX1hC2vA2fLrAEnzMEGRd77se1njTCVgz1wC60J883rfgC4FJlH1XeTSefJs2dWNcX01UURVkHaCpeWUvYO/HMV2qbvjuIHVNflQCMxLdNuMicHL7sZcTs5v/yKzxOJzPYvJNNfIwDK2b+tZjFZxqfLm3YoiiKsuxcN7CLSAZ4nFB31AH+yhjzB9G+fw18jDDX8R1jzL+7gbbeVNhyYH6KHQDPuZoIg+JdiTTVm/Cc3UxbGzjhvp/zMk0gARWyfDcm5nq8ofJ9pajic4oxTjDL4/gcYoaTzPEg7fxHNq+KTYqi3CxoKn4xysBbjTHTIuICPxaR7wJZ4H3ALcaYsoioFNgyYnMb8V7pnnMSgwuSw0/topRq40p2I+PWeZAJ4DDnGaxJu7ocxuENeNHdwWkm8QhwwmH9DWeaMn/EDzjNVXwCmmnnJ3TX9h9PNF9VFEW5EWgqfkGMMQaYjjbd6GGAfwF8zhhTjo5r7O6hvAZEsljsJuAEDm/Etu7ncsdTjDlPgIwAIxj2UJt0p0qBbiajJilChQ1kOBMFUI+AYaYY4lX0jX8V5Elxjgn8yL4pruLQU7vROMWcar4riqLcAJY0xy4iNvBzYCvwBWPMT0VkO/BmEfkjYA74uDHmyQXO/QjwEYCBgYFlM/xmIGf9FUIfIuFl8q2HgX+s7XdpSTRRzZGP6ccJt2Cxm83soZPddNCZkLG5sQhCH82cYBSAgIANuJwh7D1fIuA8FfpUHlZRlBuGpuIXxRjjA7eJSAvwDRHZG53bCtwF3AH8bxHZHI3w4+d+GfgywIEDBwzKkrFkMLGdYR8TPFLbjnd9A4tmminwa7Szlw72kF6h0flibKSlFtgB+jG04dBFiRxXmKSDPjatooWKoqxvNBV/XYwx4yLyQ+ABYBj4myiQ/0xEAqCD+QbbyrKT5ZbYlo1LkS4+TIE7KLAfm+Kq2bYQO2lhhiIZpvA4RzNVjkVTBVXgMh3s0sCuKMoNQ0fsCyIinUA1CupZ4O3AHxPOu78V+GGUlk9B1FtTuSGk2Ukb/5wcd5HlwJoL5I0MYfEkP6opzxtaEvsv6Z+LoijrCBF5APhvgA18xRjzuYb9Eu1/EJgFPmSMeTra91XgvcAlY8ze2DltwF8CQ8Ap4DeNMddc5rSUEule4O9F5DngSeAHxphvA18FNovIC8AjwEONaXhleRFcuvgkBd6y5oM6QEdsuR1AJVYBAHBJkzuKotxQ5lPxy/G4NlEt2heAdwO7gQ+KyO6Gw94NbIseHwG+GNv3Z4TZ8EYeBh4zxmwDHou2r8lSquKfA25f4PUK8E+vd75y89JEKynSVCgDMM0VLJoJCGinlZ5X2eZVURRl6axYKv4g8LIx5gSAiDxCuCT8xdgx7wP+PBoEPyEiLSLSa4wZMcY8LiJDC7zv+4D7o+dfA34IfOJahqjynHLDEIR2uplmkl766WWALjbTSy85sqttnqIoyi9Dh4g8Fdv+clQcPk8fJFpnDgN3NrzHQsf0ASPX+NxuY8wIgDFmZCmaMRrYlRvKB/mXpHRJm6Ioq8KyVsVfMcZcS5d7IVGOxunppRzzmtHArtxQNKgrirJ6rOhyt2GgP7a9ETj/Ko5p5OJ8ul5EeoHrisGtjL6ooiiKoqxvngS2icgmEUkBHwC+1XDMt4DfkZC7gIn5NPs1+BbwUPT8IeCb1zNER+yKoijKOmXl1rEbYzwR+RjwPcLlbl81xhwSkY9G+78EPEq41O1lwuVu/2z+fBH5C8IiuQ4RGQb+wBjzp8DnCAXgfhc4A7z/erZoYFcURVHWKSurPGeMeZQweMdf+1LsuQH+1SLnfnCR10eBt/0ydmgqXlEURVHWETpiVxRFUdYpKimrKIqiKOuIm7MJjKbiFUVRFGUdoSN2RVEUZZ2iqXhFURRFWUdoKl5RFEVRlNc5OmJXFEVR1imailcURVGUdYSm4hVFURRFeZ2jI3ZFURRlnXJzpuIllK5doQ8TuQycfg1v0QFcWSZzbhRr3ca1bh+ojcvBWrcP1r6Na90+eO02DhpjOpfLmLWGiPwtoY+WgyvGmAeW6b1uKCsa2F8rIvLUdRrdrzpr3ca1bh+ojcvBWrcP1r6Na90+eH3YqKw8OseuKIqiKOsIDeyKoiiKso54vQX2L6+2AUtgrdu41u0DtXE5WOv2wdq3ca3bB68PG5UV5nU1x64oiqIoyrV5vY3YFUVRFEW5BhrYFUVRFGUdseYCu4i8X0QOiUggIgca9n1SRF4WkZdE5F2LnN8mIj8QkWPRz9YbbO9fisgz0eOUiDyzyHGnROT56LinbqRNDZ/7hyJyLmbjg4sc90Dk15dF5OGVsi/67P8sIkdE5DkR+YaItCxy3Ir68Ho+kZD/Hu1/TkT232ibGj6/X0T+XkQOR/8z/3aBY+4XkYnY9f/MStoY2XDN67aafhSRHTHfPCMikyLyew3HrLgPReSrInJJRF6Ivbak77bV/F9W1gjGmDX1AHYBO4AfAgdir+8GngXSwCbgOGAvcP5/Ah6Onj8M/PEK2v5fgM8ssu8U0LEK/vxD4OPXOcaO/LkZSEV+3r2CNr4TcKLnf7zYNVtJHy7FJ8CDwHcBAe4CfrrC17YX2B89LwJHF7DxfuDbK/1398tct9X2Y8M1v0Ao2rKqPgTuBfYDL8Reu+5322r/L+tjbTzW3IjdGHPYGPPSArveBzxijCkbY04CLwMHFznua9HzrwG/dmMsTSIiAvwm8Bcr8XnLzEHgZWPMCWNMBXiE0I8rgjHm+8aYed3HJ4CNK/XZ12ApPnkf8Ocm5AmgRUR6V8pAY8yIMebp6PkUcBjoW6nPX0ZW1Y8x3gYcN8a8FnXMZcEY8zgw1vDyUr7bVvV/WVkbrLnAfg36gLOx7WEW/hLrNsaMQPjFB3StgG0AbwYuGmOOLbLfAN8XkZ+LyEdWyKZ5PhalOL+6SPpuqb5dCT5MOHpbiJX04VJ8smb8JiJDwO3ATxfYfbeIPCsi3xWRPStqWMj1rtta8eMHWPzGfLV9CEv7blsrvlRWkVVpAiMifwf0LLDrU8aYby522gKvrchavSXa+0GuPVp/ozHmvIh0AT8QkSPRXfkNtQ/4IvBZQl99lnC64MONb7HAucvq26X4UEQ+Rdix4euLvM0N8+ECLMUnq/Y3mTBCpAD8NfB7xpjJht1PE6aWp6P6iv8DbFthE6933VbdjyKSAn4V+OQCu9eCD5fKqvtSWX1WJbAbY97+Kk4bBvpj2xuB8wscd1FEeo0xI1E679KrsTHO9ewVEQf4J8AbrvEe56Ofl0TkG4Qps2UJSkv1p4j8T+DbC+xaqm9fNUvw4UPAe4G3GWMW/CK6kT5cgKX45Ib77XqIiEsY1L9ujPmbxv3xQG+MeVRE/oeIdBhjVqy5yRKu26r7EXg38LQx5mLjjrXgw4ilfLetBV8qq8zrKRX/LeADIpIWkU2Ed8w/W+S4h6LnDwGLZQCWk7cDR4wxwwvtFJG8iBTnnxMWi72w0LHLTcNc5a8v8rlPAttEZFM0cvkAoR9XBBF5APgE8KvGmNlFjllpHy7FJ98Cfieq6r4LmJhPla4EUV3HnwKHjTGfX+SYnug4ROQg4f/86ArauJTrtqp+jFg047baPoyxlO+2Vf1fVtYIq1291/ggDD7DQBm4CHwvtu9ThBWfLwHvjr3+FaIKeqAdeAw4Fv1sWwGb/wz4aMNrG4BHo+ebCatTnwUOEaafV8qf/wt4HniO8B+8t9G+aPtBwqrq4ytpX/TZLxPOCz4TPb60Fny4kE+Aj85fa8K05xei/c8TW8WxQn57E2Ga9bmY7x5ssPFjkb+eJSxMvGeFbVzwuq0xP+YIA3Vz7LVV9SHhTcYIUI2+D393se+2tfS/rI+18VBJWUVRFEVZR7yeUvGKoiiKolwHDeyKoiiKso7QwK4oiqIo6wgN7IqiKIqyjtDAriiKoijrCA3siqIoirKO0MCuKIqiKOuI/w+E8TfIGOEqggAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, figsize=(20, 7))\n", + "\n", + "gdf_grid = grid.shapefile[grid.shapefile['tzid'].isin(['Europe/Madrid', 'Europe/Lisbon', 'Europe/Paris'])]\n", + "gdf_grid.plot(ax=ax, column='O3', cmap='jet', legend=True)\n", + "\n", + "ax.set_xlim([-11, 11])\n", + "ax.set_ylim([35, 52])\n", + "ax.margins(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. With new grid" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "projection = 'regular'\n", + "lat_orig = 35\n", + "lon_orig = -11\n", + "inc_lat = 0.2\n", + "inc_lon = 0.2\n", + "n_lat = 100\n", + "n_lon = 150" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Spatial join" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "# Method can be centroid, nearest and intersection\n", + "regular_grid = from_shapefile(shapefile_path, method='centroid', projection=projection, \n", + " lat_orig=lat_orig, lon_orig=lon_orig, \n", + " inc_lat=inc_lat, inc_lon=inc_lon, \n", + " n_lat=n_lat, n_lon=n_lon)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
geometrytzid
FID
0POLYGON ((-11.00000 35.00000, -10.80000 35.000...NaN
1POLYGON ((-10.80000 35.00000, -10.60000 35.000...NaN
2POLYGON ((-10.60000 35.00000, -10.40000 35.000...NaN
3POLYGON ((-10.40000 35.00000, -10.20000 35.000...NaN
4POLYGON ((-10.20000 35.00000, -10.00000 35.000...NaN
.........
22495POLYGON ((18.00000 64.80000, 18.20000 64.80000...Europe/Stockholm
22496POLYGON ((18.20000 64.80000, 18.40000 64.80000...Europe/Stockholm
22497POLYGON ((18.40000 64.80000, 18.60000 64.80000...Europe/Stockholm
22498POLYGON ((18.60000 64.80000, 18.80000 64.80000...Europe/Stockholm
22499POLYGON ((18.80000 64.80000, 19.00000 64.80000...Europe/Stockholm
\n", + "

22500 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " geometry tzid\n", + "FID \n", + "0 POLYGON ((-11.00000 35.00000, -10.80000 35.000... NaN\n", + "1 POLYGON ((-10.80000 35.00000, -10.60000 35.000... NaN\n", + "2 POLYGON ((-10.60000 35.00000, -10.40000 35.000... NaN\n", + "3 POLYGON ((-10.40000 35.00000, -10.20000 35.000... NaN\n", + "4 POLYGON ((-10.20000 35.00000, -10.00000 35.000... NaN\n", + "... ... ...\n", + "22495 POLYGON ((18.00000 64.80000, 18.20000 64.80000... Europe/Stockholm\n", + "22496 POLYGON ((18.20000 64.80000, 18.40000 64.80000... Europe/Stockholm\n", + "22497 POLYGON ((18.40000 64.80000, 18.60000 64.80000... Europe/Stockholm\n", + "22498 POLYGON ((18.60000 64.80000, 18.80000 64.80000... Europe/Stockholm\n", + "22499 POLYGON ((18.80000 64.80000, 19.00000 64.80000... Europe/Stockholm\n", + "\n", + "[22500 rows x 2 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "regular_grid.shapefile" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot timezones in Spain, Portugal and France" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, figsize=(20, 7))\n", + "\n", + "gdf_grid = regular_grid.shapefile[regular_grid.shapefile['tzid'].isin(['Europe/Madrid', \n", + " 'Europe/Lisbon', \n", + " 'Europe/Paris'])]\n", + "gdf_grid.plot(ax=ax, column='tzid', cmap='viridis', legend=True)\n", + "\n", + "ax.set_xlim([-11, 11])\n", + "ax.set_ylim([35, 52])\n", + "ax.margins(0)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb b/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6585bf6674c4011093d085abf74a94b0f67628c1 --- /dev/null +++ b/tutorials/5.Geospatial/5.3.Add_Coordinates_Bounds.ipynb @@ -0,0 +1,1481 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to read and add coordinates bounds" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime\n", + "import numpy as np\n", + "from nes import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. File with existing bounds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1. Read dataset" + ] + }, + { + "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" + } + ], + "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'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "nessy_1 = open_netcdf(path=path_1, info=True)\n", + "nessy_1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.2. Explore spatial bounds" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "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", + " ...,\n", + " [16.45880126953125, 16.397274017333984, 16.530336380004883,\n", + " 16.59198760986328],\n", + " [16.397274017333984, 16.335533142089844, 16.46847152709961,\n", + " 16.530336380004883],\n", + " [16.335533142089844, 16.27358055114746, 16.40639305114746,\n", + " 16.46847152709961]],\n", + " \n", + " [[16.40639305114746, 16.46847152709961, 16.601383209228516,\n", + " 16.539180755615234],\n", + " [16.46847152709961, 16.530336380004883, 16.663372039794922,\n", + " 16.601383209228516],\n", + " [16.530336380004883, 16.59198760986328, 16.725149154663086,\n", + " 16.663372039794922],\n", + " ...,\n", + " [16.59198760986328, 16.530336380004883, 16.663372039794922,\n", + " 16.725149154663086],\n", + " [16.530336380004883, 16.46847152709961, 16.601383209228516,\n", + " 16.663372039794922],\n", + " [16.46847152709961, 16.40639305114746, 16.539180755615234,\n", + " 16.601383209228516]],\n", + " \n", + " [[16.539180755615234, 16.601383209228516, 16.734270095825195,\n", + " 16.67194175720215],\n", + " [16.601383209228516, 16.663372039794922, 16.796384811401367,\n", + " 16.734270095825195],\n", + " [16.663372039794922, 16.725149154663086, 16.858285903930664,\n", + " 16.796384811401367],\n", + " ...,\n", + " [16.725149154663086, 16.663372039794922, 16.796384811401367,\n", + " 16.858285903930664],\n", + " [16.663372039794922, 16.601383209228516, 16.734270095825195,\n", + " 16.796384811401367],\n", + " [16.601383209228516, 16.539180755615234, 16.67194175720215,\n", + " 16.734270095825195]],\n", + " \n", + " ...,\n", + " \n", + " [[58.31517791748047, 58.429019927978516, 58.50811004638672,\n", + " 58.3941650390625],\n", + " [58.429019927978516, 58.54280090332031, 58.62199783325195,\n", + " 58.50811004638672],\n", + " [58.54280090332031, 58.65652084350586, 58.7358283996582,\n", + " 58.62199783325195],\n", + " ...,\n", + " [58.65652084350586, 58.54280090332031, 58.62199783325195,\n", + " 58.7358283996582],\n", + " [58.54280090332031, 58.429019927978516, 58.50811004638672,\n", + " 58.62199783325195],\n", + " [58.429019927978516, 58.31517791748047, 58.3941650390625,\n", + " 58.50811004638672]],\n", + " \n", + " [[58.3941650390625, 58.50811004638672, 58.586734771728516,\n", + " 58.472686767578125],\n", + " [58.50811004638672, 58.62199783325195, 58.70072937011719,\n", + " 58.586734771728516],\n", + " [58.62199783325195, 58.7358283996582, 58.814666748046875,\n", + " 58.70072937011719],\n", + " ...,\n", + " [58.7358283996582, 58.62199783325195, 58.70072937011719,\n", + " 58.814666748046875],\n", + " [58.62199783325195, 58.50811004638672, 58.586734771728516,\n", + " 58.70072937011719],\n", + " [58.50811004638672, 58.3941650390625, 58.472686767578125,\n", + " 58.586734771728516]],\n", + " \n", + " [[58.472686767578125, 58.586734771728516, 58.664894104003906,\n", + " 58.550743103027344],\n", + " [58.586734771728516, 58.70072937011719, 58.77899169921875,\n", + " 58.664894104003906],\n", + " [58.70072937011719, 58.814666748046875, 58.89303207397461,\n", + " 58.77899169921875],\n", + " ...,\n", + " [58.814666748046875, 58.70072937011719, 58.77899169921875,\n", + " 58.89303207397461],\n", + " [58.70072937011719, 58.586734771728516, 58.664894104003906,\n", + " 58.77899169921875],\n", + " [58.586734771728516, 58.472686767578125, 58.550743103027344,\n", + " 58.664894104003906]]],\n", + " mask=[[[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", + " \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,\n", + " dtype=float32)}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_1.lat_bnds" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(\n", + " data=[[[-22.16550636291504, -22.04222297668457, -22.114652633666992,\n", + " -22.238161087036133],\n", + " [-22.04222297668457, -21.918787002563477, -21.990989685058594,\n", + " -22.114652633666992],\n", + " [-21.918787002563477, -21.795194625854492, -21.867170333862305,\n", + " -21.990989685058594],\n", + " ...,\n", + " [41.795196533203125, 41.918785095214844, 41.990989685058594,\n", + " 41.86717224121094],\n", + " [41.918785095214844, 42.0422248840332, 42.114654541015625,\n", + " 41.990989685058594],\n", + " [42.0422248840332, 42.165504455566406, 42.2381591796875,\n", + " 42.114654541015625]],\n", + " \n", + " [[-22.238161087036133, -22.114652633666992, -22.18718147277832,\n", + " -22.310914993286133],\n", + " [-22.114652633666992, -21.990989685058594, -22.06329345703125,\n", + " -22.18718147277832],\n", + " [-21.990989685058594, -21.867170333862305, -21.939247131347656,\n", + " -22.06329345703125],\n", + " ...,\n", + " [41.86717224121094, 41.990989685058594, 42.06329345703125,\n", + " 41.939247131347656],\n", + " [41.990989685058594, 42.114654541015625, 42.18718338012695,\n", + " 42.06329345703125],\n", + " [42.114654541015625, 42.2381591796875, 42.310916900634766,\n", + " 42.18718338012695]],\n", + " \n", + " [[-22.310914993286133, -22.18718147277832, -22.259811401367188,\n", + " -22.383769989013672],\n", + " [-22.18718147277832, -22.06329345703125, -22.135696411132812,\n", + " -22.259811401367188],\n", + " [-22.06329345703125, -21.939247131347656, -22.011423110961914,\n", + " -22.135696411132812],\n", + " ...,\n", + " [41.939247131347656, 42.06329345703125, 42.13569641113281,\n", + " 42.01142501831055],\n", + " [42.06329345703125, 42.18718338012695, 42.25981140136719,\n", + " 42.13569641113281],\n", + " [42.18718338012695, 42.310916900634766, 42.38376998901367,\n", + " 42.25981140136719]],\n", + " \n", + " ...,\n", + " \n", + " [[-67.64056396484375, -67.50543212890625, -67.74915313720703,\n", + " -67.88362121582031],\n", + " [-67.50543212890625, -67.36968231201172, -67.61406707763672,\n", + " -67.74915313720703],\n", + " [-67.36968231201172, -67.23330688476562, -67.47835540771484,\n", + " -67.61406707763672],\n", + " ...,\n", + " [87.23330688476562, 87.36968231201172, 87.61406707763672,\n", + " 87.47835540771484],\n", + " [87.36968231201172, 87.50543212890625, 87.74915313720703,\n", + " 87.61406707763672],\n", + " [87.50543212890625, 87.64056396484375, 87.88362121582031,\n", + " 87.74915313720703]],\n", + " \n", + " [[-67.88362121582031, -67.74915313720703, -67.99396514892578,\n", + " -68.12776184082031],\n", + " [-67.74915313720703, -67.61406707763672, -67.85955047607422,\n", + " -67.99396514892578],\n", + " [-67.61406707763672, -67.47835540771484, -67.72451782226562,\n", + " -67.85955047607422],\n", + " ...,\n", + " [87.47835540771484, 87.61406707763672, 87.85955047607422,\n", + " 87.72451782226562],\n", + " [87.61406707763672, 87.74915313720703, 87.99396514892578,\n", + " 87.85955047607422],\n", + " [87.74915313720703, 87.88362121582031, 88.12776184082031,\n", + " 87.99396514892578]],\n", + " \n", + " [[-68.12776184082031, -67.99396514892578, -68.23987579345703,\n", + " -68.37299346923828],\n", + " [-67.99396514892578, -67.85955047607422, -68.10614776611328,\n", + " -68.23987579345703],\n", + " [-67.85955047607422, -67.72451782226562, -67.9718017578125,\n", + " -68.10614776611328],\n", + " ...,\n", + " [87.72451782226562, 87.85955047607422, 88.10614776611328,\n", + " 87.9718017578125],\n", + " [87.85955047607422, 87.99396514892578, 88.23987579345703,\n", + " 88.10614776611328],\n", + " [87.99396514892578, 88.12776184082031, 88.37299346923828,\n", + " 88.23987579345703]]],\n", + " mask=[[[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", + " \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,\n", + " dtype=float32)}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_1.lon_bnds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.3. Write file with bounds" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "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" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: 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:2096: 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:2096: 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:2096: 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:2096: UserWarning: WARNING!!! Variable CO_GFAS was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: 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:2096: UserWarning: WARNING!!! Variable SO2_GFAS was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: 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:2096: UserWarning: WARNING!!! Variable DMS_GFAS was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: 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:2096: UserWarning: WARNING!!! Variable POA was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable POA_GFAS was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PEC was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PEC_GFAS was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PNO3 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PSO4 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PMFINE was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PMFINE_GFAS was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable PMC was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable EPOA_biomass was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable EPOA_anthro was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable OPOA_biomass was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable OPOA_anthro was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable SOAP_bb was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable SOAP_anthro was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable ECres was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable ECtot was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable TPM_GFAS was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2096: UserWarning: WARNING!!! Variable cell_area was not loaded. It will not be written.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "nessy_1.to_netcdf('bounds_file_1.nc', info=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.4. Reopen with NES" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_2 = open_netcdf('bounds_file_1.nc', info=True)\n", + "nessy_2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.5. Explore spatial bounds" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "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", + " ...,\n", + " [16.45880126953125, 16.397274017333984, 16.530336380004883,\n", + " 16.59198760986328],\n", + " [16.397274017333984, 16.335533142089844, 16.46847152709961,\n", + " 16.530336380004883],\n", + " [16.335533142089844, 16.27358055114746, 16.40639305114746,\n", + " 16.46847152709961]],\n", + " \n", + " [[16.40639305114746, 16.46847152709961, 16.601383209228516,\n", + " 16.539180755615234],\n", + " [16.46847152709961, 16.530336380004883, 16.663372039794922,\n", + " 16.601383209228516],\n", + " [16.530336380004883, 16.59198760986328, 16.725149154663086,\n", + " 16.663372039794922],\n", + " ...,\n", + " [16.59198760986328, 16.530336380004883, 16.663372039794922,\n", + " 16.725149154663086],\n", + " [16.530336380004883, 16.46847152709961, 16.601383209228516,\n", + " 16.663372039794922],\n", + " [16.46847152709961, 16.40639305114746, 16.539180755615234,\n", + " 16.601383209228516]],\n", + " \n", + " [[16.539180755615234, 16.601383209228516, 16.734270095825195,\n", + " 16.67194175720215],\n", + " [16.601383209228516, 16.663372039794922, 16.796384811401367,\n", + " 16.734270095825195],\n", + " [16.663372039794922, 16.725149154663086, 16.858285903930664,\n", + " 16.796384811401367],\n", + " ...,\n", + " [16.725149154663086, 16.663372039794922, 16.796384811401367,\n", + " 16.858285903930664],\n", + " [16.663372039794922, 16.601383209228516, 16.734270095825195,\n", + " 16.796384811401367],\n", + " [16.601383209228516, 16.539180755615234, 16.67194175720215,\n", + " 16.734270095825195]],\n", + " \n", + " ...,\n", + " \n", + " [[58.31517791748047, 58.429019927978516, 58.50811004638672,\n", + " 58.3941650390625],\n", + " [58.429019927978516, 58.54280090332031, 58.62199783325195,\n", + " 58.50811004638672],\n", + " [58.54280090332031, 58.65652084350586, 58.7358283996582,\n", + " 58.62199783325195],\n", + " ...,\n", + " [58.65652084350586, 58.54280090332031, 58.62199783325195,\n", + " 58.7358283996582],\n", + " [58.54280090332031, 58.429019927978516, 58.50811004638672,\n", + " 58.62199783325195],\n", + " [58.429019927978516, 58.31517791748047, 58.3941650390625,\n", + " 58.50811004638672]],\n", + " \n", + " [[58.3941650390625, 58.50811004638672, 58.586734771728516,\n", + " 58.472686767578125],\n", + " [58.50811004638672, 58.62199783325195, 58.70072937011719,\n", + " 58.586734771728516],\n", + " [58.62199783325195, 58.7358283996582, 58.814666748046875,\n", + " 58.70072937011719],\n", + " ...,\n", + " [58.7358283996582, 58.62199783325195, 58.70072937011719,\n", + " 58.814666748046875],\n", + " [58.62199783325195, 58.50811004638672, 58.586734771728516,\n", + " 58.70072937011719],\n", + " [58.50811004638672, 58.3941650390625, 58.472686767578125,\n", + " 58.586734771728516]],\n", + " \n", + " [[58.472686767578125, 58.586734771728516, 58.664894104003906,\n", + " 58.550743103027344],\n", + " [58.586734771728516, 58.70072937011719, 58.77899169921875,\n", + " 58.664894104003906],\n", + " [58.70072937011719, 58.814666748046875, 58.89303207397461,\n", + " 58.77899169921875],\n", + " ...,\n", + " [58.814666748046875, 58.70072937011719, 58.77899169921875,\n", + " 58.89303207397461],\n", + " [58.70072937011719, 58.586734771728516, 58.664894104003906,\n", + " 58.77899169921875],\n", + " [58.586734771728516, 58.472686767578125, 58.550743103027344,\n", + " 58.664894104003906]]],\n", + " mask=[[[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", + " \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": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_2.lat_bnds" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(\n", + " data=[[[-22.16550636291504, -22.04222297668457, -22.114652633666992,\n", + " -22.238161087036133],\n", + " [-22.04222297668457, -21.918787002563477, -21.990989685058594,\n", + " -22.114652633666992],\n", + " [-21.918787002563477, -21.795194625854492, -21.867170333862305,\n", + " -21.990989685058594],\n", + " ...,\n", + " [41.795196533203125, 41.918785095214844, 41.990989685058594,\n", + " 41.86717224121094],\n", + " [41.918785095214844, 42.0422248840332, 42.114654541015625,\n", + " 41.990989685058594],\n", + " [42.0422248840332, 42.165504455566406, 42.2381591796875,\n", + " 42.114654541015625]],\n", + " \n", + " [[-22.238161087036133, -22.114652633666992, -22.18718147277832,\n", + " -22.310914993286133],\n", + " [-22.114652633666992, -21.990989685058594, -22.06329345703125,\n", + " -22.18718147277832],\n", + " [-21.990989685058594, -21.867170333862305, -21.939247131347656,\n", + " -22.06329345703125],\n", + " ...,\n", + " [41.86717224121094, 41.990989685058594, 42.06329345703125,\n", + " 41.939247131347656],\n", + " [41.990989685058594, 42.114654541015625, 42.18718338012695,\n", + " 42.06329345703125],\n", + " [42.114654541015625, 42.2381591796875, 42.310916900634766,\n", + " 42.18718338012695]],\n", + " \n", + " [[-22.310914993286133, -22.18718147277832, -22.259811401367188,\n", + " -22.383769989013672],\n", + " [-22.18718147277832, -22.06329345703125, -22.135696411132812,\n", + " -22.259811401367188],\n", + " [-22.06329345703125, -21.939247131347656, -22.011423110961914,\n", + " -22.135696411132812],\n", + " ...,\n", + " [41.939247131347656, 42.06329345703125, 42.13569641113281,\n", + " 42.01142501831055],\n", + " [42.06329345703125, 42.18718338012695, 42.25981140136719,\n", + " 42.13569641113281],\n", + " [42.18718338012695, 42.310916900634766, 42.38376998901367,\n", + " 42.25981140136719]],\n", + " \n", + " ...,\n", + " \n", + " [[-67.64056396484375, -67.50543212890625, -67.74915313720703,\n", + " -67.88362121582031],\n", + " [-67.50543212890625, -67.36968231201172, -67.61406707763672,\n", + " -67.74915313720703],\n", + " [-67.36968231201172, -67.23330688476562, -67.47835540771484,\n", + " -67.61406707763672],\n", + " ...,\n", + " [87.23330688476562, 87.36968231201172, 87.61406707763672,\n", + " 87.47835540771484],\n", + " [87.36968231201172, 87.50543212890625, 87.74915313720703,\n", + " 87.61406707763672],\n", + " [87.50543212890625, 87.64056396484375, 87.88362121582031,\n", + " 87.74915313720703]],\n", + " \n", + " [[-67.88362121582031, -67.74915313720703, -67.99396514892578,\n", + " -68.12776184082031],\n", + " [-67.74915313720703, -67.61406707763672, -67.85955047607422,\n", + " -67.99396514892578],\n", + " [-67.61406707763672, -67.47835540771484, -67.72451782226562,\n", + " -67.85955047607422],\n", + " ...,\n", + " [87.47835540771484, 87.61406707763672, 87.85955047607422,\n", + " 87.72451782226562],\n", + " [87.61406707763672, 87.74915313720703, 87.99396514892578,\n", + " 87.85955047607422],\n", + " [87.74915313720703, 87.88362121582031, 88.12776184082031,\n", + " 87.99396514892578]],\n", + " \n", + " [[-68.12776184082031, -67.99396514892578, -68.23987579345703,\n", + " -68.37299346923828],\n", + " [-67.99396514892578, -67.85955047607422, -68.10614776611328,\n", + " -68.23987579345703],\n", + " [-67.85955047607422, -67.72451782226562, -67.9718017578125,\n", + " -68.10614776611328],\n", + " ...,\n", + " [87.72451782226562, 87.85955047607422, 88.10614776611328,\n", + " 87.9718017578125],\n", + " [87.85955047607422, 87.99396514892578, 88.23987579345703,\n", + " 88.10614776611328],\n", + " [87.99396514892578, 88.12776184082031, 88.37299346923828,\n", + " 88.23987579345703]]],\n", + " mask=[[[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", + " \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": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_2.lon_bnds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. File without existing bounds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1. Read dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "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", + "path_3 = '/gpfs/projects/bsc32/models/NES_tutorial_data/O3_all-000_2021080300.nc'\n", + "nessy_3 = open_netcdf(path=path_3, info=True)\n", + "nessy_3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2. Create spatial bounds" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "nessy_3.create_spatial_bounds()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.3. Explore spatial bounds" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "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" + ] + } + ], + "source": [ + "nessy_3.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "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", + " ...,\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", + " [[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]]])}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_3.lat_bnds" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([[[-22.21497021, -22.05071303, -22.14733617, -22.31199395],\n", + " [-22.0507124 , -21.88618013, -21.9824008 , -22.14733554],\n", + " [-21.8861795 , -21.72137239, -21.81718872, -21.98240017],\n", + " ...,\n", + " [ 41.72137553, 41.88618264, 41.98240332, 41.81719187],\n", + " [ 41.88618013, 42.0507124 , 42.14733554, 41.9824008 ],\n", + " [ 42.05071303, 42.21497021, 42.31199395, 42.14733617]],\n", + " \n", + " [[-22.31199432, -22.14733654, -22.24413665, -22.4091946 ],\n", + " [-22.14733591, -21.98240117, -22.07879923, -22.24413602],\n", + " [-21.98240054, -21.81718908, -21.91318321, -22.0787986 ],\n", + " ...,\n", + " [ 41.81719223, 41.98240369, 42.07880176, 41.91318637],\n", + " [ 41.98240117, 42.14733591, 42.24413602, 42.07879923],\n", + " [ 42.14733654, 42.31199432, 42.4091946 , 42.24413665]],\n", + " \n", + " [[-22.40919405, -22.2441361 , -22.34111548, -22.50657316],\n", + " [-22.24413547, -22.07879868, -22.17537644, -22.34111485],\n", + " [-22.07879805, -21.91318266, -22.00935688, -22.1753758 ],\n", + " ...,\n", + " [ 41.91318582, 42.07880121, 42.17537897, 42.00936005],\n", + " [ 42.07879868, 42.24413547, 42.34111485, 42.17537644],\n", + " [ 42.2441361 , 42.40919405, 42.50657316, 42.34111548]],\n", + " \n", + " ...,\n", + " \n", + " [[-67.50645709, -67.32583243, -67.64966627, -67.82912696],\n", + " [-67.32583174, -67.14410165, -67.46910621, -67.64966558],\n", + " [-67.14410095, -66.96124932, -67.28743133, -67.46910552],\n", + " ...,\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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4. Write file with bounds" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "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" + ] + } + ], + "source": [ + "nessy_3.to_netcdf('bounds_file_3.nc', info=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.5. Reopen with NES" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_4 = open_netcdf('bounds_file_3.nc', info=True)\n", + "nessy_4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6. Explore spatial bounds" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(\n", + " data=[[[16.220397902213396, 16.303068236068505, 16.480289793933082,\n", + " 16.397397154910678],\n", + " [16.30306855071067, 16.38536089516572, 16.56280423641698,\n", + " 16.48029010942229],\n", + " [16.385361208363484, 16.467274252013816, 16.64493884522746,\n", + " 16.56280455045976],\n", + " ...,\n", + " [16.467272693272037, 16.385359642374645, 16.5628029802458,\n", + " 16.64493728227078],\n", + " [16.38536089516572, 16.30306855071067, 16.48029010942229,\n", + " 16.56280423641698],\n", + " [16.303068236068505, 16.220397902213396, 16.397397154910678,\n", + " 16.480289793933082]],\n", + " \n", + " [[16.397397830024588, 16.48029046989534, 16.657467620465567,\n", + " 16.574352506965674],\n", + " [16.48029078538455, 16.5628049132256, 16.740204020393392,\n", + " 16.657467936802462],\n", + " [16.562805227268388, 16.644939522880435, 16.82256005992405,\n", + " 16.740204335281874],\n", + " ...,\n", + " [16.644937959923737, 16.56280365705441, 16.740202760839427,\n", + " 16.822558492749014],\n", + " [16.5628049132256, 16.48029078538455, 16.657467936802462,\n", + " 16.740204020393392],\n", + " [16.48029046989534, 16.397397830024588, 16.574352506965674,\n", + " 16.657467620465567]],\n", + " \n", + " [[16.574351494551546, 16.657466606777945, 16.834598762498768,\n", + " 16.75126100486087],\n", + " [16.657466923114836, 16.740203005435227, 16.917557294312953,\n", + " 16.83459907968401],\n", + " [16.7402033203237, 16.822559043698337, 17.00013494374161,\n", + " 16.9175576100478],\n", + " ...,\n", + " [16.822557476523315, 16.740201745881272, 16.91755603137349,\n", + " 17.000133372344752],\n", + " [16.740203005435227, 16.657466923114836, 16.83459907968401,\n", + " 16.917557294312953],\n", + " [16.657466606777945, 16.574351494551546, 16.75126100486087,\n", + " 16.834598762498768]],\n", + " \n", + " ...,\n", + " \n", + " [[58.19210947966698, 58.34380497289048, 58.44964444401728,\n", + " 58.29776031793852],\n", + " [58.34380555135856, 58.49539320689951, 58.60142470084916,\n", + " 58.44964502321138],\n", + " [58.495393784952064, 58.646871410321715, 58.7530983546572,\n", + " 58.60142527964072],\n", + " ...,\n", + " [58.64686852217872, 58.49539089468928, 58.601422385682845,\n", + " 58.75309546275335],\n", + " [58.49539320689951, 58.34380555135856, 58.44964502321138,\n", + " 58.60142470084916],\n", + " [58.34380497289048, 58.19210947966698, 58.29776031793852,\n", + " 58.44964444401728]],\n", + " \n", + " [[58.297760719410846, 58.44964484620209, 58.55466326772292,\n", + " 58.402594256433474],\n", + " [58.4496454253962, 58.60142510375937, 58.70663179672086,\n", + " 58.554663847628724],\n", + " [58.60142568255093, 58.753098758305896, 58.8584971477072,\n", + " 58.70663237623712],\n", + " ...,\n", + " [58.75309586640205, 58.60142278859304, 58.70662947865572,\n", + " 58.85849425211415],\n", + " [58.60142510375937, 58.4496454253962, 58.554663847628724,\n", + " 58.70663179672086],\n", + " [58.44964484620209, 58.297760719410846, 58.402594256433474,\n", + " 58.55466326772292]],\n", + " \n", + " [[58.40259365892516, 58.55466266916757, 58.65885171699879,\n", + " 58.50660166161425],\n", + " [58.554663249073386, 58.70663119709912, 58.81100467268263,\n", + " 58.65885229760164],\n", + " [58.706631776615396, 58.85849654699951, 58.96305787144377,\n", + " 58.81100525290894],\n", + " ...,\n", + " [58.85849365140645, 58.70662887903401, 58.8110023517774,\n", + " 58.963054972234914],\n", + " [58.70663119709912, 58.554663249073386, 58.65885229760164,\n", + " 58.81100467268263],\n", + " [58.55466266916757, 58.40259365892516, 58.50660166161425,\n", + " 58.65885171699879]]],\n", + " mask=[[[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", + " \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": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy_4.lat_bnds" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(\n", + " data=[[[-22.21497020844106, -22.05071302765748, -22.14733616935655,\n", + " -22.31199395171342],\n", + " [-22.050712400541215, -21.886180131800984, -21.98240080355447,\n", + " -22.147335540708582],\n", + " [-21.88617950363573, -21.72137238587186, -21.817188715117368,\n", + " -21.982400173850465],\n", + " ...,\n", + " [41.72137553193767, 41.886182644461655, 41.982403322370715,\n", + " 41.817191868913255],\n", + " [41.886180131800984, 42.050712400541215, 42.14733554070858,\n", + " 41.982400803554526],\n", + " [42.05071302765748, 42.21497020844106, 42.31199395171342,\n", + " 42.14733616935655]],\n", + " \n", + " [[-22.311994322164878, -22.147336538280683, -22.244136652959696,\n", + " -22.40919460454836],\n", + " [-22.14733590963266, -21.982401170944172, -22.078799234822952,\n", + " -22.244136022781618],\n", + " [-21.982400541240054, -21.817189080965306, -21.91318320664743,\n", + " -22.078798603581617],\n", + " ...,\n", + " [41.81719223476125, 41.982403689760304, 42.07880175978829,\n", + " 41.913186368165896],\n", + " [41.98240117094417, 42.14733590963266, 42.24413602278162,\n", + " 42.07879923482295],\n", + " [42.14733653828068, 42.311994322164935, 42.40919460454836,\n", + " 42.244136652959696]],\n", + " \n", + " [[-22.40919404785444, -22.24413609855435, -22.341115482516443,\n", + " -22.50657316411582],\n", + " [-22.24413546837627, -22.078798682716865, -22.175376436459942,\n", + " -22.34111485080996],\n", + " [-22.078798051475587, -21.913182656851575, -22.009356878042922,\n", + " -22.17537580368287],\n", + " ...,\n", + " [41.91318581837004, 42.078801207682204, 42.175378967568065,\n", + " 42.00936004727629],\n", + " [42.078798682716865, 42.24413546837627, 42.34111485080996,\n", + " 42.17537643645994],\n", + " [42.24413609855435, 42.40919404785444, 42.50657316411582,\n", + " 42.3411154825165]],\n", + " \n", + " ...,\n", + " \n", + " [[-67.50645709267866, -67.3258324302019, -67.64966626624692,\n", + " -67.82912695633217],\n", + " [-67.32583173907523, -67.14410164962646, -67.46910620827077,\n", + " -67.64966557957331],\n", + " [-67.14410095425234, -66.9612493170024, -67.28743133275196,\n", + " -67.46910551737545],\n", + " ...,\n", + " [86.9612528154201, 87.1441044311228, 87.469108971852,\n", + " 87.28743480864733],\n", + " [87.14410164962646, 87.32583173907523, 87.64966557957331,\n", + " 87.46910620827077],\n", + " [87.3258324302019, 87.50645709267866, 87.82912695633217,\n", + " 87.64966626624692]],\n", + " \n", + " [[-67.82912819088853, -67.64966750528527, -67.97544812209401,\n", + " -68.15372289526101],\n", + " [-67.64966681861165, -67.46910745181754, -67.79608107785475,\n", + " -67.97544743995786],\n", + " [-67.46910676092222, -67.28743258083347, -67.61560630362294,\n", + " -67.79608039152396],\n", + " ...,\n", + " [87.28743605672872, 87.46911021539864, 87.79608382317804,\n", + " 87.61560975656079],\n", + " [87.46910745181754, 87.64966681861165, 87.97544743995786,\n", + " 87.79608107785481],\n", + " [87.64966750528527, 87.82912819088853, 88.15372289526101,\n", + " 87.97544812209401]],\n", + " \n", + " [[-68.15372103240003, -67.97544625238419, -68.30317799462358,\n", + " -68.4802447924423],\n", + " [-67.97544557024798, -67.79607920125443, -68.12502637415002,\n", + " -68.30317731710966],\n", + " [-67.79607851492364, -67.61560442009056, -67.94577446945578,\n", + " -68.12502569246982],\n", + " ...,\n", + " [87.61560787302852, 87.79608194657783, 88.12502910087062,\n", + " 87.94577789899824],\n", + " [87.79607920125449, 87.97544557024798, 88.30317731710971,\n", + " 88.12502637415002],\n", + " [87.97544625238413, 88.15372103240003, 88.48024479244236,\n", + " 88.30317799462364]]],\n", + " mask=[[[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", + " \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" + } + ], + "source": [ + "nessy_4.lon_bnds" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/5.Geospatial/5.4.Calculate_Grid_Cell_Area.ipynb b/tutorials/5.Geospatial/5.4.Calculate_Grid_Cell_Area.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..17eb7d511622982f0250d6e4ea302c6ec060f9b8 --- /dev/null +++ b/tutorials/5.Geospatial/5.4.Calculate_Grid_Cell_Area.ipynb @@ -0,0 +1,740 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calculate the area of each cell of a grid" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from nes import *\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Create grid" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "lat_1 = 37\n", + "lat_2 = 43\n", + "lon_0 = -3\n", + "lat_0 = 40\n", + "nx = 10\n", + "ny = 15\n", + "inc_x = 4000\n", + "inc_y = 4000\n", + "x_0 = -807847.688\n", + "y_0 = -797137.125\n", + "lcc_grid = create_nes(comm=None, info=False, projection='lcc',\n", + " lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, \n", + " nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geometry
FID
0POLYGON ((-11.58393 32.47507, -11.54169 32.478...
1POLYGON ((-11.54169 32.47851, -11.49944 32.481...
2POLYGON ((-11.49944 32.48192, -11.45719 32.485...
3POLYGON ((-11.45719 32.48533, -11.41494 32.488...
4POLYGON ((-11.41494 32.48871, -11.37268 32.492...
......
145POLYGON ((-11.42882 32.99135, -11.38628 32.994...
146POLYGON ((-11.38628 32.99473, -11.34373 32.998...
147POLYGON ((-11.34373 32.99809, -11.30118 33.001...
148POLYGON ((-11.30118 33.00143, -11.25863 33.004...
149POLYGON ((-11.25863 33.00476, -11.21607 33.008...
\n", + "

150 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " geometry\n", + "FID \n", + "0 POLYGON ((-11.58393 32.47507, -11.54169 32.478...\n", + "1 POLYGON ((-11.54169 32.47851, -11.49944 32.481...\n", + "2 POLYGON ((-11.49944 32.48192, -11.45719 32.485...\n", + "3 POLYGON ((-11.45719 32.48533, -11.41494 32.488...\n", + "4 POLYGON ((-11.41494 32.48871, -11.37268 32.492...\n", + ".. ...\n", + "145 POLYGON ((-11.42882 32.99135, -11.38628 32.994...\n", + "146 POLYGON ((-11.38628 32.99473, -11.34373 32.998...\n", + "147 POLYGON ((-11.34373 32.99809, -11.30118 33.001...\n", + "148 POLYGON ((-11.30118 33.00143, -11.25863 33.004...\n", + "149 POLYGON ((-11.25863 33.00476, -11.21607 33.008...\n", + "\n", + "[150 rows x 1 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lcc_grid.create_shapefile()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculate grid area with NES" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "lcc_grid.calculate_grid_area()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': 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": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lcc_grid.cell_measures['cell_area']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Write" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "lcc_grid.to_netcdf('lcc_grid_nes_area.nc')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Reopen" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "nessy = open_netcdf('lcc_grid_nes_area.nc')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': masked_array(\n", + " data=[[15830419.376024913, 15830657.314647589, 15830893.981876407,\n", + " 15831129.379029078, 15831363.50737192, 15831596.368182508,\n", + " 15831827.962712158, 15832058.292232336, 15832287.358012801,\n", + " 15832515.161288435],\n", + " [15832888.43944364, 15833125.461857699, 15833361.217221119,\n", + " 15833595.706824414, 15833828.931936948, 15834060.89385057,\n", + " 15834291.593845021, 15834521.033190317, 15834749.2131148,\n", + " 15834976.134889057],\n", + " [15835346.792403538, 15835582.896606795, 15835817.738090131,\n", + " 15836051.318161163, 15836283.638106134, 15836514.699202463,\n", + " 15836744.502702465, 15836973.049887061, 15837200.342041023,\n", + " 15837426.380405283],\n", + " [15837794.423261626, 15838029.607272115, 15838263.532921247,\n", + " 15838496.201492224, 15838727.614273867, 15838957.77256741,\n", + " 15839186.677641798, 15839414.330762891, 15839640.733196164,\n", + " 15839865.88622452],\n", + " [15840231.320484577, 15840465.582296379, 15840698.590097519,\n", + " 15840930.345190752, 15841160.848857794, 15841390.102392279,\n", + " 15841618.107084159, 15841844.864210112, 15842070.375027169,\n", + " 15842294.640799781],\n", + " [15842657.472525794, 15842890.810149202, 15843122.898123644,\n", + " 15843353.737743918, 15843583.330332253, 15843811.677161248,\n", + " 15844038.779514872, 15844264.638685772, 15844489.255928697,\n", + " 15844712.63252152],\n", + " [15845072.867817752, 15845305.279238949, 15845536.445395218,\n", + " 15845766.367594492, 15845995.047125012, 15846222.485287195,\n", + " 15846448.683362775, 15846673.642637823, 15846897.364397066,\n", + " 15847119.849901745],\n", + " [15847477.494879192, 15847708.978121761, 15847939.220475506,\n", + " 15848168.223243283, 15848395.987744803, 15848622.51527306,\n", + " 15848847.8071165, 15849071.864561237, 15849294.68888149,\n", + " 15849516.28136931],\n", + " [15849871.342219478, 15850101.895263294, 15850331.211820066,\n", + " 15850559.293195998, 15850786.140695473, 15851011.755627826,\n", + " 15851236.139291776, 15851459.2929683, 15851681.217938488,\n", + " 15851901.915475631],\n", + " [15852254.398329977, 15852484.019190125, 15852712.40796391,\n", + " 15852939.565962346, 15853165.494495256, 15853390.194872674,\n", + " 15853613.668381073, 15853835.916329645, 15854056.940004325,\n", + " 15854276.740674812],\n", + " [15854626.65180506, 15854855.33845814, 15855082.797441188,\n", + " 15855309.030069793, 15855534.037656406, 15855757.82150882,\n", + " 15855980.3829386, 15856201.723231781, 15856421.843674172,\n", + " 15856640.745564846],\n", + " [15856988.091164649, 15857215.841636622, 15857442.368840463,\n", + " 15857667.674111139, 15857891.758779489, 15858114.624157269,\n", + " 15858336.271530768, 15858556.702207962, 15858775.917487847,\n", + " 15858993.918654963],\n", + " [15859338.705020806, 15859565.517275967, 15859791.110708648,\n", + " 15860015.48665859, 15860238.646438757, 15860460.59134635,\n", + " 15860681.322718704, 15860900.841846164, 15861119.150023872,\n", + " 15861336.248536212],\n", + " [15861678.48197006, 15861904.354016308, 15862129.01168405,\n", + " 15862352.456309775, 15862574.68920346, 15862795.711703217,\n", + " 15863015.52510921, 15863234.130726451, 15863451.529867679,\n", + " 15863667.72381984],\n", + " [15864007.410611445, 15864232.340437308, 15864456.060353493,\n", + " 15864678.571670454, 15864899.875713555, 15865119.973824475,\n", + " 15865338.867312277, 15865556.55748565, 15865773.045639142,\n", + " 15865988.333075456]],\n", + " mask=[[False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False],\n", + " [False, False, False, False, False, False, False, False, False,\n", + " False]],\n", + " fill_value=1e+20)}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy.cell_measures['cell_area']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Calculate cell area with CDO" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "lcc_grid.variables['aux'] = {'data': np.ones((1, 1, len(lcc_grid.y['data']), len(lcc_grid.x['data'])))}" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "lcc_grid.to_netcdf('lcc_grid.nc')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the terminal:\n", + "\n", + "```\n", + "module load CDO\n", + "\n", + "cdo gridarea lcc_grid.nc lcc_grid_cdo_area.nc\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Compare results from NES and CDO" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "shape = lcc_grid.shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " shape['area_cdo'] = xr.open_dataset('lcc_grid_cdo_area.nc').cell_area.values.ravel()\n", + "except:\n", + " print('Error: Calculate grid area using CDO and save it as lcc_grid_cdo_area.nc')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "shape['area_nes'] = lcc_grid.cell_measures['cell_area']['data'].ravel()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + " \n", + "
geometryarea_cdoarea_nes
FID
0POLYGON ((-11.58393 32.47507, -11.54169 32.478...1.583042e+071.583042e+07
1POLYGON ((-11.54169 32.47851, -11.49944 32.481...1.583066e+071.583066e+07
2POLYGON ((-11.49944 32.48192, -11.45719 32.485...1.583089e+071.583089e+07
3POLYGON ((-11.45719 32.48533, -11.41494 32.488...1.583113e+071.583113e+07
4POLYGON ((-11.41494 32.48871, -11.37268 32.492...1.583136e+071.583136e+07
............
145POLYGON ((-11.42882 32.99135, -11.38628 32.994...1.586512e+071.586512e+07
146POLYGON ((-11.38628 32.99473, -11.34373 32.998...1.586534e+071.586534e+07
147POLYGON ((-11.34373 32.99809, -11.30118 33.001...1.586556e+071.586556e+07
148POLYGON ((-11.30118 33.00143, -11.25863 33.004...1.586577e+071.586577e+07
149POLYGON ((-11.25863 33.00476, -11.21607 33.008...1.586599e+071.586599e+07
\n", + "

150 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " geometry area_cdo \\\n", + "FID \n", + "0 POLYGON ((-11.58393 32.47507, -11.54169 32.478... 1.583042e+07 \n", + "1 POLYGON ((-11.54169 32.47851, -11.49944 32.481... 1.583066e+07 \n", + "2 POLYGON ((-11.49944 32.48192, -11.45719 32.485... 1.583089e+07 \n", + "3 POLYGON ((-11.45719 32.48533, -11.41494 32.488... 1.583113e+07 \n", + "4 POLYGON ((-11.41494 32.48871, -11.37268 32.492... 1.583136e+07 \n", + ".. ... ... \n", + "145 POLYGON ((-11.42882 32.99135, -11.38628 32.994... 1.586512e+07 \n", + "146 POLYGON ((-11.38628 32.99473, -11.34373 32.998... 1.586534e+07 \n", + "147 POLYGON ((-11.34373 32.99809, -11.30118 33.001... 1.586556e+07 \n", + "148 POLYGON ((-11.30118 33.00143, -11.25863 33.004... 1.586577e+07 \n", + "149 POLYGON ((-11.25863 33.00476, -11.21607 33.008... 1.586599e+07 \n", + "\n", + " area_nes \n", + "FID \n", + "0 1.583042e+07 \n", + "1 1.583066e+07 \n", + "2 1.583089e+07 \n", + "3 1.583113e+07 \n", + "4 1.583136e+07 \n", + ".. ... \n", + "145 1.586512e+07 \n", + "146 1.586534e+07 \n", + "147 1.586556e+07 \n", + "148 1.586577e+07 \n", + "149 1.586599e+07 \n", + "\n", + "[150 rows x 3 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FID\n", + "0 0.0\n", + "1 0.0\n", + "2 0.0\n", + "3 0.0\n", + "4 0.0\n", + " ... \n", + "145 0.0\n", + "146 0.0\n", + "147 0.0\n", + "148 0.0\n", + "149 0.0\n", + "Length: 150, dtype: float64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(shape['area_nes']-shape['area_cdo'])*100/shape['area_cdo']" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "((shape['area_nes']-shape['area_cdo'])*100/shape['area_cdo']).mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "((shape['area_nes']-shape['area_cdo'])*100/shape['area_cdo']).max()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "((shape['area_nes']-shape['area_cdo'])*100/shape['area_cdo']).min()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/5.Geospatial/5.5.Calculate_Geometry_Cell_Area.ipynb b/tutorials/5.Geospatial/5.5.Calculate_Geometry_Cell_Area.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a1bf2e7f9be2f5fa7aa5b5e77fe8fdd8456d829c --- /dev/null +++ b/tutorials/5.Geospatial/5.5.Calculate_Geometry_Cell_Area.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Calculate the area of each cell of a set of geometries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from nes import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Get geometries from grid" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "lat_1 = 37\n", + "lat_2 = 43\n", + "lon_0 = -3\n", + "lat_0 = 40\n", + "nx = 10\n", + "ny = 15\n", + "inc_x = 4000\n", + "inc_y = 4000\n", + "x_0 = -807847.688\n", + "y_0 = -797137.125\n", + "lcc_grid = create_nes(comm=None, info=False, projection='lcc',\n", + " lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, \n", + " nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geometry
FID
0POLYGON ((-11.58393 32.47507, -11.54169 32.478...
1POLYGON ((-11.54169 32.47851, -11.49944 32.481...
2POLYGON ((-11.49944 32.48192, -11.45719 32.485...
3POLYGON ((-11.45719 32.48533, -11.41494 32.488...
4POLYGON ((-11.41494 32.48871, -11.37268 32.492...
......
145POLYGON ((-11.42882 32.99135, -11.38628 32.994...
146POLYGON ((-11.38628 32.99473, -11.34373 32.998...
147POLYGON ((-11.34373 32.99809, -11.30118 33.001...
148POLYGON ((-11.30118 33.00143, -11.25863 33.004...
149POLYGON ((-11.25863 33.00476, -11.21607 33.008...
\n", + "

150 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " geometry\n", + "FID \n", + "0 POLYGON ((-11.58393 32.47507, -11.54169 32.478...\n", + "1 POLYGON ((-11.54169 32.47851, -11.49944 32.481...\n", + "2 POLYGON ((-11.49944 32.48192, -11.45719 32.485...\n", + "3 POLYGON ((-11.45719 32.48533, -11.41494 32.488...\n", + "4 POLYGON ((-11.41494 32.48871, -11.37268 32.492...\n", + ".. ...\n", + "145 POLYGON ((-11.42882 32.99135, -11.38628 32.994...\n", + "146 POLYGON ((-11.38628 32.99473, -11.34373 32.998...\n", + "147 POLYGON ((-11.34373 32.99809, -11.30118 33.001...\n", + "148 POLYGON ((-11.30118 33.00143, -11.25863 33.004...\n", + "149 POLYGON ((-11.25863 33.00476, -11.21607 33.008...\n", + "\n", + "[150 rows x 1 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lcc_grid.create_shapefile()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "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", + "Length: 150, dtype: geometry" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geometry_list = lcc_grid.shapefile['geometry'].values\n", + "geometry_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Calculate area of each polygon" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "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, 15832888.43944364, 15833125.4618577 ,\n", + " 15833361.21722112, 15833595.70682441, 15833828.93193695,\n", + " 15834060.89385057, 15834291.59384502, 15834521.03319032,\n", + " 15834749.2131148 , 15834976.13488906, 15835346.79240354,\n", + " 15835582.8966068 , 15835817.73809013, 15836051.31816116,\n", + " 15836283.63810613, 15836514.69920246, 15836744.50270247,\n", + " 15836973.04988706, 15837200.34204102, 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, 15840231.32048458, 15840465.58229638,\n", + " 15840698.59009752, 15840930.34519075, 15841160.84885779,\n", + " 15841390.10239228, 15841618.10708416, 15841844.86421011,\n", + " 15842070.37502717, 15842294.64079978, 15842657.47252579,\n", + " 15842890.8101492 , 15843122.89812364, 15843353.73774392,\n", + " 15843583.33033225, 15843811.67716125, 15844038.77951487,\n", + " 15844264.63868577, 15844489.2559287 , 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, 15847477.49487919, 15847708.97812176,\n", + " 15847939.22047551, 15848168.22324328, 15848395.9877448 ,\n", + " 15848622.51527306, 15848847.8071165 , 15849071.86456124,\n", + " 15849294.68888149, 15849516.28136931, 15849871.34221948,\n", + " 15850101.89526329, 15850331.21182007, 15850559.293196 ,\n", + " 15850786.14069547, 15851011.75562783, 15851236.13929178,\n", + " 15851459.2929683 , 15851681.21793849, 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, 15854626.65180506, 15854855.33845814,\n", + " 15855082.79744119, 15855309.03006979, 15855534.03765641,\n", + " 15855757.82150882, 15855980.3829386 , 15856201.72323178,\n", + " 15856421.84367417, 15856640.74556485, 15856988.09116465,\n", + " 15857215.84163662, 15857442.36884046, 15857667.67411114,\n", + " 15857891.75877949, 15858114.62415727, 15858336.27153077,\n", + " 15858556.70220796, 15858775.91748785, 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, 15861678.48197006, 15861904.35401631,\n", + " 15862129.01168405, 15862352.45630977, 15862574.68920346,\n", + " 15862795.71170322, 15863015.52510921, 15863234.13072645,\n", + " 15863451.52986768, 15863667.72381984, 15864007.41061145,\n", + " 15864232.34043731, 15864456.06035349, 15864678.57167045,\n", + " 15864899.87571356, 15865119.97382447, 15865338.86731228,\n", + " 15865556.55748565, 15865773.04563914, 15865988.33307546])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "geometry_area = calculate_geometry_area(geometry_list)\n", + "geometry_area" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/5.Others/5.2.Add_Coordinates_Bounds.ipynb b/tutorials/5.Others/5.2.Add_Coordinates_Bounds.ipynb deleted file mode 100644 index 537349001ae305b0d7ac343a269a5bb13b18ad22..0000000000000000000000000000000000000000 --- a/tutorials/5.Others/5.2.Add_Coordinates_Bounds.ipynb +++ /dev/null @@ -1,236 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to add coordinates bounds" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr\n", - "import datetime\n", - "import numpy as np\n", - "from nes import *" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Set coordinates bounds" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "test_path = \"/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc\"\n", - "nessy = open_netcdf(path=test_path, info=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "nessy.create_spatial_bounds()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "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" - ] - } - ], - "source": [ - "nessy.load()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Explore variables" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "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", - " [[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]]])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nessy.lat_bnds" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[-22.21497021, -22.05071303, -22.14733617, -22.31199395],\n", - " [-22.0507124 , -21.88618013, -21.9824008 , -22.14733554],\n", - " [-21.8861795 , -21.72137239, -21.81718872, -21.98240017],\n", - " ...,\n", - " [ 41.72137553, 41.88618264, 41.98240332, 41.81719187],\n", - " [ 41.88618013, 42.0507124 , 42.14733554, 41.9824008 ],\n", - " [ 42.05071303, 42.21497021, 42.31199395, 42.14733617]],\n", - "\n", - " [[-22.31199432, -22.14733654, -22.24413665, -22.4091946 ],\n", - " [-22.14733591, -21.98240117, -22.07879923, -22.24413602],\n", - " [-21.98240054, -21.81718908, -21.91318321, -22.0787986 ],\n", - " ...,\n", - " [ 41.81719223, 41.98240369, 42.07880176, 41.91318637],\n", - " [ 41.98240117, 42.14733591, 42.24413602, 42.07879923],\n", - " [ 42.14733654, 42.31199432, 42.4091946 , 42.24413665]],\n", - "\n", - " [[-22.40919405, -22.2441361 , -22.34111548, -22.50657316],\n", - " [-22.24413547, -22.07879868, -22.17537644, -22.34111485],\n", - " [-22.07879805, -21.91318266, -22.00935688, -22.1753758 ],\n", - " ...,\n", - " [ 41.91318582, 42.07880121, 42.17537897, 42.00936005],\n", - " [ 42.07879868, 42.24413547, 42.34111485, 42.17537644],\n", - " [ 42.2441361 , 42.40919405, 42.50657316, 42.34111548]],\n", - "\n", - " ...,\n", - "\n", - " [[-67.50645709, -67.32583243, -67.64966627, -67.82912696],\n", - " [-67.32583174, -67.14410165, -67.46910621, -67.64966558],\n", - " [-67.14410095, -66.96124932, -67.28743133, -67.46910552],\n", - " ...,\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": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nessy.lon_bnds" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.6.8 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.8" - }, - "vscode": { - "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tutorials/5.Others/5.3.Create_Shapefiles.ipynb b/tutorials/5.Others/5.3.Create_Shapefiles.ipynb deleted file mode 100644 index 6fe590da742d02b786b853f037cf007edfe3e2fb..0000000000000000000000000000000000000000 --- a/tutorials/5.Others/5.3.Create_Shapefiles.ipynb +++ /dev/null @@ -1,504 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to create and save shapefiles" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from nes import *\n", - "import xarray as xr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. From grids that have already been created" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "grid_path = '/gpfs/scratch/bsc32/bsc32538/original_files/CAMS_MONARCH_d01_2022070412.nc'\n", - "grid = open_netcdf(path=grid_path, info=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometry
FID
0POLYGON ((-22.16553 16.27358, -22.04224 16.335...
1POLYGON ((-22.04224 16.33554, -21.91879 16.397...
2POLYGON ((-21.91879 16.39727, -21.79523 16.458...
3POLYGON ((-21.79523 16.45880, -21.67145 16.520...
4POLYGON ((-21.67145 16.52011, -21.54755 16.581...
......
168582POLYGON ((87.45258 59.04235, 87.58887 58.92854...
168583POLYGON ((87.58887 58.92854, 87.72452 58.81466...
168584POLYGON ((87.72452 58.81466, 87.85956 58.70073...
168585POLYGON ((87.85956 58.70073, 87.99396 58.58673...
168586POLYGON ((87.99396 58.58673, 88.12778 58.47268...
\n", - "

168587 rows × 1 columns

\n", - "
" - ], - "text/plain": [ - " geometry\n", - "FID \n", - "0 POLYGON ((-22.16553 16.27358, -22.04224 16.335...\n", - "1 POLYGON ((-22.04224 16.33554, -21.91879 16.397...\n", - "2 POLYGON ((-21.91879 16.39727, -21.79523 16.458...\n", - "3 POLYGON ((-21.79523 16.45880, -21.67145 16.520...\n", - "4 POLYGON ((-21.67145 16.52011, -21.54755 16.581...\n", - "... ...\n", - "168582 POLYGON ((87.45258 59.04235, 87.58887 58.92854...\n", - "168583 POLYGON ((87.58887 58.92854, 87.72452 58.81466...\n", - "168584 POLYGON ((87.72452 58.81466, 87.85956 58.70073...\n", - "168585 POLYGON ((87.85956 58.70073, 87.99396 58.58673...\n", - "168586 POLYGON ((87.99396 58.58673, 88.12778 58.47268...\n", - "\n", - "[168587 rows x 1 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grid.create_shapefile()\n", - "grid.shapefile" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add data to shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rank 000: Loading O3 var (1/1)\n", - "Rank 000: Loaded O3 var ((109, 24, 361, 467))\n" - ] - } - ], - "source": [ - "grid.keep_vars(['O3'])\n", - "grid.load()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Add data of last timestep\n", - "grid.shapefile['O3'] = grid.variables['O3']['data'][-1, -1, :].ravel()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \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", - "
geometryO3
FID
0POLYGON ((-22.16553 16.27358, -22.04224 16.335...0.037919
1POLYGON ((-22.04224 16.33554, -21.91879 16.397...0.038064
2POLYGON ((-21.91879 16.39727, -21.79523 16.458...0.038086
3POLYGON ((-21.79523 16.45880, -21.67145 16.520...0.038092
4POLYGON ((-21.67145 16.52011, -21.54755 16.581...0.038098
.........
168582POLYGON ((87.45258 59.04235, 87.58887 58.92854...0.026542
168583POLYGON ((87.58887 58.92854, 87.72452 58.81466...0.026310
168584POLYGON ((87.72452 58.81466, 87.85956 58.70073...0.027037
168585POLYGON ((87.85956 58.70073, 87.99396 58.58673...0.027271
168586POLYGON ((87.99396 58.58673, 88.12778 58.47268...0.026327
\n", - "

168587 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " geometry O3\n", - "FID \n", - "0 POLYGON ((-22.16553 16.27358, -22.04224 16.335... 0.037919\n", - "1 POLYGON ((-22.04224 16.33554, -21.91879 16.397... 0.038064\n", - "2 POLYGON ((-21.91879 16.39727, -21.79523 16.458... 0.038086\n", - "3 POLYGON ((-21.79523 16.45880, -21.67145 16.520... 0.038092\n", - "4 POLYGON ((-21.67145 16.52011, -21.54755 16.581... 0.038098\n", - "... ... ...\n", - "168582 POLYGON ((87.45258 59.04235, 87.58887 58.92854... 0.026542\n", - "168583 POLYGON ((87.58887 58.92854, 87.72452 58.81466... 0.026310\n", - "168584 POLYGON ((87.72452 58.81466, 87.85956 58.70073... 0.027037\n", - "168585 POLYGON ((87.85956 58.70073, 87.99396 58.58673... 0.027271\n", - "168586 POLYGON ((87.99396 58.58673, 88.12778 58.47268... 0.026327\n", - "\n", - "[168587 rows x 2 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grid.shapefile" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Save shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "grid.write_shapefile('rotated_1')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. From grids created from scratch" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "centre_lat = 51\n", - "centre_lon = 10\n", - "west_boundary = -35\n", - "south_boundary = -27\n", - "inc_rlat = 0.2\n", - "inc_rlon = 0.2\n", - "grid = create_nes(comm=None, info=False, projection='rotated',\n", - " centre_lat=centre_lat, centre_lon=centre_lon,\n", - " west_boundary=west_boundary, south_boundary=south_boundary,\n", - " inc_rlat=inc_rlat, inc_rlon=inc_rlon)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometry
FID
0POLYGON ((-22.21497 16.22040, -22.05071 16.303...
1POLYGON ((-22.05071 16.30307, -21.88618 16.385...
2POLYGON ((-21.88618 16.38536, -21.72137 16.467...
3POLYGON ((-21.72137 16.46727, -21.55629 16.548...
4POLYGON ((-21.55629 16.54881, -21.39094 16.629...
......
95116POLYGON ((87.25127 59.16191, 87.43401 59.01025...
95117POLYGON ((87.43401 59.01025, 87.61561 58.85850...
95118POLYGON ((87.61561 58.85850, 87.79608 58.70663...
95119POLYGON ((87.79608 58.70663, 87.97545 58.55466...
95120POLYGON ((87.97545 58.55466, 88.15372 58.40259...
\n", - "

95121 rows × 1 columns

\n", - "
" - ], - "text/plain": [ - " geometry\n", - "FID \n", - "0 POLYGON ((-22.21497 16.22040, -22.05071 16.303...\n", - "1 POLYGON ((-22.05071 16.30307, -21.88618 16.385...\n", - "2 POLYGON ((-21.88618 16.38536, -21.72137 16.467...\n", - "3 POLYGON ((-21.72137 16.46727, -21.55629 16.548...\n", - "4 POLYGON ((-21.55629 16.54881, -21.39094 16.629...\n", - "... ...\n", - "95116 POLYGON ((87.25127 59.16191, 87.43401 59.01025...\n", - "95117 POLYGON ((87.43401 59.01025, 87.61561 58.85850...\n", - "95118 POLYGON ((87.61561 58.85850, 87.79608 58.70663...\n", - "95119 POLYGON ((87.79608 58.70663, 87.97545 58.55466...\n", - "95120 POLYGON ((87.97545 58.55466, 88.15372 58.40259...\n", - "\n", - "[95121 rows x 1 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "grid.create_shapefile()\n", - "grid.shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "grid.write_shapefile('rotated_2')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tutorials/5.Others/5.4.Spatial_Join.ipynb b/tutorials/5.Others/5.4.Spatial_Join.ipynb deleted file mode 100644 index b00e62288dc5f117c373579760892b9c5d86452a..0000000000000000000000000000000000000000 --- a/tutorials/5.Others/5.4.Spatial_Join.ipynb +++ /dev/null @@ -1,892 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to make spatial joins" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from nes import *\n", - "import xarray as xr\n", - "import geopandas as gpd\n", - "import pandas as pd\n", - "\n", - "pd.options.mode.chained_assignment = None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Method 1: Centroids" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometry
FID
0POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...
1POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...
2POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...
3POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...
4POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...
......
9995POLYGON ((20.80000 60.90000, 21.00000 60.90000...
9996POLYGON ((21.00000 60.90000, 21.20000 60.90000...
9997POLYGON ((21.20000 60.90000, 21.40000 60.90000...
9998POLYGON ((21.40000 60.90000, 21.60000 60.90000...
9999POLYGON ((21.60000 60.90000, 21.80000 60.90000...
\n", - "

10000 rows × 1 columns

\n", - "
" - ], - "text/plain": [ - " geometry\n", - "FID \n", - "0 POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...\n", - "1 POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...\n", - "2 POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...\n", - "3 POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...\n", - "4 POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...\n", - "... ...\n", - "9995 POLYGON ((20.80000 60.90000, 21.00000 60.90000...\n", - "9996 POLYGON ((21.00000 60.90000, 21.20000 60.90000...\n", - "9997 POLYGON ((21.20000 60.90000, 21.40000 60.90000...\n", - "9998 POLYGON ((21.40000 60.90000, 21.60000 60.90000...\n", - "9999 POLYGON ((21.60000 60.90000, 21.80000 60.90000...\n", - "\n", - "[10000 rows x 1 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lat_orig = 41.1\n", - "lon_orig = 1.8\n", - "inc_lat = 0.2\n", - "inc_lon = 0.2\n", - "n_lat = 100\n", - "n_lon = 100\n", - "coordinates = create_nes(comm=None, info=False, projection='regular',\n", - " lat_orig=lat_orig, lon_orig=lon_orig, inc_lat=inc_lat, inc_lon=inc_lon, \n", - " n_lat=n_lat, n_lon=n_lon)\n", - "coordinates.create_shapefile()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "mask_path = '/esarchive/scratch/avilanova/software/NES/tutorials/data/timezones_2021c/timezones_2021c.shp'\n", - "mask = gpd.read_file(mask_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2190: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", - "\n", - " shapefile_aux.geometry = self.shapefile.centroid\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15.2 s, sys: 680 ms, total: 15.9 s\n", - "Wall time: 15.9 s\n" - ] - } - ], - "source": [ - "%time coordinates.spatial_join(mask)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \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", - "
geometrytzid
FID
0POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...Europe/Madrid
1POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...Europe/Madrid
2POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...Europe/Madrid
3POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...NaN
4POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...NaN
.........
9995POLYGON ((20.80000 60.90000, 21.00000 60.90000...Europe/Helsinki
9996POLYGON ((21.00000 60.90000, 21.20000 60.90000...Europe/Helsinki
9997POLYGON ((21.20000 60.90000, 21.40000 60.90000...Europe/Helsinki
9998POLYGON ((21.40000 60.90000, 21.60000 60.90000...Europe/Helsinki
9999POLYGON ((21.60000 60.90000, 21.80000 60.90000...Europe/Helsinki
\n", - "

10000 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " geometry tzid\n", - "FID \n", - "0 POLYGON ((1.80000 41.10000, 2.00000 41.10000, ... Europe/Madrid\n", - "1 POLYGON ((2.00000 41.10000, 2.20000 41.10000, ... Europe/Madrid\n", - "2 POLYGON ((2.20000 41.10000, 2.40000 41.10000, ... Europe/Madrid\n", - "3 POLYGON ((2.40000 41.10000, 2.60000 41.10000, ... NaN\n", - "4 POLYGON ((2.60000 41.10000, 2.80000 41.10000, ... NaN\n", - "... ... ...\n", - "9995 POLYGON ((20.80000 60.90000, 21.00000 60.90000... Europe/Helsinki\n", - "9996 POLYGON ((21.00000 60.90000, 21.20000 60.90000... Europe/Helsinki\n", - "9997 POLYGON ((21.20000 60.90000, 21.40000 60.90000... Europe/Helsinki\n", - "9998 POLYGON ((21.40000 60.90000, 21.60000 60.90000... Europe/Helsinki\n", - "9999 POLYGON ((21.60000 60.90000, 21.80000 60.90000... Europe/Helsinki\n", - "\n", - "[10000 rows x 2 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "coordinates.shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "coordinates.shapefile.to_file('spatial_join_method_1')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Method 2: Nearest centroids" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometry
FID
0POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...
1POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...
2POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...
3POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...
4POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...
......
95POLYGON ((2.80000 42.90000, 3.00000 42.90000, ...
96POLYGON ((3.00000 42.90000, 3.20000 42.90000, ...
97POLYGON ((3.20000 42.90000, 3.40000 42.90000, ...
98POLYGON ((3.40000 42.90000, 3.60000 42.90000, ...
99POLYGON ((3.60000 42.90000, 3.80000 42.90000, ...
\n", - "

100 rows × 1 columns

\n", - "
" - ], - "text/plain": [ - " geometry\n", - "FID \n", - "0 POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...\n", - "1 POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...\n", - "2 POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...\n", - "3 POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...\n", - "4 POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...\n", - ".. ...\n", - "95 POLYGON ((2.80000 42.90000, 3.00000 42.90000, ...\n", - "96 POLYGON ((3.00000 42.90000, 3.20000 42.90000, ...\n", - "97 POLYGON ((3.20000 42.90000, 3.40000 42.90000, ...\n", - "98 POLYGON ((3.40000 42.90000, 3.60000 42.90000, ...\n", - "99 POLYGON ((3.60000 42.90000, 3.80000 42.90000, ...\n", - "\n", - "[100 rows x 1 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lat_orig = 41.1\n", - "lon_orig = 1.8\n", - "inc_lat = 0.2\n", - "inc_lon = 0.2\n", - "n_lat = 10\n", - "n_lon = 10\n", - "coordinates = create_nes(comm=None, info=False, projection='regular',\n", - " lat_orig=lat_orig, lon_orig=lon_orig, inc_lat=inc_lat, inc_lon=inc_lon, \n", - " n_lat=n_lat, n_lon=n_lon)\n", - "coordinates.create_shapefile()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "mask_path = '/esarchive/scratch/avilanova/software/NES/tutorials/data/timezones_2021c/timezones_2021c.shp'\n", - "mask = gpd.read_file(mask_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "#%time coordinates.spatial_join(mask, method='nearest')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometry
FID
0POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...
1POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...
2POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...
3POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...
4POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...
......
95POLYGON ((2.80000 42.90000, 3.00000 42.90000, ...
96POLYGON ((3.00000 42.90000, 3.20000 42.90000, ...
97POLYGON ((3.20000 42.90000, 3.40000 42.90000, ...
98POLYGON ((3.40000 42.90000, 3.60000 42.90000, ...
99POLYGON ((3.60000 42.90000, 3.80000 42.90000, ...
\n", - "

100 rows × 1 columns

\n", - "
" - ], - "text/plain": [ - " geometry\n", - "FID \n", - "0 POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...\n", - "1 POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...\n", - "2 POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...\n", - "3 POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...\n", - "4 POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...\n", - ".. ...\n", - "95 POLYGON ((2.80000 42.90000, 3.00000 42.90000, ...\n", - "96 POLYGON ((3.00000 42.90000, 3.20000 42.90000, ...\n", - "97 POLYGON ((3.20000 42.90000, 3.40000 42.90000, ...\n", - "98 POLYGON ((3.40000 42.90000, 3.60000 42.90000, ...\n", - "99 POLYGON ((3.60000 42.90000, 3.80000 42.90000, ...\n", - "\n", - "[100 rows x 1 columns]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "coordinates.shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "coordinates.shapefile.to_file('spatial_join_method_2')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Method 3: Areas intersection" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometry
FID
0POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...
1POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...
2POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...
3POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...
4POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...
......
9995POLYGON ((20.80000 60.90000, 21.00000 60.90000...
9996POLYGON ((21.00000 60.90000, 21.20000 60.90000...
9997POLYGON ((21.20000 60.90000, 21.40000 60.90000...
9998POLYGON ((21.40000 60.90000, 21.60000 60.90000...
9999POLYGON ((21.60000 60.90000, 21.80000 60.90000...
\n", - "

10000 rows × 1 columns

\n", - "
" - ], - "text/plain": [ - " geometry\n", - "FID \n", - "0 POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...\n", - "1 POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...\n", - "2 POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...\n", - "3 POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...\n", - "4 POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...\n", - "... ...\n", - "9995 POLYGON ((20.80000 60.90000, 21.00000 60.90000...\n", - "9996 POLYGON ((21.00000 60.90000, 21.20000 60.90000...\n", - "9997 POLYGON ((21.20000 60.90000, 21.40000 60.90000...\n", - "9998 POLYGON ((21.40000 60.90000, 21.60000 60.90000...\n", - "9999 POLYGON ((21.60000 60.90000, 21.80000 60.90000...\n", - "\n", - "[10000 rows x 1 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lat_orig = 41.1\n", - "lon_orig = 1.8\n", - "inc_lat = 0.2\n", - "inc_lon = 0.2\n", - "n_lat = 100\n", - "n_lon = 100\n", - "coordinates = create_nes(comm=None, info=False, projection='regular',\n", - " lat_orig=lat_orig, lon_orig=lon_orig, inc_lat=inc_lat, inc_lon=inc_lon, \n", - " n_lat=n_lat, n_lon=n_lon)\n", - "coordinates.create_shapefile()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "mask_path = '/esarchive/scratch/avilanova/software/NES/tutorials/data/timezones_2021c/timezones_2021c.shp'\n", - "mask = gpd.read_file(mask_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Rank 000: 9270 intersected areas found\n", - "CPU times: user 8min 19s, sys: 9.29 s, total: 8min 29s\n", - "Wall time: 8min 30s\n" - ] - } - ], - "source": [ - "%time coordinates.spatial_join(mask, method='intersection')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\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", - " \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", - "
geometrytzid
FID
0POLYGON ((1.80000 41.10000, 2.00000 41.10000, ...Europe/Madrid
1POLYGON ((2.00000 41.10000, 2.20000 41.10000, ...Europe/Madrid
2POLYGON ((2.20000 41.10000, 2.40000 41.10000, ...Europe/Madrid
3POLYGON ((2.40000 41.10000, 2.60000 41.10000, ...Europe/Madrid
4POLYGON ((2.60000 41.10000, 2.80000 41.10000, ...NaN
.........
9995POLYGON ((20.80000 60.90000, 21.00000 60.90000...Europe/Helsinki
9996POLYGON ((21.00000 60.90000, 21.20000 60.90000...Europe/Helsinki
9997POLYGON ((21.20000 60.90000, 21.40000 60.90000...Europe/Helsinki
9998POLYGON ((21.40000 60.90000, 21.60000 60.90000...Europe/Helsinki
9999POLYGON ((21.60000 60.90000, 21.80000 60.90000...Europe/Helsinki
\n", - "

10000 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - " geometry tzid\n", - "FID \n", - "0 POLYGON ((1.80000 41.10000, 2.00000 41.10000, ... Europe/Madrid\n", - "1 POLYGON ((2.00000 41.10000, 2.20000 41.10000, ... Europe/Madrid\n", - "2 POLYGON ((2.20000 41.10000, 2.40000 41.10000, ... Europe/Madrid\n", - "3 POLYGON ((2.40000 41.10000, 2.60000 41.10000, ... Europe/Madrid\n", - "4 POLYGON ((2.60000 41.10000, 2.80000 41.10000, ... NaN\n", - "... ... ...\n", - "9995 POLYGON ((20.80000 60.90000, 21.00000 60.90000... Europe/Helsinki\n", - "9996 POLYGON ((21.00000 60.90000, 21.20000 60.90000... Europe/Helsinki\n", - "9997 POLYGON ((21.20000 60.90000, 21.40000 60.90000... Europe/Helsinki\n", - "9998 POLYGON ((21.40000 60.90000, 21.60000 60.90000... Europe/Helsinki\n", - "9999 POLYGON ((21.60000 60.90000, 21.80000 60.90000... Europe/Helsinki\n", - "\n", - "[10000 rows x 2 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "coordinates.shapefile" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "coordinates.shapefile.to_file('spatial_join_method_3')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tutorials/5.Others/5.1.Add_Time_Bounds.ipynb b/tutorials/6.Others/6.1.Add_Time_Bounds.ipynb similarity index 86% rename from tutorials/5.Others/5.1.Add_Time_Bounds.ipynb rename to tutorials/6.Others/6.1.Add_Time_Bounds.ipynb index 1ed4e2f29a02d9a7b08cc645a9cb39a2d8606e5a..77238010f589b7edb7564631e0d45168b1b44538 100644 --- a/tutorials/5.Others/5.1.Add_Time_Bounds.ipynb +++ b/tutorials/6.Others/6.1.Add_Time_Bounds.ipynb @@ -13,7 +13,6 @@ "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", "import datetime\n", "import numpy as np\n", "from nes import *" @@ -32,7 +31,9 @@ "metadata": {}, "outputs": [], "source": [ - "test_path = \"/gpfs/scratch/bsc32/bsc32538/mr_multiplyby/OUT/stats_bnds/monarch/a45g/regional/daily_max/O3_all/O3_all-000_2021080300.nc\"\n", + "# 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", + "test_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/O3_all-000_2021080300.nc'\n", "nessy = open_netcdf(path=test_path, info=True)" ] }, @@ -74,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -84,7 +85,7 @@ " datetime.datetime(2020, 2, 15, 0, 0)]], dtype=object)" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } diff --git a/tutorials/5.Others/5.5.Selecting.ipynb b/tutorials/6.Others/6.2.Selecting.ipynb similarity index 65% rename from tutorials/5.Others/5.5.Selecting.ipynb rename to tutorials/6.Others/6.2.Selecting.ipynb index fd7024dfeea42ff99de9fc9a225d144e5e89fab8..f4c28b4885ab6a533b76ca5b3666551e24dee4b6 100644 --- a/tutorials/5.Others/5.5.Selecting.ipynb +++ b/tutorials/6.Others/6.2.Selecting.ipynb @@ -13,8 +13,18 @@ "metadata": {}, "outputs": [], "source": [ - "from nes import *\n", - "file_path = \"/gpfs/scratch/bsc32/bsc32538/original_files/CAMS_MONARCH_d01_2022070412.nc\"" + "from nes import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Original path: /esarchive/exp/monarch/a4dd/original_files/000/2022111512/MONARCH_d01_2022111512.nc\n", + "# Rotated grid from MONARCH\n", + "file_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc'" ] }, { @@ -26,14 +36,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-07-04 12:00:00 109 2022-07-09 00:00:00\n" + "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" ] }, { @@ -42,7 +54,7 @@ "'\\nCPU times: user 1.17 s, sys: 6.79 s, total: 7.96 s\\nWall time: 31.4 s\\n'" ] }, - "execution_count": 2, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -52,10 +64,10 @@ "nessy.keep_vars('O3')\n", "\n", "print(nessy.time[0], len(nessy.time), nessy.time[-1])\n", - "# %time nessy.load()\n", + "%time nessy.load()\n", "\"\"\"\n", - "CPU times: user 1.17 s, sys: 6.79 s, total: 7.96 s\n", - "Wall time: 31.4 s\n", + "CPU times: user 244 ms, sys: 1.35 s, total: 1.59 s\n", + "Wall time: 3.33 s\n", "\"\"\"" ] }, @@ -68,14 +80,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-07-05 00:00:00 24 2022-07-05 23:00:00\n" + "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" ] }, { @@ -84,7 +98,7 @@ "'\\nCPU times: user 295 ms, sys: 1.47 s, total: 1.76 s\\nWall time: 6.77 s\\n'" ] }, - "execution_count": 3, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -93,13 +107,13 @@ "nessy = open_netcdf(file_path)\n", "nessy.keep_vars('O3')\n", "\n", - "nessy.sel(hours_start=12, hours_end=73)\n", + "nessy.sel(hours_start=12, hours_end=16)\n", "\n", "print(nessy.time[0], len(nessy.time), nessy.time[-1])\n", - "# %time nessy.load()\n", + "%time nessy.load()\n", "\"\"\"\n", - "CPU times: user 295 ms, sys: 1.47 s, total: 1.76 s\n", - "Wall time: 6.77 s\n", + "CPU times: user 70.8 ms, sys: 363 ms, total: 433 ms\n", + "Wall time: 1.57 s\n", "\"\"\"" ] }, @@ -112,14 +126,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-07-05 00:00:00 24 2022-07-05 23:00:00\n" + "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" ] }, { @@ -128,7 +144,7 @@ "'\\nCPU times: user 274 ms, sys: 1.44 s, total: 1.71 s\\nWall time: 7.53 s\\n'" ] }, - "execution_count": 4, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -139,14 +155,14 @@ "nessy = open_netcdf(file_path)\n", "nessy.keep_vars('O3')\n", "\n", - "nessy.sel(time_min=datetime(year=2022, month=7, day=5, hour=0), \n", - " time_max=datetime(year=2022, month=7, day=5, hour=23))\n", + "nessy.sel(time_min=datetime(year=2022, month=11, day=15, hour=0), \n", + " time_max=datetime(year=2022, month=11, day=15, hour=23))\n", "\n", "print(nessy.time[0], len(nessy.time), nessy.time[-1])\n", - "# %time nessy.load()\n", + "%time nessy.load()\n", "\"\"\"\n", - "CPU times: user 274 ms, sys: 1.44 s, total: 1.71 s\n", - "Wall time: 7.53 s\n", + "CPU times: user 95.1 ms, sys: 490 ms, total: 585 ms\n", + "Wall time: 1.47 s\n", "\"\"\"" ] }, @@ -159,14 +175,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1\n" + "1\n", + "CPU times: user 14.4 ms, sys: 70 ms, total: 84.4 ms\n", + "Wall time: 5.55 s\n" ] }, { @@ -175,7 +193,7 @@ "'\\nCPU times: user 77.3 ms, sys: 248 ms, total: 325 ms\\nWall time: 17.1 s\\n'" ] }, - "execution_count": 5, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -187,10 +205,10 @@ "nessy.sel(lev_min=23)\n", "\n", "print(len(nessy.lev['data']))\n", - "# %time nessy.load()\n", + "%time nessy.load()\n", "\"\"\"\n", - "CPU times: user 77.3 ms, sys: 248 ms, total: 325 ms\n", - "Wall time: 17.1 s\n", + "CPU times: user 14.4 ms, sys: 70 ms, total: 84.4 ms\n", + "Wall time: 5.55 s\n", "\"\"\"" ] }, @@ -203,15 +221,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 16.4 ms, sys: 79.8 ms, total: 96.2 ms\n", - "Wall time: 18.6 s\n" + "CPU times: user 4.11 ms, sys: 22 ms, total: 26.2 ms\n", + "Wall time: 4.2 s\n" ] }, { @@ -220,7 +238,7 @@ "'\\nCPU times: user 13.9 ms, sys: 74.9 ms, total: 88.8 ms\\nWall time: 16.3 s\\n'" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -232,19 +250,26 @@ "nessy.sel(lat_min=30, lat_max=31, lon_min=-1, lon_max=1)\n", "%time nessy.load()\n", "\"\"\"\n", - "CPU times: user 13.9 ms, sys: 74.9 ms, total: 88.8 ms\n", - "Wall time: 16.3 s\n", + "CPU times: user 4.11 ms, sys: 22 ms, total: 26.2 ms\n", + "Wall time: 4.2 s\n", "\"\"\"" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "nessy.to_netcdf(\"test_sel.nc\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -264,6 +289,11 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } } }, "nbformat": 4, diff --git a/tutorials/6.Others/6.3.Plot.ipynb b/tutorials/6.Others/6.3.Plot.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..c9b3ec986fefbd215613537ad4aeb12688eb1ba6 --- /dev/null +++ b/tutorials/6.Others/6.3.Plot.ipynb @@ -0,0 +1,481 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to plot data from NES" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from nes import *\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "file_path = \"/gpfs/projects/bsc32/models/NES_tutorial_data/sconcno2-000_2019010100.nc\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "nessy = open_netcdf(file_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "nessy.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Plot using contourf" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes()\n", + "plt.contourf(nessy.lon['data'], nessy.lat['data'], nessy.variables['sconcno2']['data'][0,0], cmap='jet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Plot using pcolormesh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1. With coordinates (warning)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes()\n", + "plt.pcolormesh(nessy.lon['data'], nessy.lat['data'], nessy.variables['sconcno2']['data'][0,0], \n", + " shading='auto', cmap='jet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2. With coordinates bounds" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "nessy.create_spatial_bounds()\n", + "lon_bnds, lat_bnds = nessy.get_spatial_bounds_mesh_format()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes()\n", + "plt.pcolormesh(lon_bnds, lat_bnds, nessy.variables['sconcno2']['data'][0,0], cmap='jet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.3. Creating a shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
geometry
FID
0POLYGON ((-12.00844 33.41901, -11.88982 33.428...
1POLYGON ((-11.88982 33.42830, -11.77116 33.437...
2POLYGON ((-11.77116 33.43746, -11.65248 33.446...
3POLYGON ((-11.65248 33.44650, -11.53377 33.455...
4POLYGON ((-11.53377 33.45539, -11.41505 33.464...
......
17056POLYGON ((5.70038 45.40981, 5.84126 45.39939, ...
17057POLYGON ((5.84126 45.39939, 5.98210 45.38881, ...
17058POLYGON ((5.98210 45.38881, 6.12288 45.37808, ...
17059POLYGON ((6.12288 45.37808, 6.26361 45.36719, ...
17060POLYGON ((6.26361 45.36719, 6.40430 45.35615, ...
\n", + "

17061 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " geometry\n", + "FID \n", + "0 POLYGON ((-12.00844 33.41901, -11.88982 33.428...\n", + "1 POLYGON ((-11.88982 33.42830, -11.77116 33.437...\n", + "2 POLYGON ((-11.77116 33.43746, -11.65248 33.446...\n", + "3 POLYGON ((-11.65248 33.44650, -11.53377 33.455...\n", + "4 POLYGON ((-11.53377 33.45539, -11.41505 33.464...\n", + "... ...\n", + "17056 POLYGON ((5.70038 45.40981, 5.84126 45.39939, ...\n", + "17057 POLYGON ((5.84126 45.39939, 5.98210 45.38881, ...\n", + "17058 POLYGON ((5.98210 45.38881, 6.12288 45.37808, ...\n", + "17059 POLYGON ((6.12288 45.37808, 6.26361 45.36719, ...\n", + "17060 POLYGON ((6.26361 45.36719, 6.40430 45.35615, ...\n", + "\n", + "[17061 rows x 1 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy.create_shapefile()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\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", + " \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", + "
geometrysconcno2
FID
0POLYGON ((-12.00844 33.41901, -11.88982 33.428...0.000896
1POLYGON ((-11.88982 33.42830, -11.77116 33.437...0.000930
2POLYGON ((-11.77116 33.43746, -11.65248 33.446...0.000961
3POLYGON ((-11.65248 33.44650, -11.53377 33.455...0.000989
4POLYGON ((-11.53377 33.45539, -11.41505 33.464...0.001015
.........
17056POLYGON ((5.70038 45.40981, 5.84126 45.39939, ...0.009605
17057POLYGON ((5.84126 45.39939, 5.98210 45.38881, ...0.008114
17058POLYGON ((5.98210 45.38881, 6.12288 45.37808, ...0.006802
17059POLYGON ((6.12288 45.37808, 6.26361 45.36719, ...0.008189
17060POLYGON ((6.26361 45.36719, 6.40430 45.35615, ...0.009688
\n", + "

17061 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " geometry sconcno2\n", + "FID \n", + "0 POLYGON ((-12.00844 33.41901, -11.88982 33.428... 0.000896\n", + "1 POLYGON ((-11.88982 33.42830, -11.77116 33.437... 0.000930\n", + "2 POLYGON ((-11.77116 33.43746, -11.65248 33.446... 0.000961\n", + "3 POLYGON ((-11.65248 33.44650, -11.53377 33.455... 0.000989\n", + "4 POLYGON ((-11.53377 33.45539, -11.41505 33.464... 0.001015\n", + "... ... ...\n", + "17056 POLYGON ((5.70038 45.40981, 5.84126 45.39939, ... 0.009605\n", + "17057 POLYGON ((5.84126 45.39939, 5.98210 45.38881, ... 0.008114\n", + "17058 POLYGON ((5.98210 45.38881, 6.12288 45.37808, ... 0.006802\n", + "17059 POLYGON ((6.12288 45.37808, 6.26361 45.36719, ... 0.008189\n", + "17060 POLYGON ((6.26361 45.36719, 6.40430 45.35615, ... 0.009688\n", + "\n", + "[17061 rows x 2 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nessy.shapefile['sconcno2'] = nessy.variables['sconcno2']['data'][0, 0, :].ravel()\n", + "nessy.shapefile" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, figsize=(10, 5))\n", + "gdf_grid = nessy.shapefile.dropna()\n", + "gdf_grid.plot(ax=ax, column='sconcno2', cmap='jet', legend=True)\n", + "ax.margins(0)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8 (default, Aug 12 2021, 07:06:15) \n[GCC 8.4.1 20200928 (Red Hat 8.4.1-1)]" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tutorials/Jupyter_bash_nord3v2.cmd b/tutorials/Jupyter_bash_nord3v2.cmd index eefd0f7219bcc337065fb5eef61df954e423919e..be19eba5303809cc295c955ab30bc5e4e1d124cc 100644 --- a/tutorials/Jupyter_bash_nord3v2.cmd +++ b/tutorials/Jupyter_bash_nord3v2.cmd @@ -1,9 +1,9 @@ #!/bin/bash #SBATCH --ntasks 1 #SBATCH --time 02:00:00 -#SBATCH --job-name NES -#SBATCH --output log_jupyter-notebook-%J.out -#SBATCH --error log_jupyter-notebook-%J.err +#SBATCH --job-name NES-tutorial +#SBATCH --output log_NES-tutorial-%J.out +#SBATCH --error log_NES-tutorial-%J.err #SBATCH --exclusive #SBATCH --qos debug @@ -25,19 +25,8 @@ localhost:${port} (prefix w/ https:// if using password) # load modules or conda environments here module load jupyterlab/3.0.9-foss-2019b-Python-3.7.4 -module load Python/3.7.4-GCCcore-8.3.0 -module load netcdf4-python/1.5.3-foss-2019b-Python-3.7.4 -module load xarray/0.19.0-foss-2019b-Python-3.7.4 -module load cftime/1.0.1-foss-2019b-Python-3.7.4 -module load cfunits/1.8-foss-2019b-Python-3.7.4 -module load filelock/3.7.1-foss-2019b-Python-3.7.4 -module load pyproj/2.5.0-foss-2019b-Python-3.7.4 -module load eccodes-python/0.9.5-foss-2019b-Python-3.7.4 -module load geopandas/0.10.2-foss-2019b-Python-3.7.4 -module load Shapely/1.7.1-foss-2019b-Python-3.7.4 -#module load NES/0.9.0-foss-2019b-Python-3.7.4 +module load NES/1.1.0-nord3-v2-foss-2019b-Python-3.7.4 -export PYTHONPATH=/esarchive/scratch/avilanova/software/NES:${PYTHONPATH} # DON'T USE ADDRESS BELOW. # DO USE TOKEN BELOW diff --git a/tutorials/data/Dades_2017.xlsx b/tutorials/data/Dades_2017.xlsx deleted file mode 100644 index 6385974cf23a4772e2125ca1edda5c5758252fc3..0000000000000000000000000000000000000000 Binary files a/tutorials/data/Dades_2017.xlsx and /dev/null differ diff --git a/tutorials/data/Dades_Port_Barcelona_2017-2021_corr.xlsx b/tutorials/data/Dades_Port_Barcelona_2017-2021_corr.xlsx deleted file mode 100644 index 4aca3769586044b3906cc5a6a901ce4376ed07ec..0000000000000000000000000000000000000000 Binary files a/tutorials/data/Dades_Port_Barcelona_2017-2021_corr.xlsx and /dev/null differ diff --git a/tutorials/data/NH3_barcelona_2019_csic.csv b/tutorials/data/NH3_barcelona_2019_csic.csv deleted file mode 100644 index 0b1fdd941b1d3dc57aea5ca3431abd63be90191c..0000000000000000000000000000000000000000 --- a/tutorials/data/NH3_barcelona_2019_csic.csv +++ /dev/null @@ -1,13 +0,0 @@ -Date-hour in,traffic_site,urban_site -01/01/2019 00:00:00,4.98898814531849,2.55323513371909 -02/01/2019 00:00:00,3.42253492604592,1.55622607730934 -03/01/2019 00:00:00,2.67506499076435,1.68635511757375 -04/01/2019 00:00:00,3.42552223044561,1.97548631953393 -05/01/2019 00:00:00,5.31480909108289,1.11924536797087 -06/01/2019 00:00:00,3.13949500437943,1.62656669999897 -07/01/2019 00:00:00,0,2.22685564636695 -08/01/2019 00:00:00,0,2.4696380137646 -09/01/2019 00:00:00,0,3.72735535650666 -10/01/2019 00:00:00,0,1.53505610127377 -11/01/2019 00:00:00,0,2.51115189477121 -12/01/2019 00:00:00,0,0 diff --git a/tutorials/data/NH3_stations_CSIC.csv b/tutorials/data/NH3_stations_CSIC.csv deleted file mode 100644 index cf9a54d4dd99109a2aee009c61f72ea663f03574..0000000000000000000000000000000000000000 --- a/tutorials/data/NH3_stations_CSIC.csv +++ /dev/null @@ -1,3 +0,0 @@ -station,Lon,Lat -urban_site,2.1151,41.3875 -traffic_site,2.1534,41.3987 diff --git a/tutorials/data/XVPCA_info.csv b/tutorials/data/XVPCA_info.csv deleted file mode 100644 index 41b94c9e79a49e9fa8bab191efe6f04705189ff3..0000000000000000000000000000000000000000 --- a/tutorials/data/XVPCA_info.csv +++ /dev/null @@ -1,135 +0,0 @@ -station.code,lat,lon,standardised_network_provided_area_classification -ES0266A,41.379322448,2.086139959,urban-centre -ES0392A,41.727703559,1.838530912,urban-suburban -ES0395A,41.567823582,2.014598316,urban-centre -ES0559A,41.387423958,2.164918317,urban-centre -ES0567A,41.384906375,2.119573944,urban-centre -ES0584A,41.482016279,2.188296656,urban-suburban -ES0586A,41.413621183,2.015985703,urban-centre -ES0691A,41.403716,2.204736,urban-centre -ES0692A,41.37076,2.114771,urban-centre -ES0694A,41.392157459,2.009802277,urban-suburban -ES0700A,41.515609252,2.124996708,urban-centre -ES0704A,41.55242119,2.265250427,urban-suburban -ES0963A,41.473378006,1.982016549,urban-suburban -ES0971A,41.450745,1.975021,urban-suburban -ES0991A,41.88489741,2.874243477,urban-suburban -ES1018A,41.556115398,2.007401267,urban-centre -ES1117A,41.111995,1.151879,urban-suburban -ES1120A,41.115880526,1.191975478,urban-suburban -ES1122A,41.193743,1.236904,rural-near_city -ES1123A,41.155004,1.217734,urban-suburban -ES1124A,41.159532,1.239709,urban-suburban -ES1125A,41.730280261,1.825306423,urban-centre -ES1126A,41.475573452,1.923189624,urban-suburban -ES1135A,41.577626892,1.625893365,urban-suburban -ES1148A,41.425620904,2.222244805,urban-centre -ES1201A,42.391975687,2.842138412,rural-regional -ES1208A,41.150778895,1.120171923,urban-suburban -ES1215A,40.706708761,0.581651482,urban-suburban -ES1220A,41.830977786,1.755282654,rural -ES1222A,41.691273195,2.440944864,rural-regional -ES1225A,41.615794867,0.615725897,urban-centre -ES1231A,41.476769365,2.088977264,urban-centre -ES1248A,42.405378523,1.129930371,rural-regional -ES1262A,41.561264,2.101288,urban-centre -ES1275A,41.689049442,2.495746675,urban-suburban -ES1310A,42.311957,2.213146,rural-regional -ES1311A,41.958676874,3.212854412,rural -ES1312A,41.103677887,1.200765062,urban-suburban -ES1339A,41.219036461,1.721248774,urban-suburban -ES1347A,42.143260622,2.510206967,rural-regional -ES1348A,42.36839,1.776814,rural-regional -ES1362A,41.383226589,2.044453466,urban-centre -ES1379A,41.058203798,0.439711691,rural-regional -ES1390A,41.530418632,2.422048428,urban-suburban -ES1396A,41.378802802,2.133098078,urban-centre -ES1397A,42.003359222,2.287072698,urban-suburban -ES1408A,41.26805333,1.595034746,urban-suburban -ES1413A,41.531750031,2.432573332,urban-suburban -ES1438A,41.385366,2.15403,urban-centre -ES1447A,41.23429,1.72692,urban-suburban -ES1453A,41.447350254,2.209511187,urban-centre -ES1480A,41.398762,2.153472,urban-centre -ES1506A,41.122896353,1.246696221,urban-centre -ES1551A,41.512675808,2.125383709,urban-centre -ES1555A,41.224091559,1.726292388,urban-centre -ES1559A,41.959328421,3.037425681,urban-suburban -ES1588A,41.906647654,1.192974755,rural -ES1642A,41.93501,2.239901,urban-suburban -ES1663A,41.39881978,2.002128908,urban-suburban -ES1665A,41.479955339,2.187268328,urban-suburban -ES1666A,41.117387934,1.241649688,urban-centre -ES1679A,41.386413934,2.187416735,urban-centre -ES1680A,41.634656545,2.162349596,urban-suburban -ES1684A,41.492082863,2.042497085,urban-centre -ES1754A,40.643004752,0.288445976,rural-near_city -ES1773A,41.20223184,1.672200024,urban-suburban -ES1775A,41.608655219,2.135874806,urban-suburban -ES1776A,41.452115161,2.208196282,urban-centre -ES1778A,41.779343452,2.358018901,rural-remote -ES1812A,41.278381095,1.179916582,rural-near_city -ES1813A,41.100692784,0.755100288,rural-regional -ES1814A,41.54924,2.212144,urban-suburban -ES1815A,41.346823038,1.686575492,urban-suburban -ES1816A,41.547159712,2.443253787,urban-centre -ES1817A,41.526705931,2.183795315,urban-suburban -ES1839A,41.617943017,2.087078604,urban-suburban -ES1841A,41.551859569,2.43729,urban-suburban -ES1842A,41.481513156,2.26915353,urban-suburban -ES1843A,42.102313372,1.858062466,urban-suburban -ES1851A,42.097931,1.848338,urban-suburban -ES1852A,41.42068447,2.170571748,urban-suburban -ES1853A,41.244890143,1.617704646,rural-near_city -ES1854A,41.00821168,0.831084709,rural-regional -ES1855A,41.009505689,0.912875859,rural-near_city -ES1856A,41.4260772,2.147992032,urban-centre -ES1861A,41.722992004,1.826905859,urban-suburban -ES1870A,41.374943279,2.186841902,urban-suburban -ES1871A,41.436016001,2.007867622,urban-suburban -ES1872A,41.548225196,2.105157341,urban-centre -ES1874A,41.919593952,2.257149921,urban-suburban -ES1887A,41.848441044,2.224108021,rural-near_city -ES1891A,41.598666901,2.287117688,urban-centre -ES1892A,41.443983561,2.237875306,urban-centre -ES1895A,41.494082865,2.029636956,urban-suburban -ES1896A,41.320516155,1.664199192,urban-suburban -ES1899A,41.308402497,1.647339954,rural-near_city -ES1900A,41.387237826,2.186736695,urban-centre -ES1903A,41.313475208,2.013824628,urban-suburban -ES1909A,40.880656635,0.799176536,rural-near_city -ES1910A,41.303112534,1.991523957,urban-suburban -ES1923A,41.846711554,2.217443537,rural-near_city -ES1928A,41.446265477,2.227517323,urban-centre -ES1929A,41.32149521,2.09772526,urban-suburban -ES1930A,40.902692904,0.809795431,rural-near_city -ES1931A,41.469800555,2.184233488,urban-suburban -ES1936A,41.746333141,2.556661265,rural-near_city -ES1948A,40.939553197,0.831336974,rural-near_city -ES1964A,41.386638989,2.057392493,urban-suburban -ES1965A,41.38193008,2.066347853,urban-suburban -ES1982A,42.051340687,0.729555647,rural-remote -ES1983A,41.321768405,2.082140639,urban-suburban -ES1992A,41.387273,2.115661,urban-centre -ES1999A,41.976385751,2.816547298,urban-centre -ES2009A,40.577777716,0.546796795,rural-near_city -ES2011A,41.400770767,1.999634718,urban-suburban -ES2012A,41.415319291,1.990521216,urban-suburban -ES2017A,40.552819474,0.529982528,rural -ES2027A,41.45131069,2.248236084,urban-centre -ES2033A,41.242375047,1.859334489,rural -ES2034A,41.544104651,0.829933196,rural -ES2035A,41.567703441,1.637614343,urban-suburban -ES2071A,41.120064,1.254472,urban-centre -ES2079A,41.559607414,1.995963067,urban-suburban -ES2043A,41.230073,0.547183, -ES2090A,41.418413,2.123899, -ES0554A,40.2891666667,0.289166666667, -ES0977A,41.6047222222,1.60472222222, -ES1398A,41.53667,2.18361111111, -ES1200A,40.2813888889,0.281388888889, -ES2087A,41.929283,2.257302, -ES2091A,40.5799,0.5535, -ES2088A,41.77106,2.250647, -ES1908A,41.239068799,1.856563752, -ES9994A,42.358363,1.459455, diff --git a/tutorials/data/estaciones.csv b/tutorials/data/estaciones.csv deleted file mode 100644 index ea01b3a0489c8a1a0699114db579c675d3492f03..0000000000000000000000000000000000000000 --- a/tutorials/data/estaciones.csv +++ /dev/null @@ -1,4 +0,0 @@ -station.code,lat,lon,standardised_network_provided_area_classification -Dàrsena sud,41.332889028571,2.1408072098364, -Unitat Mobil,41.3737770961323,2.18451410008966, -ZAL Prat,41.3172766511896,2.13450079021309, diff --git a/tutorials/data/timezones_2021c/timezones_2021c.dbf b/tutorials/data/timezones_2021c/timezones_2021c.dbf deleted file mode 100644 index aed52eac47a3f420d8ecd782863d6145e1a7f300..0000000000000000000000000000000000000000 Binary files a/tutorials/data/timezones_2021c/timezones_2021c.dbf and /dev/null differ diff --git a/tutorials/data/timezones_2021c/timezones_2021c.prj b/tutorials/data/timezones_2021c/timezones_2021c.prj deleted file mode 100644 index f45cbadf0074d8b7b2669559a93bc50bb95f82d4..0000000000000000000000000000000000000000 --- a/tutorials/data/timezones_2021c/timezones_2021c.prj +++ /dev/null @@ -1 +0,0 @@ -GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]] \ No newline at end of file diff --git a/tutorials/data/timezones_2021c/timezones_2021c.shp b/tutorials/data/timezones_2021c/timezones_2021c.shp deleted file mode 100644 index 5dbd64b964295a7689fb70646caf4db8f0745ddb..0000000000000000000000000000000000000000 Binary files a/tutorials/data/timezones_2021c/timezones_2021c.shp and /dev/null differ diff --git a/tutorials/data/timezones_2021c/timezones_2021c.shx b/tutorials/data/timezones_2021c/timezones_2021c.shx deleted file mode 100644 index dda8f5adb453c5460d2c39d7741a2c62d71c2153..0000000000000000000000000000000000000000 Binary files a/tutorials/data/timezones_2021c/timezones_2021c.shx and /dev/null differ