Source code for nes.nc_projections.points_nes_providentia

#!/usr/bin/env python

import sys
import warnings
import numpy as np
from copy import deepcopy
from netCDF4 import stringtochar
from .points_nes import PointsNes


[docs] class PointsNesProvidentia(PointsNes): """ Attributes ---------- _model_centre_lon : dict Model centre longitudes dictionary with the complete 'data' key for all the values and the rest of the attributes. _model_centre_lat : dict Model centre latitudes dictionary with the complete 'data' key for all the values and the rest of the attributes. _grid_edge_lon : dict Grid edge longitudes dictionary with the complete 'data' key for all the values and the rest of the attributes. _grid_edge_lat : dict Grid edge latitudes dictionary with the complete 'data' key for all the values and the rest of the attributes. model_centre_lon : dict Model centre longitudes dictionary with the portion of 'data' corresponding to the rank values. model_centre_lat : dict Model centre latitudes dictionary with the portion of 'data' corresponding to the rank values. grid_edge_lon : dict Grid edge longitudes dictionary with the portion of 'data' corresponding to the rank values. grid_edge_lat : dict Grid edge latitudes dictionary with the portion of 'data' corresponding to the rank values. """ def __init__(self, comm=None, path=None, info=False, dataset=None, xarray=False, parallel_method='X', avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, create_nes=False, balanced=False, times=None, model_centre_lon=None, model_centre_lat=None, grid_edge_lon=None, grid_edge_lat=None, **kwargs): """ Initialize the PointsNesProvidentia class Parameters ---------- comm: MPI.COMM MPI Communicator. path: str Path to the NetCDF to initialize the object. info: bool Indicates if you want to get reading/writing info. dataset: Dataset NetCDF4-python Dataset to initialize the class. xarray: bool: (Not working) Indicates if you want to use xarray as default. parallel_method : str Indicates the parallelization method that you want. Default: 'X'. Accepted values: ['X']. avoid_first_hours : int Number of hours to remove from first time steps. avoid_last_hours : int Number of hours to remove from last time steps. first_level : int Index of the first level to use. last_level : int, None 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. balanced : bool Indicates if you want a balanced parallelization or not. Balanced dataset cannot be written in chunking mode. times : list, None List of times to substitute the current ones while creation. model_centre_lon : dict Model centre longitudes dictionary with the portion of 'data' corresponding to the rank values. model_centre_lat : dict Model centre latitudes dictionary with the portion of 'data' corresponding to the rank values. grid_edge_lon : dict Grid edge longitudes dictionary with the portion of 'data' corresponding to the rank values. grid_edge_lat : dict Grid edge latitudes dictionary with the portion of 'data' corresponding to the rank values. """ super(PointsNesProvidentia, self).__init__(comm=comm, path=path, info=info, dataset=dataset, xarray=xarray, parallel_method=parallel_method, avoid_first_hours=avoid_first_hours, avoid_last_hours=avoid_last_hours, first_level=first_level, last_level=last_level, create_nes=create_nes, times=times, **kwargs) if create_nes: # Complete dimensions self._model_centre_lon = model_centre_lon self._model_centre_lat = model_centre_lat self._grid_edge_lon = grid_edge_lon self._grid_edge_lat = grid_edge_lat else: # Complete dimensions self._model_centre_lon = self._get_coordinate_dimension(['model_centre_longitude']) self._model_centre_lat = self._get_coordinate_dimension(['model_centre_latitude']) self._grid_edge_lon = self._get_coordinate_dimension(['grid_edge_longitude']) self._grid_edge_lat = self._get_coordinate_dimension(['grid_edge_latitude']) # Dimensions screening self.model_centre_lon = self._get_coordinate_values(self._model_centre_lon, '') self.model_centre_lat = self._get_coordinate_values(self._model_centre_lat, '') self.grid_edge_lon = self._get_coordinate_values(self._grid_edge_lon, '') self.grid_edge_lat = self._get_coordinate_values(self._grid_edge_lat, '')
[docs] @staticmethod def new(comm=None, path=None, info=False, dataset=None, xarray=False, parallel_method='X', avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, create_nes=False, balanced=False, times=None, model_centre_lon=None, model_centre_lat=None, grid_edge_lon=None, grid_edge_lat=None, **kwargs): """ Initialize the PointsNesProvidentia class. Parameters ---------- comm: MPI.COMM MPI Communicator. path: str Path to the NetCDF to initialize the object. info: bool Indicates if you want to get reading/writing info. dataset: Dataset NetCDF4-python Dataset to initialize the class. xarray: bool: (Not working) Indicates if you want to use xarray as default. parallel_method : str Indicates the parallelization method that you want. Default: 'X'. Accepted values: ['X']. avoid_first_hours : int Number of hours to remove from first time steps. avoid_last_hours : int Number of hours to remove from last time steps. first_level : int Index of the first level to use last_level : int, None Index of the last level to use. None if it is the last. balanced : bool Indicates if you want a balanced parallelization or not. Balanced dataset cannot be written in chunking mode. times : list, None List of times to substitute the current ones while creation. create_nes : bool Indicates if you want to create the object from scratch (True) or through an existing file. model_centre_lon : dict Model centre longitudes dictionary with the portion of 'data' corresponding to the rank values. model_centre_lat : dict Model centre latitudes dictionary with the portion of 'data' corresponding to the rank values. grid_edge_lon : dict Grid edge longitudes dictionary with the portion of 'data' corresponding to the rank values. grid_edge_lat : dict Grid edge latitudes dictionary with the portion of 'data' corresponding to the rank values. """ new = PointsNesProvidentia(comm=comm, path=path, info=info, dataset=dataset, xarray=xarray, parallel_method=parallel_method, avoid_first_hours=avoid_first_hours, avoid_last_hours=avoid_last_hours, first_level=first_level, last_level=last_level, create_nes=create_nes, balanced=balanced, times=times, model_centre_lon=model_centre_lon, model_centre_lat=model_centre_lat, grid_edge_lon=grid_edge_lon, grid_edge_lat=grid_edge_lat, **kwargs) return new
def _create_dimensions(self, netcdf): """ Create 'grid_edge', 'model_latitude' and 'model_longitude' dimensions and the super dimensions 'time', 'time_nv', 'station', and 'strlen'. Parameters ---------- netcdf : Dataset NetCDF object. """ super(PointsNesProvidentia, self)._create_dimensions(netcdf) # Create grid_edge, model_latitude and model_longitude dimensions netcdf.createDimension('grid_edge', len(self._grid_edge_lon['data'])) netcdf.createDimension('model_latitude', self._model_centre_lon['data'].shape[0]) netcdf.createDimension('model_longitude', self._model_centre_lon['data'].shape[1]) return None def _create_dimension_variables(self, netcdf): """ Create the 'model_centre_lon', model_centre_lat', 'grid_edge_lon' and 'grid_edge_lat' variables. Parameters ---------- netcdf : Dataset NetCDF object. """ super(PointsNesProvidentia, self)._create_dimension_variables(netcdf) # MODEL CENTRE LONGITUDES model_centre_lon = netcdf.createVariable('model_centre_longitude', 'f8', ('model_latitude', 'model_longitude',), zlib=self.zip_lvl > 0, complevel=self.zip_lvl) model_centre_lon.units = 'degrees_east' model_centre_lon.axis = 'X' model_centre_lon.long_name = 'model centre longitude' model_centre_lon.standard_name = 'model centre longitude' if self.size > 1: model_centre_lon.set_collective(True) msg = '2D meshed grid centre longitudes with ' msg += '{} longitudes in {} bands of latitude'.format(self._model_centre_lon['data'].shape[1], self._model_centre_lat['data'].shape[0]) model_centre_lon.description = msg model_centre_lon[:] = self._model_centre_lon['data'] # MODEL CENTRE LATITUDES model_centre_lat = netcdf.createVariable('model_centre_latitude', 'f8', ('model_latitude','model_longitude',), zlib=self.zip_lvl > 0, complevel=self.zip_lvl) model_centre_lat.units = 'degrees_north' model_centre_lat.axis = 'Y' model_centre_lat.long_name = 'model centre latitude' model_centre_lat.standard_name = 'model centre latitude' if self.size > 1: model_centre_lat.set_collective(True) msg = '2D meshed grid centre longitudes with ' msg += '{} longitudes in {} bands of latitude'.format(self._model_centre_lon['data'].shape[1], self._model_centre_lat['data'].shape[0]) model_centre_lat[:] = self._model_centre_lat['data'] # GRID EDGE DOMAIN LONGITUDES grid_edge_lon = netcdf.createVariable('grid_edge_longitude', 'f8', ('grid_edge')) grid_edge_lon.units = 'degrees_east' grid_edge_lon.axis = 'X' grid_edge_lon.long_name = 'grid edge longitude' grid_edge_lon.standard_name = 'grid edge longitude' if self.size > 1: grid_edge_lon.set_collective(True) msg = 'Longitude coordinate along edge of grid domain ' msg += '(going clockwise around grid boundary from bottom-left corner).' grid_edge_lon.description = msg grid_edge_lon[:] = self._grid_edge_lon['data'] # GRID EDGE DOMAIN LATITUDES grid_edge_lat = netcdf.createVariable('grid_edge_latitude', 'f8', ('grid_edge')) grid_edge_lat.units = 'degrees_north' grid_edge_lat.axis = 'Y' grid_edge_lat.long_name = 'grid edge latitude' grid_edge_lat.standard_name = 'grid edge latitude' if self.size > 1: grid_edge_lat.set_collective(True) msg = 'Latitude coordinate along edge of grid domain ' msg += '(going clockwise around grid boundary from bottom-left corner).' grid_edge_lat.description = msg grid_edge_lat[:] = self._grid_edge_lat['data'] self.free_vars(('model_centre_longitude', 'model_centre_latitude', 'grid_edge_longitude', 'grid_edge_latitude')) def _get_coordinate_values(self, coordinate_info, coordinate_axis, bounds=False): """ Get the coordinate data of the current portion. Parameters ---------- coordinate_info : dict, list 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. """ 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: values['data'] = values['data'][self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] elif coordinate_len == 2: values['data'] = values['data'][self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], self.read_axis_limits['t_min']:self.read_axis_limits['t_max']] elif coordinate_len == 3: values['data'] = values['data'][self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], self.read_axis_limits['t_min']:self.read_axis_limits['t_max'], :] else: raise NotImplementedError("The coordinate has wrong dimensions: {dim}".format( dim=values['data'].shape)) elif coordinate_axis == '': # pass for 'model_centre_lon', 'model_centre_lat', 'grid_edge_lon' and 'grid_edge_lat' pass return values def _read_variable(self, var_name): """ Read the corresponding variable data according to the current rank. Parameters ---------- var_name : str Name of the variable to read. Returns ------- data: np.array Portion of the variable data corresponding to the rank. """ nc_var = self.netcdf.variables[var_name] var_dims = nc_var.dimensions # Read data in 1, 2 or 3 dimensions 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: 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']] 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'], :] else: raise NotImplementedError('Error with {0}. Only can be read netCDF with 3 dimensions or less'.format( var_name)) # Unmask array data = self._unmask_array(data) return data def _create_variables(self, netcdf, chunking=False): """ Create the netCDF file variables. Parameters ---------- netcdf : Dataset netcdf4-python open Dataset. chunking : bool Indicates if you want to chunk the output netCDF. """ if self.variables is not None: for i, (var_name, var_dict) in enumerate(self.variables.items()): # Get data type if 'dtype' in var_dict.keys(): var_dtype = var_dict['dtype'] if (var_dict['data'] is not None) and (var_dtype != var_dict['data'].dtype): msg = "WARNING!!! " msg += "Different data types for variable {0}. ".format(var_name) msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, var_dict['data'].dtype) warnings.warn(msg) sys.stderr.flush() try: var_dict['data'] = var_dict['data'].astype(var_dtype) except Exception as e: # TODO: Detect exception raise e("It was not possible to cast the data to the input dtype.") else: var_dtype = var_dict['data'].dtype if var_dtype is np.object: raise TypeError("Data dtype is np.object. Define dtype explicitly as dictionary key 'dtype'") # Get dimensions when reading datasets if 'dimensions' in var_dict.keys(): var_dims = var_dict['dimensions'] # Get dimensions when creating new datasets else: if len(var_dict['data'].shape) == 1: # For data that depends only on station (e.g. station_code) var_dims = self._var_dim else: # For data that is dependent on time and station (e.g. PM10) var_dims = self._var_dim + ('time',) if var_dict['data'] is not None: # Ensure data is of type numpy array (to create NES) if not isinstance(var_dict['data'], (np.ndarray, np.generic)): try: var_dict['data'] = np.array(var_dict['data']) except AttributeError: raise AttributeError("Data for variable {0} must be a numpy array.".format(var_name)) # Convert list of strings to chars for parallelization if np.issubdtype(var_dtype, np.character): var_dict['data_aux'] = self.str2char(var_dict['data']) var_dims += ('strlen',) var_dtype = 'S1' if self.info: print("Rank {0:03d}: Writing {1} var ({2}/{3})".format(self.rank, var_name, i + 1, len(self.variables))) if not chunking: var = netcdf.createVariable(var_name, var_dtype, var_dims, zlib=self.zip_lvl > 0, complevel=self.zip_lvl) else: if self.master: chunk_size = var_dict['data'].shape else: chunk_size = None chunk_size = self.comm.bcast(chunk_size, root=0) var = netcdf.createVariable(var_name, var_dtype, var_dims, zlib=self.zip_lvl > 0, complevel=self.zip_lvl, chunksizes=chunk_size) if self.info: print("Rank {0:03d}: Var {1} created ({2}/{3})".format( self.rank, var_name, i + 1, len(self.variables))) if self.size > 1: var.set_collective(True) if self.info: print("Rank {0:03d}: Var {1} collective ({2}/{3})".format( self.rank, var_name, i + 1, len(self.variables))) for att_name, att_value in var_dict.items(): if att_name == 'data': if self.info: print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) if 'data_aux' in var_dict.keys(): att_value = var_dict['data_aux'] if len(att_value.shape) == 1: try: var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value except IndexError: raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, att_value.shape)) except ValueError: raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, att_value.shape)) elif len(att_value.shape) == 2: if 'strlen' in var_dims: try: var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :] = att_value except IndexError: raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, att_value.shape)) except ValueError: raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], :].shape, att_value.shape)) else: try: var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], self.write_axis_limits['t_min']:self.write_axis_limits['t_max']] = att_value except IndexError: raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], self.write_axis_limits['t_min']:self.write_axis_limits['t_max']].shape, att_value.shape)) except ValueError: raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], self.write_axis_limits['t_min']:self.write_axis_limits['t_max']].shape, att_value.shape)) elif len(att_value.shape) == 3: try: var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], :] = att_value except IndexError: raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], :].shape, att_value.shape)) except ValueError: raise ValueError("Axis limits cannot be accessed. out_shape={0}, data_shp={1}".format( var[self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], :].shape, att_value.shape)) if self.info: print("Rank {0:03d}: Var {1} data ({2}/{3})".format(self.rank, var_name, i + 1, len(self.variables))) elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype', 'data_aux']: var.setncattr(att_name, att_value) if 'data_aux' in var_dict.keys(): del var_dict['data_aux'] self._set_var_crs(var) if self.info: print("Rank {0:03d}: Var {1} completed ({2}/{3})".format(self.rank, var_name, i + 1, len(self.variables))) return None def _gather_data(self, data_to_gather): """ Gather all the variable data into the MPI rank 0 to perform a serial write. Returns ------- data_to_gather: dict Variables to gather. """ data_list = deepcopy(data_to_gather) for var_name, var_info in data_list.items(): try: # noinspection PyArgumentList data_aux = self.comm.gather(data_list[var_name]['data'], root=0) if self.rank == 0: shp_len = len(data_list[var_name]['data'].shape) # concatenate over station if self.parallel_method == 'X': if shp_len == 1: # dimensions = (station) axis = 0 elif shp_len == 2: # dimensions = (station, strlen) or # dimensions = (station, time) axis = 0 else: msg = 'The points NetCDF must have ' msg += 'surface values (without levels).' raise NotImplementedError(msg) elif self.parallel_method == 'T': # concatenate over time if shp_len == 1: # dimensions = (station) axis = None continue elif shp_len == 2: if 'strlen' in var_info['dimensions']: # dimensions = (station, strlen) axis = None continue else: # dimensions = (station, time) axis = 1 else: msg = 'The points NetCDF must have ' msg += 'surface values (without levels).' raise NotImplementedError(msg) else: raise NotImplementedError( "Parallel method '{meth}' is not implemented. Use one of these: {accept}".format( meth=self.parallel_method, accept=['X', 'T'])) data_list[var_name]['data'] = np.concatenate(data_aux, axis=axis) except Exception as e: print("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format(var_name)) sys.stderr.write("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format(var_name)) print(e) sys.stderr.write(str(e)) # print(e, file=sys.stderr) sys.stderr.flush() self.comm.Abort(1) raise e return data_list
[docs] def to_netcdf(self, path, compression_level=0, serial=False, info=False, chunking=False): """ Write the netCDF output file. Parameters ---------- path : str Path to the output netCDF file. compression_level : int Level of compression (0 to 9) Default: 0 (no compression). serial : bool Indicates if you want to write in serial or not. Default: False. info : bool Indicates if you want to print the information of each writing step by stdout Default: False. chunking : bool Indicates if you want a chunked netCDF output. Only available with non serial writes. Default: False. """ if (not serial) and (self.size > 1): msg = 'WARNING!!! ' msg += 'Providentia datasets cannot be written in parallel yet. ' msg += 'Changing to serial mode.' warnings.warn(msg) sys.stderr.flush() super(PointsNesProvidentia, self).to_netcdf(path, compression_level=compression_level, serial=True, info=info, chunking=chunking) return None
[docs] 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: # station as dimension if len(self.variables[var_name]['dimensions']) == 1: self.shapefile[var_name] = self.variables[var_name]['data'][:].ravel() # station and time as dimensions else: self.shapefile[var_name] = self.variables[var_name]['data'][:, idx_time].ravel() return None
@staticmethod def _get_axis_index_(axis): if axis == 'T': value = 1 elif axis == 'X': value = 0 else: raise ValueError("Unknown axis: {0}".format(axis)) return value @staticmethod def _set_var_crs(var): """ Set the grid_mapping Parameters ---------- var : Variable netCDF4-python variable object. """ return None