Source code for nes.nc_projections.points_nes_ghost

#!/usr/bin/env python

import sys
import warnings
import numpy as np
from netCDF4 import stringtochar, date2num
from copy import deepcopy
from .points_nes import PointsNes


[docs] class PointsNesGHOST(PointsNes): """ Attributes ---------- _qa : dict Quality flags (GHOST checks) dictionary with the complete 'data' key for all the values and the rest of the attributes. _flag : dict Data flags (given by data provider) dictionary with the complete 'data' key for all the values and the rest of the attributes. _qa : dict Quality flags (GHOST checks) dictionary with the portion of 'data' corresponding to the rank values. _flag : dict Data flags (given by data provider) 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, **kwargs): """ Initialize the PointsNesGHOST 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. """ super(PointsNesGHOST, 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) # Complete dimensions self._flag = self._get_coordinate_dimension(['flag']) self._qa = self._get_coordinate_dimension(['qa']) # Dimensions screening self.flag = self._get_coordinate_values(self._flag, 'X') self.qa = self._get_coordinate_values(self._qa, 'X')
[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, **kwargs): """ Initialize the PointsNesGHOST 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. """ new = PointsNesGHOST(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, **kwargs) return new
def _create_dimensions(self, netcdf): """ Create 'N_flag_codes' and 'N_qa_codes' dimensions and the super dimensions 'time', 'time_nv', 'station', and 'strlen'. Parameters ---------- netcdf : Dataset NetCDF object. """ super(PointsNesGHOST, self)._create_dimensions(netcdf) # Create N_flag_codes and N_qa_codes dimensions netcdf.createDimension('N_flag_codes', self._flag['data'].shape[2]) netcdf.createDimension('N_qa_codes', self._qa['data'].shape[2]) return None def _create_dimension_variables(self, netcdf): """ Create the 'time', 'time_bnds', 'station', 'lat', 'lat_bnds', 'lon' and 'lon_bnds' variables. Parameters ---------- netcdf : Dataset NetCDF object. """ # 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.standard_name = 'time' time_var.calendar = 'standard' time_var.long_name = 'time' if self._time_bnds is not None: 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 BOUNDS if self._time_bnds is not None: time_bnds_var = netcdf.createVariable('time_bnds', np.float64, ('time', 'time_nv',), zlib=self.zip_lvl, complevel=self.zip_lvl) if self.size > 1: time_bnds_var.set_collective(True) time_bnds_var[:] = date2num(self._time_bnds, time_var.units, calendar='standard') # STATIONS stations = netcdf.createVariable('station', np.float64, ('station',), zlib=self.zip_lvl > 0, complevel=self.zip_lvl) stations.units = '' stations.axis = 'X' stations.long_name = '' stations.standard_name = 'station' if self.size > 1: stations.set_collective(True) stations[:] = self._station['data'] # LATITUDES lat = netcdf.createVariable('latitude', np.float64, self._lat_dim, zlib=self.zip_lvl > 0, complevel=self.zip_lvl) lat.units = 'degrees_north' lat.axis = 'Y' lat.long_name = 'latitude coordinate' lat.standard_name = 'latitude' if self._lat_bnds is not None: lat.bounds = 'lat_bnds' if self.size > 1: lat.set_collective(True) lat[:] = self._lat['data'] # LONGITUDES lon = netcdf.createVariable('longitude', np.float64, self._lon_dim, zlib=self.zip_lvl > 0, complevel=self.zip_lvl) lon.units = 'degrees_east' lon.axis = 'X' lon.long_name = 'longitude coordinate' lon.standard_name = 'longitude' if self._lon_bnds is not None: lon.bounds = 'lon_bnds' if self.size > 1: lon.set_collective(True) lon[:] = self._lon['data']
[docs] def erase_flags(self): first_time_idx = self.get_time_id(self.hours_start, first=True) last_time_idx = self.get_time_id(self.hours_end, first=False) t_len = last_time_idx - first_time_idx self._qa['data'] = np.empty((len(self._lon['data']), t_len, 0)) self._flag['data'] = np.empty((len(self._lon['data']), t_len, 0)) return None
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)) 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 or 2 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 def _create_metadata(self, netcdf): """ Create metadata variables. Parameters ---------- netcdf : Dataset NetCDF object. """ # N FLAG CODES flag = netcdf.createVariable('flag', np.int64, ('station', 'time', 'N_flag_codes',), zlib=self.zip_lvl > 0, complevel=self.zip_lvl) flag.units = '' flag.axis = '' flag.long_name = '' flag.standard_name = 'flag' if self.size > 1: flag.set_collective(True) flag[:] = self._flag['data'] # N QA CODES qa = netcdf.createVariable('qa', np.int64, ('station', 'time', 'N_qa_codes',), zlib=self.zip_lvl > 0, complevel=self.zip_lvl) qa.units = '' qa.axis = '' qa.long_name = '' qa.standard_name = 'N_qa_codes' if self.size > 1: qa.set_collective(True) qa[:] = self._qa['data'] return None
[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 += 'GHOST datasets cannot be written in parallel yet. ' msg += 'Changing to serial mode.' warnings.warn(msg) sys.stderr.flush() super(PointsNesGHOST, self).to_netcdf(path, compression_level=compression_level, serial=True, info=info, chunking=chunking) return None
[docs] def to_points(self): """ Transform a PointsNesGHOST into a PointsNes object Returns ---------- points_nes : nes.Nes Points Nes Object (without GHOST metadata variables) """ points_nes = PointsNes(comm=self.comm, info=self.info, balanced=self.balanced, parallel_method=self.parallel_method, avoid_first_hours=self.hours_start, avoid_last_hours=self.hours_end, first_level=self.first_level, last_level=self.last_level, create_nes=True, lat=self.lat['data'], lon=self.lon['data'], times=self.time ) # 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') points_nes.variables = deepcopy(self.variables) return points_nes
[docs] def get_standard_metadata(self, GHOST_version): """ Get all possible GHOST variables for each version. Parameters ---------- GHOST_version : str Version of GHOST file. Returns ---------- metadata_variables[GHOST_version] : list List of metadata variables for a certain GHOST version """ # This metadata variables are metadata_variables = {'1.4': ['GHOST_version', 'station_reference', 'station_timezone', 'latitude', 'longitude', 'altitude', 'sampling_height', 'measurement_altitude', 'ellipsoid', 'horizontal_datum', 'vertical_datum', 'projection', 'distance_to_building', 'distance_to_kerb', 'distance_to_junction', 'distance_to_source', 'street_width', 'street_type', 'daytime_traffic_speed', 'daily_passing_vehicles', 'data_level', 'climatology', 'station_name', 'city', 'country', 'administrative_country_division_1', 'administrative_country_division_2', 'population', 'representative_radius', 'network', 'associated_networks', 'area_classification', 'station_classification', 'main_emission_source', 'land_use', 'terrain', 'measurement_scale', 'ESDAC_Iwahashi_landform_classification', 'ESDAC_modal_Iwahashi_landform_classification_5km', 'ESDAC_modal_Iwahashi_landform_classification_25km', 'ESDAC_Meybeck_landform_classification', 'ESDAC_modal_Meybeck_landform_classification_5km', 'ESDAC_modal_Meybeck_landform_classification_25km', 'GHSL_settlement_model_classification', 'GHSL_modal_settlement_model_classification_5km', 'GHSL_modal_settlement_model_classification_25km', 'Joly-Peuch_classification_code', 'Koppen-Geiger_classification', 'Koppen-Geiger_modal_classification_5km', 'Koppen-Geiger_modal_classification_25km', 'MODIS_MCD12C1_v6_IGBP_land_use', 'MODIS_MCD12C1_v6_modal_IGBP_land_use_5km', 'MODIS_MCD12C1_v6_modal_IGBP_land_use_25km', 'MODIS_MCD12C1_v6_UMD_land_use', 'MODIS_MCD12C1_v6_modal_UMD_land_use_5km', 'MODIS_MCD12C1_v6_modal_UMD_land_use_25km', 'MODIS_MCD12C1_v6_LAI', 'MODIS_MCD12C1_v6_modal_LAI_5km', 'MODIS_MCD12C1_v6_modal_LAI_25km', 'WMO_region', 'WWF_TEOW_terrestrial_ecoregion', 'WWF_TEOW_biogeographical_realm', 'WWF_TEOW_biome', 'UMBC_anthrome_classification', 'UMBC_modal_anthrome_classification_5km', 'UMBC_modal_anthrome_classification_25km', 'EDGAR_v4.3.2_annual_average_BC_emissions', 'EDGAR_v4.3.2_annual_average_CO_emissions', 'EDGAR_v4.3.2_annual_average_NH3_emissions', 'EDGAR_v4.3.2_annual_average_NMVOC_emissions', 'EDGAR_v4.3.2_annual_average_NOx_emissions', 'EDGAR_v4.3.2_annual_average_OC_emissions', 'EDGAR_v4.3.2_annual_average_PM10_emissions', 'EDGAR_v4.3.2_annual_average_biogenic_PM2.5_emissions', 'EDGAR_v4.3.2_annual_average_fossilfuel_PM2.5_emissions', 'EDGAR_v4.3.2_annual_average_SO2_emissions', 'ASTER_v3_altitude', 'ETOPO1_altitude', 'ETOPO1_max_altitude_difference_5km', 'GHSL_built_up_area_density', 'GHSL_average_built_up_area_density_5km', 'GHSL_average_built_up_area_density_25km', 'GHSL_max_built_up_area_density_5km', 'GHSL_max_built_up_area_density_25km', 'GHSL_population_density', 'GHSL_average_population_density_5km', 'GHSL_average_population_density_25km', 'GHSL_max_population_density_5km', 'GHSL_max_population_density_25km', 'GPW_population_density', 'GPW_average_population_density_5km', 'GPW_average_population_density_25km', 'GPW_max_population_density_5km', 'GPW_max_population_density_25km', 'NOAA-DMSP-OLS_v4_nighttime_stable_lights', 'NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_5km', 'NOAA-DMSP-OLS_v4_average_nighttime_stable_lights_25km', 'NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_5km', 'NOAA-DMSP-OLS_v4_max_nighttime_stable_lights_25km', 'OMI_level3_column_annual_average_NO2', 'OMI_level3_column_cloud_screened_annual_average_NO2', 'OMI_level3_tropospheric_column_annual_average_NO2', 'OMI_level3_tropospheric_column_cloud_screened_annual_average_NO2', 'GSFC_coastline_proximity', 'primary_sampling_type', 'primary_sampling_instrument_name', 'primary_sampling_instrument_documented_flow_rate', 'primary_sampling_instrument_reported_flow_rate', 'primary_sampling_process_details', 'primary_sampling_instrument_manual_name', 'primary_sampling_further_details', 'sample_preparation_types', 'sample_preparation_techniques', 'sample_preparation_process_details', 'sample_preparation_further_details', 'measurement_methodology', 'measuring_instrument_name', 'measuring_instrument_sampling_type', 'measuring_instrument_documented_flow_rate', 'measuring_instrument_reported_flow_rate', 'measuring_instrument_process_details', 'measuring_instrument_process_details', 'measuring_instrument_manual_name', 'measuring_instrument_further_details', 'measuring_instrument_reported_units', 'measuring_instrument_reported_lower_limit_of_detection', 'measuring_instrument_documented_lower_limit_of_detection', 'measuring_instrument_reported_upper_limit_of_detection', 'measuring_instrument_documented_upper_limit_of_detection', 'measuring_instrument_reported_uncertainty', 'measuring_instrument_documented_uncertainty', 'measuring_instrument_reported_accuracy', 'measuring_instrument_documented_accuracy', 'measuring_instrument_reported_precision', 'measuring_instrument_documented_precision', 'measuring_instrument_reported_zero_drift', 'measuring_instrument_documented_zero_drift', 'measuring_instrument_reported_span_drift', 'measuring_instrument_documented_span_drift', 'measuring_instrument_reported_zonal_drift', 'measuring_instrument_documented_zonal_drift', 'measuring_instrument_reported_measurement_resolution', 'measuring_instrument_documented_measurement_resolution', 'measuring_instrument_reported_absorption_cross_section', 'measuring_instrument_documented_absorption_cross_section', 'measuring_instrument_inlet_information', 'measuring_instrument_calibration_scale', 'network_provided_volume_standard_temperature', 'network_provided_volume_standard_pressure', 'retrieval_algorithm', 'principal_investigator_name', 'principal_investigator_institution', 'principal_investigator_email_address', 'contact_name', 'contact_institution', 'contact_email_address', 'meta_update_stamp', 'data_download_stamp', 'data_revision_stamp', 'network_sampling_details', 'network_uncertainty_details', 'network_maintenance_details', 'network_qa_details', 'network_miscellaneous_details', 'data_licence', 'process_warnings', 'temporal_resolution', 'reported_lower_limit_of_detection_per_measurement', 'reported_upper_limit_of_detection_per_measurement', 'reported_uncertainty_per_measurement', 'derived_uncertainty_per_measurement', 'day_night_code', 'weekday_weekend_code', 'season_code', 'hourly_native_representativity_percent', 'hourly_native_max_gap_percent', 'daily_native_representativity_percent', 'daily_representativity_percent', 'daily_native_max_gap_percent', 'daily_max_gap_percent', 'monthly_native_representativity_percent', 'monthly_representativity_percent', 'monthly_native_max_gap_percent', 'monthly_max_gap_percent', 'annual_native_representativity_percent', 'annual_native_max_gap_percent', 'all_representativity_percent', 'all_max_gap_percent'], } return metadata_variables[GHOST_version]
[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