# 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

    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')

        @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']

        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

        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

        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

        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]

        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