# 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

    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, '')

        @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

        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

        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