# Source code for nes.methods.horizontal_interpolation

    #!/usr/bin/env python

    import sys
    import warnings
    import numpy as np
    import pandas as pd
    from geopandas import GeoSeries
    import os
    import nes
    from mpi4py import MPI
    from scipy import spatial
    from filelock import FileLock
    from datetime import datetime
    from warnings import warn
    import copy
    import pyproj
    import gc
    import psutil

    # CONSTANTS
    NEAREST_OPTS = ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN']
    CONSERVATIVE_OPTS = ['Conservative', 'Area_Conservative', 'cons', 'conservative', 'area']

    def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='NearestNeighbour', n_neighbours=4,
                               info=False, to_providentia=False, only_create_wm=False, wm=None, flux=False):
        """
        Horizontal methods from one grid to another one.

        Parameters
        ----------
        self : nes.Nes
            Source projection Nes Object.
        dst_grid : nes.Nes
            Final projection Nes object.
        weight_matrix_path : str, None
            Path to the weight matrix to read/create.
        kind : str
            Kind of horizontal interpolation. Accepted values: ['NearestNeighbour', 'Conservative'].
        n_neighbours : int
            Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4.
        info : bool
            Indicates if you want to print extra info during the methods process.
        to_providentia : bool
            Indicates if we want the interpolated grid in Providentia format.
        only_create_wm : bool
            Indicates if you want to only create the Weight Matrix.
        wm : Nes
            Weight matrix Nes File.
        flux : bool
            Indicates if you want to calculate the weight matrix for flux variables.
        """

        if info and self.master:
            print("Creating Weight Matrix")
        # Obtain weight matrix
        if self.parallel_method == 'T':
            weights, idx = get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours,
                                                  only_create_wm, wm, flux)
        elif self.parallel_method in ['Y', 'X']:
            weights, idx = get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours,
                                                   only_create_wm, wm, flux)
        else:
            raise NotImplemented("Parallel method {0} is not implemented yet for horizontal interpolations. Use 'T'".format(
                self.parallel_method))
        if info and self.master:
            print("Weight Matrix done!")
        if only_create_wm:
            # weights for only_create is the WM NES object
            return weights

        # idx[idx < 0] = np.nan
        idx = np.ma.masked_array(idx, mask=idx == -999)
        # idx = np.array(idx, dtype=float)
        # idx[idx < 0] = np.nan
        # weights[weights < 0] = np.nan
        weights = np.ma.masked_array(weights, mask=weights == -999)
        # weights = np.array(weights, dtype=float)
        # weights[weights < 0] = np.nan

        # Copy NES
        final_dst = dst_grid.copy()
        final_dst.set_communicator(dst_grid.comm)

        # Remove original file information
        final_dst.__ini_path = None
        final_dst.netcdf = None
        final_dst.dataset = None

        # Return final_dst
        final_dst.lev = self.lev
        final_dst._lev = self._lev
        final_dst.time = self.time
        final_dst._time = self._time
        final_dst.hours_start = self.hours_start
        final_dst.hours_end = self.hours_end

        if info and self.master:
            print("Applying weights")
        # Apply weights
        for var_name, var_info in self.variables.items():
            if info and self.master:
                print("\t{var} horizontal interpolation".format(var=var_name))
                sys.stdout.flush()
            src_shape = var_info['data'].shape
            if isinstance(dst_grid, nes.PointsNes):
                dst_shape = (src_shape[0], src_shape[1], idx.shape[-1])
            else:
                dst_shape = (src_shape[0], src_shape[1], idx.shape[-2], idx.shape[-1])
            # Creating new variable without data
            final_dst.variables[var_name] = {attr_name: attr_value for attr_name, attr_value in var_info.items()
                                             if attr_name != 'data'}
            # Creating empty data
            final_dst.variables[var_name]['data'] = np.empty(dst_shape)

            # src_data = var_info['data'].reshape((src_shape[0], src_shape[1], src_shape[2] * src_shape[3]))
            for time in range(dst_shape[0]):
                for lev in range(dst_shape[1]):
                    src_aux = get_src_data(self.comm, var_info['data'][time, lev], idx, self.parallel_method)
                    final_dst.variables[var_name]['data'][time, lev] = np.nansum(weights * src_aux, axis=1)

            if isinstance(dst_grid, nes.PointsNes):
                # Removing level axis
                if src_shape[1] != 1:
                    raise IndexError("Data with vertical levels cannot be interpolated to points")
                final_dst.variables[var_name]['data'] = final_dst.variables[var_name]['data'].reshape(
                    (src_shape[0], idx.shape[-1]))
                if isinstance(dst_grid, nes.PointsNesGHOST) and not to_providentia:
                    final_dst = final_dst.to_points()

        final_dst.global_attrs = self.global_attrs

        if info and self.master:
            print("Formatting")

        if to_providentia:
            # self = experiment to interpolate (regular, rotated, etc.)
            # final_dst = interpolated experiment (points)
            if isinstance(final_dst, nes.PointsNes):
                model_centre_lat, model_centre_lon = self.create_providentia_exp_centre_coordinates()
                grid_edge_lat, grid_edge_lon = self.create_providentia_exp_grid_edge_coordinates()
                final_dst = final_dst.to_providentia(model_centre_lon=model_centre_lon,
                                                     model_centre_lat=model_centre_lat,
                                                     grid_edge_lon=grid_edge_lon,
                                                     grid_edge_lat=grid_edge_lat)
            else:
                msg = "The final projection must be points to interpolate an experiment and get it in Providentia format."
                warnings.warn(msg)
                sys.stderr.flush()
        else:
            # Convert dimensions (time, lev, lat, lon) or (time, lat, lon) to (time, station) for interpolated variables
            # and reshape data
            if isinstance(final_dst, nes.PointsNes):
                for var_name, var_info in final_dst.variables.items():
                    if len(var_info['dimensions']) != len(var_info['data'].shape):
                        final_dst.variables[var_name]['dimensions'] = ('time', 'station')

        return final_dst

    def get_src_data(comm, var_data, idx, parallel_method):
        """
        To obtain the needed src data to interpolate.

        Parameters
        ----------
        comm : MPI.Communicator.
            MPI communicator.
        var_data : np.array
            Rank source data.
        idx : np.array
            Index of the needed data in a 2D flatten way.
        parallel_method: str
            Source parallel method.

        Returns
        -------
        np.array
            Flatten source needed data.
        """

        if parallel_method == 'T':
            var_data = var_data.flatten()
        else:
            var_data = comm.gather(var_data, root=0)
            if comm.Get_rank() == 0:
                if parallel_method == 'Y':
                    axis = 0
                elif parallel_method == 'X':
                    axis = 1
                else:
                    raise NotImplementedError(parallel_method)
                var_data = np.concatenate(var_data, axis=axis)
                var_data = var_data.flatten()

            var_data = comm.bcast(var_data)

        var_data = np.pad(var_data, [1, 1], 'constant', constant_values=np.nan).take(idx + 1, mode='clip')
        #var_data = np.take(var_data, idx)

        return var_data

    # noinspection DuplicatedCode

    def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, only_create, wm, flux):
        """
        To obtain the weights and source data index through the T axis.

        Parameters
        ----------
        self : nes.Nes
            Source projection Nes Object.
        dst_grid : nes.Nes
            Final projection Nes object.
        weight_matrix_path : str, None
            Path to the weight matrix to read/create.
        kind : str
            Kind of horizontal interpolation. Accepted values: ['NearestNeighbour', 'Conservative'].
        n_neighbours : int
            Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4.
        only_create : bool
            Indicates if you want to only create the Weight Matrix.
        wm : Nes
            Weight matrix Nes File.
        flux : bool
            Indicates if you want to calculate the weight matrix for flux variables.

        Returns
        -------
        tuple
            Weights and source data index.
        """

        if wm is not None:
            weight_matrix = wm

        elif weight_matrix_path is not None:
            with FileLock(weight_matrix_path + "{0:03d}.lock".format(self.rank)):
                if os.path.isfile(weight_matrix_path):
                    if self.master:
                        weight_matrix = read_weight_matrix(weight_matrix_path, comm=MPI.COMM_SELF)
                    else:
                        weight_matrix = True
                    if kind in NEAREST_OPTS:
                        if self.master:
                            if len(weight_matrix.lev['data']) != n_neighbours:
                                warn("The selected weight matrix does not have the same number of nearest neighbours." +
                                     "Re-calculating again but not saving it.")
                                sys.stderr.flush()
                                weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours)
                        else:
                            weight_matrix = True

                else:
                    if self.master:
                        if kind in NEAREST_OPTS:
                            weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours,
                                                                    wm_path=weight_matrix_path)
                        elif kind in CONSERVATIVE_OPTS:
                            weight_matrix = create_area_conservative_weight_matrix(
                                self, dst_grid, wm_path=weight_matrix_path, flux=flux)
                        else:
                            raise NotImplementedError(kind)
                    else:
                        weight_matrix = True

            if os.path.exists(weight_matrix_path + "{0:03d}.lock".format(self.rank)):
                os.remove(weight_matrix_path + "{0:03d}.lock".format(self.rank))
        else:
            if self.master:
                if kind in NEAREST_OPTS:
                    weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours)
                elif kind in CONSERVATIVE_OPTS:
                    weight_matrix = create_area_conservative_weight_matrix(self, dst_grid, flux=flux)
                else:
                    raise NotImplementedError(kind)
            else:
                weight_matrix = True

        if only_create:
            return weight_matrix, None

        if self.master:
            if kind in NEAREST_OPTS:
                # Normalize to 1
                weights = np.array(np.array(weight_matrix.variables['weight']['data'], dtype=np.float64) /
                                   np.array(weight_matrix.variables['weight']['data'], dtype=np.float64).sum(axis=1),
                                   dtype=np.float64)
            else:
                weights = np.array(weight_matrix.variables['weight']['data'], dtype=np.float64)
            idx = np.array(weight_matrix.variables['idx']['data'][0], dtype=int)
        else:
            weights = None
            idx = None

        weights = self.comm.bcast(weights, root=0)
        idx = self.comm.bcast(idx, root=0)

        return weights, idx

    # noinspection DuplicatedCode

    def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, only_create, wm, flux):
        """
        To obtain the weights and source data index through the X or Y axis.

        Parameters
        ----------
        self : nes.Nes
            Source projection Nes Object.
        dst_grid : nes.Nes
            Final projection Nes object.
        weight_matrix_path : str, None
            Path to the weight matrix to read/create.
        kind : str
            Kind of horizontal interpolation. Accepted values: ['NearestNeighbour', 'Conservative'].
        n_neighbours : int
            Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4.
        only_create : bool
            Indicates if you want to only create the Weight Matrix.
        wm : Nes
            Weight matrix Nes File.
        flux : bool
            Indicates if you want to calculate the weight matrix for flux variables.

        Returns
        -------
        tuple
            Weights and source data index.
        """

        if isinstance(dst_grid, nes.PointsNes) and weight_matrix_path is not None:
            if self.master:
                warn("To point weight matrix cannot be saved.")
                sys.stderr.flush()
            weight_matrix_path = None

        if wm is not None:
           weight_matrix = wm

        elif weight_matrix_path is not None:
            with FileLock(weight_matrix_path + "{0:03d}.lock".format(self.rank)):
                if os.path.isfile(weight_matrix_path):
                    if self.master:
                        weight_matrix = read_weight_matrix(weight_matrix_path, comm=MPI.COMM_SELF)
                    else:
                        weight_matrix = True
                    if kind in NEAREST_OPTS:
                        if self.master:
                            if len(weight_matrix.lev['data']) != n_neighbours:
                                warn("The selected weight matrix does not have the same number of nearest neighbours." +
                                     "Re-calculating again but not saving it.")
                                sys.stderr.flush()
                                weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours)
                        else:
                            weight_matrix = True
                else:
                    if kind in NEAREST_OPTS:
                        if self.master:
                            weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours,
                                                                    wm_path=weight_matrix_path)
                        else:
                            weight_matrix = True
                    elif kind in CONSERVATIVE_OPTS:
                        weight_matrix = create_area_conservative_weight_matrix(
                            self, dst_grid, wm_path=weight_matrix_path, flux=flux)
                    else:
                        raise NotImplementedError(kind)

            if os.path.exists(weight_matrix_path + "{0:03d}.lock".format(self.rank)):
                os.remove(weight_matrix_path + "{0:03d}.lock".format(self.rank))
        else:
            if kind in NEAREST_OPTS:
                weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours)
            elif kind in CONSERVATIVE_OPTS:
                weight_matrix = create_area_conservative_weight_matrix(self, dst_grid, flux=flux)
            else:
                raise NotImplementedError(kind)

        if only_create:
            return weight_matrix, None

        # Normalize to 1
        if self.master:
            if kind in NEAREST_OPTS:
                weights = np.array(np.array(weight_matrix.variables['weight']['data'], dtype=np.float64) /
                                   np.array(weight_matrix.variables['weight']['data'], dtype=np.float64).sum(axis=1),
                                   dtype=np.float64)
            else:
                weights = np.array(weight_matrix.variables['weight']['data'], dtype=np.float64)
            idx = np.array(weight_matrix.variables['idx']['data'][0], dtype=np.int64)
        else:
            weights = None
            idx = None

        weights = self.comm.bcast(weights, root=0)
        idx = self.comm.bcast(idx, root=0)

        # if isinstance(dst_grid, nes.PointsNes):
        # print("weights 1 ->", weights.shape)
        # print("idx 1 ->", idx.shape)
        #     weights = weights[:, dst_grid.write_axis_limits['x_min']:dst_grid.write_axis_limits['x_max']]
        #     idx = idx[dst_grid.write_axis_limits['x_min']:dst_grid.write_axis_limits['x_max']]
        # else:
        weights = weights[:, :, dst_grid.write_axis_limits['y_min']:dst_grid.write_axis_limits['y_max'],
                          dst_grid.write_axis_limits['x_min']:dst_grid.write_axis_limits['x_max']]
        idx = idx[:, dst_grid.write_axis_limits['y_min']:dst_grid.write_axis_limits['y_max'],
                  dst_grid.write_axis_limits['x_min']:dst_grid.write_axis_limits['x_max']]
        # print("weights 2 ->", weights.shape)
        # print("idx 2 ->", idx.shape)

        return weights, idx

    def read_weight_matrix(weight_matrix_path, comm=None, parallel_method='T'):
        """
        Read weight matrix.

        Parameters
        ----------
        weight_matrix_path : str
            Path of the weight matrix.
        comm : MPI.Communicator
            Communicator to read the weight matrix.
        parallel_method : str
            Nes parallel method to read the weight matrix.

        Returns
        -------
        nes.Nes
            Weight matrix.
        """

        weight_matrix = nes.open_netcdf(path=weight_matrix_path, comm=comm, parallel_method=parallel_method, balanced=True)
        weight_matrix.load()

        # In previous versions of NES weight was called inverse_dists
        if 'inverse_dists' in weight_matrix.variables.keys():
            weight_matrix.variables['weight'] = weight_matrix.variables['inverse_dists']

        weight_matrix.variables['weight']['data'][weight_matrix.variables['weight']['data'] <= 0] = np.nan
        weight_matrix.variables['weight']['data'][weight_matrix.variables['idx']['data'] <= 0] = np.nan

        return weight_matrix

    def create_nn_weight_matrix(self, dst_grid, n_neighbours=4, wm_path=None, info=False):
        """
        To create the weight matrix with the nearest neighbours method.

        Parameters
        ----------
        self : nes.Nes
            Source projection Nes Object.
        dst_grid : nes.Nes
            Final projection Nes object.
        n_neighbours : int
            Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4.
        wm_path : str
            Path where write the weight matrix.
        info: bool
            Indicates if you want to print extra info during the methods process.

        Returns
        -------
        nes.Nes
            Weight matrix.
        """

        if info and self.master:
            print("\tCreating Nearest Neighbour Weight Matrix with {0} neighbours".format(n_neighbours))
            sys.stdout.flush()
        # Source
        src_lat = np.array(self._lat['data'], dtype=np.float32)
        src_lon = np.array(self._lon['data'], dtype=np.float32)

        # 1D to 2D coordinates
        if len(src_lon.shape) == 1:
            src_lon, src_lat = np.meshgrid(src_lon, src_lat)

        # Destination
        dst_lat = np.array(dst_grid._lat['data'], dtype=np.float32)
        dst_lon = np.array(dst_grid._lon['data'], dtype=np.float32)

        if isinstance(dst_grid, nes.PointsNes):
            dst_lat = np.expand_dims(dst_grid._lat['data'], axis=0)
            dst_lon = np.expand_dims(dst_grid._lon['data'], axis=0)
        else:
            # 1D to 2D coordinates
            if len(dst_lon.shape) == 1:
                dst_lon, dst_lat = np.meshgrid(dst_lon, dst_lat)

        # calculate N nearest neighbour inverse distance weights (and indices)
        # from gridcells centres of model 1 to each gridcell centre of model 2
        # model geographic longitude/latitude coordinates are first converted
        # to cartesian ECEF (Earth Centred, Earth Fixed) coordinates, before
        # calculating distances.

        # src_mod_xy = lon_lat_to_cartesian(src_lon.flatten(), src_lat.flatten())
        # dst_mod_xy = lon_lat_to_cartesian(dst_lon.flatten(), dst_lat.flatten())

        src_mod_xy = lon_lat_to_cartesian_ecef(src_lon.flatten(), src_lat.flatten())
        dst_mod_xy = lon_lat_to_cartesian_ecef(dst_lon.flatten(), dst_lat.flatten())

        # generate KDtree using model 1 coordinates (i.e. the model grid you are
        # interpolating from)
        src_tree = spatial.cKDTree(src_mod_xy)

        # get n-neighbour nearest distances/indices (ravel form) of model 1 gridcell
        # centres from each model 2 gridcell centre

        dists, idx = src_tree.query(dst_mod_xy, k=n_neighbours)
        # self.nearest_neighbour_inds = \
        #     np.column_stack(np.unravel_index(idx, lon.shape))

        weight_matrix = dst_grid.copy()
        weight_matrix.time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)]
        weight_matrix._time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)]
        weight_matrix._time_bnds = None
        weight_matrix.time_bnds = None
        weight_matrix.last_level = None
        weight_matrix.first_level = 0
        weight_matrix.hours_start = 0
        weight_matrix.hours_end = 0

        weight_matrix.set_communicator(MPI.COMM_SELF)
        # take the reciprocals of the nearest neighbours distances
        dists[dists < 1] = 1
        inverse_dists = np.reciprocal(dists)

        inverse_dists_transf = inverse_dists.T.reshape((1, n_neighbours, dst_lon.shape[0], dst_lon.shape[1]))
        weight_matrix.variables['weight'] = {'data': inverse_dists_transf, 'units': 'm'}
        idx_transf = idx.T.reshape((1, n_neighbours, dst_lon.shape[0], dst_lon.shape[1]))
        weight_matrix.variables['idx'] = {'data': idx_transf, 'units': ''}
        weight_matrix.lev = {'data': np.arange(inverse_dists_transf.shape[1]), 'units': ''}
        weight_matrix._lev = {'data': np.arange(inverse_dists_transf.shape[1]), 'units': ''}
        if wm_path is not None:
            weight_matrix.to_netcdf(wm_path)

        return weight_matrix

    def create_area_conservative_weight_matrix(self, dst_nes, wm_path=None, flux=False, info=False):
        """
        To create the weight matrix with the area conservative method.

        Parameters
        ----------
        self : nes.Nes
            Source projection Nes Object.
        dst_nes : nes.Nes
            Final projection Nes object.
        wm_path : str
            Path where write the weight matrix.
        flux : bool
            Indicates if you want to calculate the weight matrix for flux variables.
        info: bool
            Indicates if you want to print extra info during the methods process.

        Returns
        -------
        nes.Nes
            Weight matrix.
        """

        if info and self.master:
            print("\tCreating area conservative Weight Matrix")
            sys.stdout.flush()

        my_crs = pyproj.CRS.from_proj4("+proj=latlon")  # Common projection for both shapefiles

        # Get a portion of the destiny grid
        if dst_nes.shapefile is None:
            dst_nes.create_shapefile()
        dst_grid = copy.deepcopy(dst_nes.shapefile)

        # Formatting Destination grid
        dst_grid.to_crs(crs=my_crs, inplace=True)
        dst_grid['FID_dst'] = dst_grid.index

        # Preparing Source grid
        if self.shapefile is None:
            self.create_shapefile()
        src_grid = copy.deepcopy(self.shapefile)

        # Formatting Source grid
        src_grid.to_crs(crs=my_crs, inplace=True)

        # Serialize index intersection function to avoid memory problems
        if self.size > 1 and self.parallel_method != 'T':
            src_grid = self.comm.gather(src_grid, root=0)
            dst_grid = self.comm.gather(dst_grid, root=0)
            if self.master:
                src_grid = pd.concat(src_grid)
                dst_grid = pd.concat(dst_grid)
        if self.master:
            src_grid['FID_src'] = src_grid.index
            src_grid = src_grid.reset_index()
            dst_grid = dst_grid.reset_index()
            fid_src, fid_dst = dst_grid.sindex.query_bulk(src_grid.geometry, predicate='intersects')

            # Calculate intersected areas and fractions
            intersection_df = pd.DataFrame(columns=["FID_src", "FID_dst"])

            intersection_df['FID_src'] = np.array(src_grid.loc[fid_src, 'FID_src'], dtype=np.uint32)
            intersection_df['FID_dst'] = np.array(dst_grid.loc[fid_dst, 'FID_dst'], dtype=np.uint32)

            intersection_df['geometry_src'] = src_grid.loc[fid_src, 'geometry'].values
            intersection_df['geometry_dst'] = dst_grid.loc[fid_dst, 'geometry'].values
            del src_grid, dst_grid, fid_src, fid_dst
            # Split the array into smaller arrays in order to scatter the data among the processes
            intersection_df = np.array_split(intersection_df, self.size)
        else:
            intersection_df = None

        intersection_df = self.comm.scatter(intersection_df, root=0)

        if info and self.master:
            print("\t\tGrids created and ready to interpolate")
            sys.stdout.flush()
        if True:
            # No Warnings Zone
            warnings.filterwarnings('ignore')
            # intersection_df['weight'] = np.array(intersection_df.apply(
            #     lambda x: x['geometry_src'].intersection(x['geometry_dst']).buffer(0).area / x['geometry_src'].area,
            #     axis=1), dtype=np.float64)
            if flux:
                intersection_df['weight'] = np.array(intersection_df.apply(
                    lambda x: (x['geometry_src'].intersection(x['geometry_dst']).buffer(0).area / x['geometry_src'].area) *
                              (nes.Nes.calculate_geometry_area([x['geometry_src']])[0] /
                               nes.Nes.calculate_geometry_area([x['geometry_dst']])[0]),
                    axis=1), dtype=np.float64)
            else:
                intersection_df['weight'] = np.array(intersection_df.apply(
                    lambda x: x['geometry_src'].intersection(x['geometry_dst']).buffer(0).area / x['geometry_src'].area,
                    axis=1), dtype=np.float64)

            intersection_df.drop(columns=["geometry_src", "geometry_dst"], inplace=True)
            gc.collect()
            warnings.filterwarnings('default')

        # Format & Clean
        if info and self.master:
            print("\t\tWeights calculated. Formatting weight matrix.")
            sys.stdout.flush()

        # Initialising weight matrix
        if self.parallel_method != 'T':
            intersection_df = self.comm.gather(intersection_df, root=0)
        if self.master:
            if self.parallel_method != 'T':
                intersection_df = pd.concat(intersection_df)
            intersection_df = intersection_df.set_index(['FID_dst', intersection_df.groupby('FID_dst').cumcount()]).rename_axis(
                ('FID', 'level')).sort_index()
            intersection_df.rename(columns={"FID_src": "idx"}, inplace=True)
            weight_matrix = dst_nes.copy()
            weight_matrix.time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)]
            weight_matrix._time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)]
            weight_matrix._time_bnds = None
            weight_matrix.time_bnds = None
            weight_matrix.last_level = None
            weight_matrix.first_level = 0
            weight_matrix.hours_start = 0
            weight_matrix.hours_end = 0

            weight_matrix.set_communicator(MPI.COMM_SELF)

            weight_matrix.set_levels({'data': np.arange(intersection_df.index.get_level_values('level').max() + 1),
                                      'dimensions': ('lev',),
                                      'units': '',
                                      'positive': 'up'})

            # Creating Weight matrix empty variables
            if len(weight_matrix._lat['data'].shape) == 1:
                shape = (1, len(weight_matrix.lev['data']),
                         weight_matrix._lat['data'].shape[0], weight_matrix._lon['data'].shape[0],)
                shape_flat = (1, len(weight_matrix.lev['data']),
                              weight_matrix._lat['data'].shape[0] * weight_matrix._lon['data'].shape[0],)
            else:
                shape = (1, len(weight_matrix.lev['data']),
                         weight_matrix._lat['data'].shape[0], weight_matrix._lat['data'].shape[1],)
                shape_flat = (1, len(weight_matrix.lev['data']),
                              weight_matrix._lat['data'].shape[0] * weight_matrix._lat['data'].shape[1],)

            weight_matrix.variables['weight'] = {'data': np.empty(shape_flat), 'units': '-'}
            weight_matrix.variables['weight']['data'][:] = -999
            weight_matrix.variables['idx'] = {'data': np.empty(shape_flat), 'units': '-'}
            weight_matrix.variables['idx']['data'][:] = -999

            # Filling Weight matrix variables
            for aux_lev in weight_matrix.lev['data']:
                aux_data = intersection_df.xs(level='level', key=aux_lev)
                weight_matrix.variables['weight']['data'][0, aux_lev, aux_data.index] = aux_data.loc[:, 'weight'].values
                weight_matrix.variables['idx']['data'][0, aux_lev, aux_data.index] = aux_data.loc[:, 'idx'].values
            # Re-shaping
            weight_matrix.variables['weight']['data'] = weight_matrix.variables['weight']['data'].reshape(shape)
            weight_matrix.variables['idx']['data'] = weight_matrix.variables['idx']['data'].reshape(shape)
            if wm_path is not None:
                if info and self.master:
                    print("\t\tWeight matrix saved at {0}".format(wm_path))
                    sys.stdout.flush()
                weight_matrix.to_netcdf(wm_path)
        else:
            weight_matrix = True
        return weight_matrix

    def lon_lat_to_cartesian(lon, lat, radius=6378137.0):
        """
        Calculate lon, lat coordinates of a point on a sphere.

        DEPRECATED!!!!

        Parameters
        ----------
        lon : np.array
            Longitude values.
        lat : np.array
            Latitude values.
        radius : float
            Radius of the sphere to get the distances.
        """

        lon_r = np.radians(lon)
        lat_r = np.radians(lat)

        x = radius * np.cos(lat_r) * np.cos(lon_r)
        y = radius * np.cos(lat_r) * np.sin(lon_r)
        z = radius * np.sin(lat_r)

        return np.column_stack([x, y, z])

    def lon_lat_to_cartesian_ecef(lon, lat):
        """
        Convert observational/model geographic longitude/latitude coordinates to cartesian ECEF (Earth Centred,
        Earth Fixed) coordinates, assuming WGS84 datum and ellipsoid, and that all heights = 0.
        ECEF coordiantes represent positions (in meters) as X, Y, Z coordinates, approximating the earth surface
        as an ellipsoid of revolution.
        This conversion is for the subsequent calculation of euclidean distances of the model gridcell centres
        from each observational station.
        Defining the distance between two points on the earth's surface as simply the euclidean distance
        between the two lat/lon pairs could lead to inaccurate results depending on the distance
        between two points (i.e. 1 deg. of longitude varies with latitude).

        Parameters
        ----------
        lon : np.array
            Longitude values.
        lat : np.array
            Latitude values.
        """

        lla = pyproj.Proj(proj='latlong', ellps='WGS84', datum='WGS84')
        ecef = pyproj.Proj(proj='geocent', ellps='WGS84', datum='WGS84')

        x, y, z = pyproj.transform(lla, ecef, lon, lat, np.zeros(lon.shape), radians=False)

        return np.column_stack([x, y, z])