# Source code for nes.methods.spatial_join

    #!/usr/bin/env python

    import sys
    import warnings
    import geopandas as gpd
    from geopandas import GeoDataFrame
    import nes
    import numpy as np
    import pandas as pd
    from shapely.geos import TopologicalError

    def spatial_join(self, ext_shp, method=None, var_list=None, info=False):
        """
        Compute overlay intersection of two GeoPandasDataFrames.

        Parameters
        ----------
        self : nes.Nes
        ext_shp : GeoPandasDataFrame or str
            File or path from where the data will be obtained on the intersection.
        method : str
            Overlay method. Accepted values: ['nearest', 'intersection', 'centroid'].
        var_list : List or None or str
            Variables that will be included in the resulting shapefile.
        info : bool
            Indicates if you want to print the process info or no
        """

        if self.master and info:
            print("Starting spatial join")
        if isinstance(var_list, str):
            # Transforming string (variable name) to a list with length 0
            var_list = [var_list]

        # Create source shapefile if it does not exist
        if self.shapefile is None:
            if self.master and info:
                print("\tCreating shapefile")
                sys.stdout.flush()
            self.create_shapefile()

        ext_shp = prepare_external_shapefile(self, ext_shp=ext_shp, var_list=var_list, info=info)

        if method == 'nearest':
            # Nearest centroids to the shapefile polygons
            spatial_join_nearest(self, ext_shp=ext_shp, info=info)
        elif method == 'intersection':
            # Intersect the areas of the shapefile polygons, outside the shapefile there will be NaN
            spatial_join_intersection(self, ext_shp=ext_shp, info=info)
        elif method == 'centroid':
            # Centroids that fall on the shapefile polygons, outside the shapefile there will be NaN
            spatial_join_centroid(self, ext_shp=ext_shp, info=info)

        else:
            accepted_values = ['nearest', 'intersection', 'centroid']
            raise NotImplementedError('{0} is not implemented. Choose from: {1}'.format(method, accepted_values))

        return None

    def prepare_external_shapefile(self, ext_shp, var_list, info=False):
        """
        Prepare the external shapefile.

        It is high recommended to pass ext_shp parameter as string because it will clip the external shapefile to the rank.

        1. Read if it is not already read
        2. Filter variables list
        3. Standardize projections

        Parameters
        ----------
        self : nes.Nes
        ext_shp : GeoDataFrame or str
            External shapefile or path to it
        var_list : List[str] or None
            External shapefile variables to be computed
        info : bool
            Indicates if you want to print the information

        Returns
        -------
        GeoDataFrame
            External shapefile
        """

        if isinstance(ext_shp, str):
            # Reading external shapefile
            if self.master and info:
                print("\tReading external shapefile")
            # ext_shp = gpd.read_file(ext_shp, include_fields=var_list, mask=self.shapefile.geometry)
            ext_shp = gpd.read_file(ext_shp, include_fields=var_list, bbox=get_bbox(self))

        else:
            msg = "WARNING!!! "
            msg += "External shapefile already read. If you pass the path to the shapefile instead of the opened shapefile "
            msg += "a best usage of memory is performed because the external shape will be clipped while reading."
            warnings.warn(msg)
            sys.stderr.flush()
            ext_shp.reset_index(inplace=True)
            if var_list is not None:
                ext_shp = ext_shp.loc[:, var_list + ['geometry']]

        self.comm.Barrier()
        if self.master and info:
            print("\t\tReading external shapefile done!")

        # Standardizing projection
        ext_shp = ext_shp.to_crs(self.shapefile.crs)

        return ext_shp

    def get_bbox(self):
        """
        Obtain the bounding box of the rank data

        (lon_min, lat_min, lon_max, lat_max)

        Parameters
        ----------
        self : nes.Nes

        Returns
        -------
        tuple
            Bounding box
        """

        bbox = (self.lon_bnds['data'].min(), self.lat_bnds['data'].min(),
                self.lon_bnds['data'].max(), self.lat_bnds['data'].max(), )

        return bbox

    def spatial_join_nearest(self, ext_shp, info=False):
        """
        Perform the spatial join using the nearest method

        Parameters
        ----------
        self : nes.Nes
        ext_shp : GeoDataFrame
            External shapefile
        info : bool
            Indicates if you want to print the information
        """

        if self.master and info:
            print("\tNearest spatial joint")
            sys.stdout.flush()
        grid_shp = self.get_centroids_from_coordinates()

        # From geodetic coordinates (e.g. 4326) to meters (e.g. 4328) to use sjoin_nearest
        # TODO: Check if the projection 4328 does not distort the coordinates too much
        # https://gis.stackexchange.com/questions/372564/
        #   userwarning-when-trying-to-get-centroid-from-a-polygon-geopandas
        # ext_shp = ext_shp.to_crs('EPSG:4328')
        # grid_shp = grid_shp.to_crs('EPSG:4328')

        # Calculate spatial joint by distance
        aux_grid = gpd.sjoin_nearest(grid_shp, ext_shp, distance_col='distance')

        # Get data from closest shapes to centroids
        del aux_grid['geometry'], aux_grid['index_right']
        self.shapefile.loc[aux_grid.index, aux_grid.columns] = aux_grid

        var_list = list(ext_shp.columns)
        var_list.remove('geometry')
        for var_name in var_list:
            self.shapefile.loc[:, var_name] = np.array(self.shapefile.loc[:, var_name], dtype=ext_shp[var_name].dtype)

        return None

    def spatial_join_centroid(self, ext_shp, info=False):
        """
        Perform the spatial join using the centroid method

        Parameters
        ----------
        self : nes.Nes
        ext_shp : GeoDataFrame
            External shapefile
        info : bool
            Indicates if you want to print the information
        """

        if self.master and info:
            print("\tCentroid spatial join")
            sys.stdout.flush()
        if info and self.master:
            print("\t\tCalculating centroids")
            sys.stdout.flush()

        # Get centroids
        grid_shp = self.get_centroids_from_coordinates()

        # Calculate spatial joint
        if info and self.master:
            print("\t\tCalculating centroid spatial join")
            sys.stdout.flush()
        aux_grid = gpd.sjoin(grid_shp, ext_shp, predicate='within')

        # Get data from shapes where there are centroids, rest will be NaN
        del aux_grid['geometry'], aux_grid['index_right']
        self.shapefile.loc[aux_grid.index, aux_grid.columns] = aux_grid

        var_list = list(ext_shp.columns)
        var_list.remove('geometry')
        for var_name in var_list:
            self.shapefile.loc[:, var_name] = np.array(self.shapefile.loc[:, var_name], dtype=ext_shp[var_name].dtype)

        return None

    def spatial_join_intersection(self, ext_shp, info=False):
        """
        Perform the spatial join using the intersection method

        Parameters
        ----------
        self : nes.Nes
        ext_shp : GeoDataFrame
            External shapefile
        info : bool
            Indicates if you want to print the information
        """

        var_list = list(ext_shp.columns)
        var_list.remove('geometry')

        grid_shp = self.shapefile
        grid_shp['FID_grid'] = grid_shp.index
        grid_shp = grid_shp.reset_index()

        # Get intersected areas
        # inp, res = ext_shp.sindex.query_bulk(grid_shp.geometry, predicate='intersects')
        inp, res = grid_shp.sindex.query_bulk(ext_shp.geometry, predicate='intersects')

        if info:
            print('\t\tRank {0:03d}: {1} intersected areas found'.format(self.rank, len(inp)))
            sys.stdout.flush()

        # Calculate intersected areas and fractions
        intersection = pd.DataFrame(columns=['FID', 'ext_shp_id', 'weight'])
        intersection['FID'] = np.array(grid_shp.loc[res, 'FID_grid'], dtype=np.uint32)
        intersection['ext_shp_id'] = np.array(inp, dtype=np.uint32)

        if len(intersection) > 0:
            if True:
                # No Warnings Zone
                counts = intersection['FID'].value_counts()
                warnings.filterwarnings('ignore')
                intersection.loc[:, 'weight'] = 1.

                for i, row in intersection.iterrows():
                    if isinstance(i, int) and i % 1000 == 0 and info:
                        print('\t\t\tRank {0:03d}: {1:.3f} %'.format(self.rank, i * 100 / len(intersection)))
                        sys.stdout.flush()
                    # Filter to do not calculate percentages over 100% grid cells spatial joint
                    if counts[row['FID']] > 1:
                        try:
                            intersection.loc[i, 'weight'] = grid_shp.loc[res[i], 'geometry'].intersection(
                                ext_shp.loc[inp[i], 'geometry']).area / grid_shp.loc[res[i], 'geometry'].area
                        except TopologicalError:
                            # If for some reason the geometry is corrupted it should work with the buffer function
                            ext_shp.loc[[inp[i]], 'geometry'] = ext_shp.loc[[inp[i]], 'geometry'].buffer(0)
                            intersection.loc[i, 'weight'] = grid_shp.loc[res[i], 'geometry'].intersection(
                                ext_shp.loc[inp[i], 'geometry']).area / grid_shp.loc[res[i], 'geometry'].area
                # intersection['intersect_area'] = intersection.apply(
                #     lambda x: x['geometry_grid'].intersection(x['geometry_ext']).area, axis=1)
                intersection.drop(intersection[intersection['weight'] <= 0].index, inplace=True)

                warnings.filterwarnings('default')

            # Choose the biggest area from intersected areas with multiple options
            intersection.sort_values('weight', ascending=False, inplace=True)
            intersection = intersection.drop_duplicates(subset='FID', keep="first")
            intersection = intersection.sort_values('FID').set_index('FID')

            for var_name in var_list:
                self.shapefile.loc[intersection.index, var_name] = np.array(
                    ext_shp.loc[intersection['ext_shp_id'], var_name])

        else:
            for var_name in var_list:
                self.shapefile.loc[:, var_name] = np.nan

        for var_name in var_list:
            self.shapefile.loc[:, var_name] = np.array(self.shapefile.loc[:, var_name], dtype=ext_shp[var_name].dtype)

        return None