# Source code for nes.methods.spatial_join

    #!/usr/bin/env python

    import sys
    from warnings import warn, filterwarnings
    from geopandas import sjoin_nearest, sjoin, read_file
    from pandas import DataFrame
    from numpy import array, uint32, nan
    from shapely.errors import TopologicalError

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

        Parameters
        ----------
        self : nes.Nes
            A Nes Object.
        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.
        apply_bbox : bool
            Indicates if you want to reduce the shapefile to a bbox.
        """

        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,
                                               apply_bbox=apply_bbox)

        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, apply_bbox=True):
        """
        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
            A Nes Object.
        ext_shp : geopandas.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.
        apply_bbox : bool
            Indicates if you want to reduce the shapefile to a bbox.

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

        if isinstance(ext_shp, str):
            # Reading external shapefile
            if self.master and info:
                print("\tReading external shapefile")
            # ext_shp = read_file(ext_shp, include_fields=var_list, mask=self.shapefile.geometry)
            if apply_bbox:
                ext_shp = read_file(ext_shp, include_fields=var_list, bbox=__get_bbox(self))
            else:
                ext_shp = read_file(ext_shp, include_fields=var_list)
        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."
            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
            A Nes Object.

        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

    # noinspection DuplicatedCode
    def __spatial_join_nearest(self, ext_shp, info=False):
        """
        Perform the spatial join using the nearest method.

        Parameters
        ----------
        self : nes.Nes
            A Nes Object.
        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 = 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] = array(self.shapefile.loc[:, var_name], dtype=ext_shp[var_name].dtype)

        return None

    # noinspection DuplicatedCode
    def __spatial_join_centroid(self, ext_shp, info=False):
        """
        Perform the spatial join using the centroid method.

        Parameters
        ----------
        self : nes.Nes
            A Nes Object.
        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 = 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] = 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
            A Nes Object.
        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(grid_shp.geometry, predicate="intersects")
        inp, res = grid_shp.sindex.query(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 = DataFrame(columns=["FID", "ext_shp_id", "weight"])
        intersection["FID"] = array(grid_shp.loc[res, "FID_grid"], dtype=uint32)
        intersection["ext_shp_id"] = array(inp, dtype=uint32)

        if len(intersection) > 0:
            if True:
                # No Warnings Zone
                counts = intersection["FID"].value_counts()
                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)

                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] = array(
                    ext_shp.loc[intersection["ext_shp_id"], var_name])

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

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

        return None