# Source code for nes.nc_projections.rotated_nes

    #!/usr/bin/env python

    from numpy import (float64, linspace, cos, sin, arcsin, arctan2, array, mean, diff, append, flip, repeat, concatenate,
                       vstack)
    from math import pi
    from geopandas import GeoDataFrame
    from pandas import Index
    from pyproj import Proj
    from copy import deepcopy
    from typing import Dict, Any
    from shapely.geometry import Polygon, Point
    from .default_nes import Nes

    class RotatedNes(Nes):
        """

        Attributes
        ----------
        _full_rlat : dict
            Rotated latitudes dictionary with the complete "data" key for all the values and the rest of the attributes.
        _full_rlon : dict
            Rotated longitudes dictionary with the complete "data" key for all the values and the rest of the attributes.
        rlat : dict
            Rotated latitudes dictionary with the portion of "data" corresponding to the rank values.
        rlon : dict
            Rotated longitudes dictionary with the portion of "data" corresponding to the rank values.
        _var_dim : tuple
            A Tuple with the name of the Y and X dimensions for the variables.
            ("rlat", "rlon") for a rotated projection.
        _lat_dim : tuple
            A Tuple with the name of the dimensions of the Latitude values.
            ("rlat", "rlon") for a rotated projection.
        _lon_dim : tuple
            A Tuple with the name of the dimensions of the Longitude values.
            ("rlat", "rlon") for a rotated projection.
        """
        def __init__(self, comm=None, path=None, info=False, dataset=None, parallel_method="Y",
                     avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, create_nes=False,
                     balanced=False, times=None, **kwargs):
            """
            Initialize the RotatedNes 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.
            parallel_method : str
                Indicates the parallelization method that you want. Default: "Y".
                Accepted values: ["X", "Y", "T"].
            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.
            """
            self._full_rlat = None
            self._full_rlon = None

            super(RotatedNes, self).__init__(comm=comm, path=path,
                                             info=info, dataset=dataset, balanced=balanced,
                                             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._full_rlat, self._full_rlon = self._create_rotated_coordinates()
                # Dimensions screening
                self.lat = self._get_coordinate_values(self.get_full_latitudes(), "Y")
                self.lon = self._get_coordinate_values(self.get_full_longitudes(), "X")
            else:
                # Complete dimensions
                self._full_rlat = self._get_coordinate_dimension("rlat")
                self._full_rlon = self._get_coordinate_dimension("rlon")

            # Dimensions screening
            self.rlat = self._get_coordinate_values(self.get_full_rlat(), "Y")
            self.rlon = self._get_coordinate_values(self.get_full_rlon(), "X")

            # Set axis limits for parallel writing
            self.write_axis_limits = self._get_write_axis_limits()

            self._var_dim = ("rlat", "rlon")
            self._lat_dim = ("rlat", "rlon")
            self._lon_dim = ("rlat", "rlon")

        @staticmethod
        def new(comm=None, path=None, info=False, dataset=None, parallel_method="Y",
                avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, create_nes=False,
                balanced=False, times=None, **kwargs):
            """
            Initialize the Nes 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.
            parallel_method : str
                Indicates the parallelization method that you want. Default over Y axis
                accepted values: ["X", "Y", "T"].
            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 or 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[datetime] or None
                List of times to substitute the current ones while creation.
            """

            new = RotatedNes(comm=comm, path=path, info=info, dataset=dataset,
                             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 get_full_rlat(self) -> Dict[str, Any]:
            """
            Retrieve the complete rotated latitude information.

            Returns
            -------
            Dict[str, Any]
                A dictionary containing the complete latitude data and its attributes.
                The dictionary structure is:
                {
                    "data": ndarray,  # Array of latitude values.
                    attr_name: attr_value,  # Latitude attributes.
                    ...
                }
            """
            data = self.comm.bcast(self._full_rlat)

            return data

        def get_full_rlon(self) -> Dict[str, Any]:
            """
            Retrieve the complete rotated longitude information.

            Returns
            -------
            Dict[str, Any]
                A dictionary containing the complete longitude data and its attributes.
                The dictionary structure is:
                {
                    "data": ndarray,  # Array of longitude values.
                    attr_name: attr_value,  # Longitude attributes.
                    ...
                }
            """
            data = self.comm.bcast(self._full_rlon)
            return data

        def set_full_rlat(self, data: Dict[str, Any]) -> None:
            """
            Set the complete rotated latitude information.

            Parameters
            ----------
            data : Dict[str, Any]
                A dictionary containing the complete latitude data and its attributes.
                The dictionary structure is:
                {
                    "data": ndarray,  # Array of latitude values.
                    attr_name: attr_value,  # Latitude attributes.
                    ...
                }
            """
            if self.master:
                self._full_rlat = data
            return None

        def set_full_rlon(self, data: Dict[str, Any]) -> None:
            """
            Set the complete rotated longitude information.

            Parameters
            ----------
            data : Dict[str, Any]
                A dictionary containing the complete longitude data and its attributes.
                The dictionary structure is:
                {
                    "data": ndarray,  # Array of longitude values.
                    attr_name: attr_value,  # Longitude attributes.
                    ...
                }
            """
            if self.master:
                self._full_rlon = data
            return None

        # noinspection DuplicatedCode
        def _filter_coordinates_selection(self):
            """
            Use the selection limits to filter rlat, rlon, time, lev, lat, lon, lon_bnds and lat_bnds.
            """

            idx = self._get_idx_intervals()

            full_rlat = self.get_full_rlat()
            full_rlon = self.get_full_rlon()

            self.rlat = self._get_coordinate_values(full_rlat, "Y")
            self.rlon = self._get_coordinate_values(full_rlon, "X")

            if self.master:
                self.set_full_rlat({'data': full_rlat["data"][idx["idx_y_min"]:idx["idx_y_max"]]})
                self.set_full_rlon({'data': full_rlon["data"][idx["idx_x_min"]:idx["idx_x_max"]]})

            super(RotatedNes, self)._filter_coordinates_selection()

            return None

        def _get_pyproj_projection(self):
            """
            Get projection data as in Pyproj library.

            Returns
            ----------
            projection : pyproj.Proj
                Grid projection.
            """

            projection = Proj(proj="ob_tran",
                              o_proj="longlat",
                              ellps="WGS84",
                              R=self.earth_radius[0],
                              o_lat_p=float64(self.projection_data["grid_north_pole_latitude"]),
                              o_lon_p=float64(self.projection_data["grid_north_pole_longitude"]),
                              )

            return projection

        # noinspection DuplicatedCode
        def _get_projection_data(self, create_nes, **kwargs):
            """
            Retrieves projection data based on grid details.

            Parameters
            ----------
            create_nes : bool
                Flag indicating whether to create new object (True) or use existing (False).
            **kwargs : dict
                Additional keyword arguments for specifying projection details.
            """
            if create_nes:
                projection_data = {"grid_mapping_name": "rotated_latitude_longitude",
                                   "grid_north_pole_latitude": 90 - kwargs["centre_lat"],
                                   "grid_north_pole_longitude": -180 + kwargs["centre_lon"],
                                   "inc_rlat": kwargs["inc_rlat"],
                                   "inc_rlon": kwargs["inc_rlon"],
                                   "south_boundary": kwargs["south_boundary"],
                                   "west_boundary": kwargs["west_boundary"],
                                   }
            else:
                if "rotated_pole" in self.variables.keys():
                    projection_data = self.variables["rotated_pole"]
                    self.free_vars("rotated_pole")
                else:
                    msg = "There is no variable called rotated_pole, projection has not been defined."
                    raise RuntimeError(msg)

                if "dtype" in projection_data.keys():
                    del projection_data["dtype"]

                if "data" in projection_data.keys():
                    del projection_data["data"]

                if "dimensions" in projection_data.keys():
                    del projection_data["dimensions"]

            return projection_data

        def _create_dimensions(self, netcdf):
            """
            Create "rlat", "rlon"  and "spatial_nv" dimensions and the dimensions "lev", "time", "time_nv", "lon" and "lat".

            Parameters
            ----------
            netcdf : Dataset
                NetCDF object.
            """

            super(RotatedNes, self)._create_dimensions(netcdf)

            shape = self.get_full_shape()
            # Create rlat and rlon dimensions
            netcdf.createDimension("rlon", shape[1])
            netcdf.createDimension("rlat", shape[0])

            # Create spatial_nv (number of vertices) dimension
            if (self.lat_bnds is not None) and (self.lon_bnds is not None):
                netcdf.createDimension("spatial_nv", 4)
                pass

            return None

        def _create_dimension_variables(self, netcdf):
            """
            Create the "rlat" and "rlon" variables.

            Parameters
            ----------
            netcdf : Dataset
                NetCDF object.
            """
            super(RotatedNes, self)._create_dimension_variables(netcdf)

            # ROTATED LATITUDES
            full_rlat = self.get_full_rlat()
            rlat = netcdf.createVariable("rlat", full_rlat["data"].dtype, ("rlat",))
            rlat.long_name = "latitude in rotated pole grid"
            if "units" in full_rlat.keys():
                rlat.units = full_rlat["units"]
            else:
                rlat.units = "degrees"
            rlat.standard_name = "grid_latitude"
            if self.size > 1:
                rlat.set_collective(True)
            rlat[:] = full_rlat["data"]

            # ROTATED LONGITUDES
            full_rlon = self.get_full_rlon()
            rlon = netcdf.createVariable("rlon", full_rlon["data"].dtype, ("rlon",))
            rlon.long_name = "longitude in rotated pole grid"
            if "units" in full_rlon.keys():
                rlon.units = full_rlon["units"]
            else:
                rlon.units = "degrees"
            rlon.standard_name = "grid_longitude"
            if self.size > 1:
                rlon.set_collective(True)
            rlon[:] = full_rlon["data"]

            return None

        def _create_rotated_coordinates(self):
            """
            Calculate rotated latitudes and longitudes from grid details.

            Returns
            ----------
            _rlat : dict
                Rotated latitudes dictionary with the "data" key for all the values and the rest of the attributes.
            _rlon : dict
                Rotated longitudes dictionary with the "data" key for all the values and the rest of the attributes.
            """
            # Get grid resolution
            inc_rlon = float64(self.projection_data["inc_rlon"])
            inc_rlat = float64(self.projection_data["inc_rlat"])

            # Get south and west boundaries
            south_boundary = float64(self.projection_data["south_boundary"])
            west_boundary = float64(self.projection_data["west_boundary"])

            # Calculate rotated latitudes
            n_lat = int((abs(south_boundary) / inc_rlat) * 2 + 1)
            rlat = linspace(south_boundary, south_boundary + (inc_rlat * (n_lat - 1)), n_lat, dtype=float64)

            # Calculate rotated longitudes
            n_lon = int((abs(west_boundary) / inc_rlon) * 2 + 1)
            rlon = linspace(west_boundary, west_boundary + (inc_rlon * (n_lon - 1)), n_lon, dtype=float64)

            return {"data": rlat}, {"data": rlon}

        def rotated2latlon(self, lon_deg, lat_deg, lon_min=-180):
            """
            Calculate the unrotated coordinates using the rotated ones.

            Parameters
            ----------
            lon_deg : array
                Rotated longitude coordinate.
            lat_deg : array
                Rotated latitude coordinate.
            lon_min : float
                Minimum value for the longitudes: -180 (-180 to 180) or 0 (0 to 360).

            Returns
            ----------
            almd : array
                Unrotated longitudes.
            aphd : array
                Unrotated latitudes.
            """

            # Get centre coordinates
            centre_lat = 90 - float64(self.projection_data["grid_north_pole_latitude"])
            centre_lon = float64(self.projection_data["grid_north_pole_longitude"]) + 180

            # Convert to radians
            degrees_to_radians = pi / 180.
            tph0 = centre_lat * degrees_to_radians
            tlm = lon_deg * degrees_to_radians
            tph = lat_deg * degrees_to_radians

            tlm0d = -180 + centre_lon
            ctph0 = cos(tph0)
            stph0 = sin(tph0)
            stlm = sin(tlm)
            ctlm = cos(tlm)
            stph = sin(tph)
            ctph = cos(tph)

            # Calculate unrotated latitudes
            sph = (ctph0 * stph) + (stph0 * ctph * ctlm)
            sph[sph > 1.] = 1.
            sph[sph < -1.] = -1.
            aph = arcsin(sph)
            aphd = aph / degrees_to_radians

            # Calculate rotated longitudes
            anum = ctph * stlm
            denom = (ctlm * ctph - stph0 * sph) / ctph0
            relm = arctan2(anum, denom) - pi
            almd = relm / degrees_to_radians + tlm0d
            almd[almd > (lon_min + 360)] -= 360
            almd[almd < lon_min] += 360

            return almd, aphd

        def _create_centre_coordinates(self, **kwargs):
            """
            Calculate centre latitudes and longitudes from grid details.

            Returns
            ----------
            centre_lat : dict
                Dictionary with data of centre coordinates for latitude in 2D (latitude, longitude).
            centre_lon : dict
                Dictionary with data of centre coordinates for longitude in 2D (latitude, longitude).
            """
            if self.master:
                # Complete dimensions
                self._full_rlat, self._full_rlon = self._create_rotated_coordinates()

                # Calculate centre latitudes and longitudes (1D to 2D)
                centre_lon, centre_lat = self.rotated2latlon(
                    array([self._full_rlon["data"]] * len(self._full_rlat["data"])),
                    array([self._full_rlat["data"]] * len(self._full_rlon["data"])).T)

                return {"data": centre_lat}, {"data": centre_lon}
            else:
                return None, None

        def create_providentia_exp_centre_coordinates(self):
            """
            Calculate centre latitudes and longitudes from original coordinates and store as 2D arrays.

            Returns
            ----------
            model_centre_lat : dict
                Dictionary with data of centre coordinates for latitude in 2D (latitude, longitude).
            model_centre_lon : dict
                Dictionary with data of centre coordinates for longitude in 2D (latitude, longitude).
            """

            # Get centre latitudes
            model_centre_lat = self.lat

            # Get centre longitudes
            model_centre_lon = self.lon

            return model_centre_lat, model_centre_lon

        # noinspection DuplicatedCode

        def create_providentia_exp_grid_edge_coordinates(self):
            """
            Calculate grid edge latitudes and longitudes and get model grid outline.

            Returns
            ----------
            grid_edge_lat : dict
                Dictionary with data of grid edge latitudes.
            grid_edge_lon : dict
                Dictionary with data of grid edge longitudes.
            """

            # Get grid resolution
            inc_rlon = abs(mean(diff(self.rlon["data"])))
            inc_rlat = abs(mean(diff(self.rlat["data"])))

            # Get bounds for rotated coordinates
            rlat_bounds = self._create_single_spatial_bounds(self.rlat["data"], inc_rlat)
            rlon_bounds = self._create_single_spatial_bounds(self.rlon["data"], inc_rlon)

            # Get rotated latitudes for grid edge
            left_edge_rlat = append(rlat_bounds.flatten()[::2], rlat_bounds.flatten()[-1])
            right_edge_rlat = flip(left_edge_rlat, 0)
            top_edge_rlat = repeat(rlat_bounds[-1][-1], len(self.rlon["data"]) - 1)
            bottom_edge_rlat = repeat(rlat_bounds[0][0], len(self.rlon["data"]))
            rlat_grid_edge = concatenate((left_edge_rlat, top_edge_rlat, right_edge_rlat, bottom_edge_rlat))

            # Get rotated longitudes for grid edge
            left_edge_rlon = repeat(rlon_bounds[0][0], len(self.rlat["data"]) + 1)
            top_edge_rlon = rlon_bounds.flatten()[1:-1:2]
            right_edge_rlon = repeat(rlon_bounds[-1][-1], len(self.rlat["data"]) + 1)
            bottom_edge_rlon = flip(rlon_bounds.flatten()[:-1:2], 0)
            rlon_grid_edge = concatenate((left_edge_rlon, top_edge_rlon, right_edge_rlon, bottom_edge_rlon))

            # Get edges for regular coordinates
            grid_edge_lon_data, grid_edge_lat_data = self.rotated2latlon(rlon_grid_edge, rlat_grid_edge)

            # Create grid outline by stacking the edges in both coordinates
            model_grid_outline = vstack((grid_edge_lon_data, grid_edge_lat_data)).T

            grid_edge_lat = {"data": model_grid_outline[:, 1]}
            grid_edge_lon = {"data": model_grid_outline[:, 0]}

            return grid_edge_lat, grid_edge_lon

        # noinspection DuplicatedCode

        def create_spatial_bounds(self):
            """
            Calculate longitude and latitude bounds and set them.
            """

            # Calculate rotated coordinates bounds
            full_rlat = self.get_full_rlat()
            full_rlon = self.get_full_rlon()
            inc_rlat = abs(mean(diff(full_rlat["data"])))
            rlat_bnds = self._create_single_spatial_bounds(array([full_rlat["data"]] * len(full_rlon["data"])).T,
                                                           inc_rlat, spatial_nv=4, inverse=True)

            inc_rlon = abs(mean(diff(full_rlon["data"])))
            rlon_bnds = self._create_single_spatial_bounds(array([full_rlon["data"]] * len(full_rlat["data"])),
                                                           inc_rlon, spatial_nv=4)

            # Transform rotated bounds to regular bounds
            lon_bnds, lat_bnds = self.rotated2latlon(rlon_bnds, rlat_bnds)

            # Obtain regular coordinates bounds
            self.set_full_latitudes_boundaries({"data": deepcopy(lat_bnds)})
            self.lat_bnds = {"data": lat_bnds[self.write_axis_limits["y_min"]:self.write_axis_limits["y_max"],
                                              self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"],
                                              :]}

            self.set_full_longitudes_boundaries({"data": deepcopy(lon_bnds)})
            self.lon_bnds = {"data": lon_bnds[self.write_axis_limits["y_min"]:self.write_axis_limits["y_max"],
                                              self.write_axis_limits["x_min"]:self.write_axis_limits["x_max"],
                                              :]}

            return None

        @staticmethod
        def _set_var_crs(var):
            """
            Set the grid_mapping to "rotated_pole".

            Parameters
            ----------
            var : Variable
                netCDF4-python variable object.
            """

            var.grid_mapping = "rotated_pole"
            var.coordinates = "lat lon"

            return None

        def _create_metadata(self, netcdf):
            """
            Create the "crs" variable for the rotated latitude longitude grid_mapping.

            Parameters
            ----------
            netcdf : Dataset
                netcdf4-python Dataset.
            """

            if self.projection_data is not None:
                mapping = netcdf.createVariable("rotated_pole", "i")
                mapping.grid_mapping_name = self.projection_data["grid_mapping_name"]
                mapping.grid_north_pole_latitude = self.projection_data["grid_north_pole_latitude"]
                mapping.grid_north_pole_longitude = self.projection_data["grid_north_pole_longitude"]

            return None

        def to_grib2(self, path, grib_keys, grib_template_path, lat_flip=False, info=False):
            """
            Write output file with grib2 format.

            Parameters
            ----------
            lat_flip : bool
                Indicates if you want to flip the latitude coordinates.
            path : str
                Path to the output file.
            grib_keys : dict
                Dictionary with the grib2 keys.
            grib_template_path : str
                Path to the grib2 file to use as template.
            info : bool
                Indicates if you want to print extra information during the process.
            """

            raise NotImplementedError("Grib2 format cannot be written in a Rotated pole projection.")

        # noinspection DuplicatedCode

        def create_shapefile(self):
            """
            Create spatial geodataframe (shapefile).

            Returns
            -------
            shapefile : GeoPandasDataFrame
                Shapefile dataframe.
            """

            if self.shapefile is None:

                if self.lat_bnds is None or self.lon_bnds is None:
                    self.create_spatial_bounds()

                # Reshape arrays to create geometry
                aux_b_lats = self.lat_bnds["data"].reshape((self.lat_bnds["data"].shape[0] * self.lat_bnds["data"].shape[1],
                                                            self.lat_bnds["data"].shape[2]))
                aux_b_lons = self.lon_bnds["data"].reshape((self.lon_bnds["data"].shape[0] * self.lon_bnds["data"].shape[1],
                                                            self.lon_bnds["data"].shape[2]))

                # Get polygons from bounds
                geometry = []
                for i in range(aux_b_lons.shape[0]):
                    geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]),
                                             (aux_b_lons[i, 1], aux_b_lats[i, 1]),
                                             (aux_b_lons[i, 2], aux_b_lats[i, 2]),
                                             (aux_b_lons[i, 3], aux_b_lats[i, 3]),
                                             (aux_b_lons[i, 0], aux_b_lats[i, 0])]))

                # Create dataframe cointaining all polygons
                fids = self.get_fids()
                gdf = GeoDataFrame(index=Index(name="FID", data=fids.ravel()), geometry=geometry, crs="EPSG:4326")
                self.shapefile = gdf

            else:
                gdf = self.shapefile

            return gdf

        # noinspection DuplicatedCode

        def get_centroids_from_coordinates(self):
            """
            Get centroids from geographical coordinates.

            Returns
            -------
            centroids_gdf: GeoPandasDataFrame
                Centroids dataframe.
            """

            # Get centroids from coordinates
            centroids = []
            for lat_ind in range(0, self.lon["data"].shape[0]):
                for lon_ind in range(0, self.lon["data"].shape[1]):
                    centroids.append(Point(self.lon["data"][lat_ind, lon_ind],
                                           self.lat["data"][lat_ind, lon_ind]))

            # Create dataframe cointaining all points
            fids = self.get_fids()
            centroids_gdf = GeoDataFrame(index=Index(name="FID", data=fids.ravel()), geometry=centroids, crs="EPSG:4326")

            return centroids_gdf