# Source code for nes.nc_projections.lcc_nes

    #!/usr/bin/env python

    import warnings
    import sys
    import numpy as np
    import pandas as pd
    from cfunits import Units
    from pyproj import Proj
    from copy import deepcopy
    import geopandas as gpd
    from shapely.geometry import Polygon, Point
    from .default_nes import Nes

    class LCCNes(Nes):
        """

        Attributes
        ----------
        _y : dict
            Y coordinates dictionary with the complete 'data' key for all the values and the rest of the attributes.
        _x : dict
            X coordinates dictionary with the complete 'data' key for all the values and the rest of the attributes.
        y : dict
            Y coordinates dictionary with the portion of 'data' corresponding to the rank values.
        x : dict
            X coordinates dictionary with the portion of 'data' corresponding to the rank values.
        _var_dim : tuple
            Tuple with the name of the Y and X dimensions for the variables.
            ('y', 'x',) for a LCC projection.
        _lat_dim : tuple
            Tuple with the name of the dimensions of the Latitude values.
            ('y', 'x',) for a LCC projection.
        _lon_dim : tuple
            Tuple with the name of the dimensions of the Longitude values.
            ('y', 'x') for a LCC projection.
        """
        def __init__(self, comm=None, path=None, info=False, dataset=None, xarray=False, 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 LCCNes 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: '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.
            """

            super(LCCNes, self).__init__(comm=comm, path=path, info=info, dataset=dataset,
                                         xarray=xarray, parallel_method=parallel_method, balanced=balanced,
                                         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:
                # Dimensions screening
                self.lat = self._get_coordinate_values(self._lat, 'Y')
                self.lon = self._get_coordinate_values(self._lon, 'X')
            else:
                # Complete dimensions
                self._y = self._get_coordinate_dimension('y')
                self._x = self._get_coordinate_dimension('x')

            # Dimensions screening
            self.y = self._get_coordinate_values(self._y, 'Y')
            self.x = self._get_coordinate_values(self._x, 'X')

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

            self._var_dim = ('y', 'x')
            self._lat_dim = ('y', 'x')
            self._lon_dim = ('y', 'x')

            self.free_vars('crs')

        @staticmethod
        def new(comm=None, path=None, info=False, dataset=None, xarray=False, 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.
            xarray: bool:
                (Not working) Indicates if you want to use xarray as default.
            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.
            """

            new = LCCNes(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, **kwargs)

            return new

        def filter_coordinates_selection(self):
            """
            Use the selection limits to filter y, x, time, lev, lat, lon, lon_bnds and lat_bnds.
            """

            idx = self.get_idx_intervals()

            self.y = self._get_coordinate_values(self._y, 'Y')
            self.x = self._get_coordinate_values(self._x, 'X')

            self._y['data'] = self._y['data'][idx['idx_y_min']:idx['idx_y_max']]
            self._x['data'] = self._x['data'][idx['idx_x_min']:idx['idx_x_max']]

            super(LCCNes, 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='lcc',
                              ellps='WGS84',
                              R=self.earth_radius[0],
                              lat_1=np.float64(self.projection_data['standard_parallel'][0]),
                              lat_2=np.float64(self.projection_data['standard_parallel'][1]),
                              lon_0=np.float64(self.projection_data['longitude_of_central_meridian']),
                              lat_0=np.float64(self.projection_data['latitude_of_projection_origin']),
                              to_meter=1,
                              x_0=0,
                              y_0=0,
                              a=self.earth_radius[1],
                              k_0=1.0,
                             )

            return projection

        def _get_projection(self):
            """
            Get 'projection' and 'projection_data' from grid details.
            """

            if 'Lambert_Conformal' in self.variables.keys():
                projection_data = self.variables['Lambert_Conformal']
                self.free_vars('Lambert_Conformal')
            elif 'Lambert_conformal' in self.variables.keys():
                projection_data = self.variables['Lambert_conformal']
                self.free_vars('Lambert_conformal')
            else:
                # We will never have this condition since the LCC grid will never be correctly detected
                # since the function __is_lcc in load_nes only detects LCC grids when there is Lambert_conformal
                msg = 'There is no variable called Lambert_Conformal, 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']

            if isinstance(projection_data['standard_parallel'], str):
                projection_data['standard_parallel'] = [projection_data['standard_parallel'].split(', ')[0],
                                                        projection_data['standard_parallel'].split(', ')[1]]

            self.projection_data = projection_data
            self.projection = self._get_pyproj_projection()

            return None

        def _create_projection(self, **kwargs):
            """
            Create 'projection' and 'projection_data' from projection arguments.
            """

            projection_data = {'grid_mapping_name': 'lambert_conformal_conic',
                               'standard_parallel': [kwargs['lat_1'], kwargs['lat_2']],
                               'longitude_of_central_meridian': kwargs['lon_0'],
                               'latitude_of_projection_origin': kwargs['lat_0'],
                               'x_0': kwargs['x_0'], 'y_0': kwargs['y_0'],
                               'inc_x': kwargs['inc_x'], 'inc_y': kwargs['inc_y'],
                               'nx': kwargs['nx'], 'ny': kwargs['ny'],

                              }

            self.projection_data = projection_data
            self.projection = self._get_pyproj_projection()

            return None

        def _create_dimensions(self, netcdf):
            """
            Create 'y', 'x' and 'spatial_nv' dimensions and the super dimensions 'lev', 'time', 'time_nv', 'lon' and 'lat'

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

            super(LCCNes, self)._create_dimensions(netcdf)

            # Create y and x dimensions
            netcdf.createDimension('y', len(self._y['data']))
            netcdf.createDimension('x', len(self._x['data']))

            # 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)

            return None

        def _create_dimension_variables(self, netcdf):
            """
            Create the 'y' and 'x' variables.

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

            super(LCCNes, self)._create_dimension_variables(netcdf)

            # LCC Y COORDINATES
            y = netcdf.createVariable('y', self._y['data'].dtype, ('y',))
            y.long_name = 'y coordinate of projection'
            if 'units' in self._y.keys():
                y.units = Units(self._y['units'], formatted=True).units
            else:
                y.units = 'm'
            y.standard_name = 'projection_y_coordinate'
            if self.size > 1:
                y.set_collective(True)
            y[:] = self._y['data']

            # LCC X COORDINATES
            x = netcdf.createVariable('x', self._x['data'].dtype, ('x',))
            x.long_name = 'x coordinate of projection'
            if 'units' in self._x.keys():
                x.units = Units(self._x['units'], formatted=True).units
            else:
                x.units = 'm'
            x.standard_name = 'projection_x_coordinate'
            if self.size > 1:
                x.set_collective(True)
            x[:] = self._x['data']

            return None

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

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

            # Get projection details on x
            x_0 = np.float64(self.projection_data['x_0'])
            inc_x = np.float64(self.projection_data['inc_x'])
            nx = int(self.projection_data['nx'])

            # Get projection details on y
            y_0 = np.float64(self.projection_data['y_0'])
            inc_y = np.float64(self.projection_data['inc_y'])
            ny = int(self.projection_data['ny'])

            # Create a regular grid in metres (1D)
            self._x = {'data': np.linspace(x_0 + (inc_x / 2),
                                                  x_0 + (inc_x / 2) + (inc_x * (nx - 1)),
                                                  nx, dtype=np.float64)}
            self._y = {'data': np.linspace(y_0 + (inc_y / 2),
                                                  y_0 + (inc_y / 2) + (inc_y * (ny - 1)),
                                                  ny, dtype=np.float64)}

            # Create a regular grid in metres (1D to 2D)
            x = np.array([self._x['data']] * len(self._y['data']))
            y = np.array([self._y['data']] * len(self._x['data'])).T

            # Calculate centre latitudes and longitudes (UTM to LCC)
            centre_lon, centre_lat = self.projection(x, y, inverse=True)

            return {'data': centre_lat}, {'data': centre_lon}

        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

        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_x = np.abs(np.mean(np.diff(self.x['data'])))
            inc_y = np.abs(np.mean(np.diff(self.y['data'])))

            # Get bounds for rotated coordinates
            y_bnds = self.create_single_spatial_bounds(self.y['data'], inc_y)
            x_bnds = self.create_single_spatial_bounds(self.x['data'], inc_x)

            # Get rotated latitudes for grid edge
            left_edge_y = np.append(y_bnds.flatten()[::2], y_bnds.flatten()[-1])
            right_edge_y = np.flip(left_edge_y, 0)
            top_edge_y = np.repeat(y_bnds[-1][-1], len(self.x['data']) - 1)
            bottom_edge_y = np.repeat(y_bnds[0][0], len(self.x['data']))
            y_grid_edge = np.concatenate((left_edge_y, top_edge_y, right_edge_y, bottom_edge_y))

            # Get rotated longitudes for grid edge
            left_edge_x = np.repeat(x_bnds[0][0], len(self.y['data']) + 1)
            top_edge_x = x_bnds.flatten()[1:-1:2]
            right_edge_x = np.repeat(x_bnds[-1][-1], len(self.y['data']) + 1)
            bottom_edge_x = np.flip(x_bnds.flatten()[:-1:2], 0)
            x_grid_edge = np.concatenate((left_edge_x, top_edge_x, right_edge_x, bottom_edge_x))

            # Get edges for regular coordinates
            grid_edge_lon_data, grid_edge_lat_data = self.projection(x_grid_edge, y_grid_edge, inverse=True)

            # Create grid outline by stacking the edges in both coordinates
            model_grid_outline = np.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

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

            # Calculate LCC coordinates bounds
            inc_x = np.abs(np.mean(np.diff(self._x['data'])))
            x_bnds = self.create_single_spatial_bounds(np.array([self._x['data']] * len(self._y['data'])),
                                                       inc_x, spatial_nv=4)

            inc_y = np.abs(np.mean(np.diff(self._y['data'])))
            y_bnds = self.create_single_spatial_bounds(np.array([self._y['data']] * len(self._x['data'])).T,
                                                       inc_y, spatial_nv=4, inverse=True)

            # Transform LCC bounds to regular bounds
            lon_bnds, lat_bnds = self.projection(x_bnds, y_bnds, inverse=True)

            # Obtain regular coordinates bounds
            self._lat_bnds = {}
            self._lat_bnds['data'] = deepcopy(lat_bnds)
            self.lat_bnds = {}
            self.lat_bnds['data'] = lat_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
                                             self.read_axis_limits['x_min']:self.read_axis_limits['x_max'],
                                             :]

            self._lon_bnds = {}
            self._lon_bnds['data'] = deepcopy(lon_bnds)
            self.lon_bnds = {}
            self.lon_bnds['data'] = lon_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'],
                                             self.read_axis_limits['x_min']:self.read_axis_limits['x_max'],
                                             :]

            return None

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

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

            var.grid_mapping = 'Lambert_Conformal'
            var.coordinates = "lat lon"

            return None

        def _create_metadata(self, netcdf):
            """
            Create the 'crs' variable for the lambert conformal grid_mapping.

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

            if self.projection_data is not None:
                mapping = netcdf.createVariable('Lambert_Conformal', 'i')
                mapping.grid_mapping_name = self.projection_data['grid_mapping_name']
                mapping.standard_parallel = self.projection_data['standard_parallel']
                mapping.longitude_of_central_meridian = self.projection_data['longitude_of_central_meridian']
                mapping.latitude_of_projection_origin = self.projection_data['latitude_of_projection_origin']

            return None

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

            Parameters
            ----------
            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 Lambert Conformal Conic projection.")

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

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

            if self.shapefile is None:

                # Get latitude and longitude cell boundaries
                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 = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()),
                                       geometry=geometry,
                                       crs="EPSG:4326")
                self.shapefile = gdf

            else:
                gdf = self.shapefile

            return gdf

        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 = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()),
                                             geometry=centroids,
                                             crs="EPSG:4326")

            return centroids_gdf