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


[docs] 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")
[docs] @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
[docs] 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
[docs] 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
[docs] 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
[docs] 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}
[docs] 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
[docs] 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
[docs] 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
[docs] 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
[docs] 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
[docs] 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
[docs] 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