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


[docs] 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')
[docs] @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
[docs] 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}
[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
[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_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
[docs] 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
[docs] 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.")
[docs] 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
[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 = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), geometry=centroids, crs="EPSG:4326") return centroids_gdf