#!/usr/bin/env python
from numpy import float64, linspace, array, mean, diff, append, flip, repeat, concatenate, vstack
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 LCCNes(Nes):
"""
Attributes
----------
_full_y : dict
Y coordinates dictionary with the complete "data" key for all the values and the rest of the attributes.
_full_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
A Tuple with the name of the Y and X dimensions for the variables.
("y", "x", ) for an LCC projection.
_lat_dim : tuple
A Tuple with the name of the dimensions of the Latitude values.
("y", "x", ) for an LCC projection.
_lon_dim : tuple
ATuple with the name of the dimensions of the Longitude values.
("y", "x") for an LCC 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 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.
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_y = None
self._full_x = None
super(LCCNes, self).__init__(comm=comm, path=path, info=info, dataset=dataset,
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.get_full_latitudes(), "Y")
self.lon = self._get_coordinate_values(self.get_full_longitudes(), "X")
else:
# Complete dimensions
self._full_y = self._get_coordinate_dimension("y")
self._full_x = self._get_coordinate_dimension("x")
# Dimensions screening
self.y = self._get_coordinate_values(self.get_full_y(), "Y")
self.x = self._get_coordinate_values(self.get_full_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, 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: "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,
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_y(self) -> Dict[str, Any]:
"""
Retrieve the complete Y 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_y)
return data
[docs]
def get_full_x(self) -> Dict[str, Any]:
"""
Retrieve the complete X 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_x)
return data
[docs]
def set_full_y(self, data: Dict[str, Any]) -> None:
"""
Set the complete Y 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_y = data
return None
[docs]
def set_full_x(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_x = data
return None
# noinspection DuplicatedCode
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.get_full_y(), "Y")
self.x = self._get_coordinate_values(self.get_full_x(), "X")
self.set_full_y({'data': self.y["data"][idx["idx_y_min"]:idx["idx_y_max"]]})
self.set_full_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=float64(self.projection_data["standard_parallel"][0]),
lat_2=float64(self.projection_data["standard_parallel"][1]),
lon_0=float64(self.projection_data["longitude_of_central_meridian"]),
lat_0=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_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": "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"],
}
else:
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]]
return projection_data
# noinspection DuplicatedCode
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.get_full_y()["data"]))
netcdf.createDimension("x", len(self.get_full_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
# noinspection DuplicatedCode
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
full_y = self.get_full_y()
y = netcdf.createVariable("y", full_y["data"].dtype, ("y",))
y.long_name = "y coordinate of projection"
if "units" in full_y.keys():
y.units = full_y["units"]
else:
y.units = "m"
y.standard_name = "projection_y_coordinate"
if self.size > 1:
y.set_collective(True)
y[:] = full_y["data"]
# LCC X COORDINATES
full_x = self.get_full_x()
x = netcdf.createVariable("x", full_x["data"].dtype, ("x",))
x.long_name = "x coordinate of projection"
if "units" in full_x.keys():
x.units = full_x["units"]
else:
x.units = "m"
x.standard_name = "projection_x_coordinate"
if self.size > 1:
x.set_collective(True)
x[:] = full_x["data"]
return None
# noinspection DuplicatedCode
def _create_centre_coordinates(self, **kwargs):
"""
Calculate centre latitudes and longitudes from grid details.
Parameters
----------
netcdf : Dataset
NetCDF object.
"""
if self.master:
# Get projection details on x
x_0 = float64(self.projection_data["x_0"])
inc_x = float64(self.projection_data["inc_x"])
nx = int(self.projection_data["nx"])
# Get projection details on y
y_0 = float64(self.projection_data["y_0"])
inc_y = float64(self.projection_data["inc_y"])
ny = int(self.projection_data["ny"])
# Create a regular grid in metres (1D)
self._full_x = {"data": linspace(x_0 + (inc_x / 2), x_0 + (inc_x / 2) + (inc_x * (nx - 1)), nx,
dtype=float64)}
self._full_y = {"data": linspace(y_0 + (inc_y / 2), y_0 + (inc_y / 2) + (inc_y * (ny - 1)), ny,
dtype=float64)}
# Create a regular grid in metres (1D to 2D)
x = array([self._full_x["data"]] * len(self._full_y["data"]))
y = array([self._full_y["data"]] * len(self._full_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}
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_x = abs(mean(diff(self.x["data"])))
inc_y = abs(mean(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 = append(y_bnds.flatten()[::2], y_bnds.flatten()[-1])
right_edge_y = flip(left_edge_y, 0)
top_edge_y = repeat(y_bnds[-1][-1], len(self.x["data"]) - 1)
bottom_edge_y = repeat(y_bnds[0][0], len(self.x["data"]))
y_grid_edge = concatenate((left_edge_y, top_edge_y, right_edge_y, bottom_edge_y))
# Get rotated longitudes for grid edge
left_edge_x = repeat(x_bnds[0][0], len(self.y["data"]) + 1)
top_edge_x = x_bnds.flatten()[1:-1:2]
right_edge_x = repeat(x_bnds[-1][-1], len(self.y["data"]) + 1)
bottom_edge_x = flip(x_bnds.flatten()[:-1:2], 0)
x_grid_edge = 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 = 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 LCC coordinates bounds
full_x = self.get_full_x()
full_y = self.get_full_y()
inc_x = abs(mean(diff(full_x["data"])))
x_bnds = self._create_single_spatial_bounds(array([full_x["data"]] * len(full_y["data"])),
inc_x, spatial_nv=4)
inc_y = abs(mean(diff(full_y["data"])))
y_bnds = self._create_single_spatial_bounds(array([full_y["data"]] * len(full_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.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 "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
----------
lat_flip : bool
Indicates if the latitudes need to be flipped Up-Down or Down-Up. Default False.
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.")
# noinspection DuplicatedCode
[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_lat = 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_lon = 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_lon.shape[0]):
geometry.append(Polygon([(aux_b_lon[i, 0], aux_b_lat[i, 0]),
(aux_b_lon[i, 1], aux_b_lat[i, 1]),
(aux_b_lon[i, 2], aux_b_lat[i, 2]),
(aux_b_lon[i, 3], aux_b_lat[i, 3]),
(aux_b_lon[i, 0], aux_b_lat[i, 0])]))
# Create dataframe containing 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 containing all points
fids = self.get_fids()
centroids_gdf = GeoDataFrame(index=Index(name="FID", data=fids.ravel()), geometry=centroids, crs="EPSG:4326")
return centroids_gdf