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# SPDX-FileCopyrightText: Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES.
# SPDX-FileCopyrightText: All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import netCDF4 as nc
import torch
from torch import Tensor
from torch.nn.functional import interpolate
from .graph_utils import deg2rad
class StaticData:
"""Class to load static data from netCDF files. Static data includes land-sea mask,
geopotential, and latitude-longitude coordinates.
Parameters
----------
static_dataset_path : str
Path to directory containing static data.
latitudes : Tensor
Tensor with shape (lat,) that includes latitudes.
longitudes : Tensor
Tensor with shape (lon,) that includes longitudes.
"""
def __init__(
self,
static_dataset_path: str,
latitudes: Tensor,
longitudes: Tensor,
) -> None: # pragma: no cover
self.lsm_path = os.path.join(static_dataset_path, "land_sea_mask.nc")
self.geop_path = os.path.join(static_dataset_path, "geopotential.nc")
self.lat = latitudes
self.lon = longitudes
def get_lsm(self) -> Tensor: # pragma: no cover
"""Get land-sea mask from netCDF file.
Returns
-------
Tensor
Land-sea mask with shape (1, 1, lat, lon).
"""
ds = torch.tensor(nc.Dataset(self.lsm_path)["lsm"], dtype=torch.float32)
ds = torch.unsqueeze(ds, dim=0)
ds = interpolate(ds, size=(self.lat.size(0), self.lon.size(0)), mode="bilinear")
return ds
def get_geop(self, normalize: bool = True) -> Tensor: # pragma: no cover
"""Get geopotential from netCDF file.
Parameters
----------
normalize : bool, optional
Whether to normalize the geopotential, by default True
Returns
-------
Tensor
Normalized geopotential with shape (1, 1, lat, lon).
"""
ds = torch.tensor(nc.Dataset(self.geop_path)["z"], dtype=torch.float32)
ds = torch.unsqueeze(ds, dim=0)
ds = interpolate(ds, size=(self.lat.size(0), self.lon.size(0)), mode="bilinear")
if normalize:
ds = (ds - ds.mean()) / ds.std()
return ds
def get_lat_lon(self) -> Tensor: # pragma: no cover
"""Computes cosine of latitudes and sine and cosine of longitudes.
Returns
-------
Tensor
Tensor with shape (1, 3, lat, lon) tha includes cosine of latitudes,
sine and cosine of longitudes.
"""
# cos latitudes
cos_lat = torch.cos(deg2rad(self.lat))
cos_lat = cos_lat.view(1, 1, self.lat.size(0), 1)
cos_lat_mg = cos_lat.expand(1, 1, self.lat.size(0), self.lon.size(0))
# sin longitudes
sin_lon = torch.sin(deg2rad(self.lon))
sin_lon = sin_lon.view(1, 1, 1, self.lon.size(0))
sin_lon_mg = sin_lon.expand(1, 1, self.lat.size(0), self.lon.size(0))
# cos longitudes
cos_lon = torch.cos(deg2rad(self.lon))
cos_lon = cos_lon.view(1, 1, 1, self.lon.size(0))
cos_lon_mg = cos_lon.expand(1, 1, self.lat.size(0), self.lon.size(0))
outvar = torch.cat((cos_lat_mg, sin_lon_mg, cos_lon_mg), dim=1)
return outvar
def get(self) -> Tensor: # pragma: no cover
"""Get all static data.
Returns
-------
Tensor
Tensor with shape (1, 5, lat, lon) that includes land-sea mask,
geopotential, cosine of latitudes, sine and cosine of longitudes.
"""
lsm = self.get_lsm()
geop = self.get_geop()
lat_lon = self.get_lat_lon()
return torch.concat((lsm, geop, lat_lon), dim=1)
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