deeplearning/modulus/modulus-core-v030/_modules/modulus/utils/graphcast/data_utils.html

Core v0.3.0

Source code for modulus.utils.graphcast.data_utils

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import netCDF4 as nc
import numpy as np
import torch
from torch import Tensor
import os

from .graph_utils import deg2rad


[docs]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
[docs] 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 = nc.Dataset(self.lsm_path) lsm = np.expand_dims(ds["lsm"], axis=0) return torch.tensor(lsm, dtype=torch.float32)
[docs] 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 = nc.Dataset(self.geop_path) geop = np.expand_dims(ds["z"], axis=0) if normalize: geop = (geop - geop.mean()) / geop.std() return torch.tensor(geop, dtype=torch.float32)
[docs] 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
[docs] 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)
© Copyright 2023, NVIDIA Modulus Team. Last updated on Jan 25, 2024.