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#
# 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
#
# https://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 importlib.resources
import pickle
from dinosaur import coordinate_systems
from dinosaur import horizontal_interpolation
from dinosaur import spherical_harmonic
import model as neuralgcm
import numpy as np
import xarray
def _horizontal_regrid(
regridder: horizontal_interpolation.Regridder, dataset: xarray.Dataset
) -> xarray.Dataset:
"""Horizontally regrid an xarray Dataset."""
# TODO(shoyer): consider moving to public API
regridded = xarray.apply_ufunc(
regridder,
dataset,
input_core_dims=[['longitude', 'latitude']],
output_core_dims=[['longitude', 'latitude']],
exclude_dims={'longitude', 'latitude'},
vectorize=True, # loops over level, for lower memory usage
)
regridded.coords['longitude'] = np.rad2deg(regridder.target_grid.longitudes)
regridded.coords['latitude'] = np.rad2deg(regridder.target_grid.latitudes)
return regridded
def load_checkpoint_tl63_stochastic():
"""Load a checkpoint for a toy TL63 stochastic model."""
package = importlib.resources.files(neuralgcm)
file = package.joinpath('data/tl63_stochastic_mini.pkl')
return pickle.loads(file.read_bytes())
def load_data(coords: coordinate_systems.CoordinateSystem) -> xarray.Dataset:
"""Load demo data for the given coordinate system."""
if coords.vertical.layers != 37:
raise ValueError('can only load demo data for 37 pressure levels')
package = importlib.resources.files(neuralgcm)
with package.joinpath('data/era5_tl31_19590102T00.nc').open('rb') as f:
ds = xarray.load_dataset(f).expand_dims('time')
regridder = horizontal_interpolation.ConservativeRegridder(
spherical_harmonic.Grid.TL31(), coords.horizontal
)
return _horizontal_regrid(regridder, ds)
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