# The MIT License (MIT)
# Copyright (c) 2022 by the xcube development team and contributors
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
from typing import Sequence, Any, Dict, Callable, Optional
import xarray as xr
from .abc import MultiLevelDataset
from .lazy import LazyMultiLevelDataset
[docs]
class CombinedMultiLevelDataset(LazyMultiLevelDataset):
"""A multi-level dataset that is a combination of other
multi-level datasets.
:param ml_datasets: The multi-level datasets to be combined.
At least two must be provided.
:param ds_id: Optional dataset identifier.
:param combiner_function: An optional function used to combine the
datasets, for example ``xarray.merge``.
If given, it receives a list of datasets
(``xarray.Dataset`` instances) and *combiner_params* as keyword
arguments.
If not given or ``None`` is passed, a copy of the first dataset
is made, which is then subsequently updated by the remaining datasets
using ``xarray.Dataset.update()``.
:param combiner_params: Parameters to the *combiner_function*
passed as keyword arguments.
"""
def __init__(self,
ml_datasets: Sequence[MultiLevelDataset],
ds_id: Optional[str] = None,
combiner_function: Optional[Callable] = None,
combiner_params: Optional[Dict[str, Any]] = None):
if not ml_datasets or len(ml_datasets) < 2:
raise ValueError('ml_datasets must have at least two elements')
super().__init__(ds_id=ds_id,
parameters=combiner_params)
self._ml_datasets = ml_datasets
self._combiner_function = combiner_function
def _get_num_levels_lazily(self) -> int:
return self._ml_datasets[0].num_levels
def _get_dataset_lazily(self, index: int,
combiner_params: Dict[str, Any]) -> xr.Dataset:
datasets = [ml_dataset.get_dataset(index)
for ml_dataset in self._ml_datasets]
if self._combiner_function is None:
combined_dataset = datasets[0].copy()
for dataset in datasets[1:]:
combined_dataset.update(dataset)
return combined_dataset
else:
return self._combiner_function(datasets, **combiner_params)
[docs]
def close(self):
for ml_dataset in self._ml_datasets:
ml_dataset.close()