Source code for xcube.core.unchunk

# The MIT License (MIT)
# Copyright (c) 2019 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.

import json
import os.path
from typing import List, Sequence

import numpy as np
import xarray as xr
import zarr


[docs]def unchunk_dataset(dataset_path: str, var_names: Sequence[str] = None, coords_only: bool = False): """ Unchunk dataset variables in-place. :param dataset_path: Path to ZARR dataset directory. :param var_names: Optional list of variable names. :param coords_only: Un-chunk coordinate variables only. """ is_zarr = os.path.isfile(os.path.join(dataset_path, '.zgroup')) if not is_zarr: raise ValueError(f'{dataset_path!r} is not a valid Zarr directory') with xr.open_zarr(dataset_path) as dataset: if var_names is None: if coords_only: var_names = list(dataset.coords) else: var_names = list(dataset.variables) else: for var_name in var_names: if coords_only: if var_name not in dataset.coords: raise ValueError(f'variable {var_name!r} is not a coordinate variable in {dataset_path!r}') else: if var_name not in dataset.variables: raise ValueError(f'variable {var_name!r} is not a variable in {dataset_path!r}') _unchunk_vars(dataset_path, var_names)
def _unchunk_vars(dataset_path: str, var_names: List[str]): for var_name in var_names: var_path = os.path.join(dataset_path, var_name) # Optimization: if "shape" and "chunks" are equal in ${var}/.zarray, we are done var_array_info_path = os.path.join(var_path, '.zarray') with open(var_array_info_path, 'r') as fp: var_array_info = json.load(fp) if var_array_info.get('shape') == var_array_info.get('chunks'): continue # Open array and remove chunks from the data var_array = zarr.convenience.open_array(var_path, 'r+') if var_array.shape != var_array.chunks: # TODO (forman): Fully loading data is inefficient and dangerous for large arrays. # Instead save unchunked to temp and replace existing chunked array dir with temp. # Fully load data and attrs so we no longer depend on files data = np.array(var_array) attributes = var_array.attrs.asdict() # Save array data zarr.convenience.save_array(var_path, data, chunks=False, fill_value=var_array.fill_value) # zarr.convenience.save_array() does not seem save user attributes (file ".zattrs" not written), # therefore we must modify attrs explicitly: var_array = zarr.convenience.open_array(var_path, 'r+') var_array.attrs.update(attributes) zarr.consolidate_metadata(dataset_path)