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0.7.0
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@forman forman released this 05 Mar 14:32
v0.7.0
300dcab

Changes in 0.7.0

  • Introduced abstract base class xcube.util.jsonschema.JsonObject which
    is now the super class of many classes that have JSON object representations.
    In Jupyter notebooks, instances of such classes are automatically rendered
    as JSON trees.
  • xcube gen2 CLI tool can now have multiple -v options, e.g. -vvv
    will now output detailed requests and responses.
  • Added new Jupyter notebooks in examples/notebooks/gen2
    for the data cube generators in the package xcube.core.gen2.
  • Fixed a problem in JsonArraySchema that occurred if a valid
    instance was None. A TypeError TypeError: 'NoneType' object is not iterable was
    raised in this case.
  • The S3 data store xcube.core.store.stores.s3.S3DataStore now implements the describe_data() method.
    It therefore can also be used as a data store from which data is queried and read.
  • The xcube gen2 data cube generator tool has been hidden from
    the set of "official" xcube tools. It is considered as an internal tool
    that is subject to change at any time until its interface has stabilized.
    Please refer to xcube gen2 --help for more information.
  • Added coords property to DatasetDescriptor class.
    The data_vars property of the DatasetDescriptor class is now a dictionary.
  • Added chunks property to VariableDescriptor class.
  • Removed function reproject_crs_to_wgs84() and tests (#375) because
    • it seemed to be no longer be working with GDAL 3.1+;
    • there was no direct use in xcube itself;
    • xcube plans to get rid of GDAL dependencies.
  • CLI tool xcube gen2 may now also ingest non-cube datasets.
  • Fixed unit tests broken by accident. (#396)
  • Added new context manager xcube.util.observe_dask_progress() that can be used
    to observe tasks that known to be dominated by Dask computations: 
    with observe_dask_progress('Writing dataset', 100):
    dataset.to_zarr(store)
  • The xcube normalisation process, which ensures that a dataset meets the requirements
    of a cube, internally requested a lot of data, causing the process to be slow and
    expensive in terms of memory consumption. This problem was resolved by avoiding to
    read in these large amounts of data. (#392)
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