1.0.0

Changes in 1.0.0

Enhancements

• Added a catalog API compliant to STAC to
  xcube server. (#455)

  • It serves a single collection named "datacubes" whose items are the
    datasets published by the service.
  • The collection items make use the STAC
    datacube extension.

• Simplified the cloud deployment of xcube server/viewer applications (#815).
  This has been achieved by the following new xcube server features:

  • Configuration files can now also be URLs which allows
    provisioning from S3-compatible object storage.
    For example, it is now possible to invoke xcube server as follows:

    $ xcube serve --config s3://cyanoalert/xcube/demo.yaml ...

  • A new endpoint /viewer/config/{*path} allows
    for configuring the viewer accessible via endpoint /viewer.
    The actual source for the configuration items is configured by xcube
    server configuration using the new entry Viewer/Configuration/Path,
    for example:

    Viewer:
      Configuration:
        Path: s3://cyanoalert/xcube/viewer-config

  • A typical xcube server configuration comprises many paths, and
    relative paths of known configuration parameters are resolved against
    the base_dir configuration parameter. However, for values of
    parameters passed to user functions that represent paths in user code,
    this cannot be done automatically. For such situations, expressions
    can be used. An expression is any string between "${" and "}" in a
    configuration value. An expression can contain the variables
    base_dir (a string), ctx the current server context
    (type xcube.webapi.datasets.DatasetsContext), as well as the function
    resolve_config_path(path) that is used to make a path absolut with
    respect to base_dir and to normalize it. For example

    Augmentation:
      Path: augmentation/metadata.py
      Function: metadata:update_metadata
      InputParameters:
        bands_config: ${resolve_config_path("../common/bands.yaml")}

• xcube's spatial resampling functions resample_in_space(),
  affine_transform_dataset(), and rectify_dataset() exported
  from module xcube.core.resampling now encode the target grid mapping
  into the resampled datasets. (#822)

  This new default behaviour can be switched off by keyword argument
  encode_cf=False.
  The grid mapping name can be set by keyword argument gm_name.
  If gm_name is not given a grid mapping will not be encoded if
  all the following conditions are true:

  • The target CRS is geographic;
  • The spatial dimension names are "lon" and "lat";
  • The spatial 1-D coordinate variables are named "lon" and "lat"
    and are evenly spaced.

  The encoding of the grid mapping is done according to CF conventions:

  • The CRS is encoded as attributes of a 0-D data variable named by gm_name
  • All spatial data variables receive an attribute grid_mapping that is
    set to the value of gm_name.

• Added Notebook
  xcube-viewer-in-jl.ipynb
  that explains how xcube Viewer can now be utilised in JupyterLab
  using the new (still experimental) xcube JupyterLab extension
  xcube-jl-ext.
  The xcube-jl-ext package is also available on PyPI.

• Updated example
  Notebook for CMEMS data store
  to reflect changes of parameter names that provide CMEMS API credentials.

• Included support for Azure Blob Storage filesystem by adding a new
  data store abfs. Many thanks to Ed!
  (#752)

  These changes will enable access to data cubes (.zarr or .levels)
  in Azure blob storage as shown here:

  store = new_data_store(
      "abfs",                    # Azure filesystem protocol
      root="my_blob_container",  # Azure blob container name
      storage_options= {'anon': True, 
                        # Alternatively, use 'connection_string': 'xxx'
                        'account_name': 'xxx', 
                        'account_key':'xxx'}  
  )
  store.list_data_ids()

  Same configuration for xcube Server:

  DataStores:
  - Identifier: siec
    StoreId: abfs
    StoreParams:
      root: my_blob_container
      max_depth: 1
      storage_options:
        anon: true
        account_name: "xxx"
        account_key': "xxx"
        # or
        # connection_string: "xxx"
    Datasets:
      - Path: "*.levels"
        Style: default

• Added Notebook
  8_azure_blob_filesystem.ipynb.
  This notebook shows how a new data store instance can connect and list
  Zarr files from Azure bolb storage using the new abfs data store.

• xcube's Dockerfile no longer creates a conda environment xcube.
  All dependencies are now installed into the base environment making it
  easier to use the container as an executable for xcube applications.
  We are now also using a micromamba base image instead of miniconda.
  The result is a much faster build and smaller image size.

• Added a new_cluster function to xcube.util.dask, which can create
  Dask clusters with various configuration options.

• The xcube multi-level dataset specification has been enhanced. (#802)

  • When writing multi-level datasets (*.levels/) we now create a new
    JSON file .zlevels that contains the parameters used to create the
    dataset.
  • A new class xcube.core.mldataset.FsMultiLevelDataset that represents
    a multi-level dataset persisted to some filesystem, like
    "file", "s3", "memory". It can also write datasets to the filesystem.

• Changed the behaviour of the class
  xcube.core.mldataset.CombinedMultiLevelDataset to do what we
  actually expect:
  If the keyword argument combiner_func is 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().
  The former default was using the xarray.merge(), which for some reason
  can eagerly load Dask array chunks into memory that won't be released.

Fixes

• Tiles of datasets with forward slashes in their identifiers
  (originated from nested directories) now display again correctly
  in xcube Viewer. Tile URLs have not been URL-encoded in such cases. (#817)

• The xcube server configuration parameters url_prefix and
  reverse_url_prefix can now be absolute URLs. This fixes a problem for
  relative prefixes such as "proxy/8000" used for xcube server running
  inside JupyterLab. Here, the expected returned self-referencing URL was
  https://{host}/users/{user}/proxy/8000/{path} but we got
  http://{host}/proxy/8000/{path}. (#806)

New Contributors

• @edd3x made their first contribution in #798

Full Changelog: v0.13.0...v1.0.0

1.0.0.dev3

Changes in 1.0.0.dev3

Enhancements

• Included support for Azure Blob Storage filesystem by adding a new
  data store abfs. Many thanks to Ed!
  (#752)

  These changes will enable access to data cubes (.zarr or .levels)
  in Azure blob storage as shown here:

  store = new_data_store(
      "abfs",                    # Azure filesystem protocol
      root="my_blob_container",  # Azure blob container name
      storage_options= {'anon': True, 
                        # Alternatively, use 'connection_string': 'xxx'
                        'account_name': 'xxx', 
                        'account_key':'xxx'}  
  )
  store.list_data_ids()

  Same configuration for xcube Server:

  DataStores:
  - Identifier: siec
    StoreId: abfs
    StoreParams:
      root: my_blob_container
      max_depth: 1
      storage_options:
        anon: true
        account_name: "xxx"
        account_key': "xxx"
        # or
        # connection_string: "xxx"
    Datasets:
      - Path: "*.levels"
        Style: default

• Added Notebook
  8_azure_blob_filesystem.ipynb.
  This notebook shows how a new data store instance can connect and list
  Zarr files from Azure bolb storage using the new abfs data store.

• Added a catalog API compliant to STAC to
  xcube server. (#455)

  • It serves a single collection named "datacubes" whose items are the
    datasets published by the service.
  • The collection items make use the STAC
    datacube extension.

• Simplified the cloud deployment of xcube server/viewer applications (#815).
  This has been achieved by the following new xcube server features:

  • Configuration files can now also be URLs which allows
    provisioning from S3-compatible object storage.
    For example, it is now possible to invoke xcube server as follows:

    $ xcube serve --config s3://cyanoalert/xcube/demo.yaml ...

  • A new endpoint /viewer/config/{*path} allows
    for configuring the viewer accessible via endpoint /viewer.
    The actual source for the configuration items is configured by xcube
    server configuration using the new entry Viewer/Configuration/Path,
    for example:

    Viewer:
      Configuration:
        Path: s3://cyanoalert/xcube/viewer/ 

  • A typical xcube server configuration comprises many paths, and
    relative paths of known configuration parameters are resolved against
    the base_dir configuration parameter. However, for values of
    parameters passed to user functions that represent paths in user code,
    this cannot be done automatically. For such situations, expressions
    can be used. An expression is any string between "${" and "}" in a
    configuration value. An expression can contain the variables
    base_dir (a string), ctx the current server context
    (type xcube.webapi.datasets.DatasetsContext), as well as the function
    resolve_config_path(path) that is used to make a path absolut with
    respect to base_dir and to normalize it. For example

    Augmentation:
      Path: augmentation/metadata.py
      Function: metadata:update_metadata
      InputParameters:
        bands_config: ${resolve_config_path("../common/bands.yaml")}

• xcube's Dockerfile no longer creates a conda environment xcube.
  All dependencies are now installed into the base environment making it
  easier to use the container as an executable for xcube applications.
  We are now also using a micromamba base image instead of miniconda.
  The result is a much faster build and smaller image size.

• Added a new_cluster function to xcube.util.dask, which can create
  Dask clusters with various configuration options.

• The xcube multi-level dataset specification has been enhanced. (#802)

  • When writing multi-level datasets (*.levels/) we now create a new
    JSON file .zlevels that contains the parameters used to create the
    dataset.
  • A new class xcube.core.mldataset.FsMultiLevelDataset that represents
    a multi-level dataset persisted to some filesystem, like
    "file", "s3", "memory". It can also write datasets to the filesystem.

• Changed the behaviour of the class
  xcube.core.mldataset.CombinedMultiLevelDataset to do what we
  actually expect:
  If the keyword argument combiner_func is 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().
  The former default was using the xarray.merge(), which for some reason
  can eagerly load Dask array chunks into memory that won't be released.

Fixes

• Tiles of datasets with forward slashes in their identifiers
  (originated from nested directories) now display again correctly
  in xcube Viewer. Tile URLs have not been URL-encoded in such cases. (#817)

• The xcube server configuration parameters url_prefix and
  reverse_url_prefix can now be absolute URLs. This fixes a problem for
  relative prefixes such as "proxy/8000" used for xcube server running
  inside JupyterLab. Here, the expected returned self-referencing URL was
  https://{host}/users/{user}/proxy/8000/{path} but we got
  http://{host}/proxy/8000/{path}. (#806)

New Contributors

• @edd3x made their first contribution in #798

Full Changelog: v1.0.0.dev2...v1.0.0.dev3

1.0.0.dev2

Changes in 1.0.0.dev2

Enhancements

• Added a catalog API compliant to STAC to
  xcube server.
  It serves a single collection named "datasets" whose items are the
  datasets published by the service. (#455)

• Simplified the cloud deployment of xcube server/viewer applications (#815).
  This has been achieved by the following new xcube server features:

  • Configuration files can now also be URLs which allows
    provisioning from S3-compatible object storage.
    For example, it is now possible to invoke xcube server as follows:

    $ xcube serve --config s3://cyanoalert/xcube/demo.yaml ...

  • A new endpoint /viewer/config/{*path} allows
    for configuring the viewer accessible via endpoint /viewer.
    The actual source for the configuration items is configured by xcube
    server configuration using the new entry Viewer/Configuration/Path,
    for example:

    Viewer:
      Configuration:
        Path: s3://cyanoalert/xcube/viewer/ 

  • A typical xcube server configuration comprises many paths, and
    relative paths of known configuration parameters are resolved against
    the base_dir configuration parameter. However, for values of
    parameters passed to user functions that represent paths in user code,
    this cannot be done automatically. For such situations, expressions
    can be used. An expression is any string between "${" and "}" in a
    configuration value. An expression can contain the variables
    base_dir (a string), ctx the current server context
    (type xcube.webapi.datasets.DatasetsContext), as well as the function
    resolve_config_path(path) that is used to make a path absolut with
    respect to base_dir and to normalize it. For example

    Augmentation:
      Path: augmentation/metadata.py
      Function: metadata:update_metadata
      InputParameters:
        bands_config: ${resolve_config_path("../common/bands.yaml")}

• xcube's Dockerfile no longer creates a conda environment xcube.
  All dependencies are now installed into the base environment making it
  easier to use the container as an executable for xcube applications.
  We are now also using a micromamba base image instead of miniconda.
  The result is a much faster build and smaller image size.

• Added a new_cluster function to xcube.util.dask, which can create
  Dask clusters with various configuration options.

• The xcube multi-level dataset specification has been enhanced. (#802)

  • When writing multi-level datasets (*.levels/) we now create a new
    JSON file .zlevels that contains the parameters used to create the
    dataset.
  • A new class xcube.core.mldataset.FsMultiLevelDataset that represents
    a multi-level dataset persisted to some filesystem, like
    "file", "s3", "memory". It can also write datasets to the filesystem.

• Changed the behaviour of the class
  xcube.core.mldataset.CombinedMultiLevelDataset to do what we
  actually expect:
  If the keyword argument combiner_func is 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().
  The former default was using the xarray.merge(), which for some reason
  can eagerly load Dask array chunks into memory that won't be released.

Fixes

• Tiles of datasets with forward slashes in their identifiers
  (originated from nested directories) now display again correctly
  in xcube Viewer. Tile URLs have not been URL-encoded in such cases. (#817)

• The xcube server configuration parameters url_prefix and
  reverse_url_prefix can now be absolute URLs. This fixes a problem for
  relative prefixes such as "proxy/8000" used for xcube server running
  inside JupyterLab. Here, the expected returned self-referencing URL was
  https://{host}/users/{user}/proxy/8000/{path} but we got
  http://{host}/proxy/8000/{path}. (#806)

Full Changelog: v1.0.0.dev1...v1.0.0.dev2

0.13.0

Changes in 0.13.0

Enhancements

• xcube Server has been rewritten almost from scratch.

  • Introduced a new endpoint ${server_url}/s3 that emulates
    and AWS S3 object storage for the published datasets. (#717)
    The bucket name can be either:

    • s3://datasets - publishes all datasets in Zarr format.
    • s3://pyramids - publishes all datasets in a multi-level levels
      format (multi-resolution N-D images)
      that comprises level datasets in Zarr format.

    Datasets published through the S3 API are slightly
    renamed for clarity. For bucket s3://pyramids:

    • if a dataset identifier has suffix .levels, the identifier remains;
    • if a dataset identifier has suffix .zarr, it will be replaced by
      .levels only if such a dataset doesn't exist;
    • otherwise, the suffix .levels is appended to the identifier.
      For bucket s3://datasets the opposite is true:
    • if a dataset identifier has suffix .zarr, the identifier remains;
    • if a dataset identifier has suffix .levels, it will be replaced by
      .zarr only if such a dataset doesn't exist;
    • otherwise, the suffix .zarr is appended to the identifier.

    With the new S3 endpoints in place, xcube Server instances can be used
    as xcube data stores as follows:

    store = new_data_store(
        "s3", 
        root="datasets",   # bucket "datasets", use also "pyramids"
        max_depth=2,       # optional, but we may have nested datasets
        storage_options=dict(
            anon=True,
            client_kwargs=dict(
                endpoint_url='http://localhost:8080/s3' 
            )
        )
    )

  • The limited s3bucket endpoints are no longer available and are
    replaced by s3 endpoints.

  • Added new endpoint /viewer that serves a self-contained,
    packaged build of
    xcube Viewer.
    The packaged viewer can be overridden by environment variable
    XCUBE_VIEWER_PATH that must point to a directory with a
    build of a compatible viewer.

  • The --show option of xcube serve
    has been renamed to --open-viewer.
    It now uses the self-contained, packaged build of
    xcube Viewer. (#750)

  • The --show option of xcube serve
    now outputs various aspects of the server configuration.

  • Added experimental endpoint /volumes.
    It is used by xcube Viewer to render 3-D volumes.

• xcube Server is now more tolerant with respect to datasets it can not
  open without errors. Implementation detail: It no longer fails if
  opening datasets raises any exception other than DatasetIsNotACubeError.
  (#789)

• xcube Server's colormap management has been improved in several ways:

  • Colormaps are no longer managed globally. E.g., on server configuration
    change, new custom colormaps are reloaded from files.
  • Colormaps are loaded dynamically from underlying
    matplotlib and cmocean registries, and custom SNAP color palette files.
    That means, latest matplotlib colormaps are now always available. (#687)
  • Colormaps can now be reversed (name suffix "_r"),
    can have alpha blending (name suffix "_alpha"),
    or both (name suffix "_r_alpha").
  • Loading of custom colormaps from SNAP *.cpd has been rewritten.
    Now also the isLogScaled property of the colormap is recognized. (#661)
  • The module xcube.util.cmaps has been redesigned and now offers
    three new classes for colormap management:
    • Colormap - a colormap
    • ColormapCategory - represents a colormap category
    • ColormapRegistry - manages colormaps and their categories

• The xcube filesystem data stores such as "file", "s3", "memory"
  can now filter the data identifiers reported by get_data_ids(). (#585)
  For this purpose, the data stores now accept two new optional keywords
  which both can take the form of a wildcard pattern or a sequence
  of wildcard patterns:

  1. excludes: if given and if any pattern matches the identifier,
     the identifier is not reported.
  2. includes: if not given or if any pattern matches the identifier,
     the identifier is reported.

• Added convenience method DataStore.list_data_ids() that works
  like get_data_ids(), but returns a list instead of an iterator. (#776)

• Added Notebook
  xcube-viewer-in-jl.ipynb
  that explains how xcube Viewer can now be utilised in JupyterLab
  using the new (still experimental) xcube JupyterLab extension
  xcube-jl-ext.

• Replaced usages of deprecated numpy dtype numpy.bool
  by Python type bool.

Fixes

• xcube CLI tools no longer emit warnings when trying to import
  installed packages named xcube_* as xcube plugins.

• The xcube.util.timeindex module can now handle 0-dimensional
  ndarrays as indexers. This effectively avoids the warning
  Can't determine indexer timezone; leaving it unmodified.
  which was emitted in such cases.

• xcube serve will now also accept datasets with coordinate names
  longitude and latitude, even if the attribute long_name isn't set.
  (#763)

• Function xcube.core.resampling.affine.affine_transform_dataset()
  now assumes that geographic coordinate systems are equal by default and
  hence a resampling based on an affine transformation can be performed.

• Fixed a problem with xcube server's WMTS implementation.
  For multi-level resolution datasets with very coarse low resolution levels,
  the tile matrix sets WorldCRS84Quad and WorldWebMercatorQuad have
  reported a negative minimum z-level.

• Implementation of function xcube.core.geom.rasterize_features()
  has been changed to account for consistent use of a target variable's
  fill_value and dtype for a given feature.
  In-memory (decoded) variables now always use dtype float64 and use
  np.nan to represent missing values. Persisted (encoded) variable data
  will make use of the target fill_value and dtype. (#778)

• Relative local filesystem paths to datasets are now correctly resolved
  against the base directory of the xcube Server's configuration, i.e.
  configuration parameter base_dir. (#758)

• Fixed problem with xcube gen raising FileNotFoundError
  with Zarr >= 2.13.

• Provided backward compatibility with Python 3.8. (#760)

Other

• The CLI tool xcube edit has been deprecated in favour of the
  xcube patch. (#748)

• Deprecated CLI xcube tile has been removed.

• Deprecated modules, classes, methods, and functions
  have finally been removed:

  • xcube.core.geom.get_geometry_mask()
  • xcube.core.mldataset.FileStorageMultiLevelDataset
  • xcube.core.mldataset.open_ml_dataset()
  • xcube.core.mldataset.open_ml_dataset_from_local_fs()
  • xcube.core.mldataset.open_ml_dataset_from_object_storage()
  • xcube.core.subsampling.get_dataset_subsampling_slices()
  • xcube.core.tiledimage
  • xcube.core.tilegrid

• The following classes, methods, and functions have been deprecated:

  • xcube.core.xarray.DatasetAccessor.levels()
  • xcube.util.cmaps.get_cmap()
  • xcube.util.cmaps.get_cmaps()

• A new function compute_tiles() has been
  refactored out from function xcube.core.tile.compute_rgba_tile().

• Added method get_level_for_resolution(xy_res) to
  abstract base class xcube.core.mldataset.MultiLevelDataset.

• Removed outdated example resources from examples/serve/demo.

• Account for different spatial resolutions in x and y in
  xcube.core.geom.get_dataset_bounds().

• Make code robust against 0-size coordinates in
  xcube.core.update._update_dataset_attrs().

• xcube Server has been enhanced to load multi-module Python code
  for dynamic cubes both from both directories and zip archives.
  For example, the following dataset definition computes a dynamic
  cube from dataset "local" using function "compute_dataset" in
  Python module "resample_in_time.py":

    Path: resample_in_time.py
    Function: compute_dataset
    InputDatasets: ["local"]

  Users can now pack "resample_in_time.py" among any other modules and
  packages into a zip archive. Note that the original module name
  is now a prefix to the function name:

    Path: modules.zip
    Function: resample_in_time:compute_dataset
    InputDatasets: ["local"]

  Implementation note: this has been achieved by using
  xcube.core.byoa.CodeConfig in
  xcube.core.mldataset.ComputedMultiLevelDataset.

• Instead of the Function keyword it is now
  possible to use the Class keyword.
  While Function references a function that receives one or
  more datasets (type xarray.Dataset) and returns a new one,
  Class references a callable that receives one or
  more multi-level datasets and returns a new one.
  The callable is either a class derived from
  or a function that returns an instance of
  xcube.core.mldataset.MultiLevelDataset.

• Module xcube.core.mldataset has been refactored into
  a sub-package for clarity and maintainability.

• Removed deprecated example examples/tile.

New Contributors

• @thomasstorm made their first contribution in #699
• @ymoisan made their first contribution in #759

Full Changelog: v0.12.1...v0.13.0

0.13.0.dev12

Changes in 0.13.0.dev12

Fixes

• Intermediate: Fixed Viewer.show() and Viewer.info().

Full Changelog: v0.13.0.dev11...v0.13.0.dev12

0.13.0.dev11

Changes in 0.13.0.dev11

Enhancements

• Added Notebook
  xcube-viewer-in-jl.ipynb
  that explains how xcube Viewer can now be utilised in JupyterLab
  using the new (still experimental) xcube JupyterLab extension
  xcube-jl-ext.

Fixes

• Intermediate: Ensure Viewer() creates a server with reverse prefix set.

• Intermediate: Ensure Viewer.add_dataset() provides a dataset title.

• Intermediate: Fixed xcube.webapi.viewer.Viewer
  so it can find ~/.xcube/jupyterlab/lab-info.json.

Other

• Intermediate: Added important TODO, should be made a GH issue after 0.13.

• Removed deprecated example examples/tile.

Full Changelog: v0.13.0.dev10...v0.13.0.dev11

0.13.0.dev10

What's Changed

• Replaced numpy.bool by Python bool by @forman in #796

Full Changelog: v0.13.0.dev9...v0.13.0.dev10

0.13.0.dev9

Changes in 0.13.0.dev9

Enhancements

• xcube Server has been rewritten almost from scratch.

  • Introduced a new endpoint ${server_url}/s3 that emulates
    and AWS S3 object storage for the published datasets. (#717)
    The bucket name can be either:

    • s3://datasets - publishes all datasets in Zarr format.
    • s3://pyramids - publishes all datasets in a multi-level levels
      format (multi-resolution N-D images)
      that comprises level datasets in Zarr format.

    Datasets published through the S3 API are slightly
    renamed for clarity. For bucket s3://pyramids:

    • if a dataset identifier has suffix .levels, the identifier remains;
    • if a dataset identifier has suffix .zarr, it will be replaced by
      .levels only if such a dataset doesn't exist;
    • otherwise, the suffix .levels is appended to the identifier.
      For bucket s3://datasets the opposite is true:
    • if a dataset identifier has suffix .zarr, the identifier remains;
    • if a dataset identifier has suffix .levels, it will be replaced by
      .zarr only if such a dataset doesn't exist;
    • otherwise, the suffix .zarr is appended to the identifier.

    With the new S3 endpoints in place, xcube Server instances can be used
    as xcube data stores as follows:

    store = new_data_store(
        "s3", 
        root="datasets",   # bucket "datasets", use also "pyramids"
        max_depth=2,       # optional, but we may have nested datasets
        storage_options=dict(
            anon=True,
            client_kwargs=dict(
                endpoint_url='http://localhost:8080/s3' 
            )
        )
    )

  • The limited s3bucket endpoints are no longer available and are
    replaced by s3 endpoints.

  • Added new endpoint /viewer that serves a self-contained,
    packaged build of
    xcube Viewer.
    The packaged viewer can be overridden by environment variable
    XCUBE_VIEWER_PATH that must point to a directory with a
    build of a compatible viewer.

  • The --show option of xcube serve
    has been renamed to --open-viewer.
    It now uses the self-contained, packaged build of
    xcube Viewer. (#750)

  • The --show option of xcube serve
    now outputs various aspects of the server configuration.

  • Added experimental endpoint /volumes.
    It is used by xcube Viewer to render 3-D volumes.

• xcube Server is now more tolerant with respect to datasets it can not
  open without errors. Implementation detail: It no longer fails if
  opening datasets raises any exception other than DatasetIsNotACubeError.
  (#789)

• xcube Server's colormap management has been improved in several ways:

  • Colormaps are no longer managed globally. E.g., on server configuration
    change, new custom colormaps are reloaded from files.
  • Colormaps are loaded dynamically from underlying
    matplotlib and cmocean registries, and custom SNAP color palette files.
    That means, latest matplotlib colormaps are now always available. (#687)
  • Colormaps can now be reversed (name suffix "_r"),
    can have alpha blending (name suffix "_alpha"),
    or both (name suffix "_r_alpha").
  • Loading of custom colormaps from SNAP *.cpd has been rewritten.
    Now also the isLogScaled property of the colormap is recognized. (#661)
  • The module xcube.util.cmaps has been redesigned and now offers
    three new classes for colormap management:
    • Colormap - a colormap
    • ColormapCategory - represents a colormap category
    • ColormapRegistry - manages colormaps and their categories

• The xcube filesystem data stores such as "file", "s3", "memory"
  can now filter the data identifiers reported by get_data_ids(). (#585)
  For this purpose, the data stores now accept two new optional keywords
  which both can take the form of a wildcard pattern or a sequence
  of wildcard patterns:

  1. excludes: if given and if any pattern matches the identifier,
     the identifier is not reported.
  2. includes: if not given or if any pattern matches the identifier,
     the identifier is reported.

• Added convenience method DataStore.list_data_ids() that works
  like get_data_ids(), but returns a list instead of an iterator. (#776)

Fixes

• xcube CLI tools no longer emit warnings when trying to import
  installed packages named xcube_* as xcube plugins.

• The xcube.util.timeindex module can now handle 0-dimensional
  ndarrays as indexers. This effectively avoids the warning
  Can't determine indexer timezone; leaving it unmodified.
  which was emitted in such cases.

• xcube serve will now also accept datasets with coordinate names
  longitude and latitude, even if the attribute long_name isn't set.
  (#763)

• Function xcube.core.resampling.affine.affine_transform_dataset()
  now assumes that geographic coordinate systems are equal by default and
  hence a resampling based on an affine transformation can be performed.

• Fixed a problem with xcube server's WMTS implementation.
  For multi-level resolution datasets with very coarse low resolution levels,
  the tile matrix sets WorldCRS84Quad and WorldWebMercatorQuad have
  reported a negative minimum z-level.

• Implementation of function xcube.core.geom.rasterize_features()
  has been changed to account for consistent use of a target variable's
  fill_value and dtype for a given feature.
  In-memory (decoded) variables now always use dtype float64 and use
  np.nan to represent missing values. Persisted (encoded) variable data
  will make use of the target fill_value and dtype. (#778)

• Relative local filesystem paths to datasets are now correctly resolved
  against the base directory of the xcube Server's configuration, i.e.
  configuration parameter base_dir. (#758)

• Fixed problem with xcube gen raising FileNotFoundError
  with Zarr >= 2.13.

• Provided backward compatibility with Python 3.8. (#760)

Other

• The CLI tool xcube edit has been deprecated in favour of the
  xcube patch. (#748)

• Deprecated CLI xcube tile has been removed.

• Deprecated modules, classes, methods, and functions
  have finally been removed:

  • xcube.core.geom.get_geometry_mask()
  • xcube.core.mldataset.FileStorageMultiLevelDataset
  • xcube.core.mldataset.open_ml_dataset()
  • xcube.core.mldataset.open_ml_dataset_from_local_fs()
  • xcube.core.mldataset.open_ml_dataset_from_object_storage()
  • xcube.core.subsampling.get_dataset_subsampling_slices()
  • xcube.core.tiledimage
  • xcube.core.tilegrid

• The following classes, methods, and functions have been deprecated:

  • xcube.core.xarray.DatasetAccessor.levels()
  • xcube.util.cmaps.get_cmap()
  • xcube.util.cmaps.get_cmaps()

• A new function compute_tiles() has been
  refactored out from function xcube.core.tile.compute_rgba_tile().

• Added method get_level_for_resolution(xy_res) to
  abstract base class xcube.core.mldataset.MultiLevelDataset.

• Removed outdated example resources from examples/serve/demo.

• Account for different spatial resolutions in x and y in
  xcube.core.geom.get_dataset_bounds().

• Make code robust against 0-size coordinates in
  xcube.core.update._update_dataset_attrs().

• xcube Server has been enhanced to load multi-module Python code
  for dynamic cubes both from both directories and zip archives.
  For example, the following dataset definition computes a dynamic
  cube from dataset "local" using function "compute_dataset" in
  Python module "resample_in_time.py":

    Path: resample_in_time.py
    Function: compute_dataset
    InputDatasets: ["local"]

  Users can now pack "resample_in_time.py" among any other modules and
  packages into a zip archive. Note that the original module name
  is now a prefix to the function name:

    Path: modules.zip
    Function: resample_in_time:compute_dataset
    InputDatasets: ["local"]

  Implementation note: this has been achieved by using
  xcube.core.byoa.CodeConfig in
  xcube.core.mldataset.ComputedMultiLevelDataset.

• Instead of the Function keyword it is now
  possible to use the Class keyword.
  While Function references a function that receives one or
  more datasets (type xarray.Dataset) and returns a new one,
  Class references a callable that receives one or
  more multi-level datasets and returns a new one.
  The callable is either a class derived from
  or a function that returns an instance of
  xcube.core.mldataset.MultiLevelDataset.

• Module xcube.core.mldataset has been refactored into
  a sub-package for clarity and maintainability.

Full Changelog: v0.13.0.dev8...v0.13.0.dev9

0.13.0.dev8

Changes in 0.13.0.dev8

Other

• Added convenience method DataStore.list_data_ids() that works
  like get_data_ids(), but returns a list instead of an iterator. (#776)

Fixes

• Implementation of function xcube.core.geom.rasterize_features()
  has been changed to account for consistent use of a target variable's
  fill_value and dtype for a given feature.
  In-memory (decoded) variables now always use dtype float64 and use
  np.nan to represent missing values. Persisted (encoded) variable data
  will make use of the target fill_value and dtype. (#778)

Full Changelog: v0.13.0.dev7...v0.13.0.dev8

0.13.0.dev7

Changes in 0.13.0.dev7

Other

• xcube Server has been enhanced to load multi-module Python code
  for dynamic cubes both from both directories and zip archives.
  For example, the following dataset definition computes a dynamic
  cube from dataset "local" using function "compute_dataset" in
  Python module "resample_in_time.py":

    Path: resample_in_time.py
    Function: compute_dataset
    InputDatasets: ["local"]

  Users can now pack "resample_in_time.py" among any other modules and
  packages into a zip archive. Note that the original module name
  is now a prefix to the function name:

    Path: modules.zip
    Function: resample_in_time:compute_dataset
    InputDatasets: ["local"]

  Implementation note: this has been achieved by using
  xcube.core.byoa.CodeConfig in
  xcube.core.mldataset.ComputedMultiLevelDataset.

• Instead of the Function keyword it is now
  possible to use the Class keyword.
  While Function references a function that receives one or
  more datasets (type xarray.Dataset) and returns a new one,
  Class references a callable that receives one or
  more multi-level datasets and returns a new one.
  The callable is either a class derived from
  or a function that returns an instance of
  xcube.core.mldataset.MultiLevelDataset.

• Module xcube.core.mldataset has been refactored into
  a sub-package for clarity and maintainability.

• Provided backward compatibility with Python 3.8. (#760)