Release notes

2.2.0 (release candidate)

Version 2.2.0 is currently at the release candidate stage. To install the latest release candidate version using pip:

$ pip install --pre zarr

Alternatively, to install the latest release candidate using conda:

$ conda install -c conda-forge/label/rc zarr

Enhancements

  • Advanced indexing. The Array class has several new methods and properties that enable a selection of items in an array to be retrieved or updated. See the Advanced indexing tutorial section for more information. There is also a notebook with extended examples and performance benchmarks. #78, #89, #112, #172.
  • New package for compressor and filter codecs. The classes previously defined in the zarr.codecs module have been factored out into a separate package called NumCodecs. The NumCodecs package also includes several new codec classes not previously available in Zarr, including compressor codecs for Zstd and LZ4. This change is backwards-compatible with existing code, as all codec classes defined by NumCodecs are imported into the zarr.codecs namespace. However, it is recommended to import codecs from the new package, see the tutorial sections on Compressors and Filters for examples. With contributions by John Kirkham; #74, #102, #120, #123, #139.
  • New storage class for DBM-style databases. The zarr.storage.DBMStore class enables any DBM-style database such as gdbm, ndbm or Berkeley DB, to be used as the backing store for an array or group. See the tutorial section on Storage alternatives for some examples. #133, #186.
  • New storage class for LMDB databases. The zarr.storage.LMDBStore class enables an LMDB “Lightning” database to be used as the backing store for an array or group. #192.
  • New storage class using a nested directory structure for chunk files. The zarr.storage.NestedDirectoryStore has been added, which is similar to the existing zarr.storage.DirectoryStore class but nests chunk files for multidimensional arrays into sub-directories. #155, #177.
  • New tree() method for printing hierarchies. The Group class has a new zarr.hierarchy.Group.tree() method which enables a tree representation of a group hierarchy to be printed. Also provides an interactive tree representation when used within a Jupyter notebook. See the Array and group diagnostics tutorial section for examples. By John Kirkham; #82, #140, #184.
  • Visitor API. The Group class now implements the h5py visitor API, see docs for the zarr.hierarchy.Group.visit(), zarr.hierarchy.Group.visititems() and zarr.hierarchy.Group.visitvalues() methods. By John Kirkham, #92, #122.
  • Viewing an array as a different dtype. The Array class has a new zarr.core.Array.astype() method, which is a convenience that enables an array to be viewed as a different dtype. By John Kirkham, #94, #96.
  • New open(), save(), load() convenience functions. The function zarr.convenience.open() provides a convenient way to open a persistent array or group, using either a DirectoryStore or ZipStore as the backing store. The functions zarr.convenience.save() and zarr.convenience.load() are also available and provide a convenient way to save an entire NumPy array to disk and load back into memory later. See the tutorial section Persistent arrays for examples. #104, #105, #141, #181.
  • IPython completions. The Group class now implements __dir__() and _ipython_key_completions_() which enables tab-completion for group members to be used in any IPython interactive environment. #170.
  • New info property; changes to __repr__. The Group and Array classes have a new info property which can be used to print diagnostic information, including compression ratio where available. See the tutorial section on Array and group diagnostics for examples. The string representation (__repr__) of these classes has been simplified to ensure it is cheap and quick to compute in all circumstances. #83, #115, #132, #148.
  • Chunk options. When creating an array, chunks=False can be specified, which will result in an array with a single chunk only. Alternatively, chunks=True will trigger an automatic chunk shape guess. See Chunk optimizations for more on the chunks parameter. #106, #107, #183.
  • Zero-dimensional arrays and are now supported; by Prakhar Goel, #154, #161.
  • Arrays with one or more zero-length dimensions are now fully supported; by Prakhar Goel, #150, #154, #160.
  • The .zattrs key is now optional and will now only be created when the first custom attribute is set; #121, #200.
  • New Group.move() method supports moving a sub-group or array to a different location within the same hierarchy. By John Kirkham, #191, #193, #196.
  • ZipStore is now thread-safe; #194, #192.
  • New Array.hexdigest() method computes an Array’s hash with hashlib. By John Kirkham, #98, #203.
  • Improved support for object arrays. In previous versions of Zarr, creating an array with dtype=object was possible but could under certain circumstances lead to unexpected errors and/or segmentation faults. To make it easier to properly configure an object array, a new object_codec parameter has been added to array creation functions. See the tutorial section on Object arrays for more information and examples. Also, runtime checks have been added in both Zarr and Numcodecs so that segmentation faults are no longer possible, even with a badly configured array. This API change is backwards compatible and previous code that created an object array and provided an object codec via the filters parameter will continue to work, however a warning will be raised to encourage use of the object_codec parameter. #208, #212.
  • Added support for datetime64 and timedelta64 data types; #85, #215.
  • Array and group attributes are now cached by default to improve performance with slow stores, e.g., stores accessing data via the network; #220, #218, #204.
  • New LRUStoreCache class. The class zarr.storage.LRUStoreCache has been added and provides a means to locally cache data in memory from a store that may be slow, e.g., a store that retrieves data from a remote server via the network; #223.
  • New copy functions. The new functions zarr.convenience.copy() and zarr.convenience.copy_all() provide a way to copy groups and/or arrays between HDF5 and Zarr, or between two Zarr groups. The zarr.convenience.copy_store() provides a more efficient way to copy data directly between two Zarr stores. #87, #113, #137, #217.

Bug fixes

  • Fixed bug where read_only keyword argument was ignored when creating an array; #151, #179.
  • Fixed bugs when using a ZipStore opened in ‘w’ mode; #158, #182.
  • Fill values can now be provided for fixed-length string arrays; #165, #176.
  • Fixed a bug where the number of chunks initialized could be counted incorrectly; #97, #174.
  • Fixed a bug related to the use of an ellipsis (…) in indexing statements; #93, #168, #172.
  • Fixed a bug preventing use of other integer types for indexing; #143, #147.

Documentation

Maintenance

  • A data fixture has been included in the test suite to ensure data format compatibility is maintained; #83, #146.
  • The test suite has been migrated from nosetests to pytest; #189, #225.
  • Various continuous integration updates and improvements; #118, #124, #125, #126, #109, #114, #171.
  • Bump numcodecs dependency to 0.5.3, completely remove nose dependency, #237.
  • Fix compatibility issues with NumPy 1.14 regarding fill values for structured arrays, #222, #238, #239.

Acknowledgments

Code was contributed to this release by Alistair Miles, John Kirkham and Prakhar Goel.

Documentation was contributed to this release by Mamy Ratsimbazafy and Charles Noyes.

Thank you to John Kirkham, Stephan Hoyer, Francesc Alted, and Matthew Rocklin for code reviews and/or comments on pull requests.

2.1.4

  • Resolved an issue where calling hasattr on a Group object erroneously returned a KeyError. By Vincent Schut; #88, #95.

2.1.3

2.1.2

  • Resolved an issue when no compression is used and chunks are stored in memory (#79).

2.1.1

Various minor improvements, including: Group objects support member access via dot notation (__getattr__); fixed metadata caching for Array.shape property and derivatives; added Array.ndim property; fixed Array.__array__ method arguments; fixed bug in pickling Array state; fixed bug in pickling ThreadSynchronizer.

2.1.0

  • Group objects now support member deletion via del statement (#65).
  • Added zarr.storage.TempStore class for convenience to provide storage via a temporary directory (#59).
  • Fixed performance issues with zarr.storage.ZipStore class (#66).
  • The Blosc extension has been modified to return bytes instead of array objects from compress and decompress function calls. This should improve compatibility and also provides a small performance increase for compressing high compression ratio data (#55).
  • Added overwrite keyword argument to array and group creation methods on the zarr.hierarchy.Group class (#71).
  • Added cache_metadata keyword argument to array creation methods.
  • The functions zarr.creation.open_array() and zarr.hierarchy.open_group() now accept any store as first argument (#56).

2.0.1

The bundled Blosc library has been upgraded to version 1.11.1.

2.0.0

Hierarchies

Support has been added for organizing arrays into hierarchies via groups. See the tutorial section on Groups and the zarr.hierarchy API docs for more information.

Filters

Support has been added for configuring filters to preprocess chunk data prior to compression. See the tutorial section on Filters and the zarr.codecs API docs for more information.

Other changes

To accommodate support for hierarchies and filters, the Zarr metadata format has been modified. See the Zarr storage specification version 2 for more information. To migrate an array stored using Zarr version 1.x, use the zarr.storage.migrate_1to2() function.

The bundled Blosc library has been upgraded to version 1.11.0.

Acknowledgments

Thanks to Matthew Rocklin, Stephan Hoyer and Francesc Alted for contributions and comments.

1.1.0

  • The bundled Blosc library has been upgraded to version 1.10.0. The ‘zstd’ internal compression library is now available within Blosc. See the tutorial section on Compressors for an example.
  • When using the Blosc compressor, the default internal compression library is now ‘lz4’.
  • The default number of internal threads for the Blosc compressor has been increased to a maximum of 8 (previously 4).
  • Added convenience functions zarr.blosc.list_compressors() and zarr.blosc.get_nthreads().

1.0.0

This release includes a complete re-organization of the code base. The major version number has been bumped to indicate that there have been backwards-incompatible changes to the API and the on-disk storage format. However, Zarr is still in an early stage of development, so please do not take the version number as an indicator of maturity.

Storage

The main motivation for re-organizing the code was to create an abstraction layer between the core array logic and data storage (#21). In this release, any object that implements the MutableMapping interface can be used as an array store. See the tutorial sections on Persistent arrays and Storage alternatives, the Zarr storage specification version 1, and the zarr.storage module documentation for more information.

Please note also that the file organization and file name conventions used when storing a Zarr array in a directory on the file system have changed. Persistent Zarr arrays created using previous versions of the software will not be compatible with this version. See the zarr.storage API docs and the Zarr storage specification version 1 for more information.

Compression

An abstraction layer has also been created between the core array logic and the code for compressing and decompressing array chunks. This release still bundles the c-blosc library and uses Blosc as the default compressor, however other compressors including zlib, BZ2 and LZMA are also now supported via the Python standard library. New compressors can also be dynamically registered for use with Zarr. See the tutorial sections on Compressors and Configuring Blosc, the Zarr storage specification version 1, and the zarr.compressors module documentation for more information.

Synchronization

The synchronization code has also been refactored to create a layer of abstraction, enabling Zarr arrays to be used in parallel computations with a number of alternative synchronization methods. For more information see the tutorial section on Parallel computing and synchronization and the zarr.sync module documentation.

Changes to the Blosc extension

NumPy is no longer a build dependency for the zarr.blosc Cython extension, so setup.py will run even if NumPy is not already installed, and should automatically install NumPy as a runtime dependency. Manual installation of NumPy prior to installing Zarr is still recommended, however, as the automatic installation of NumPy may fail or be sub-optimal on some platforms.

Some optimizations have been made within the zarr.blosc extension to avoid unnecessary memory copies, giving a ~10-20% performance improvement for multi-threaded compression operations.

The zarr.blosc extension now automatically detects whether it is running within a single-threaded or multi-threaded program and adapts its internal behaviour accordingly (#27). There is no need for the user to make any API calls to switch Blosc between contextual and non-contextual (global lock) mode. See also the tutorial section on Configuring Blosc.

Other changes

The internal code for managing chunks has been rewritten to be more efficient. Now no state is maintained for chunks outside of the array store, meaning that chunks do not carry any extra memory overhead not accounted for by the store. This negates the need for the “lazy” option present in the previous release, and this has been removed.

The memory layout within chunks can now be set as either “C” (row-major) or “F” (column-major), which can help to provide better compression for some data (#7). See the tutorial section on Chunk memory layout for more information.

A bug has been fixed within the __getitem__ and __setitem__ machinery for slicing arrays, to properly handle getting and setting partial slices.

Acknowledgments

Thanks to Matthew Rocklin, Stephan Hoyer, Francesc Alted, Anthony Scopatz and Martin Durant for contributions and comments.