Announcing HDF5 for Python (h5py) 1.2
I'm pleased to announce the availability of HDF5 for Python 1.2 final!
This release represents a significant update to the h5py feature set.
Some of the new new features are:
- Support for variable-length strings!
- Use of built-in Python exceptions (KeyError, etc), alongside H5Error
- Top-level support for HDF5 CORE, SEC2, STDIO, WINDOWS and FAMILY drivers
- Support for ENUM and ARRAY types
- Support for Unicode file names
- Big speedup (~3x) when using single-index slicing on a chunked dataset
What is h5py?
HDF5 for Python (h5py) is a general-purpose Python interface to the
Hierarchical Data Format library, version 5. HDF5 is a versatile,
mature scientific software library designed for the fast, flexible
storage of enormous amounts of data.
From a Python programmer's perspective, HDF5 provides a robust way to
store data, organized by name in a tree-like fashion. You can create
datasets (arrays on disk) hundreds of gigabytes in size, and perform
random-access I/O on desired sections. Datasets are organized in a
filesystem-like hierarchy using containers called "groups", and
accesed using the tradional POSIX /path/to/resource syntax.
In addition to providing interoperability with existing HDF5 datasets
and platforms, h5py is a convienient way to store and retrieve
arbitrary NumPy data and metadata.
Full list of new features in 1.2
- Variable-length strings are now supported! They are mapped to native
Python strings via the NumPy "object" type. VL strings may be read,
written and created from h5py, and are allowed in all HDF5 contexts,
even as members of compound or array types.
- HDF5 exceptions now inherit from common Python built-ins like TypeError
and ValueError (in addition to current HDF5 error hierarchy), freeing
the user from knowledge of the HDF5 error system. Existing code which
uses H5Error will continue to work.
- Many different low-level HDF5 drivers can now be used when creating
a file, which allows purely in-memory ("core") files, multi-volume
("family") files, and files which use low-level buffered I/O.
- Groups and attributes now support the standard Python dictionary
interface methods, including keys(), values() and friends. The existing
methods (listnames(), listobjects(), etc.) remain and will not be
removed until at least h5py 1.4 or equivalent.
- Workaround for an HDF5 bug has sped up reading/writing of chunked
datasets. When using a slice with fewer dimensions than the dataset,
there can be as much as a 3x improvement in write times over h5py 1.1.
- Enumerated types are now fully supported; they can be used in NumPy
anywhere integer types are allowed, and are stored as native HDF5
enums. Conversion between integers and enums is supported.
- The NumPy "array" dtype is now allowed as a top-level type when
creating a dataset, not just as a member of a compound type.
- Unicode file names are now supported
- It's now possible to explicitly set the type of an attribute, and to
preserve the type of an attribute while modifying it.
- High-level objects now have .parent and .file attributes, to make the
navigation of HDF5 files more convenient.
Design revisions since 1.1
- The role of the "name" attribute on File objects has changed. "name"
now returns the HDF5 path of the File object ('/'); the file name on
disk is available at File.filename.
- Dictionary-interface methods for Group and AttributeManager objects have
been renamed to follow the standard Python convention (keys(), values(),
etc). The old method names are still available but deprecated.
- The HDF5 shuffle filter is no longer automatically activated when
GZIP or LZF compression is used; many datasets "in the wild" do not
benefit from shuffling.
- Supports storage of NumPy data of the following types:
* Integer/Unsigned Integer
* Complex/Double Complex
* Compound ("recarray")
* Enumeration (integers)
- Random access to datasets using the standard NumPy slicing syntax,
including a subset of fancy indexing and point-based selection
- Transparent compression of datasets using GZIP, LZF or SZIP,
and error-detection using Fletcher32
- "Pythonic" interface supporting dictionary and NumPy-array metaphors
for the high-level HDF5 abstrations like groups and datasets
- A comprehensive, object-oriented wrapping of the HDF5 low-level C API
via Cython, in addition to the NumPy-like high-level interface.
- Supports many new features of HDF5 1.8, including recursive iteration
over entire files and in-library copy operations on the file tree
Where to get it
* Main website, documentation: http://h5py.alfven.org
* Downloads, bug tracker: http://h5py.googlecode.com
* Linux, Mac OS-X or Windows
* Python 2.5 (Windows), Python 2.5 or 2.6 (Linux/Mac OS-X)
* NumPy 1.0.3 or later
* HDF5 1.6.5 or later (including 1.8); HDF5 is included with
the Windows version.
Thanks to D. Dale, E. Lawrence and other for their continued support
and comments. Also thanks to the Francesc Alted and the PyTables project,
for inspiration and generously providing their code to the community. Thanks
to everyone at the HDF Group for creating such a useful piece of software.
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