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.
Standard features -----------------
- Supports storage of NumPy data of the following types:
* Integer/Unsigned Integer * Float/Double * Complex/Double Complex * Compound ("recarray") * Strings * Boolean * Array * Enumeration (integers) * Void
- 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.