What's new in h5py 2.2
======================


Support for Parallel HDF5
-------------------------

On UNIX platforms, you can now take advantage of MPI and Parallel HDF5.
Cython, ``mpi4py`` and an MPI-enabled build of HDF5 are required..
See :ref:`parallel` in the documentation for details.


Support for Python 3.3
----------------------

Python 3.3 is now officially supported.


Mini float support (issue #141)
-------------------------------

Two-byte floats (NumPy ``float16``) are supported.

HDF5 scale/offset filter
------------------------

The Scale/Offset filter added in HDF5 1.8 is now available.


Field indexing is now allowed when writing to a dataset (issue #42)
-------------------------------------------------------------------

H5py has long supported reading only certain fields from a dataset::

    >>> dset = f.create_dataset('x', (100,), dtype=np.dtype([('a', 'f'), ('b', 'i')]))
    >>> out = dset['a', 0:100:10]
    >>> out.dtype
    dtype('float32')

Now, field names are also allowed when writing to a dataset:

    >>> dset['a', 20:50] = 1.0


Region references preserve shape (issue #295)
---------------------------------------------

Previously, region references always resulted in a 1D selection, even when
2D slicing was used::

    >>> dset = f.create_dataset('x', (10, 10))
    >>> ref = dset.regionref[0:5,0:5]
    >>> out = dset[ref]
    >>> out.shape
    (25,)

Shape is now preserved::

    >>> out = dset[ref]
    >>> out.shape
    (5, 5)

Additionally, the shape of both the target dataspace and the selection shape
can be determined via new methods on the ``regionref`` proxy (now available
on both datasets and groups)::

    >>> f.regionref.shape(ref)
    (10, 10)
    >>> f.regionref.selection(ref)
    (5, 5)


Committed types can be linked to datasets and attributes
--------------------------------------------------------

HDF5 supports "shared" named types stored in the file::

    >>> f['name'] = np.dtype("int64")

You can now use these types when creating a new dataset or attribute, and
HDF5 will "link" the dataset type to the named type::

    >>> dset = f.create_dataset('int dataset', (10,), dtype=f['name'])
    >>> f.attrs.create('int scalar attribute', shape=(), dtype=f['name'])


``move`` method on Group objects
--------------------------------

It's no longer necessary to move objects in a file by manually re-linking them::

    >>> f.create_group('a')
    >>> f['b'] = f['a']
    >>> del f['a']

The method ``Group.move`` allows this to be performed in one step::

    >>> f.move('a', 'b')

Both the source and destination must be in the same file.