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======================
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.
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